added new process the video
This commit is contained in:
kicap
parent
28eebbe33a
commit
9b30890b15
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "93b77493-0a01-4421-b2a0-380991740ff6",
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import cv2\n",
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"import pandas as pd\n",
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"\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "80b4ff7c-1f3b-4e1d-896c-d88c0966f33e",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"6868.0 30.03550936578534 848 478\n"
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]
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}
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],
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"source": [
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"cap = cv2.VideoCapture('video/video.mp4')\n",
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"# mendapatkan jumlah frame, fps, lebar, dan tinggi dari video\n",
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"frames_count, fps, width, height = cap.get(cv2.CAP_PROP_FRAME_COUNT), cap.get(cv2.CAP_PROP_FPS), cap.get(\n",
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" cv2.CAP_PROP_FRAME_WIDTH), cap.get(cv2.CAP_PROP_FRAME_HEIGHT)\n",
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"width = int(width)\n",
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"height = int(height)\n",
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"print(frames_count, fps, width, height)\n",
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"\n",
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"# membuat sebuah frame pandas dengan jumlah baris yang sama dengan jumlah frame\n",
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"df = pd.DataFrame(index=range(int(frames_count)))\n",
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"df.index.name = \"Frame\" # menandai kolom frame\n",
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"\n",
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"framenumber = 0 # mencatat frame saat ini\n",
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"carscrossedup = 0 # mencatat mobil yang melintasi jalan ke atas\n",
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"carscrosseddown = 0 # mencatat mobil yang melintasi jalan ke bawah\n",
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"carids = [] # daftar kosong untuk menyimpan ID mobil\n",
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"caridscrossed = [] # daftar kosong untuk menyimpan ID mobil yang sudah melintasi\n",
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"totalcars = 0 # mencatat jumlah total mobil\n",
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"\n",
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"fgbg = cv2.createBackgroundSubtractorMOG2() # membuat pengambil gambar latar belakang\n",
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"\n",
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"# informasi untuk mulai menyimpan video\n",
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"ret, frame = cap.read() # mengimpor gambar\n",
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"ratio = .5 # rasio ukuran pengubahan ukuran\n",
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"image = cv2.resize(frame, (0, 0), None, ratio, ratio) # mengubah ukuran gambar\n",
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"width2, height2, channels = image.shape\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "5c8d5645-9df8-457c-88d7-2d3bbc0fade9",
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"metadata": {},
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"outputs": [
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{
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"ename": "KeyboardInterrupt",
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"evalue": "",
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"output_type": "error",
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"traceback": [
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
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"Cell \u001b[0;32mIn[3], line 265\u001b[0m\n\u001b[1;32m 260\u001b[0m \u001b[38;5;66;03m# video.write(image) # save the current image to video file from earlier\u001b[39;00m\n\u001b[1;32m 261\u001b[0m \n\u001b[1;32m 262\u001b[0m \u001b[38;5;66;03m# adds to framecount\u001b[39;00m\n\u001b[1;32m 263\u001b[0m framenumber \u001b[38;5;241m=\u001b[39m framenumber \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[0;32m--> 265\u001b[0m k \u001b[38;5;241m=\u001b[39m \u001b[43mcv2\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mwaitKey\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mint\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m1000\u001b[39;49m\u001b[38;5;241;43m/\u001b[39;49m\u001b[43mfps\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;241m&\u001b[39m \u001b[38;5;241m0xff\u001b[39m \u001b[38;5;66;03m# int(1000/fps) is normal speed since waitkey is in ms\u001b[39;00m\n\u001b[1;32m 266\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m k \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m27\u001b[39m:\n\u001b[1;32m 267\u001b[0m \u001b[38;5;28;01mbreak\u001b[39;00m\n",
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"\u001b[0;31mKeyboardInterrupt\u001b[0m: "
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]
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}
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],
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"source": [
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"while True:\n",
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"\n",
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" ret, frame = cap.read() # mengimpor gambar\n",
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"\n",
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" if ret: # jika ada frame lanjutkan dengan kode\n",
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"\n",
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" image = cv2.resize(frame, (0, 0), None, ratio, ratio) # mengubah ukuran gambar\n",
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"\n",
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" gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) # mengubah gambar ke hitam putih\n",
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"\n",
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" fgmask = fgbg.apply(gray) # menggunakan pengambil gambar latar belakang\n",
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"\n",
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" # menerapkan berbagai batasan pada fgmask untuk menyaring mobil\n",
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" # perlu bermain dengan setelan tersebut hingga mobil dapat diidentifikasi dengan mudah\n",
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" kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5)) # kernel untuk dilakukan pada morphology\n",
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" closing = cv2.morphologyEx(fgmask, cv2.MORPH_CLOSE, kernel)\n",
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" opening = cv2.morphologyEx(closing, cv2.MORPH_OPEN, kernel)\n",
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" dilation = cv2.dilate(opening, kernel)\n",
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" retvalbin, bins = cv2.threshold(dilation, 220, 255, cv2.THRESH_BINARY) # menghapus shadow\n",
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"\n",
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" # membuat kontur\n",
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" contours, hierarchy = cv2.findContours(bins, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)[-2:]\n",
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"\n",
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" # menggunakan konveks hull untuk membuat poligon di sekitar kontur\n",
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" hull = [cv2.convexHull(c) for c in contours]\n",
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"\n",
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" # menggambar kontur\n",
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" cv2.drawContours(image, hull, -1, (0, 255, 0), 3)\n",
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"\n",
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" # garis dibuat untuk menghentikan menghitung kontur, perlu dilakukan karena mobil yang jauh akan menjadi satu kontur besar\n",
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" lineypos = 225\n",
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" cv2.line(image, (0, lineypos), (width, lineypos), (255, 0, 0), 5)\n",
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"\n",
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" # garis y pos dibuat untuk menghitung kontur\n",
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" lineypos2 = 250\n",
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" cv2.line(image, (0, lineypos2), (width, lineypos2), (0, 255, 0), 5)\n",
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"\n",
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" # minimum area untuk kontur\n",
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" minarea = 300\n",
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"\n",
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" # maksimum area untuk kontur\n",
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" maxarea = 50000\n",
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"\n",
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" # vektor untuk x dan y lokasi centroid di frame saat ini\n",
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" cxx = np.zeros(len(contours))\n",
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" cyy = np.zeros(len(contours))\n",
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"\n",
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" for i in range(len(contours)): # mengulangi seluruh kontur dalam frame saat ini\n",
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"\n",
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" if hierarchy[0, i, 3] == -1: # menggunakan hierarchy untuk hanya menghitung kontur induk (tidak termasuk dalam kontur lain)\n",
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"\n",
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" area = cv2.contourArea(contours[i]) # menghitung area kontur\n",
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"\n",
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" if minarea < area < maxarea: # area threshold untuk kontur\n",
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"\n",
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" # menghitung centroid dari kontur\n",
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" cnt = contours[i]\n",
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" M = cv2.moments(cnt)\n",
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" cx = int(M['m10'] / M['m00'])\n",
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" cy = int(M['m01'] / M['m00'])\n",
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"\n",
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" if cy > lineypos: # menghapus kontur yang di atas garis\n",
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"\n",
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" # mengambil titik teratas, kiri, dan lebar dari kontur untuk membuat kotak\n",
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" # x,y adalah kiri atas dan w,h adalah lebar dan tinggi\n",
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" x, y, w, h = cv2.boundingRect(cnt)\n",
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"\n",
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" # membuat kotak di sekitar kontur\n",
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" cv2.rectangle(image, (x, y), (x + w, y + h), (255, 0, 0), 2)\n",
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"\n",
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" # Menuliskan teks centroid untuk memastikan kembali nanti\n",
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" cv2.putText(image, str(cx) + \",\" + str(cy), (cx + 10, cy + 10), cv2.FONT_HERSHEY_SIMPLEX,\n",
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" .3, (0, 0, 255), 1)\n",
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"\n",
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" cv2.drawMarker(image, (cx, cy), (0, 0, 255), cv2.MARKER_STAR, markerSize=5, thickness=1,\n",
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" line_type=cv2.LINE_AA)\n",
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"\n",
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" # menambahkan centroid yang lulus pada kriteria ke dalam list centroid\n",
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" cxx[i] = cx\n",
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" cyy[i] = cy\n",
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"\n",
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" # menghapus entri 0 dari list centroid\n",
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" cxx = cxx[cxx != 0]\n",
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" cyy = cyy[cyy != 0]\n",
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"\n",
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" # list kosong untuk nanti menyimpan indices centroid yang di tambahkan ke dataframe\n",
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" minx_index2 = []\n",
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" miny_index2 = []\n",
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"\n",
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" # batas maksimum untuk radius dari centroid dari frame saat ini untuk dianggap sama dengan centroid dari frame sebelumnya\n",
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" maxrad = 25\n",
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"\n",
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" # Bagian ini mengelola centroid dan menetapkan mereka untuk carid lama atau carid baru\n",
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"\n",
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" if len(cxx): # jika ada centroid dalam area yang ditentukan\n",
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"\n",
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" if not carids: # jika carids kosong\n",
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"\n",
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" for i in range(len(cxx)): # melalui semua centroid\n",
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"\n",
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" carids.append(i) # menambahkan car id ke list carids kosong\n",
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" df[str(carids[i])] = \"\" # menambahkan kolom ke dataframe sesuai carid\n",
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"\n",
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" # menetapkan nilai centroid ke frame (baris) dan carid (kolom) yang sesuai\n",
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" df.at[int(framenumber), str(carids[i])] = [cxx[i], cyy[i]]\n",
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"\n",
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" totalcars = carids[i] + 1 # menambahkan count car\n",
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"\n",
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" else: # jika carids sudah ada\n",
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"\n",
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" dx = np.zeros((len(cxx), len(carids))) # array baru untuk menghitung deltas\n",
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" dy = np.zeros((len(cyy), len(carids))) # array baru untuk menghitung deltas\n",
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"\n",
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" for i in range(len(cxx)): # melalui semua centroid\n",
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"\n",
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" for j in range(len(carids)): # melalui semua car id yang sudah ada\n",
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"\n",
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" # mengambil centroid dari frame sebelumnya untuk carid tertentu\n",
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" oldcxcy = df.iloc[int(framenumber - 1)][str(carids[j])]\n",
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"\n",
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" # mengambil centroid dari frame saat ini yang tidak selalu sesuai dengan centroid frame sebelumnya\n",
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" curcxcy = np.array([cxx[i], cyy[i]])\n",
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"\n",
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" if not oldcxcy: # periksa apakah centroid sebelumnya kosong jika arah sudah tidak ada di layar\n",
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"\n",
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" continue # lanjutkan ke carid berikutnya\n",
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"\n",
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" else: # hitung delta centroid untuk membandingkan dengan centroid frame saat ini\n",
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"\n",
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" dx[i, j] = oldcxcy[0] - curcxcy[0]\n",
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" dy[i, j] = oldcxcy[1] - curcxcy[1]\n",
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"\n",
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" for j in range(len(carids)): # melalui semua car id saat ini\n",
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"\n",
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" sumsum = np.abs(dx[:, j]) + np.abs(dy[:, j]) # menghitung delta wrt car id\n",
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"\n",
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" # mengambil indeks centroid yang memiliki nilai delta minimum dan ini indeks benar\n",
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" correctindextrue = np.argmin(np.abs(sumsum))\n",
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" minx_index = correctindextrue\n",
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" miny_index = correctindextrue\n",
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"\n",
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" # mengambil delta nilai minimum untuk dibandingkan dengan radius\n",
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" mindx = dx[minx_index, j]\n",
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" mindy = dy[miny_index, j]\n",
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"\n",
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" if mindx == 0 and mindy == 0 and np.all(dx[:, j] == 0) and np.all(dy[:, j] == 0):\n",
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" # periksa apakah minimum nilai adalah 0 dan semua delta adalah nol\n",
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" # delta dapat berupa nol jika centroid tidak bergerak\n",
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"\n",
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" continue # lanjutkan ke carid berikutnya\n",
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"\n",
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" else:\n",
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"\n",
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" # jika delta nilai adalah kurang dari maksimal radius maka tambahkan centroid ke carid sebelumnya\n",
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" if np.abs(mindx) < maxrad and np.abs(mindy) < maxrad:\n",
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"\n",
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" # tambahkan centroid ke carid yang sudah ada\n",
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" df.at[int(framenumber), str(carids[j])] = [cxx[minx_index], cyy[miny_index]]\n",
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" minx_index2.append(minx_index) # tambahkan semua indeks yang ditambahkan ke carid ke list\n",
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" miny_index2.append(miny_index)\n",
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"\n",
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" currentcars = 0 # current cars on screen\n",
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" currentcarsindex = [] # current cars on screen carid index\n",
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"\n",
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" for i in range(len(carids)): # loops through all carids\n",
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"\n",
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" if df.at[int(framenumber), str(carids[i])] != '':\n",
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" # checks the current frame to see which car ids are active\n",
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" # by checking in centroid exists on current frame for certain car id\n",
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"\n",
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" currentcars = currentcars + 1 # adds another to current cars on screen\n",
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" currentcarsindex.append(i) # adds car ids to current cars on screen\n",
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"\n",
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" for i in range(currentcars): # loops through all current car ids on screen\n",
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"\n",
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" # grabs centroid of certain carid for current frame\n",
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" curcent = df.iloc[int(framenumber)][str(carids[currentcarsindex[i]])]\n",
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"\n",
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" # grabs centroid of certain carid for previous frame\n",
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" oldcent = df.iloc[int(framenumber - 1)][str(carids[currentcarsindex[i]])]\n",
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"\n",
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" if curcent: # if there is a current centroid\n",
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"\n",
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" # On-screen text for current centroid\n",
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" cv2.putText(image, \"Centroid\" + str(curcent[0]) + \",\" + str(curcent[1]),\n",
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" (int(curcent[0]), int(curcent[1])), cv2.FONT_HERSHEY_SIMPLEX, .5, (0, 255, 255), 2)\n",
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"\n",
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" cv2.putText(image, \"ID:\" + str(carids[currentcarsindex[i]]), (int(curcent[0]), int(curcent[1] - 15)),\n",
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" cv2.FONT_HERSHEY_SIMPLEX, .5, (0, 255, 255), 2)\n",
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"\n",
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" cv2.drawMarker(image, (int(curcent[0]), int(curcent[1])), (0, 0, 255), cv2.MARKER_STAR, markerSize=5,\n",
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" thickness=1, line_type=cv2.LINE_AA)\n",
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"\n",
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" if oldcent: # checks if old centroid exists\n",
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" # adds radius box from previous centroid to current centroid for visualization\n",
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" xstart = oldcent[0] - maxrad\n",
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" ystart = oldcent[1] - maxrad\n",
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" xwidth = oldcent[0] + maxrad\n",
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" yheight = oldcent[1] + maxrad\n",
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" cv2.rectangle(image, (int(xstart), int(ystart)), (int(xwidth), int(yheight)), (0, 125, 0), 1)\n",
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"\n",
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" # checks if old centroid is on or below line and curcent is on or above line\n",
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" # to count cars and that car hasn't been counted yet\n",
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" if oldcent[1] >= lineypos2 and curcent[1] <= lineypos2 and carids[\n",
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" currentcarsindex[i]] not in caridscrossed:\n",
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"\n",
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" carscrossedup = carscrossedup + 1\n",
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" cv2.line(image, (0, lineypos2), (width, lineypos2), (0, 0, 255), 5)\n",
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" caridscrossed.append(\n",
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" currentcarsindex[i]) # adds car id to list of count cars to prevent double counting\n",
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"\n",
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" # checks if old centroid is on or above line and curcent is on or below line\n",
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" # to count cars and that car hasn't been counted yet\n",
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" elif oldcent[1] <= lineypos2 and curcent[1] >= lineypos2 and carids[\n",
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" currentcarsindex[i]] not in caridscrossed:\n",
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"\n",
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" carscrosseddown = carscrosseddown + 1\n",
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" cv2.line(image, (0, lineypos2), (width, lineypos2), (0, 0, 125), 5)\n",
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" caridscrossed.append(currentcarsindex[i])\n",
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"\n",
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" # Top left hand corner on-screen text\n",
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" cv2.rectangle(image, (0, 0), (250, 100), (255, 0, 0), -1) # background rectangle for on-screen text\n",
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"\n",
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" cv2.putText(image, \"Cars in Area: \" + str(currentcars), (0, 15), cv2.FONT_HERSHEY_SIMPLEX, .5, (0, 170, 0), 1)\n",
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"\n",
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" cv2.putText(image, \"Cars Crossed Up: \" + str(carscrossedup), (0, 30), cv2.FONT_HERSHEY_SIMPLEX, .5, (0, 170, 0),\n",
|
||||
" 1)\n",
|
||||
"\n",
|
||||
" cv2.putText(image, \"Cars Crossed Down: \" + str(carscrosseddown), (0, 45), cv2.FONT_HERSHEY_SIMPLEX, .5,\n",
|
||||
" (0, 170, 0), 1)\n",
|
||||
"\n",
|
||||
" cv2.putText(image, \"Total Cars Detected: \" + str(len(carids)), (0, 60), cv2.FONT_HERSHEY_SIMPLEX, .5,\n",
|
||||
" (0, 170, 0), 1)\n",
|
||||
"\n",
|
||||
" cv2.putText(image, \"Frame: \" + str(framenumber) + ' of ' + str(frames_count), (0, 75), cv2.FONT_HERSHEY_SIMPLEX,\n",
|
||||
" .5, (0, 170, 0), 1)\n",
|
||||
"\n",
|
||||
" cv2.putText(image, 'Time: ' + str(round(framenumber / fps, 2)) + ' sec of ' + str(round(frames_count / fps, 2))\n",
|
||||
" + ' sec', (0, 90), cv2.FONT_HERSHEY_SIMPLEX, .5, (0, 170, 0), 1)\n",
|
||||
"\n",
|
||||
" # displays images and transformations\n",
|
||||
" cv2.imshow(\"countours\", image)\n",
|
||||
" cv2.moveWindow(\"countours\", 0, 0)\n",
|
||||
"\n",
|
||||
" cv2.imshow(\"fgmask\", fgmask)\n",
|
||||
" cv2.moveWindow(\"fgmask\", int(width * ratio), 0)\n",
|
||||
"\n",
|
||||
" cv2.imshow(\"closing\", closing)\n",
|
||||
" cv2.moveWindow(\"closing\", width, 0)\n",
|
||||
"\n",
|
||||
" cv2.imshow(\"opening\", opening)\n",
|
||||
" cv2.moveWindow(\"opening\", 0, int(height * ratio))\n",
|
||||
"\n",
|
||||
" cv2.imshow(\"dilation\", dilation)\n",
|
||||
" cv2.moveWindow(\"dilation\", int(width * ratio), int(height * ratio))\n",
|
||||
"\n",
|
||||
" cv2.imshow(\"binary\", bins)\n",
|
||||
" cv2.moveWindow(\"binary\", width, int(height * ratio))\n",
|
||||
"\n",
|
||||
" # video.write(image) # save the current image to video file from earlier\n",
|
||||
"\n",
|
||||
" # adds to framecount\n",
|
||||
" framenumber = framenumber + 1\n",
|
||||
"\n",
|
||||
" k = cv2.waitKey(int(1000/fps)) & 0xff # int(1000/fps) is normal speed since waitkey is in ms\n",
|
||||
" if k == 27:\n",
|
||||
" break\n",
|
||||
"\n",
|
||||
" else: # if video is finished then break loop\n",
|
||||
"\n",
|
||||
" break\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "af84e6b4-dd55-447e-ac8c-a02a5f6f34be",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"cap.release()\n",
|
||||
"cv2.destroyAllWindows()\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3 (ipykernel)",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.10.10"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
|
|
@ -0,0 +1,344 @@
|
|||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "37fe6724-f5fe-412a-ab9a-6a1df878c308",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Import Library"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"id": "11b66fe3-8d38-4bf9-b9c5-f8bd3213bd55",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Selesai Import Library\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"import cv2 # Import library OpenCV untuk pengolahan citra dan video\n",
|
||||
"import imutils # Import library imutils untuk mempermudah manipulasi citra\n",
|
||||
"import numpy as np # Import library numpy untuk operasi numerik\n",
|
||||
"from ultralytics import YOLO # Import class YOLO dari library ultralytics untuk deteksi objek\n",
|
||||
"from collections import defaultdict # Import class defaultdict dari library collections untuk struktur data default dictionary\n",
|
||||
"\n",
|
||||
"print(\"Selesai Import Library\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "243e5a8f-46c2-4fe1-b174-52a46f0a26ee",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Deklarasi Variable"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"id": "bbeb303b-5683-44cc-a924-0f2481d75528",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"selesai deklarasi variable\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"color = (0, 255, 0) # Warna hijau untuk penggambaran objek dan garis\n",
|
||||
"color_red = (0, 0, 255) # Warna merah untuk teks dan garis\n",
|
||||
"thickness = 2 # Ketebalan garis untuk penggambaran objek dan garis\n",
|
||||
"\n",
|
||||
"font = cv2.FONT_HERSHEY_SIMPLEX # Jenis font untuk teks\n",
|
||||
"font_scale = 0.5 # Skala font untuk teks\n",
|
||||
"\n",
|
||||
"# Path video yang akan diproses\n",
|
||||
"video_path = \"video/videonya.mp4\"\n",
|
||||
"model_path = \"models/yolov8n.pt\"\n",
|
||||
"\n",
|
||||
"# Buka video\n",
|
||||
"cap = cv2.VideoCapture(video_path)\n",
|
||||
"# Inisialisasi model YOLO dengan file weight yang telah dilatih sebelumnya\n",
|
||||
"model = YOLO(model_path)\n",
|
||||
"\n",
|
||||
"# Ukuran frame video\n",
|
||||
"width = 1280\n",
|
||||
"height = 720\n",
|
||||
"\n",
|
||||
"# Inisialisasi objek untuk menyimpan video hasil pemrosesan\n",
|
||||
"# fourcc = cv2.VideoWriter_fourcc(*'XVID')\n",
|
||||
"# writer = cv2.VideoWriter(\"video.avi\", fourcc, 20.0, (width, height))\n",
|
||||
"\n",
|
||||
"# Id objek kendaraan yang ingin dilacak berdasarkan kelas di file coco-classes.txt\n",
|
||||
"vehicle_ids = [1,2, 3, 5, 6,7]\n",
|
||||
"# Dictionary untuk menyimpan sejarah pergerakan setiap kendaraan yang terdeteksi\n",
|
||||
"track_history = defaultdict(lambda: [])\n",
|
||||
"\n",
|
||||
"up = {} # Dictionary untuk kendaraan yang melewati garis atas\n",
|
||||
"down = {} # Dictionary untuk kendaraan yang melewati garis bawah\n",
|
||||
"threshold = 400 # Ambang batas garis pemisah kendaraan\n",
|
||||
"\n",
|
||||
"print(\"selesai deklarasi variable\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "00596875-56e1-445a-bd8b-b2b3a73a411a",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Fungsi untuk mengambil titik tengah dari bounding box objek "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"id": "ffcffbd1-ad9b-4908-8930-bea2ba6b6ecb",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Selesai membuat fungsi\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"def pega_centro(x, y, w, h):\n",
|
||||
" x1 = int(w / 2)\n",
|
||||
" y1 = int(h / 2)\n",
|
||||
" cx = x + x1\n",
|
||||
" cy = y + y1\n",
|
||||
" return cx, cy\n",
|
||||
"\n",
|
||||
"print(\"Selesai membuat fungsi\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "9f2e6c12-a70b-49f2-9083-a9c85b04e842",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Background subtraction menggunakan MOG2"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"id": "4b0f68b8-9216-49e6-892e-bbf2282d73b3",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"selesai\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"subtracao = cv2.createBackgroundSubtractorMOG2()\n",
|
||||
"print(\"selesai\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "0e9ea925-a617-45d3-b50c-273f4ee0163b",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Proses Video "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "705c59f4-fba5-498d-9e51-d002a0dc3226",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Terjadi kesalahan: not enough values to unpack (expected 7, got 6)\n",
|
||||
"Terjadi kesalahan: not enough values to unpack (expected 7, got 6)\n",
|
||||
"Terjadi kesalahan: not enough values to unpack (expected 7, got 6)\n",
|
||||
"Terjadi kesalahan: not enough values to unpack (expected 7, got 6)\n",
|
||||
"Terjadi kesalahan: not enough values to unpack (expected 7, got 6)\n",
|
||||
"Terjadi kesalahan: not enough values to unpack (expected 7, got 6)\n",
|
||||
"Terjadi kesalahan: not enough values to unpack (expected 7, got 6)\n",
|
||||
"Terjadi kesalahan: not enough values to unpack (expected 7, got 6)\n",
|
||||
"Terjadi kesalahan: not enough values to unpack (expected 7, got 6)\n",
|
||||
"Terjadi kesalahan: not enough values to unpack (expected 7, got 6)\n",
|
||||
"Terjadi kesalahan: not enough values to unpack (expected 7, got 6)\n",
|
||||
"Terjadi kesalahan: not enough values to unpack (expected 7, got 6)\n",
|
||||
"Terjadi kesalahan: not enough values to unpack (expected 7, got 6)\n",
|
||||
"Terjadi kesalahan: not enough values to unpack (expected 7, got 6)\n",
|
||||
"Terjadi kesalahan: not enough values to unpack (expected 7, got 6)\n",
|
||||
"Terjadi kesalahan: not enough values to unpack (expected 7, got 6)\n",
|
||||
"Terjadi kesalahan: not enough values to unpack (expected 7, got 6)\n",
|
||||
"Terjadi kesalahan: not enough values to unpack (expected 7, got 6)\n",
|
||||
"Terjadi kesalahan: not enough values to unpack (expected 7, got 6)\n",
|
||||
"Terjadi kesalahan: not enough values to unpack (expected 7, got 6)\n",
|
||||
"Terjadi kesalahan: not enough values to unpack (expected 7, got 6)\n",
|
||||
"Terjadi kesalahan: not enough values to unpack (expected 7, got 6)\n",
|
||||
"Terjadi kesalahan: not enough values to unpack (expected 7, got 6)\n",
|
||||
"Terjadi kesalahan: not enough values to unpack (expected 7, got 6)\n",
|
||||
"Terjadi kesalahan: not enough values to unpack (expected 7, got 6)\n",
|
||||
"Terjadi kesalahan: not enough values to unpack (expected 7, got 6)\n",
|
||||
"Terjadi kesalahan: not enough values to unpack (expected 7, got 6)\n",
|
||||
"Terjadi kesalahan: not enough values to unpack (expected 7, got 6)\n",
|
||||
"Terjadi kesalahan: not enough values to unpack (expected 7, got 6)\n",
|
||||
"Terjadi kesalahan: not enough values to unpack (expected 7, got 6)\n",
|
||||
"Terjadi kesalahan: not enough values to unpack (expected 7, got 6)\n",
|
||||
"Terjadi kesalahan: not enough values to unpack (expected 7, got 6)\n",
|
||||
"Terjadi kesalahan: not enough values to unpack (expected 7, got 6)\n",
|
||||
"Terjadi kesalahan: not enough values to unpack (expected 7, got 6)\n",
|
||||
"Terjadi kesalahan: not enough values to unpack (expected 7, got 6)\n",
|
||||
"Terjadi kesalahan: not enough values to unpack (expected 7, got 6)\n",
|
||||
"Terjadi kesalahan: not enough values to unpack (expected 7, got 6)\n",
|
||||
"Terjadi kesalahan: not enough values to unpack (expected 7, got 6)\n",
|
||||
"Terjadi kesalahan: not enough values to unpack (expected 7, got 6)\n",
|
||||
"Terjadi kesalahan: not enough values to unpack (expected 7, got 6)\n",
|
||||
"Terjadi kesalahan: not enough values to unpack (expected 7, got 6)\n",
|
||||
"Terjadi kesalahan: not enough values to unpack (expected 7, got 6)\n",
|
||||
"Terjadi kesalahan: not enough values to unpack (expected 7, got 6)\n",
|
||||
"Terjadi kesalahan: not enough values to unpack (expected 7, got 6)\n",
|
||||
"Terjadi kesalahan: not enough values to unpack (expected 7, got 6)\n",
|
||||
"Terjadi kesalahan: not enough values to unpack (expected 7, got 6)\n",
|
||||
"Terjadi kesalahan: not enough values to unpack (expected 7, got 6)\n",
|
||||
"Terjadi kesalahan: not enough values to unpack (expected 7, got 6)\n",
|
||||
"Terjadi kesalahan: not enough values to unpack (expected 7, got 6)\n",
|
||||
"Terjadi kesalahan: not enough values to unpack (expected 7, got 6)\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Loop utama untuk membaca setiap frame dari video\n",
|
||||
"while True:\n",
|
||||
" ret, frame = cap.read() # Membaca frame dari video\n",
|
||||
" if ret == False: # Keluar dari loop jika tidak ada frame yang dapat dibaca\n",
|
||||
" break\n",
|
||||
" \n",
|
||||
" try:\n",
|
||||
" frame = imutils.resize(frame, width = 1280, height = 720) # ubah frame menjadi tinggi 720 x lebar 1280\n",
|
||||
" frame_color = frame.copy() # Salin frame ke mode warna untuk pengolahan dan penggambaran\n",
|
||||
" frame_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) # Konversi frame ke citra grayscale\n",
|
||||
" frame_gray = cv2.cvtColor(frame_gray, cv2.COLOR_GRAY2BGR) # Konversi kembali ke citra BGR untuk tampilan grayscale\n",
|
||||
" frame_bw = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) # Konversi ke citra grayscale untuk mode black and white\n",
|
||||
"\n",
|
||||
" # Deteksi objek menggunakan model YOLO\n",
|
||||
" results = model.track(frame_color, persist=True, verbose=False)[0]\n",
|
||||
" bboxes = np.array(results.boxes.data.tolist(), dtype=\"int\") # Koordinat bounding box objek yang terdeteksi\n",
|
||||
"\n",
|
||||
" # Gambar garis pembatas untuk menghitung jumlah kendaraan yang melewati garis\n",
|
||||
" cv2.line(frame_color, (0, threshold), (1280, threshold), color, thickness)\n",
|
||||
" cv2.putText(frame_color, \"Pembatas Jalan\", (620, 445), font, 0.7, color_red, thickness)\n",
|
||||
"\n",
|
||||
" # Loop untuk setiap objek yang terdeteksi\n",
|
||||
" for box in bboxes:\n",
|
||||
" x1, y1, x2, y2, track_id, score, class_id = box # Ambil koordinat dan informasi lainnya\n",
|
||||
" cx = int((x1 + x2) / 2) # Hitung koordinat x pusat objek\n",
|
||||
" cy = int((y1 + y2) / 2) # Hitung koordinat y pusat objek\n",
|
||||
" if class_id in vehicle_ids: # Periksa apakah objek merupakan kendaraan yang ingin dilacak\n",
|
||||
" class_name = results.names[int(class_id)].upper() # Dapatkan nama kelas objek\n",
|
||||
"\n",
|
||||
" track = track_history[track_id] # Ambil sejarah pergerakan objek berdasarkan ID\n",
|
||||
" track.append((cx, cy)) # Tambahkan koordinat pusat objek ke dalam sejarah pergerakan\n",
|
||||
" if len(track) > 20: # Batasi panjang sejarah pergerakan agar tidak terlalu panjang\n",
|
||||
" track.pop(0) # Hapus elemen pertama jika sejarah sudah melebihi batas\n",
|
||||
"\n",
|
||||
" points = np.hstack(track).astype(\"int32\").reshape(-1, 1, 2) # Konversi sejarah pergerakan ke format yang sesuai untuk penggambaran\n",
|
||||
" cv2.polylines(frame_color, [points], isClosed=False, color=color, thickness=thickness) # Gambar garis yang merepresentasikan sejarah pergerakan\n",
|
||||
" cv2.rectangle(frame_color, (x1, y1), (x2, y2), color, thickness) # Gambar bounding box objek\n",
|
||||
" text = \"ID: {} {}\".format(track_id, class_name) # Buat teks ID objek dan nama kelasnya\n",
|
||||
" cv2.putText(frame_color, text, (x1, y1 - 5), font, font_scale, color, thickness) # Tampilkan teks di atas objek\n",
|
||||
"\n",
|
||||
" if cy > threshold - 5 and cy < threshold + 5 and cx < 670: # Periksa apakah objek melewati garis atas\n",
|
||||
" down[track_id] = x1, y1, x2, y2 # Simpan informasi objek yang melewati garis atas\n",
|
||||
"\n",
|
||||
" if cy > threshold - 5 and cy < threshold + 5 and cx > 670: # Periksa apakah objek melewati garis bawah\n",
|
||||
" up[track_id] = x1, y1, x2, y2 # Simpan informasi objek yang melewati garis bawah\n",
|
||||
"\n",
|
||||
" up_text = \"Kanan:{}\".format(len(list(up.keys()))) # Buat teks jumlah kendaraan yang melewati garis atas\n",
|
||||
" down_text = \"Kiri:{}\".format(len(list(down.keys()))) # Buat teks jumlah kendaraan yang melewati garis bawah\n",
|
||||
"\n",
|
||||
" cv2.putText(frame_color, up_text, (1150, threshold - 5), font, 0.8, color_red, thickness) # Tampilkan teks jumlah kendaraan yang melewati garis atas\n",
|
||||
" cv2.putText(frame_color, down_text, (0, threshold - 5), font, 0.8, color_red, thickness) # Tampilkan teks jumlah kendaraan yang melewati garis bawah\n",
|
||||
"\n",
|
||||
" # Background subtraction dan deteksi kontur\n",
|
||||
" grey = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) # Konversi frame ke citra grayscale\n",
|
||||
" blur = cv2.GaussianBlur(grey, (3, 3), 5) # Reduksi noise menggunakan Gaussian Blur\n",
|
||||
" img_sub = subtracao.apply(blur) # Background subtraction\n",
|
||||
" dilat = cv2.dilate(img_sub, np.ones((5, 5))) # Dilasi untuk meningkatkan ketebalan objek\n",
|
||||
" kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5)) # Kernel untuk operasi morfologi\n",
|
||||
" dilatada = cv2.morphologyEx(dilat, cv2.MORPH_CLOSE, kernel) # Operasi closing untuk mengisi lubang kecil pada objek\n",
|
||||
" dilatada = cv2.morphologyEx(dilatada, cv2.MORPH_CLOSE, kernel) # Operasi closing tambahan\n",
|
||||
" contorno, h = cv2.findContours(dilatada, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) # Deteksi kontur objek\n",
|
||||
"\n",
|
||||
" #writer.write(frame_color) # Menyimpan frame hasil pemrosesan\n",
|
||||
" # Menampilkan gambar\n",
|
||||
" cv2.imshow(\"Input\",frame) # inputan video\n",
|
||||
" cv2.imshow(\"Warna\", frame_color) # Tampilkan mode warna\n",
|
||||
" cv2.imshow(\"Grayscale\", frame_gray) # Tampilkan mode grayscale\n",
|
||||
" cv2.imshow(\"Detectar\", dilatada) # Tampilkan mode Detectar dilatada\n",
|
||||
" if cv2.waitKey(10) & 0xFF == ord(\"q\"): # Keluar saat tombol q ditekan\n",
|
||||
" break\n",
|
||||
"\n",
|
||||
" except Exception as e:\n",
|
||||
" print(\"Terjadi kesalahan:\", str(e)) # Tangkap dan tampilkan kesalahan yang terjadi\n",
|
||||
" continue # Lanjutkan ke iterasi berikutnya\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "ae345f06-2af7-4b93-b833-a14cc20f7d64",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Menutup Window OpenCV"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "15c70b25-1b92-43d8-9167-ebb88b2a8df7",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"cap.release() # Bebaskan sumber daya setelah selesai pemrosesan video\n",
|
||||
"writer.release() # Tutup objek writer\n",
|
||||
"cv2.destroyAllWindows() # Tutup semua jendela yang dibuka oleh OpenCV"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3 (ipykernel)",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.10.10"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
|
|
@ -52,13 +52,6 @@
|
|||
"text": [
|
||||
"selesai deklarasi variable\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"OpenCV: Couldn't read video stream from file \"vivideo2.mp4\"\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
|
|
@ -70,7 +63,7 @@
|
|||
"font_scale = 0.5 # Skala font untuk teks\n",
|
||||
"\n",
|
||||
"# Path video yang akan diproses\n",
|
||||
"video_path = \"video/video2.mp4\"\n",
|
||||
"video_path = \"video/videonya.mp4\"\n",
|
||||
"model_path = \"models/yolov8n.pt\"\n",
|
||||
"\n",
|
||||
"# Buka video\n",
|
||||
|
|
@ -83,11 +76,11 @@
|
|||
"height = 720\n",
|
||||
"\n",
|
||||
"# Inisialisasi objek untuk menyimpan video hasil pemrosesan\n",
|
||||
"fourcc = cv2.VideoWriter_fourcc(*'XVID')\n",
|
||||
"writer = cv2.VideoWriter(\"video.avi\", fourcc, 20.0, (width, height))\n",
|
||||
"# fourcc = cv2.VideoWriter_fourcc(*'XVID')\n",
|
||||
"# writer = cv2.VideoWriter(\"video.avi\", fourcc, 20.0, (width, height))\n",
|
||||
"\n",
|
||||
"# Id objek kendaraan yang ingin dilacak berdasarkan kelas di file coco-classes.txt\n",
|
||||
"vehicle_ids = [2, 3, 5, 7]\n",
|
||||
"vehicle_ids = [1,2, 3, 5, 6,7]\n",
|
||||
"# Dictionary untuk menyimpan sejarah pergerakan setiap kendaraan yang terdeteksi\n",
|
||||
"track_history = defaultdict(lambda: [])\n",
|
||||
"\n",
|
||||
|
|
@ -168,10 +161,40 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"execution_count": null,
|
||||
"id": "705c59f4-fba5-498d-9e51-d002a0dc3226",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Terjadi kesalahan: not enough values to unpack (expected 7, got 6)\n",
|
||||
"Terjadi kesalahan: not enough values to unpack (expected 7, got 6)\n",
|
||||
"Terjadi kesalahan: not enough values to unpack (expected 7, got 6)\n",
|
||||
"Terjadi kesalahan: not enough values to unpack (expected 7, got 6)\n",
|
||||
"Terjadi kesalahan: not enough values to unpack (expected 7, got 6)\n",
|
||||
"Terjadi kesalahan: not enough values to unpack (expected 7, got 6)\n",
|
||||
"Terjadi kesalahan: not enough values to unpack (expected 7, got 6)\n",
|
||||
"Terjadi kesalahan: not enough values to unpack (expected 7, got 6)\n",
|
||||
"Terjadi kesalahan: not enough values to unpack (expected 7, got 6)\n",
|
||||
"Terjadi kesalahan: not enough values to unpack (expected 7, got 6)\n",
|
||||
"Terjadi kesalahan: not enough values to unpack (expected 7, got 6)\n",
|
||||
"Terjadi kesalahan: not enough values to unpack (expected 7, got 6)\n",
|
||||
"Terjadi kesalahan: not enough values to unpack (expected 7, got 6)\n",
|
||||
"Terjadi kesalahan: not enough values to unpack (expected 7, got 6)\n",
|
||||
"Terjadi kesalahan: not enough values to unpack (expected 7, got 6)\n",
|
||||
"Terjadi kesalahan: not enough values to unpack (expected 7, got 6)\n",
|
||||
"Terjadi kesalahan: not enough values to unpack (expected 7, got 6)\n",
|
||||
"Terjadi kesalahan: not enough values to unpack (expected 7, got 6)\n",
|
||||
"Terjadi kesalahan: not enough values to unpack (expected 7, got 6)\n",
|
||||
"Terjadi kesalahan: not enough values to unpack (expected 7, got 6)\n",
|
||||
"Terjadi kesalahan: not enough values to unpack (expected 7, got 6)\n",
|
||||
"Terjadi kesalahan: not enough values to unpack (expected 7, got 6)\n",
|
||||
"Terjadi kesalahan: not enough values to unpack (expected 7, got 6)\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Loop utama untuk membaca setiap frame dari video\n",
|
||||
"while True:\n",
|
||||
|
|
@ -235,8 +258,9 @@
|
|||
" dilatada = cv2.morphologyEx(dilatada, cv2.MORPH_CLOSE, kernel) # Operasi closing tambahan\n",
|
||||
" contorno, h = cv2.findContours(dilatada, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) # Deteksi kontur objek\n",
|
||||
"\n",
|
||||
" writer.write(frame_color) # Menyimpan frame hasil pemrosesan\n",
|
||||
" #writer.write(frame_color) # Menyimpan frame hasil pemrosesan\n",
|
||||
" # Menampilkan gambar\n",
|
||||
" cv2.imshow(\"Input\",frame) # inputan video\n",
|
||||
" cv2.imshow(\"Warna\", frame_color) # Tampilkan mode warna\n",
|
||||
" cv2.imshow(\"Grayscale\", frame_gray) # Tampilkan mode grayscale\n",
|
||||
" cv2.imshow(\"Detectar\", dilatada) # Tampilkan mode Detectar dilatada\n",
|
||||
|
|
@ -258,7 +282,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"execution_count": null,
|
||||
"id": "15c70b25-1b92-43d8-9167-ebb88b2a8df7",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
|
|
@ -0,0 +1,6 @@
|
|||
{
|
||||
"cells": [],
|
||||
"metadata": {},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
|
|
@ -0,0 +1,384 @@
|
|||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"id": "93b77493-0a01-4421-b2a0-380991740ff6",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import numpy as np\n",
|
||||
"import cv2\n",
|
||||
"import pandas as pd\n",
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"id": "80b4ff7c-1f3b-4e1d-896c-d88c0966f33e",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"6868.0 30.03550936578534 848 478\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"cap = cv2.VideoCapture('video/video.mp4')\n",
|
||||
"# mendapatkan jumlah frame, fps, lebar, dan tinggi dari video\n",
|
||||
"frames_count, fps, width, height = cap.get(cv2.CAP_PROP_FRAME_COUNT), cap.get(cv2.CAP_PROP_FPS), cap.get(\n",
|
||||
" cv2.CAP_PROP_FRAME_WIDTH), cap.get(cv2.CAP_PROP_FRAME_HEIGHT)\n",
|
||||
"width = int(width)\n",
|
||||
"height = int(height)\n",
|
||||
"print(frames_count, fps, width, height)\n",
|
||||
"\n",
|
||||
"# membuat sebuah frame pandas dengan jumlah baris yang sama dengan jumlah frame\n",
|
||||
"df = pd.DataFrame(index=range(int(frames_count)))\n",
|
||||
"df.index.name = \"Frame\" # menandai kolom frame\n",
|
||||
"\n",
|
||||
"framenumber = 0 # mencatat frame saat ini\n",
|
||||
"carscrossedup = 0 # mencatat mobil yang melintasi jalan ke atas\n",
|
||||
"carscrosseddown = 0 # mencatat mobil yang melintasi jalan ke bawah\n",
|
||||
"carids = [] # daftar kosong untuk menyimpan ID mobil\n",
|
||||
"caridscrossed = [] # daftar kosong untuk menyimpan ID mobil yang sudah melintasi\n",
|
||||
"totalcars = 0 # mencatat jumlah total mobil\n",
|
||||
"\n",
|
||||
"fgbg = cv2.createBackgroundSubtractorMOG2() # membuat pengambil gambar latar belakang\n",
|
||||
"\n",
|
||||
"# informasi untuk mulai menyimpan video\n",
|
||||
"ret, frame = cap.read() # mengimpor gambar\n",
|
||||
"ratio = .5 # rasio ukuran pengubahan ukuran\n",
|
||||
"image = cv2.resize(frame, (0, 0), None, ratio, ratio) # mengubah ukuran gambar\n",
|
||||
"width2, height2, channels = image.shape\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"id": "5c8d5645-9df8-457c-88d7-2d3bbc0fade9",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"ename": "KeyboardInterrupt",
|
||||
"evalue": "",
|
||||
"output_type": "error",
|
||||
"traceback": [
|
||||
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
|
||||
"\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
|
||||
"Cell \u001b[0;32mIn[3], line 265\u001b[0m\n\u001b[1;32m 260\u001b[0m \u001b[38;5;66;03m# video.write(image) # save the current image to video file from earlier\u001b[39;00m\n\u001b[1;32m 261\u001b[0m \n\u001b[1;32m 262\u001b[0m \u001b[38;5;66;03m# adds to framecount\u001b[39;00m\n\u001b[1;32m 263\u001b[0m framenumber \u001b[38;5;241m=\u001b[39m framenumber \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[0;32m--> 265\u001b[0m k \u001b[38;5;241m=\u001b[39m \u001b[43mcv2\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mwaitKey\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mint\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m1000\u001b[39;49m\u001b[38;5;241;43m/\u001b[39;49m\u001b[43mfps\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;241m&\u001b[39m \u001b[38;5;241m0xff\u001b[39m \u001b[38;5;66;03m# int(1000/fps) is normal speed since waitkey is in ms\u001b[39;00m\n\u001b[1;32m 266\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m k \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m27\u001b[39m:\n\u001b[1;32m 267\u001b[0m \u001b[38;5;28;01mbreak\u001b[39;00m\n",
|
||||
"\u001b[0;31mKeyboardInterrupt\u001b[0m: "
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"while True:\n",
|
||||
"\n",
|
||||
" ret, frame = cap.read() # mengimpor gambar\n",
|
||||
"\n",
|
||||
" if ret: # jika ada frame lanjutkan dengan kode\n",
|
||||
"\n",
|
||||
" image = cv2.resize(frame, (0, 0), None, ratio, ratio) # mengubah ukuran gambar\n",
|
||||
"\n",
|
||||
" gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) # mengubah gambar ke hitam putih\n",
|
||||
"\n",
|
||||
" fgmask = fgbg.apply(gray) # menggunakan pengambil gambar latar belakang\n",
|
||||
"\n",
|
||||
" # menerapkan berbagai batasan pada fgmask untuk menyaring mobil\n",
|
||||
" # perlu bermain dengan setelan tersebut hingga mobil dapat diidentifikasi dengan mudah\n",
|
||||
" kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5)) # kernel untuk dilakukan pada morphology\n",
|
||||
" closing = cv2.morphologyEx(fgmask, cv2.MORPH_CLOSE, kernel)\n",
|
||||
" opening = cv2.morphologyEx(closing, cv2.MORPH_OPEN, kernel)\n",
|
||||
" dilation = cv2.dilate(opening, kernel)\n",
|
||||
" retvalbin, bins = cv2.threshold(dilation, 220, 255, cv2.THRESH_BINARY) # menghapus shadow\n",
|
||||
"\n",
|
||||
" # membuat kontur\n",
|
||||
" contours, hierarchy = cv2.findContours(bins, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)[-2:]\n",
|
||||
"\n",
|
||||
" # menggunakan konveks hull untuk membuat poligon di sekitar kontur\n",
|
||||
" hull = [cv2.convexHull(c) for c in contours]\n",
|
||||
"\n",
|
||||
" # menggambar kontur\n",
|
||||
" cv2.drawContours(image, hull, -1, (0, 255, 0), 3)\n",
|
||||
"\n",
|
||||
" # garis dibuat untuk menghentikan menghitung kontur, perlu dilakukan karena mobil yang jauh akan menjadi satu kontur besar\n",
|
||||
" lineypos = 225\n",
|
||||
" cv2.line(image, (0, lineypos), (width, lineypos), (255, 0, 0), 5)\n",
|
||||
"\n",
|
||||
" # garis y pos dibuat untuk menghitung kontur\n",
|
||||
" lineypos2 = 250\n",
|
||||
" cv2.line(image, (0, lineypos2), (width, lineypos2), (0, 255, 0), 5)\n",
|
||||
"\n",
|
||||
" # minimum area untuk kontur\n",
|
||||
" minarea = 300\n",
|
||||
"\n",
|
||||
" # maksimum area untuk kontur\n",
|
||||
" maxarea = 50000\n",
|
||||
"\n",
|
||||
" # vektor untuk x dan y lokasi centroid di frame saat ini\n",
|
||||
" cxx = np.zeros(len(contours))\n",
|
||||
" cyy = np.zeros(len(contours))\n",
|
||||
"\n",
|
||||
" for i in range(len(contours)): # mengulangi seluruh kontur dalam frame saat ini\n",
|
||||
"\n",
|
||||
" if hierarchy[0, i, 3] == -1: # menggunakan hierarchy untuk hanya menghitung kontur induk (tidak termasuk dalam kontur lain)\n",
|
||||
"\n",
|
||||
" area = cv2.contourArea(contours[i]) # menghitung area kontur\n",
|
||||
"\n",
|
||||
" if minarea < area < maxarea: # area threshold untuk kontur\n",
|
||||
"\n",
|
||||
" # menghitung centroid dari kontur\n",
|
||||
" cnt = contours[i]\n",
|
||||
" M = cv2.moments(cnt)\n",
|
||||
" cx = int(M['m10'] / M['m00'])\n",
|
||||
" cy = int(M['m01'] / M['m00'])\n",
|
||||
"\n",
|
||||
" if cy > lineypos: # menghapus kontur yang di atas garis\n",
|
||||
"\n",
|
||||
" # mengambil titik teratas, kiri, dan lebar dari kontur untuk membuat kotak\n",
|
||||
" # x,y adalah kiri atas dan w,h adalah lebar dan tinggi\n",
|
||||
" x, y, w, h = cv2.boundingRect(cnt)\n",
|
||||
"\n",
|
||||
" # membuat kotak di sekitar kontur\n",
|
||||
" cv2.rectangle(image, (x, y), (x + w, y + h), (255, 0, 0), 2)\n",
|
||||
"\n",
|
||||
" # Menuliskan teks centroid untuk memastikan kembali nanti\n",
|
||||
" cv2.putText(image, str(cx) + \",\" + str(cy), (cx + 10, cy + 10), cv2.FONT_HERSHEY_SIMPLEX,\n",
|
||||
" .3, (0, 0, 255), 1)\n",
|
||||
"\n",
|
||||
" cv2.drawMarker(image, (cx, cy), (0, 0, 255), cv2.MARKER_STAR, markerSize=5, thickness=1,\n",
|
||||
" line_type=cv2.LINE_AA)\n",
|
||||
"\n",
|
||||
" # menambahkan centroid yang lulus pada kriteria ke dalam list centroid\n",
|
||||
" cxx[i] = cx\n",
|
||||
" cyy[i] = cy\n",
|
||||
"\n",
|
||||
" # menghapus entri 0 dari list centroid\n",
|
||||
" cxx = cxx[cxx != 0]\n",
|
||||
" cyy = cyy[cyy != 0]\n",
|
||||
"\n",
|
||||
" # list kosong untuk nanti menyimpan indices centroid yang di tambahkan ke dataframe\n",
|
||||
" minx_index2 = []\n",
|
||||
" miny_index2 = []\n",
|
||||
"\n",
|
||||
" # batas maksimum untuk radius dari centroid dari frame saat ini untuk dianggap sama dengan centroid dari frame sebelumnya\n",
|
||||
" maxrad = 25\n",
|
||||
"\n",
|
||||
" # Bagian ini mengelola centroid dan menetapkan mereka untuk carid lama atau carid baru\n",
|
||||
"\n",
|
||||
" if len(cxx): # jika ada centroid dalam area yang ditentukan\n",
|
||||
"\n",
|
||||
" if not carids: # jika carids kosong\n",
|
||||
"\n",
|
||||
" for i in range(len(cxx)): # melalui semua centroid\n",
|
||||
"\n",
|
||||
" carids.append(i) # menambahkan car id ke list carids kosong\n",
|
||||
" df[str(carids[i])] = \"\" # menambahkan kolom ke dataframe sesuai carid\n",
|
||||
"\n",
|
||||
" # menetapkan nilai centroid ke frame (baris) dan carid (kolom) yang sesuai\n",
|
||||
" df.at[int(framenumber), str(carids[i])] = [cxx[i], cyy[i]]\n",
|
||||
"\n",
|
||||
" totalcars = carids[i] + 1 # menambahkan count car\n",
|
||||
"\n",
|
||||
" else: # jika carids sudah ada\n",
|
||||
"\n",
|
||||
" dx = np.zeros((len(cxx), len(carids))) # array baru untuk menghitung deltas\n",
|
||||
" dy = np.zeros((len(cyy), len(carids))) # array baru untuk menghitung deltas\n",
|
||||
"\n",
|
||||
" for i in range(len(cxx)): # melalui semua centroid\n",
|
||||
"\n",
|
||||
" for j in range(len(carids)): # melalui semua car id yang sudah ada\n",
|
||||
"\n",
|
||||
" # mengambil centroid dari frame sebelumnya untuk carid tertentu\n",
|
||||
" oldcxcy = df.iloc[int(framenumber - 1)][str(carids[j])]\n",
|
||||
"\n",
|
||||
" # mengambil centroid dari frame saat ini yang tidak selalu sesuai dengan centroid frame sebelumnya\n",
|
||||
" curcxcy = np.array([cxx[i], cyy[i]])\n",
|
||||
"\n",
|
||||
" if not oldcxcy: # periksa apakah centroid sebelumnya kosong jika arah sudah tidak ada di layar\n",
|
||||
"\n",
|
||||
" continue # lanjutkan ke carid berikutnya\n",
|
||||
"\n",
|
||||
" else: # hitung delta centroid untuk membandingkan dengan centroid frame saat ini\n",
|
||||
"\n",
|
||||
" dx[i, j] = oldcxcy[0] - curcxcy[0]\n",
|
||||
" dy[i, j] = oldcxcy[1] - curcxcy[1]\n",
|
||||
"\n",
|
||||
" for j in range(len(carids)): # melalui semua car id saat ini\n",
|
||||
"\n",
|
||||
" sumsum = np.abs(dx[:, j]) + np.abs(dy[:, j]) # menghitung delta wrt car id\n",
|
||||
"\n",
|
||||
" # mengambil indeks centroid yang memiliki nilai delta minimum dan ini indeks benar\n",
|
||||
" correctindextrue = np.argmin(np.abs(sumsum))\n",
|
||||
" minx_index = correctindextrue\n",
|
||||
" miny_index = correctindextrue\n",
|
||||
"\n",
|
||||
" # mengambil delta nilai minimum untuk dibandingkan dengan radius\n",
|
||||
" mindx = dx[minx_index, j]\n",
|
||||
" mindy = dy[miny_index, j]\n",
|
||||
"\n",
|
||||
" if mindx == 0 and mindy == 0 and np.all(dx[:, j] == 0) and np.all(dy[:, j] == 0):\n",
|
||||
" # periksa apakah minimum nilai adalah 0 dan semua delta adalah nol\n",
|
||||
" # delta dapat berupa nol jika centroid tidak bergerak\n",
|
||||
"\n",
|
||||
" continue # lanjutkan ke carid berikutnya\n",
|
||||
"\n",
|
||||
" else:\n",
|
||||
"\n",
|
||||
" # jika delta nilai adalah kurang dari maksimal radius maka tambahkan centroid ke carid sebelumnya\n",
|
||||
" if np.abs(mindx) < maxrad and np.abs(mindy) < maxrad:\n",
|
||||
"\n",
|
||||
" # tambahkan centroid ke carid yang sudah ada\n",
|
||||
" df.at[int(framenumber), str(carids[j])] = [cxx[minx_index], cyy[miny_index]]\n",
|
||||
" minx_index2.append(minx_index) # tambahkan semua indeks yang ditambahkan ke carid ke list\n",
|
||||
" miny_index2.append(miny_index)\n",
|
||||
"\n",
|
||||
" currentcars = 0 # current cars on screen\n",
|
||||
" currentcarsindex = [] # current cars on screen carid index\n",
|
||||
"\n",
|
||||
" for i in range(len(carids)): # loops through all carids\n",
|
||||
"\n",
|
||||
" if df.at[int(framenumber), str(carids[i])] != '':\n",
|
||||
" # checks the current frame to see which car ids are active\n",
|
||||
" # by checking in centroid exists on current frame for certain car id\n",
|
||||
"\n",
|
||||
" currentcars = currentcars + 1 # adds another to current cars on screen\n",
|
||||
" currentcarsindex.append(i) # adds car ids to current cars on screen\n",
|
||||
"\n",
|
||||
" for i in range(currentcars): # loops through all current car ids on screen\n",
|
||||
"\n",
|
||||
" # grabs centroid of certain carid for current frame\n",
|
||||
" curcent = df.iloc[int(framenumber)][str(carids[currentcarsindex[i]])]\n",
|
||||
"\n",
|
||||
" # grabs centroid of certain carid for previous frame\n",
|
||||
" oldcent = df.iloc[int(framenumber - 1)][str(carids[currentcarsindex[i]])]\n",
|
||||
"\n",
|
||||
" if curcent: # if there is a current centroid\n",
|
||||
"\n",
|
||||
" # On-screen text for current centroid\n",
|
||||
" cv2.putText(image, \"Centroid\" + str(curcent[0]) + \",\" + str(curcent[1]),\n",
|
||||
" (int(curcent[0]), int(curcent[1])), cv2.FONT_HERSHEY_SIMPLEX, .5, (0, 255, 255), 2)\n",
|
||||
"\n",
|
||||
" cv2.putText(image, \"ID:\" + str(carids[currentcarsindex[i]]), (int(curcent[0]), int(curcent[1] - 15)),\n",
|
||||
" cv2.FONT_HERSHEY_SIMPLEX, .5, (0, 255, 255), 2)\n",
|
||||
"\n",
|
||||
" cv2.drawMarker(image, (int(curcent[0]), int(curcent[1])), (0, 0, 255), cv2.MARKER_STAR, markerSize=5,\n",
|
||||
" thickness=1, line_type=cv2.LINE_AA)\n",
|
||||
"\n",
|
||||
" if oldcent: # checks if old centroid exists\n",
|
||||
" # adds radius box from previous centroid to current centroid for visualization\n",
|
||||
" xstart = oldcent[0] - maxrad\n",
|
||||
" ystart = oldcent[1] - maxrad\n",
|
||||
" xwidth = oldcent[0] + maxrad\n",
|
||||
" yheight = oldcent[1] + maxrad\n",
|
||||
" cv2.rectangle(image, (int(xstart), int(ystart)), (int(xwidth), int(yheight)), (0, 125, 0), 1)\n",
|
||||
"\n",
|
||||
" # checks if old centroid is on or below line and curcent is on or above line\n",
|
||||
" # to count cars and that car hasn't been counted yet\n",
|
||||
" if oldcent[1] >= lineypos2 and curcent[1] <= lineypos2 and carids[\n",
|
||||
" currentcarsindex[i]] not in caridscrossed:\n",
|
||||
"\n",
|
||||
" carscrossedup = carscrossedup + 1\n",
|
||||
" cv2.line(image, (0, lineypos2), (width, lineypos2), (0, 0, 255), 5)\n",
|
||||
" caridscrossed.append(\n",
|
||||
" currentcarsindex[i]) # adds car id to list of count cars to prevent double counting\n",
|
||||
"\n",
|
||||
" # checks if old centroid is on or above line and curcent is on or below line\n",
|
||||
" # to count cars and that car hasn't been counted yet\n",
|
||||
" elif oldcent[1] <= lineypos2 and curcent[1] >= lineypos2 and carids[\n",
|
||||
" currentcarsindex[i]] not in caridscrossed:\n",
|
||||
"\n",
|
||||
" carscrosseddown = carscrosseddown + 1\n",
|
||||
" cv2.line(image, (0, lineypos2), (width, lineypos2), (0, 0, 125), 5)\n",
|
||||
" caridscrossed.append(currentcarsindex[i])\n",
|
||||
"\n",
|
||||
" # Top left hand corner on-screen text\n",
|
||||
" cv2.rectangle(image, (0, 0), (250, 100), (255, 0, 0), -1) # background rectangle for on-screen text\n",
|
||||
"\n",
|
||||
" cv2.putText(image, \"Cars in Area: \" + str(currentcars), (0, 15), cv2.FONT_HERSHEY_SIMPLEX, .5, (0, 170, 0), 1)\n",
|
||||
"\n",
|
||||
" cv2.putText(image, \"Cars Crossed Up: \" + str(carscrossedup), (0, 30), cv2.FONT_HERSHEY_SIMPLEX, .5, (0, 170, 0),\n",
|
||||
" 1)\n",
|
||||
"\n",
|
||||
" cv2.putText(image, \"Cars Crossed Down: \" + str(carscrosseddown), (0, 45), cv2.FONT_HERSHEY_SIMPLEX, .5,\n",
|
||||
" (0, 170, 0), 1)\n",
|
||||
"\n",
|
||||
" cv2.putText(image, \"Total Cars Detected: \" + str(len(carids)), (0, 60), cv2.FONT_HERSHEY_SIMPLEX, .5,\n",
|
||||
" (0, 170, 0), 1)\n",
|
||||
"\n",
|
||||
" cv2.putText(image, \"Frame: \" + str(framenumber) + ' of ' + str(frames_count), (0, 75), cv2.FONT_HERSHEY_SIMPLEX,\n",
|
||||
" .5, (0, 170, 0), 1)\n",
|
||||
"\n",
|
||||
" cv2.putText(image, 'Time: ' + str(round(framenumber / fps, 2)) + ' sec of ' + str(round(frames_count / fps, 2))\n",
|
||||
" + ' sec', (0, 90), cv2.FONT_HERSHEY_SIMPLEX, .5, (0, 170, 0), 1)\n",
|
||||
"\n",
|
||||
" # displays images and transformations\n",
|
||||
" cv2.imshow(\"countours\", image)\n",
|
||||
" cv2.moveWindow(\"countours\", 0, 0)\n",
|
||||
"\n",
|
||||
" cv2.imshow(\"fgmask\", fgmask)\n",
|
||||
" cv2.moveWindow(\"fgmask\", int(width * ratio), 0)\n",
|
||||
"\n",
|
||||
" cv2.imshow(\"closing\", closing)\n",
|
||||
" cv2.moveWindow(\"closing\", width, 0)\n",
|
||||
"\n",
|
||||
" cv2.imshow(\"opening\", opening)\n",
|
||||
" cv2.moveWindow(\"opening\", 0, int(height * ratio))\n",
|
||||
"\n",
|
||||
" cv2.imshow(\"dilation\", dilation)\n",
|
||||
" cv2.moveWindow(\"dilation\", int(width * ratio), int(height * ratio))\n",
|
||||
"\n",
|
||||
" cv2.imshow(\"binary\", bins)\n",
|
||||
" cv2.moveWindow(\"binary\", width, int(height * ratio))\n",
|
||||
"\n",
|
||||
" # video.write(image) # save the current image to video file from earlier\n",
|
||||
"\n",
|
||||
" # adds to framecount\n",
|
||||
" framenumber = framenumber + 1\n",
|
||||
"\n",
|
||||
" k = cv2.waitKey(int(1000/fps)) & 0xff # int(1000/fps) is normal speed since waitkey is in ms\n",
|
||||
" if k == 27:\n",
|
||||
" break\n",
|
||||
"\n",
|
||||
" else: # if video is finished then break loop\n",
|
||||
"\n",
|
||||
" break\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "af84e6b4-dd55-447e-ac8c-a02a5f6f34be",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"cap.release()\n",
|
||||
"cv2.destroyAllWindows()\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3 (ipykernel)",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.10.10"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
|
|
@ -0,0 +1,344 @@
|
|||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "37fe6724-f5fe-412a-ab9a-6a1df878c308",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Import Library"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"id": "11b66fe3-8d38-4bf9-b9c5-f8bd3213bd55",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Selesai Import Library\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"import cv2 # Import library OpenCV untuk pengolahan citra dan video\n",
|
||||
"import imutils # Import library imutils untuk mempermudah manipulasi citra\n",
|
||||
"import numpy as np # Import library numpy untuk operasi numerik\n",
|
||||
"from ultralytics import YOLO # Import class YOLO dari library ultralytics untuk deteksi objek\n",
|
||||
"from collections import defaultdict # Import class defaultdict dari library collections untuk struktur data default dictionary\n",
|
||||
"\n",
|
||||
"print(\"Selesai Import Library\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "243e5a8f-46c2-4fe1-b174-52a46f0a26ee",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Deklarasi Variable"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"id": "bbeb303b-5683-44cc-a924-0f2481d75528",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"selesai deklarasi variable\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"color = (0, 255, 0) # Warna hijau untuk penggambaran objek dan garis\n",
|
||||
"color_red = (0, 0, 255) # Warna merah untuk teks dan garis\n",
|
||||
"thickness = 2 # Ketebalan garis untuk penggambaran objek dan garis\n",
|
||||
"\n",
|
||||
"font = cv2.FONT_HERSHEY_SIMPLEX # Jenis font untuk teks\n",
|
||||
"font_scale = 0.5 # Skala font untuk teks\n",
|
||||
"\n",
|
||||
"# Path video yang akan diproses\n",
|
||||
"video_path = \"video/videonya.mp4\"\n",
|
||||
"model_path = \"models/yolov8n.pt\"\n",
|
||||
"\n",
|
||||
"# Buka video\n",
|
||||
"cap = cv2.VideoCapture(video_path)\n",
|
||||
"# Inisialisasi model YOLO dengan file weight yang telah dilatih sebelumnya\n",
|
||||
"model = YOLO(model_path)\n",
|
||||
"\n",
|
||||
"# Ukuran frame video\n",
|
||||
"width = 1280\n",
|
||||
"height = 720\n",
|
||||
"\n",
|
||||
"# Inisialisasi objek untuk menyimpan video hasil pemrosesan\n",
|
||||
"# fourcc = cv2.VideoWriter_fourcc(*'XVID')\n",
|
||||
"# writer = cv2.VideoWriter(\"video.avi\", fourcc, 20.0, (width, height))\n",
|
||||
"\n",
|
||||
"# Id objek kendaraan yang ingin dilacak berdasarkan kelas di file coco-classes.txt\n",
|
||||
"vehicle_ids = [1,2, 3, 5, 6,7]\n",
|
||||
"# Dictionary untuk menyimpan sejarah pergerakan setiap kendaraan yang terdeteksi\n",
|
||||
"track_history = defaultdict(lambda: [])\n",
|
||||
"\n",
|
||||
"up = {} # Dictionary untuk kendaraan yang melewati garis atas\n",
|
||||
"down = {} # Dictionary untuk kendaraan yang melewati garis bawah\n",
|
||||
"threshold = 400 # Ambang batas garis pemisah kendaraan\n",
|
||||
"\n",
|
||||
"print(\"selesai deklarasi variable\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "00596875-56e1-445a-bd8b-b2b3a73a411a",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Fungsi untuk mengambil titik tengah dari bounding box objek "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"id": "ffcffbd1-ad9b-4908-8930-bea2ba6b6ecb",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Selesai membuat fungsi\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"def pega_centro(x, y, w, h):\n",
|
||||
" x1 = int(w / 2)\n",
|
||||
" y1 = int(h / 2)\n",
|
||||
" cx = x + x1\n",
|
||||
" cy = y + y1\n",
|
||||
" return cx, cy\n",
|
||||
"\n",
|
||||
"print(\"Selesai membuat fungsi\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "9f2e6c12-a70b-49f2-9083-a9c85b04e842",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Background subtraction menggunakan MOG2"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"id": "4b0f68b8-9216-49e6-892e-bbf2282d73b3",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"selesai\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"subtracao = cv2.createBackgroundSubtractorMOG2()\n",
|
||||
"print(\"selesai\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "0e9ea925-a617-45d3-b50c-273f4ee0163b",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Proses Video "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "705c59f4-fba5-498d-9e51-d002a0dc3226",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Terjadi kesalahan: not enough values to unpack (expected 7, got 6)\n",
|
||||
"Terjadi kesalahan: not enough values to unpack (expected 7, got 6)\n",
|
||||
"Terjadi kesalahan: not enough values to unpack (expected 7, got 6)\n",
|
||||
"Terjadi kesalahan: not enough values to unpack (expected 7, got 6)\n",
|
||||
"Terjadi kesalahan: not enough values to unpack (expected 7, got 6)\n",
|
||||
"Terjadi kesalahan: not enough values to unpack (expected 7, got 6)\n",
|
||||
"Terjadi kesalahan: not enough values to unpack (expected 7, got 6)\n",
|
||||
"Terjadi kesalahan: not enough values to unpack (expected 7, got 6)\n",
|
||||
"Terjadi kesalahan: not enough values to unpack (expected 7, got 6)\n",
|
||||
"Terjadi kesalahan: not enough values to unpack (expected 7, got 6)\n",
|
||||
"Terjadi kesalahan: not enough values to unpack (expected 7, got 6)\n",
|
||||
"Terjadi kesalahan: not enough values to unpack (expected 7, got 6)\n",
|
||||
"Terjadi kesalahan: not enough values to unpack (expected 7, got 6)\n",
|
||||
"Terjadi kesalahan: not enough values to unpack (expected 7, got 6)\n",
|
||||
"Terjadi kesalahan: not enough values to unpack (expected 7, got 6)\n",
|
||||
"Terjadi kesalahan: not enough values to unpack (expected 7, got 6)\n",
|
||||
"Terjadi kesalahan: not enough values to unpack (expected 7, got 6)\n",
|
||||
"Terjadi kesalahan: not enough values to unpack (expected 7, got 6)\n",
|
||||
"Terjadi kesalahan: not enough values to unpack (expected 7, got 6)\n",
|
||||
"Terjadi kesalahan: not enough values to unpack (expected 7, got 6)\n",
|
||||
"Terjadi kesalahan: not enough values to unpack (expected 7, got 6)\n",
|
||||
"Terjadi kesalahan: not enough values to unpack (expected 7, got 6)\n",
|
||||
"Terjadi kesalahan: not enough values to unpack (expected 7, got 6)\n",
|
||||
"Terjadi kesalahan: not enough values to unpack (expected 7, got 6)\n",
|
||||
"Terjadi kesalahan: not enough values to unpack (expected 7, got 6)\n",
|
||||
"Terjadi kesalahan: not enough values to unpack (expected 7, got 6)\n",
|
||||
"Terjadi kesalahan: not enough values to unpack (expected 7, got 6)\n",
|
||||
"Terjadi kesalahan: not enough values to unpack (expected 7, got 6)\n",
|
||||
"Terjadi kesalahan: not enough values to unpack (expected 7, got 6)\n",
|
||||
"Terjadi kesalahan: not enough values to unpack (expected 7, got 6)\n",
|
||||
"Terjadi kesalahan: not enough values to unpack (expected 7, got 6)\n",
|
||||
"Terjadi kesalahan: not enough values to unpack (expected 7, got 6)\n",
|
||||
"Terjadi kesalahan: not enough values to unpack (expected 7, got 6)\n",
|
||||
"Terjadi kesalahan: not enough values to unpack (expected 7, got 6)\n",
|
||||
"Terjadi kesalahan: not enough values to unpack (expected 7, got 6)\n",
|
||||
"Terjadi kesalahan: not enough values to unpack (expected 7, got 6)\n",
|
||||
"Terjadi kesalahan: not enough values to unpack (expected 7, got 6)\n",
|
||||
"Terjadi kesalahan: not enough values to unpack (expected 7, got 6)\n",
|
||||
"Terjadi kesalahan: not enough values to unpack (expected 7, got 6)\n",
|
||||
"Terjadi kesalahan: not enough values to unpack (expected 7, got 6)\n",
|
||||
"Terjadi kesalahan: not enough values to unpack (expected 7, got 6)\n",
|
||||
"Terjadi kesalahan: not enough values to unpack (expected 7, got 6)\n",
|
||||
"Terjadi kesalahan: not enough values to unpack (expected 7, got 6)\n",
|
||||
"Terjadi kesalahan: not enough values to unpack (expected 7, got 6)\n",
|
||||
"Terjadi kesalahan: not enough values to unpack (expected 7, got 6)\n",
|
||||
"Terjadi kesalahan: not enough values to unpack (expected 7, got 6)\n",
|
||||
"Terjadi kesalahan: not enough values to unpack (expected 7, got 6)\n",
|
||||
"Terjadi kesalahan: not enough values to unpack (expected 7, got 6)\n",
|
||||
"Terjadi kesalahan: not enough values to unpack (expected 7, got 6)\n",
|
||||
"Terjadi kesalahan: not enough values to unpack (expected 7, got 6)\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Loop utama untuk membaca setiap frame dari video\n",
|
||||
"while True:\n",
|
||||
" ret, frame = cap.read() # Membaca frame dari video\n",
|
||||
" if ret == False: # Keluar dari loop jika tidak ada frame yang dapat dibaca\n",
|
||||
" break\n",
|
||||
" \n",
|
||||
" try:\n",
|
||||
" frame = imutils.resize(frame, width = 1280, height = 720) # ubah frame menjadi tinggi 720 x lebar 1280\n",
|
||||
" frame_color = frame.copy() # Salin frame ke mode warna untuk pengolahan dan penggambaran\n",
|
||||
" frame_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) # Konversi frame ke citra grayscale\n",
|
||||
" frame_gray = cv2.cvtColor(frame_gray, cv2.COLOR_GRAY2BGR) # Konversi kembali ke citra BGR untuk tampilan grayscale\n",
|
||||
" frame_bw = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) # Konversi ke citra grayscale untuk mode black and white\n",
|
||||
"\n",
|
||||
" # Deteksi objek menggunakan model YOLO\n",
|
||||
" results = model.track(frame_color, persist=True, verbose=False)[0]\n",
|
||||
" bboxes = np.array(results.boxes.data.tolist(), dtype=\"int\") # Koordinat bounding box objek yang terdeteksi\n",
|
||||
"\n",
|
||||
" # Gambar garis pembatas untuk menghitung jumlah kendaraan yang melewati garis\n",
|
||||
" cv2.line(frame_color, (0, threshold), (1280, threshold), color, thickness)\n",
|
||||
" cv2.putText(frame_color, \"Pembatas Jalan\", (620, 445), font, 0.7, color_red, thickness)\n",
|
||||
"\n",
|
||||
" # Loop untuk setiap objek yang terdeteksi\n",
|
||||
" for box in bboxes:\n",
|
||||
" x1, y1, x2, y2, track_id, score, class_id = box # Ambil koordinat dan informasi lainnya\n",
|
||||
" cx = int((x1 + x2) / 2) # Hitung koordinat x pusat objek\n",
|
||||
" cy = int((y1 + y2) / 2) # Hitung koordinat y pusat objek\n",
|
||||
" if class_id in vehicle_ids: # Periksa apakah objek merupakan kendaraan yang ingin dilacak\n",
|
||||
" class_name = results.names[int(class_id)].upper() # Dapatkan nama kelas objek\n",
|
||||
"\n",
|
||||
" track = track_history[track_id] # Ambil sejarah pergerakan objek berdasarkan ID\n",
|
||||
" track.append((cx, cy)) # Tambahkan koordinat pusat objek ke dalam sejarah pergerakan\n",
|
||||
" if len(track) > 20: # Batasi panjang sejarah pergerakan agar tidak terlalu panjang\n",
|
||||
" track.pop(0) # Hapus elemen pertama jika sejarah sudah melebihi batas\n",
|
||||
"\n",
|
||||
" points = np.hstack(track).astype(\"int32\").reshape(-1, 1, 2) # Konversi sejarah pergerakan ke format yang sesuai untuk penggambaran\n",
|
||||
" cv2.polylines(frame_color, [points], isClosed=False, color=color, thickness=thickness) # Gambar garis yang merepresentasikan sejarah pergerakan\n",
|
||||
" cv2.rectangle(frame_color, (x1, y1), (x2, y2), color, thickness) # Gambar bounding box objek\n",
|
||||
" text = \"ID: {} {}\".format(track_id, class_name) # Buat teks ID objek dan nama kelasnya\n",
|
||||
" cv2.putText(frame_color, text, (x1, y1 - 5), font, font_scale, color, thickness) # Tampilkan teks di atas objek\n",
|
||||
"\n",
|
||||
" if cy > threshold - 5 and cy < threshold + 5 and cx < 670: # Periksa apakah objek melewati garis atas\n",
|
||||
" down[track_id] = x1, y1, x2, y2 # Simpan informasi objek yang melewati garis atas\n",
|
||||
"\n",
|
||||
" if cy > threshold - 5 and cy < threshold + 5 and cx > 670: # Periksa apakah objek melewati garis bawah\n",
|
||||
" up[track_id] = x1, y1, x2, y2 # Simpan informasi objek yang melewati garis bawah\n",
|
||||
"\n",
|
||||
" up_text = \"Kanan:{}\".format(len(list(up.keys()))) # Buat teks jumlah kendaraan yang melewati garis atas\n",
|
||||
" down_text = \"Kiri:{}\".format(len(list(down.keys()))) # Buat teks jumlah kendaraan yang melewati garis bawah\n",
|
||||
"\n",
|
||||
" cv2.putText(frame_color, up_text, (1150, threshold - 5), font, 0.8, color_red, thickness) # Tampilkan teks jumlah kendaraan yang melewati garis atas\n",
|
||||
" cv2.putText(frame_color, down_text, (0, threshold - 5), font, 0.8, color_red, thickness) # Tampilkan teks jumlah kendaraan yang melewati garis bawah\n",
|
||||
"\n",
|
||||
" # Background subtraction dan deteksi kontur\n",
|
||||
" grey = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) # Konversi frame ke citra grayscale\n",
|
||||
" blur = cv2.GaussianBlur(grey, (3, 3), 5) # Reduksi noise menggunakan Gaussian Blur\n",
|
||||
" img_sub = subtracao.apply(blur) # Background subtraction\n",
|
||||
" dilat = cv2.dilate(img_sub, np.ones((5, 5))) # Dilasi untuk meningkatkan ketebalan objek\n",
|
||||
" kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5)) # Kernel untuk operasi morfologi\n",
|
||||
" dilatada = cv2.morphologyEx(dilat, cv2.MORPH_CLOSE, kernel) # Operasi closing untuk mengisi lubang kecil pada objek\n",
|
||||
" dilatada = cv2.morphologyEx(dilatada, cv2.MORPH_CLOSE, kernel) # Operasi closing tambahan\n",
|
||||
" contorno, h = cv2.findContours(dilatada, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) # Deteksi kontur objek\n",
|
||||
"\n",
|
||||
" #writer.write(frame_color) # Menyimpan frame hasil pemrosesan\n",
|
||||
" # Menampilkan gambar\n",
|
||||
" cv2.imshow(\"Input\",frame) # inputan video\n",
|
||||
" cv2.imshow(\"Warna\", frame_color) # Tampilkan mode warna\n",
|
||||
" cv2.imshow(\"Grayscale\", frame_gray) # Tampilkan mode grayscale\n",
|
||||
" cv2.imshow(\"Detectar\", dilatada) # Tampilkan mode Detectar dilatada\n",
|
||||
" if cv2.waitKey(10) & 0xFF == ord(\"q\"): # Keluar saat tombol q ditekan\n",
|
||||
" break\n",
|
||||
"\n",
|
||||
" except Exception as e:\n",
|
||||
" print(\"Terjadi kesalahan:\", str(e)) # Tangkap dan tampilkan kesalahan yang terjadi\n",
|
||||
" continue # Lanjutkan ke iterasi berikutnya\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "ae345f06-2af7-4b93-b833-a14cc20f7d64",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Menutup Window OpenCV"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "15c70b25-1b92-43d8-9167-ebb88b2a8df7",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"cap.release() # Bebaskan sumber daya setelah selesai pemrosesan video\n",
|
||||
"writer.release() # Tutup objek writer\n",
|
||||
"cv2.destroyAllWindows() # Tutup semua jendela yang dibuka oleh OpenCV"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3 (ipykernel)",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.10.10"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
322
Pengujian.ipynb
322
Pengujian.ipynb
|
|
@ -1,322 +0,0 @@
|
|||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "37fe6724-f5fe-412a-ab9a-6a1df878c308",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Import Library"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"id": "11b66fe3-8d38-4bf9-b9c5-f8bd3213bd55",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Selesai Import Library\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"import cv2 # Import library OpenCV untuk pengolahan citra dan video\n",
|
||||
"import imutils # Import library imutils untuk mempermudah manipulasi citra\n",
|
||||
"import numpy as np # Import library numpy untuk operasi numerik\n",
|
||||
"from ultralytics import YOLO # Import class YOLO dari library ultralytics untuk deteksi objek\n",
|
||||
"from collections import defaultdict # Import class defaultdict dari library collections untuk struktur data default dictionary\n",
|
||||
"\n",
|
||||
"print(\"Selesai Import Library\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "243e5a8f-46c2-4fe1-b174-52a46f0a26ee",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Deklarasi Variable"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"id": "bbeb303b-5683-44cc-a924-0f2481d75528",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"selesai deklarasi variable\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"color = (0, 255, 0) # Warna hijau untuk penggambaran objek dan garis\n",
|
||||
"color_red = (0, 0, 255) # Warna merah untuk teks dan garis\n",
|
||||
"thickness = 2 # Ketebalan garis untuk penggambaran objek dan garis\n",
|
||||
"\n",
|
||||
"font = cv2.FONT_HERSHEY_SIMPLEX # Jenis font untuk teks\n",
|
||||
"font_scale = 0.5 # Skala font untuk teks\n",
|
||||
"\n",
|
||||
"# Path video yang akan diproses\n",
|
||||
"video_path = \"video/video2.mp4\"\n",
|
||||
"model_path = \"models/yolov8n.pt\"\n",
|
||||
"\n",
|
||||
"# Buka video\n",
|
||||
"cap = cv2.VideoCapture(video_path)\n",
|
||||
"# Inisialisasi model YOLO dengan file weight yang telah dilatih sebelumnya\n",
|
||||
"model = YOLO(model_path)\n",
|
||||
"\n",
|
||||
"# Ukuran frame video\n",
|
||||
"width = 1280\n",
|
||||
"height = 720\n",
|
||||
"\n",
|
||||
"# Inisialisasi objek untuk menyimpan video hasil pemrosesan\n",
|
||||
"fourcc = cv2.VideoWriter_fourcc(*'XVID')\n",
|
||||
"writer = cv2.VideoWriter(\"video.avi\", fourcc, 20.0, (width, height))\n",
|
||||
"\n",
|
||||
"# Id objek kendaraan yang ingin dilacak berdasarkan kelas di file coco-classes.txt\n",
|
||||
"vehicle_ids = [2, 3, 5, 7]\n",
|
||||
"# Dictionary untuk menyimpan sejarah pergerakan setiap kendaraan yang terdeteksi\n",
|
||||
"track_history = defaultdict(lambda: [])\n",
|
||||
"\n",
|
||||
"up = {} # Dictionary untuk kendaraan yang melewati garis atas\n",
|
||||
"down = {} # Dictionary untuk kendaraan yang melewati garis bawah\n",
|
||||
"threshold = 400 # Ambang batas garis pemisah kendaraan\n",
|
||||
"\n",
|
||||
"print(\"selesai deklarasi variable\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "00596875-56e1-445a-bd8b-b2b3a73a411a",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Fungsi untuk mengambil titik tengah dari bounding box objek "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"id": "ffcffbd1-ad9b-4908-8930-bea2ba6b6ecb",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Selesai membuat fungsi\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"def pega_centro(x, y, w, h):\n",
|
||||
" x1 = int(w / 2)\n",
|
||||
" y1 = int(h / 2)\n",
|
||||
" cx = x + x1\n",
|
||||
" cy = y + y1\n",
|
||||
" return cx, cy\n",
|
||||
"\n",
|
||||
"print(\"Selesai membuat fungsi\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "9f2e6c12-a70b-49f2-9083-a9c85b04e842",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Background subtraction menggunakan MOG2"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"id": "4b0f68b8-9216-49e6-892e-bbf2282d73b3",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"selesai\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"subtracao = cv2.createBackgroundSubtractorMOG2()\n",
|
||||
"print(\"selesai\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "0e9ea925-a617-45d3-b50c-273f4ee0163b",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Proses Video "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 11,
|
||||
"id": "705c59f4-fba5-498d-9e51-d002a0dc3226",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"ename": "KeyboardInterrupt",
|
||||
"evalue": "",
|
||||
"output_type": "error",
|
||||
"traceback": [
|
||||
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
|
||||
"\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
|
||||
"Cell \u001b[0;32mIn[11], line 15\u001b[0m\n\u001b[1;32m 12\u001b[0m frame_bw \u001b[38;5;241m=\u001b[39m cv2\u001b[38;5;241m.\u001b[39mcvtColor(frame, cv2\u001b[38;5;241m.\u001b[39mCOLOR_BGR2GRAY) \u001b[38;5;66;03m# Konversi ke citra grayscale untuk mode black and white\u001b[39;00m\n\u001b[1;32m 14\u001b[0m \u001b[38;5;66;03m# Deteksi objek menggunakan model YOLO\u001b[39;00m\n\u001b[0;32m---> 15\u001b[0m results \u001b[38;5;241m=\u001b[39m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrack\u001b[49m\u001b[43m(\u001b[49m\u001b[43mframe_color\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpersist\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mverbose\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m[\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m 16\u001b[0m bboxes \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marray(results\u001b[38;5;241m.\u001b[39mboxes\u001b[38;5;241m.\u001b[39mdata\u001b[38;5;241m.\u001b[39mtolist(), dtype\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mint\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;66;03m# Koordinat bounding box objek yang terdeteksi\u001b[39;00m\n\u001b[1;32m 18\u001b[0m \u001b[38;5;66;03m# Gambar garis pembatas untuk menghitung jumlah kendaraan yang melewati garis\u001b[39;00m\n",
|
||||
"File \u001b[0;32m~/Desktop/Python/YOLOv8-Projects/Traffic Analysis Projects/Highway Car Counter/env/lib/python3.10/site-packages/ultralytics/engine/model.py:469\u001b[0m, in \u001b[0;36mModel.track\u001b[0;34m(self, source, stream, persist, **kwargs)\u001b[0m\n\u001b[1;32m 467\u001b[0m kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mconf\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m kwargs\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mconf\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;241m0.1\u001b[39m \u001b[38;5;66;03m# ByteTrack-based method needs low confidence predictions as input\u001b[39;00m\n\u001b[1;32m 468\u001b[0m kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmode\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtrack\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m--> 469\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpredict\u001b[49m\u001b[43m(\u001b[49m\u001b[43msource\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msource\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstream\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstream\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
|
||||
"File \u001b[0;32m~/Desktop/Python/YOLOv8-Projects/Traffic Analysis Projects/Highway Car Counter/env/lib/python3.10/site-packages/ultralytics/engine/model.py:430\u001b[0m, in \u001b[0;36mModel.predict\u001b[0;34m(self, source, stream, predictor, **kwargs)\u001b[0m\n\u001b[1;32m 428\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m prompts \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpredictor, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mset_prompts\u001b[39m\u001b[38;5;124m\"\u001b[39m): \u001b[38;5;66;03m# for SAM-type models\u001b[39;00m\n\u001b[1;32m 429\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpredictor\u001b[38;5;241m.\u001b[39mset_prompts(prompts)\n\u001b[0;32m--> 430\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpredictor\u001b[38;5;241m.\u001b[39mpredict_cli(source\u001b[38;5;241m=\u001b[39msource) \u001b[38;5;28;01mif\u001b[39;00m is_cli \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpredictor\u001b[49m\u001b[43m(\u001b[49m\u001b[43msource\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msource\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstream\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstream\u001b[49m\u001b[43m)\u001b[49m\n",
|
||||
"File \u001b[0;32m~/Desktop/Python/YOLOv8-Projects/Traffic Analysis Projects/Highway Car Counter/env/lib/python3.10/site-packages/ultralytics/engine/predictor.py:204\u001b[0m, in \u001b[0;36mBasePredictor.__call__\u001b[0;34m(self, source, model, stream, *args, **kwargs)\u001b[0m\n\u001b[1;32m 202\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstream_inference(source, model, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m 203\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 204\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mlist\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstream_inference\u001b[49m\u001b[43m(\u001b[49m\u001b[43msource\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n",
|
||||
"File \u001b[0;32m~/Desktop/Python/YOLOv8-Projects/Traffic Analysis Projects/Highway Car Counter/env/lib/python3.10/site-packages/torch/utils/_contextlib.py:35\u001b[0m, in \u001b[0;36m_wrap_generator.<locals>.generator_context\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 32\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 33\u001b[0m \u001b[38;5;66;03m# Issuing `None` to a generator fires it up\u001b[39;00m\n\u001b[1;32m 34\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m ctx_factory():\n\u001b[0;32m---> 35\u001b[0m response \u001b[38;5;241m=\u001b[39m \u001b[43mgen\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msend\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m 37\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[1;32m 38\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 39\u001b[0m \u001b[38;5;66;03m# Forward the response to our caller and get its next request\u001b[39;00m\n",
|
||||
"File \u001b[0;32m~/Desktop/Python/YOLOv8-Projects/Traffic Analysis Projects/Highway Car Counter/env/lib/python3.10/site-packages/ultralytics/engine/predictor.py:283\u001b[0m, in \u001b[0;36mBasePredictor.stream_inference\u001b[0;34m(self, source, model, *args, **kwargs)\u001b[0m\n\u001b[1;32m 281\u001b[0m \u001b[38;5;66;03m# Inference\u001b[39;00m\n\u001b[1;32m 282\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m profilers[\u001b[38;5;241m1\u001b[39m]:\n\u001b[0;32m--> 283\u001b[0m preds \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minference\u001b[49m\u001b[43m(\u001b[49m\u001b[43mim\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 284\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39margs\u001b[38;5;241m.\u001b[39membed:\n\u001b[1;32m 285\u001b[0m \u001b[38;5;28;01myield from\u001b[39;00m [preds] \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(preds, torch\u001b[38;5;241m.\u001b[39mTensor) \u001b[38;5;28;01melse\u001b[39;00m preds \u001b[38;5;66;03m# yield embedding tensors\u001b[39;00m\n",
|
||||
"File \u001b[0;32m~/Desktop/Python/YOLOv8-Projects/Traffic Analysis Projects/Highway Car Counter/env/lib/python3.10/site-packages/ultralytics/engine/predictor.py:140\u001b[0m, in \u001b[0;36mBasePredictor.inference\u001b[0;34m(self, im, *args, **kwargs)\u001b[0m\n\u001b[1;32m 134\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Runs inference on a given image using the specified model and arguments.\"\"\"\u001b[39;00m\n\u001b[1;32m 135\u001b[0m visualize \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m 136\u001b[0m increment_path(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msave_dir \u001b[38;5;241m/\u001b[39m Path(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbatch[\u001b[38;5;241m0\u001b[39m][\u001b[38;5;241m0\u001b[39m])\u001b[38;5;241m.\u001b[39mstem, mkdir\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m 137\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39margs\u001b[38;5;241m.\u001b[39mvisualize \u001b[38;5;129;01mand\u001b[39;00m (\u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msource_type\u001b[38;5;241m.\u001b[39mtensor)\n\u001b[1;32m 138\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[1;32m 139\u001b[0m )\n\u001b[0;32m--> 140\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmodel\u001b[49m\u001b[43m(\u001b[49m\u001b[43mim\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maugment\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43maugment\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvisualize\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mvisualize\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43membed\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43membed\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
|
||||
"File \u001b[0;32m~/Desktop/Python/YOLOv8-Projects/Traffic Analysis Projects/Highway Car Counter/env/lib/python3.10/site-packages/torch/nn/modules/module.py:1511\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1509\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m 1510\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1511\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
|
||||
"File \u001b[0;32m~/Desktop/Python/YOLOv8-Projects/Traffic Analysis Projects/Highway Car Counter/env/lib/python3.10/site-packages/torch/nn/modules/module.py:1520\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1515\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1516\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1517\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m 1518\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1519\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1520\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1522\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 1523\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
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"File \u001b[0;32m~/Desktop/Python/YOLOv8-Projects/Traffic Analysis Projects/Highway Car Counter/env/lib/python3.10/site-packages/ultralytics/nn/autobackend.py:384\u001b[0m, in \u001b[0;36mAutoBackend.forward\u001b[0;34m(self, im, augment, visualize, embed)\u001b[0m\n\u001b[1;32m 381\u001b[0m im \u001b[38;5;241m=\u001b[39m im\u001b[38;5;241m.\u001b[39mpermute(\u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m2\u001b[39m, \u001b[38;5;241m3\u001b[39m, \u001b[38;5;241m1\u001b[39m) \u001b[38;5;66;03m# torch BCHW to numpy BHWC shape(1,320,192,3)\u001b[39;00m\n\u001b[1;32m 383\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpt \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnn_module: \u001b[38;5;66;03m# PyTorch\u001b[39;00m\n\u001b[0;32m--> 384\u001b[0m y \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmodel\u001b[49m\u001b[43m(\u001b[49m\u001b[43mim\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maugment\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maugment\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvisualize\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mvisualize\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43membed\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43membed\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 385\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mjit: \u001b[38;5;66;03m# TorchScript\u001b[39;00m\n\u001b[1;32m 386\u001b[0m y \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmodel(im)\n",
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"File \u001b[0;32m~/Desktop/Python/YOLOv8-Projects/Traffic Analysis Projects/Highway Car Counter/env/lib/python3.10/site-packages/torch/nn/modules/module.py:1511\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1509\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m 1510\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1511\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
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"File \u001b[0;32m~/Desktop/Python/YOLOv8-Projects/Traffic Analysis Projects/Highway Car Counter/env/lib/python3.10/site-packages/torch/nn/modules/module.py:1520\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1515\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1516\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1517\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m 1518\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1519\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1520\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1522\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 1523\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
|
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"File \u001b[0;32m~/Desktop/Python/YOLOv8-Projects/Traffic Analysis Projects/Highway Car Counter/env/lib/python3.10/site-packages/ultralytics/nn/tasks.py:83\u001b[0m, in \u001b[0;36mBaseModel.forward\u001b[0;34m(self, x, *args, **kwargs)\u001b[0m\n\u001b[1;32m 81\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(x, \u001b[38;5;28mdict\u001b[39m): \u001b[38;5;66;03m# for cases of training and validating while training.\u001b[39;00m\n\u001b[1;32m 82\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mloss(x, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m---> 83\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpredict\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
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"File \u001b[0;32m~/Desktop/Python/YOLOv8-Projects/Traffic Analysis Projects/Highway Car Counter/env/lib/python3.10/site-packages/ultralytics/nn/tasks.py:101\u001b[0m, in \u001b[0;36mBaseModel.predict\u001b[0;34m(self, x, profile, visualize, augment, embed)\u001b[0m\n\u001b[1;32m 99\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m augment:\n\u001b[1;32m 100\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_predict_augment(x)\n\u001b[0;32m--> 101\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_predict_once\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprofile\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvisualize\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43membed\u001b[49m\u001b[43m)\u001b[49m\n",
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"File \u001b[0;32m~/Desktop/Python/YOLOv8-Projects/Traffic Analysis Projects/Highway Car Counter/env/lib/python3.10/site-packages/ultralytics/nn/tasks.py:122\u001b[0m, in \u001b[0;36mBaseModel._predict_once\u001b[0;34m(self, x, profile, visualize, embed)\u001b[0m\n\u001b[1;32m 120\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m profile:\n\u001b[1;32m 121\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_profile_one_layer(m, x, dt)\n\u001b[0;32m--> 122\u001b[0m x \u001b[38;5;241m=\u001b[39m \u001b[43mm\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;66;03m# run\u001b[39;00m\n\u001b[1;32m 123\u001b[0m y\u001b[38;5;241m.\u001b[39mappend(x \u001b[38;5;28;01mif\u001b[39;00m m\u001b[38;5;241m.\u001b[39mi \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msave \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m) \u001b[38;5;66;03m# save output\u001b[39;00m\n\u001b[1;32m 124\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m visualize:\n",
|
||||
"File \u001b[0;32m~/Desktop/Python/YOLOv8-Projects/Traffic Analysis Projects/Highway Car Counter/env/lib/python3.10/site-packages/torch/nn/modules/module.py:1511\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1509\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m 1510\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1511\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
|
||||
"File \u001b[0;32m~/Desktop/Python/YOLOv8-Projects/Traffic Analysis Projects/Highway Car Counter/env/lib/python3.10/site-packages/torch/nn/modules/module.py:1520\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1515\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1516\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1517\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m 1518\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1519\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1520\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1522\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 1523\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
|
||||
"File \u001b[0;32m~/Desktop/Python/YOLOv8-Projects/Traffic Analysis Projects/Highway Car Counter/env/lib/python3.10/site-packages/ultralytics/nn/modules/block.py:171\u001b[0m, in \u001b[0;36mSPPF.forward\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m 169\u001b[0m y1 \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mm(x)\n\u001b[1;32m 170\u001b[0m y2 \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mm(y1)\n\u001b[0;32m--> 171\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcv2\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcat\u001b[49m\u001b[43m(\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my1\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my2\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mm\u001b[49m\u001b[43m(\u001b[49m\u001b[43my2\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n",
|
||||
"File \u001b[0;32m~/Desktop/Python/YOLOv8-Projects/Traffic Analysis Projects/Highway Car Counter/env/lib/python3.10/site-packages/torch/nn/modules/module.py:1511\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1509\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m 1510\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1511\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
|
||||
"File \u001b[0;32m~/Desktop/Python/YOLOv8-Projects/Traffic Analysis Projects/Highway Car Counter/env/lib/python3.10/site-packages/torch/nn/modules/module.py:1520\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1515\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1516\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1517\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m 1518\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1519\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1520\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1522\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 1523\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
|
||||
"File \u001b[0;32m~/Desktop/Python/YOLOv8-Projects/Traffic Analysis Projects/Highway Car Counter/env/lib/python3.10/site-packages/ultralytics/nn/modules/conv.py:54\u001b[0m, in \u001b[0;36mConv.forward_fuse\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m 52\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward_fuse\u001b[39m(\u001b[38;5;28mself\u001b[39m, x):\n\u001b[1;32m 53\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Perform transposed convolution of 2D data.\"\"\"\u001b[39;00m\n\u001b[0;32m---> 54\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mact(\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconv\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m)\n",
|
||||
"File \u001b[0;32m~/Desktop/Python/YOLOv8-Projects/Traffic Analysis Projects/Highway Car Counter/env/lib/python3.10/site-packages/torch/nn/modules/module.py:1511\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1509\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m 1510\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1511\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
|
||||
"File \u001b[0;32m~/Desktop/Python/YOLOv8-Projects/Traffic Analysis Projects/Highway Car Counter/env/lib/python3.10/site-packages/torch/nn/modules/module.py:1520\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1515\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1516\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1517\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m 1518\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1519\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1520\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1522\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 1523\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
|
||||
"File \u001b[0;32m~/Desktop/Python/YOLOv8-Projects/Traffic Analysis Projects/Highway Car Counter/env/lib/python3.10/site-packages/torch/nn/modules/conv.py:460\u001b[0m, in \u001b[0;36mConv2d.forward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m 459\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;28minput\u001b[39m: Tensor) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Tensor:\n\u001b[0;32m--> 460\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_conv_forward\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mweight\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbias\u001b[49m\u001b[43m)\u001b[49m\n",
|
||||
"File \u001b[0;32m~/Desktop/Python/YOLOv8-Projects/Traffic Analysis Projects/Highway Car Counter/env/lib/python3.10/site-packages/torch/nn/modules/conv.py:456\u001b[0m, in \u001b[0;36mConv2d._conv_forward\u001b[0;34m(self, input, weight, bias)\u001b[0m\n\u001b[1;32m 452\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpadding_mode \u001b[38;5;241m!=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mzeros\u001b[39m\u001b[38;5;124m'\u001b[39m:\n\u001b[1;32m 453\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m F\u001b[38;5;241m.\u001b[39mconv2d(F\u001b[38;5;241m.\u001b[39mpad(\u001b[38;5;28minput\u001b[39m, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_reversed_padding_repeated_twice, mode\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpadding_mode),\n\u001b[1;32m 454\u001b[0m weight, bias, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstride,\n\u001b[1;32m 455\u001b[0m _pair(\u001b[38;5;241m0\u001b[39m), \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdilation, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mgroups)\n\u001b[0;32m--> 456\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mF\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconv2d\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mweight\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbias\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstride\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 457\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpadding\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdilation\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgroups\u001b[49m\u001b[43m)\u001b[49m\n",
|
||||
"\u001b[0;31mKeyboardInterrupt\u001b[0m: "
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Loop utama untuk membaca setiap frame dari video\n",
|
||||
"while True:\n",
|
||||
" ret, frame = cap.read() # Membaca frame dari video\n",
|
||||
" if ret == False: # Keluar dari loop jika tidak ada frame yang dapat dibaca\n",
|
||||
" break\n",
|
||||
" \n",
|
||||
" try:\n",
|
||||
" frame = imutils.resize(frame, width = 1280, height = 720) # ubah frame menjadi tinggi 720 x lebar 1280\n",
|
||||
" frame_color = frame.copy() # Salin frame ke mode warna untuk pengolahan dan penggambaran\n",
|
||||
" frame_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) # Konversi frame ke citra grayscale\n",
|
||||
" frame_gray = cv2.cvtColor(frame_gray, cv2.COLOR_GRAY2BGR) # Konversi kembali ke citra BGR untuk tampilan grayscale\n",
|
||||
" frame_bw = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) # Konversi ke citra grayscale untuk mode black and white\n",
|
||||
"\n",
|
||||
" # Deteksi objek menggunakan model YOLO\n",
|
||||
" results = model.track(frame_color, persist=True, verbose=False)[0]\n",
|
||||
" bboxes = np.array(results.boxes.data.tolist(), dtype=\"int\") # Koordinat bounding box objek yang terdeteksi\n",
|
||||
"\n",
|
||||
" # Gambar garis pembatas untuk menghitung jumlah kendaraan yang melewati garis\n",
|
||||
" cv2.line(frame_color, (0, threshold), (1280, threshold), color, thickness)\n",
|
||||
" cv2.putText(frame_color, \"Pembatas Jalan\", (620, 445), font, 0.7, color_red, thickness)\n",
|
||||
"\n",
|
||||
" # Loop untuk setiap objek yang terdeteksi\n",
|
||||
" for box in bboxes:\n",
|
||||
" x1, y1, x2, y2, track_id, score, class_id = box # Ambil koordinat dan informasi lainnya\n",
|
||||
" cx = int((x1 + x2) / 2) # Hitung koordinat x pusat objek\n",
|
||||
" cy = int((y1 + y2) / 2) # Hitung koordinat y pusat objek\n",
|
||||
" if class_id in vehicle_ids: # Periksa apakah objek merupakan kendaraan yang ingin dilacak\n",
|
||||
" class_name = results.names[int(class_id)].upper() # Dapatkan nama kelas objek\n",
|
||||
"\n",
|
||||
" track = track_history[track_id] # Ambil sejarah pergerakan objek berdasarkan ID\n",
|
||||
" track.append((cx, cy)) # Tambahkan koordinat pusat objek ke dalam sejarah pergerakan\n",
|
||||
" if len(track) > 20: # Batasi panjang sejarah pergerakan agar tidak terlalu panjang\n",
|
||||
" track.pop(0) # Hapus elemen pertama jika sejarah sudah melebihi batas\n",
|
||||
"\n",
|
||||
" points = np.hstack(track).astype(\"int32\").reshape(-1, 1, 2) # Konversi sejarah pergerakan ke format yang sesuai untuk penggambaran\n",
|
||||
" cv2.polylines(frame_color, [points], isClosed=False, color=color, thickness=thickness) # Gambar garis yang merepresentasikan sejarah pergerakan\n",
|
||||
" cv2.rectangle(frame_color, (x1, y1), (x2, y2), color, thickness) # Gambar bounding box objek\n",
|
||||
" text = \"ID: {} {}\".format(track_id, class_name) # Buat teks ID objek dan nama kelasnya\n",
|
||||
" cv2.putText(frame_color, text, (x1, y1 - 5), font, font_scale, color, thickness) # Tampilkan teks di atas objek\n",
|
||||
"\n",
|
||||
" if cy > threshold - 5 and cy < threshold + 5 and cx < 670: # Periksa apakah objek melewati garis atas\n",
|
||||
" down[track_id] = x1, y1, x2, y2 # Simpan informasi objek yang melewati garis atas\n",
|
||||
"\n",
|
||||
" if cy > threshold - 5 and cy < threshold + 5 and cx > 670: # Periksa apakah objek melewati garis bawah\n",
|
||||
" up[track_id] = x1, y1, x2, y2 # Simpan informasi objek yang melewati garis bawah\n",
|
||||
"\n",
|
||||
" up_text = \"Kanan:{}\".format(len(list(up.keys()))) # Buat teks jumlah kendaraan yang melewati garis atas\n",
|
||||
" down_text = \"Kiri:{}\".format(len(list(down.keys()))) # Buat teks jumlah kendaraan yang melewati garis bawah\n",
|
||||
"\n",
|
||||
" cv2.putText(frame_color, up_text, (1150, threshold - 5), font, 0.8, color_red, thickness) # Tampilkan teks jumlah kendaraan yang melewati garis atas\n",
|
||||
" cv2.putText(frame_color, down_text, (0, threshold - 5), font, 0.8, color_red, thickness) # Tampilkan teks jumlah kendaraan yang melewati garis bawah\n",
|
||||
"\n",
|
||||
" # Background subtraction dan deteksi kontur\n",
|
||||
" grey = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) # Konversi frame ke citra grayscale\n",
|
||||
" blur = cv2.GaussianBlur(grey, (3, 3), 5) # Reduksi noise menggunakan Gaussian Blur\n",
|
||||
" img_sub = subtracao.apply(blur) # Background subtraction\n",
|
||||
" dilat = cv2.dilate(img_sub, np.ones((5, 5))) # Dilasi untuk meningkatkan ketebalan objek\n",
|
||||
" kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5)) # Kernel untuk operasi morfologi\n",
|
||||
" dilatada = cv2.morphologyEx(dilat, cv2.MORPH_CLOSE, kernel) # Operasi closing untuk mengisi lubang kecil pada objek\n",
|
||||
" dilatada = cv2.morphologyEx(dilatada, cv2.MORPH_CLOSE, kernel) # Operasi closing tambahan\n",
|
||||
" contorno, h = cv2.findContours(dilatada, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) # Deteksi kontur objek\n",
|
||||
"\n",
|
||||
" writer.write(frame_color) # Menyimpan frame hasil pemrosesan\n",
|
||||
" # Menampilkan gambar\n",
|
||||
" cv2.imshow(\"Warna\", frame_color) # Tampilkan mode warna\n",
|
||||
" cv2.imshow(\"Grayscale\", frame_gray) # Tampilkan mode grayscale\n",
|
||||
" cv2.imshow(\"Detectar\", dilatada) # Tampilkan mode Detectar dilatada\n",
|
||||
" if cv2.waitKey(10) & 0xFF == ord(\"q\"): # Keluar saat tombol q ditekan\n",
|
||||
" break\n",
|
||||
"\n",
|
||||
" except Exception as e:\n",
|
||||
" print(\"Terjadi kesalahan:\", str(e)) # Tangkap dan tampilkan kesalahan yang terjadi\n",
|
||||
" continue # Lanjutkan ke iterasi berikutnya\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "ae345f06-2af7-4b93-b833-a14cc20f7d64",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Menutup Window OpenCV"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "15c70b25-1b92-43d8-9167-ebb88b2a8df7",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"cap.release() # Bebaskan sumber daya setelah selesai pemrosesan video\n",
|
||||
"writer.release() # Tutup objek writer\n",
|
||||
"cv2.destroyAllWindows() # Tutup semua jendela yang dibuka oleh OpenCV"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3 (ipykernel)",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.10.10"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
532
app.py
532
app.py
|
|
@ -5,20 +5,21 @@ import numpy as np
|
|||
from ultralytics import YOLO
|
||||
from collections import defaultdict
|
||||
import os
|
||||
import pandas as pd
|
||||
|
||||
app = Flask(__name__, static_folder='assets')
|
||||
|
||||
video_list = []
|
||||
|
||||
color = (0, 255, 0)
|
||||
color_red = (0, 0, 255)
|
||||
thickness = 2
|
||||
# color = (0, 255, 0)
|
||||
# color_red = (0, 0, 255)
|
||||
# thickness = 2
|
||||
|
||||
font = cv2.FONT_HERSHEY_SIMPLEX
|
||||
font_scale = 0.5
|
||||
# font = cv2.FONT_HERSHEY_SIMPLEX
|
||||
# font_scale = 0.5
|
||||
|
||||
# Background subtraction menggunakan MOG2
|
||||
subtracao = cv2.createBackgroundSubtractorMOG2()
|
||||
# # Background subtraction menggunakan MOG2
|
||||
# subtracao = cv2.createBackgroundSubtractorMOG2()
|
||||
|
||||
jumlah_kenderaan = 0
|
||||
kenderaan_kiri = 0
|
||||
|
|
@ -27,17 +28,115 @@ kenderaan_kanan = 0
|
|||
|
||||
|
||||
# Define the generate_frames function with parameters for video, threshold, and state
|
||||
def generate_frames(video, threshold, stat):
|
||||
model_path = "models/yolov8n.pt"
|
||||
cap = cv2.VideoCapture(video)
|
||||
model = YOLO(model_path)
|
||||
# def generate_frames(video, threshold, stat):
|
||||
# model_path = "models/yolov8n.pt"
|
||||
# cap = cv2.VideoCapture(video)
|
||||
# model = YOLO(model_path)
|
||||
|
||||
vehicle_ids = [2, 3, 5, 7]
|
||||
track_history = defaultdict(lambda: [])
|
||||
# vehicle_ids = [2, 3, 5, 7]
|
||||
# track_history = defaultdict(lambda: [])
|
||||
|
||||
up = {}
|
||||
down = {}
|
||||
# up = {}
|
||||
# down = {}
|
||||
|
||||
# global jumlah_kenderaan
|
||||
# global kenderaan_kiri
|
||||
# global kenderaan_kanan
|
||||
|
||||
# jumlah_kenderaan = 0
|
||||
# kenderaan_kiri = 0
|
||||
# kenderaan_kanan = 0
|
||||
|
||||
# while True:
|
||||
# ret, frame = cap.read()
|
||||
# if not ret:
|
||||
# break
|
||||
|
||||
|
||||
# try:
|
||||
# frame = imutils.resize(frame, width=1280, height=720)
|
||||
# # freame_original = frame.copy()
|
||||
# frame_color = frame.copy()
|
||||
# frame_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
|
||||
# frame_gray = cv2.cvtColor(frame_gray, cv2.COLOR_GRAY2BGR)
|
||||
# frame_bw = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
|
||||
|
||||
# results = model.track(frame_color, persist=True, verbose=False)[0]
|
||||
# bboxes = np.array(results.boxes.data.tolist(), dtype="int")
|
||||
|
||||
# # Gambar garis pembatas untuk menghitung jumlah kendaraan yang melewati garis
|
||||
# cv2.line(frame_color, (0, threshold), (1280, threshold), color, thickness)
|
||||
# text_position = (620, threshold - 5) # Adjust the Y coordinate to place the text just above the line
|
||||
# cv2.putText(frame_color, "Pembatas Jalan", text_position, font, 0.7, color_red, thickness)
|
||||
|
||||
|
||||
# for box in bboxes:
|
||||
# x1, y1, x2, y2, track_id, score, class_id = box
|
||||
# cx = int((x1 + x2) / 2)
|
||||
# cy = int((y1 + y2) / 2)
|
||||
# if class_id in vehicle_ids:
|
||||
# class_name = results.names[int(class_id)].upper()
|
||||
|
||||
# track = track_history[track_id]
|
||||
# track.append((cx, cy))
|
||||
# if len(track) > 20:
|
||||
# track.pop(0)
|
||||
|
||||
# points = np.hstack(track).astype("int32").reshape(-1, 1, 2)
|
||||
# cv2.polylines(frame_color, [points], isClosed=False, color=color, thickness=thickness)
|
||||
# cv2.rectangle(frame_color, (x1, y1), (x2, y2), color, thickness)
|
||||
# text = "ID: {} {}".format(track_id, class_name)
|
||||
# cv2.putText(frame_color, text, (x1, y1 - 5), font, font_scale, color, thickness)
|
||||
|
||||
# if cy > threshold - 5 and cy < threshold + 5 and cx < 670:
|
||||
# down[track_id] = x1, y1, x2, y2
|
||||
|
||||
# if cy > threshold - 5 and cy < threshold + 5 and cx > 670:
|
||||
# up[track_id] = x1, y1, x2, y2
|
||||
|
||||
# up_text = "Kanan:{}".format(len(list(up.keys())))
|
||||
# down_text = "Kiri:{}".format(len(list(down.keys())))
|
||||
# kenderaan_kanan = len(list(up.keys()))
|
||||
# kenderaan_kiri = len(list(down.keys()))
|
||||
# cv2.putText(frame_color, up_text, (1150, threshold - 5), font, 0.8, color_red, thickness)
|
||||
# cv2.putText(frame_color, down_text, (0, threshold - 5), font, 0.8, color_red, thickness)
|
||||
|
||||
# # Background subtraction dan deteksi kontur
|
||||
# grey = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) # Konversi frame ke citra grayscale
|
||||
# blur = cv2.GaussianBlur(grey, (3, 3), 5) # Reduksi noise menggunakan Gaussian Blur
|
||||
# img_sub = subtracao.apply(blur) # Background subtraction
|
||||
# dilat = cv2.dilate(img_sub, np.ones((5, 5))) # Dilasi untuk meningkatkan ketebalan objek
|
||||
# kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5)) # Kernel untuk operasi morfologi
|
||||
# dilatada = cv2.morphologyEx(dilat, cv2.MORPH_CLOSE, kernel) # Operasi closing untuk mengisi lubang kecil pada objek
|
||||
# dilatada = cv2.morphologyEx(dilatada, cv2.MORPH_CLOSE, kernel) # Operasi closing tambahan
|
||||
# contorno, h = cv2.findContours(dilatada, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) # Deteksi kontur objek
|
||||
# frame_bw = cv2.cvtColor(dilatada, cv2.COLOR_GRAY2BGR) # Konversi frame grayscale ke BGR
|
||||
|
||||
# if stat == 'color':
|
||||
# frame_to_encode = frame_color
|
||||
# elif stat == 'grayscale':
|
||||
# frame_to_encode = frame_gray
|
||||
# elif stat == 'original':
|
||||
# frame_to_encode = frame
|
||||
# else: # Assuming 'detectar' state
|
||||
# frame_to_encode = frame_bw
|
||||
|
||||
# _, buffer = cv2.imencode('.jpg', frame_to_encode)
|
||||
# frame_bytes = buffer.tobytes()
|
||||
|
||||
# yield (b'--frame\r\n'
|
||||
# b'Content-Type: image/jpeg\r\n\r\n' + frame_bytes + b'\r\n')
|
||||
# except Exception as e:
|
||||
# print("Terjadi kesalahan:", str(e))
|
||||
# continue
|
||||
|
||||
# jumlah_kenderaan = kenderaan_kiri + kenderaan_kanan
|
||||
|
||||
|
||||
# cap.release()
|
||||
|
||||
|
||||
def generate_frames2(video, threshold,stat):
|
||||
global jumlah_kenderaan
|
||||
global kenderaan_kiri
|
||||
global kenderaan_kanan
|
||||
|
|
@ -46,94 +145,355 @@ def generate_frames(video, threshold, stat):
|
|||
kenderaan_kiri = 0
|
||||
kenderaan_kanan = 0
|
||||
|
||||
cap = cv2.VideoCapture(video)
|
||||
frames_count, fps, width, height = cap.get(cv2.CAP_PROP_FRAME_COUNT), cap.get(cv2.CAP_PROP_FPS), cap.get(
|
||||
cv2.CAP_PROP_FRAME_WIDTH), cap.get(cv2.CAP_PROP_FRAME_HEIGHT)
|
||||
width = int(width)
|
||||
height = int(height)
|
||||
print(frames_count, fps, width, height)
|
||||
|
||||
# creates a pandas data frame with the number of rows the same length as frame count
|
||||
df = pd.DataFrame(index=range(int(frames_count)))
|
||||
df.index.name = "Frames"
|
||||
|
||||
framenumber = 0 # keeps track of current frame
|
||||
carscrossedup = 0 # keeps track of cars that crossed up
|
||||
carscrosseddown = 0 # keeps track of cars that crossed down
|
||||
carids = [] # blank list to add car ids
|
||||
caridscrossed = [] # blank list to add car ids that have crossed
|
||||
totalcars = 0 # keeps track of total cars
|
||||
|
||||
fgbg = cv2.createBackgroundSubtractorMOG2() # create background subtractor
|
||||
|
||||
# information to start saving a video file
|
||||
ret, frame = cap.read() # import image
|
||||
ratio = .5 # resize ratio
|
||||
image = cv2.resize(frame, (0, 0), None, ratio, ratio) # resize image
|
||||
width2, height2, channels = image.shape
|
||||
video = cv2.VideoWriter('traffic_counter.avi', cv2.VideoWriter_fourcc('M', 'J', 'P', 'G'), fps, (height2, width2), 1)
|
||||
|
||||
while True:
|
||||
ret, frame = cap.read()
|
||||
if not ret:
|
||||
break
|
||||
|
||||
|
||||
try:
|
||||
frame = imutils.resize(frame, width=1280, height=720)
|
||||
# freame_original = frame.copy()
|
||||
frame_color = frame.copy()
|
||||
frame_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
|
||||
frame_gray = cv2.cvtColor(frame_gray, cv2.COLOR_GRAY2BGR)
|
||||
frame_bw = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
|
||||
ret, frame = cap.read() # import image
|
||||
|
||||
results = model.track(frame_color, persist=True, verbose=False)[0]
|
||||
bboxes = np.array(results.boxes.data.tolist(), dtype="int")
|
||||
if ret: # if there is a frame continue with code
|
||||
|
||||
# Gambar garis pembatas untuk menghitung jumlah kendaraan yang melewati garis
|
||||
cv2.line(frame_color, (0, threshold), (1280, threshold), color, thickness)
|
||||
text_position = (620, threshold - 5) # Adjust the Y coordinate to place the text just above the line
|
||||
cv2.putText(frame_color, "Pembatas Jalan", text_position, font, 0.7, color_red, thickness)
|
||||
image = cv2.resize(frame, (0, 0), None, ratio, ratio) # resize image
|
||||
|
||||
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) # converts image to gray
|
||||
|
||||
fgmask = fgbg.apply(gray) # uses the background subtraction
|
||||
|
||||
# applies different thresholds to fgmask to try and isolate cars
|
||||
# just have to keep playing around with settings until cars are easily identifiable
|
||||
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5)) # kernel to apply to the morphology
|
||||
closing = cv2.morphologyEx(fgmask, cv2.MORPH_CLOSE, kernel)
|
||||
opening = cv2.morphologyEx(closing, cv2.MORPH_OPEN, kernel)
|
||||
dilation = cv2.dilate(opening, kernel)
|
||||
retvalbin, bins = cv2.threshold(dilation, 220, 255, cv2.THRESH_BINARY) # removes the shadows
|
||||
|
||||
# creates contours
|
||||
contours, hierarchy = cv2.findContours(bins, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)[-2:]
|
||||
|
||||
# use convex hull to create polygon around contours
|
||||
hull = [cv2.convexHull(c) for c in contours]
|
||||
|
||||
# draw contours
|
||||
cv2.drawContours(image, hull, -1, (0, 255, 0), 3)
|
||||
|
||||
# line created to stop counting contours, needed as cars in distance become one big contour
|
||||
lineypos = 100
|
||||
cv2.line(image, (0, lineypos), (width, lineypos), (255, 0, 0), 5)
|
||||
|
||||
# line y position created to count contours
|
||||
lineypos2 = 125
|
||||
cv2.line(image, (0, lineypos2), (width, lineypos2), (0, 255, 0), 5)
|
||||
|
||||
# min area for contours in case a bunch of small noise contours are created
|
||||
minarea = 400
|
||||
|
||||
# max area for contours, can be quite large for buses
|
||||
maxarea = 50000
|
||||
|
||||
# vectors for the x and y locations of contour centroids in current frame
|
||||
cxx = np.zeros(len(contours))
|
||||
cyy = np.zeros(len(contours))
|
||||
|
||||
for i in range(len(contours)): # cycles through all contours in current frame
|
||||
|
||||
if hierarchy[0, i, 3] == -1: # using hierarchy to only count parent contours (contours not within others)
|
||||
|
||||
area = cv2.contourArea(contours[i]) # area of contour
|
||||
|
||||
if minarea < area < maxarea: # area threshold for contour
|
||||
|
||||
# calculating centroids of contours
|
||||
cnt = contours[i]
|
||||
M = cv2.moments(cnt)
|
||||
cx = int(M['m10'] / M['m00'])
|
||||
cy = int(M['m01'] / M['m00'])
|
||||
|
||||
if cy > lineypos: # filters out contours that are above line (y starts at top)
|
||||
|
||||
# gets bounding points of contour to create rectangle
|
||||
# x,y is top left corner and w,h is width and height
|
||||
x, y, w, h = cv2.boundingRect(cnt)
|
||||
|
||||
# creates a rectangle around contour
|
||||
cv2.rectangle(image, (x, y), (x + w, y + h), (255, 0, 0), 2)
|
||||
|
||||
# Prints centroid text in order to double check later on
|
||||
cv2.putText(image, str(cx) + "," + str(cy), (cx + 10, cy + 10), cv2.FONT_HERSHEY_SIMPLEX,
|
||||
.3, (0, 0, 255), 1)
|
||||
|
||||
cv2.drawMarker(image, (cx, cy), (0, 0, 255), cv2.MARKER_STAR, markerSize=5, thickness=1,
|
||||
line_type=cv2.LINE_AA)
|
||||
|
||||
# adds centroids that passed previous criteria to centroid list
|
||||
cxx[i] = cx
|
||||
cyy[i] = cy
|
||||
|
||||
# eliminates zero entries (centroids that were not added)
|
||||
cxx = cxx[cxx != 0]
|
||||
cyy = cyy[cyy != 0]
|
||||
|
||||
# empty list to later check which centroid indices were added to dataframe
|
||||
minx_index2 = []
|
||||
miny_index2 = []
|
||||
|
||||
# maximum allowable radius for current frame centroid to be considered the same centroid from previous frame
|
||||
maxrad = 25
|
||||
|
||||
# The section below keeps track of the centroids and assigns them to old carids or new carids
|
||||
|
||||
if len(cxx): # if there are centroids in the specified area
|
||||
|
||||
if not carids: # if carids is empty
|
||||
|
||||
for i in range(len(cxx)): # loops through all centroids
|
||||
|
||||
carids.append(i) # adds a car id to the empty list carids
|
||||
df[str(carids[i])] = "" # adds a column to the dataframe corresponding to a carid
|
||||
|
||||
# assigns the centroid values to the current frame (row) and carid (column)
|
||||
df.at[int(framenumber), str(carids[i])] = [cxx[i], cyy[i]]
|
||||
|
||||
totalcars = carids[i] + 1 # adds one count to total cars
|
||||
|
||||
else: # if there are already car ids
|
||||
|
||||
dx = np.zeros((len(cxx), len(carids))) # new arrays to calculate deltas
|
||||
dy = np.zeros((len(cyy), len(carids))) # new arrays to calculate deltas
|
||||
|
||||
for i in range(len(cxx)): # loops through all centroids
|
||||
|
||||
for j in range(len(carids)): # loops through all recorded car ids
|
||||
|
||||
# acquires centroid from previous frame for specific carid
|
||||
oldcxcy = df.iloc[int(framenumber - 1)][str(carids[j])]
|
||||
|
||||
# acquires current frame centroid that doesn't necessarily line up with previous frame centroid
|
||||
curcxcy = np.array([cxx[i], cyy[i]])
|
||||
|
||||
if not oldcxcy: # checks if old centroid is empty in case car leaves screen and new car shows
|
||||
|
||||
continue # continue to next carid
|
||||
|
||||
else: # calculate centroid deltas to compare to current frame position later
|
||||
|
||||
dx[i, j] = oldcxcy[0] - curcxcy[0]
|
||||
dy[i, j] = oldcxcy[1] - curcxcy[1]
|
||||
|
||||
for j in range(len(carids)): # loops through all current car ids
|
||||
|
||||
sumsum = np.abs(dx[:, j]) + np.abs(dy[:, j]) # sums the deltas wrt to car ids
|
||||
|
||||
# finds which index carid had the min difference and this is true index
|
||||
correctindextrue = np.argmin(np.abs(sumsum))
|
||||
minx_index = correctindextrue
|
||||
miny_index = correctindextrue
|
||||
|
||||
# acquires delta values of the minimum deltas in order to check if it is within radius later on
|
||||
mindx = dx[minx_index, j]
|
||||
mindy = dy[miny_index, j]
|
||||
|
||||
if mindx == 0 and mindy == 0 and np.all(dx[:, j] == 0) and np.all(dy[:, j] == 0):
|
||||
# checks if minimum value is 0 and checks if all deltas are zero since this is empty set
|
||||
# delta could be zero if centroid didn't move
|
||||
|
||||
continue # continue to next carid
|
||||
|
||||
else:
|
||||
|
||||
# if delta values are less than maximum radius then add that centroid to that specific carid
|
||||
if np.abs(mindx) < maxrad and np.abs(mindy) < maxrad:
|
||||
|
||||
# adds centroid to corresponding previously existing carid
|
||||
df.at[int(framenumber), str(carids[j])] = [cxx[minx_index], cyy[miny_index]]
|
||||
minx_index2.append(minx_index) # appends all the indices that were added to previous carids
|
||||
miny_index2.append(miny_index)
|
||||
|
||||
for i in range(len(cxx)): # loops through all centroids
|
||||
|
||||
# if centroid is not in the minindex list then another car needs to be added
|
||||
if i not in minx_index2 and miny_index2:
|
||||
|
||||
df[str(totalcars)] = "" # create another column with total cars
|
||||
totalcars = totalcars + 1 # adds another total car the count
|
||||
t = totalcars - 1 # t is a placeholder to total cars
|
||||
carids.append(t) # append to list of car ids
|
||||
df.at[int(framenumber), str(t)] = [cxx[i], cyy[i]] # add centroid to the new car id
|
||||
|
||||
elif curcxcy[0] and not oldcxcy and not minx_index2 and not miny_index2:
|
||||
# checks if current centroid exists but previous centroid does not
|
||||
# new car to be added in case minx_index2 is empty
|
||||
|
||||
df[str(totalcars)] = "" # create another column with total cars
|
||||
totalcars = totalcars + 1 # adds another total car the count
|
||||
t = totalcars - 1 # t is a placeholder to total cars
|
||||
carids.append(t) # append to list of car ids
|
||||
df.at[int(framenumber), str(t)] = [cxx[i], cyy[i]] # add centroid to the new car id
|
||||
|
||||
# The section below labels the centroids on screen
|
||||
|
||||
currentcars = 0 # current cars on screen
|
||||
currentcarsindex = [] # current cars on screen carid index
|
||||
|
||||
for i in range(len(carids)): # loops through all carids
|
||||
|
||||
if df.at[int(framenumber), str(carids[i])] != '':
|
||||
# checks the current frame to see which car ids are active
|
||||
# by checking in centroid exists on current frame for certain car id
|
||||
|
||||
currentcars = currentcars + 1 # adds another to current cars on screen
|
||||
currentcarsindex.append(i) # adds car ids to current cars on screen
|
||||
|
||||
for i in range(currentcars): # loops through all current car ids on screen
|
||||
|
||||
# grabs centroid of certain carid for current frame
|
||||
curcent = df.iloc[int(framenumber)][str(carids[currentcarsindex[i]])]
|
||||
|
||||
# grabs centroid of certain carid for previous frame
|
||||
oldcent = df.iloc[int(framenumber - 1)][str(carids[currentcarsindex[i]])]
|
||||
|
||||
if curcent: # if there is a current centroid
|
||||
|
||||
# On-screen text for current centroid
|
||||
cv2.putText(image, "Centroid" + str(curcent[0]) + "," + str(curcent[1]),
|
||||
(int(curcent[0]), int(curcent[1])), cv2.FONT_HERSHEY_SIMPLEX, .5, (0, 255, 255), 2)
|
||||
|
||||
cv2.putText(image, "ID:" + str(carids[currentcarsindex[i]]), (int(curcent[0]), int(curcent[1] - 15)),
|
||||
cv2.FONT_HERSHEY_SIMPLEX, .5, (0, 255, 255), 2)
|
||||
|
||||
cv2.drawMarker(image, (int(curcent[0]), int(curcent[1])), (0, 0, 255), cv2.MARKER_STAR, markerSize=5,
|
||||
thickness=1, line_type=cv2.LINE_AA)
|
||||
|
||||
if oldcent: # checks if old centroid exists
|
||||
# adds radius box from previous centroid to current centroid for visualization
|
||||
xstart = oldcent[0] - maxrad
|
||||
ystart = oldcent[1] - maxrad
|
||||
xwidth = oldcent[0] + maxrad
|
||||
yheight = oldcent[1] + maxrad
|
||||
cv2.rectangle(image, (int(xstart), int(ystart)), (int(xwidth), int(yheight)), (0, 125, 0), 1)
|
||||
|
||||
# checks if old centroid is on or below line and curcent is on or above line
|
||||
# to count cars and that car hasn't been counted yet
|
||||
if oldcent[1] >= lineypos2 and curcent[1] <= lineypos2 and carids[
|
||||
currentcarsindex[i]] not in caridscrossed:
|
||||
|
||||
carscrossedup = carscrossedup + 1
|
||||
cv2.line(image, (0, lineypos2), (width, lineypos2), (0, 0, 255), 5)
|
||||
caridscrossed.append(
|
||||
currentcarsindex[i]) # adds car id to list of count cars to prevent double counting
|
||||
|
||||
# checks if old centroid is on or above line and curcent is on or below line
|
||||
# to count cars and that car hasn't been counted yet
|
||||
elif oldcent[1] <= lineypos2 and curcent[1] >= lineypos2 and carids[
|
||||
currentcarsindex[i]] not in caridscrossed:
|
||||
|
||||
carscrosseddown = carscrosseddown + 1
|
||||
cv2.line(image, (0, lineypos2), (width, lineypos2), (0, 0, 125), 5)
|
||||
caridscrossed.append(currentcarsindex[i])
|
||||
|
||||
# Top left hand corner on-screen text
|
||||
#cv2.rectangle(image, (0, 0), (250, 100), (255, 0, 0), -1) # background rectangle for on-screen text
|
||||
|
||||
cv2.putText(image, "Kenderaan Sebelah Kiri: " + str(carscrossedup), (0, 15), cv2.FONT_HERSHEY_SIMPLEX, .5, (0, 170, 0),
|
||||
1)
|
||||
|
||||
cv2.putText(image, "Kenderaan Sebelah Kanan: " + str(carscrosseddown), (0, 30), cv2.FONT_HERSHEY_SIMPLEX, .5,
|
||||
(0, 170, 0), 1)
|
||||
|
||||
# cv2.putText(image, "Total Cars Detected: " + str(len(carids)), (0, 60), cv2.FONT_HERSHEY_SIMPLEX, .5,
|
||||
# (0, 170, 0), 1)
|
||||
|
||||
cv2.putText(image, "Frame: " + str(framenumber) + ' dari ' + str(frames_count), (0, 45), cv2.FONT_HERSHEY_SIMPLEX,
|
||||
.5, (0, 170, 0), 1)
|
||||
|
||||
cv2.putText(image, 'Waktu: ' + str(round(framenumber / fps, 2)) + ' detik dari ' + str(round(frames_count / fps, 2))
|
||||
+ ' detik', (0, 60), cv2.FONT_HERSHEY_SIMPLEX, .5, (0, 170, 0), 1)
|
||||
|
||||
kenderaan_kanan = carscrosseddown
|
||||
kenderaan_kiri = carscrossedup
|
||||
jumlah_kenderaan = carscrossedup + carscrosseddown
|
||||
|
||||
for box in bboxes:
|
||||
x1, y1, x2, y2, track_id, score, class_id = box
|
||||
cx = int((x1 + x2) / 2)
|
||||
cy = int((y1 + y2) / 2)
|
||||
if class_id in vehicle_ids:
|
||||
class_name = results.names[int(class_id)].upper()
|
||||
|
||||
track = track_history[track_id]
|
||||
track.append((cx, cy))
|
||||
if len(track) > 20:
|
||||
track.pop(0)
|
||||
|
||||
points = np.hstack(track).astype("int32").reshape(-1, 1, 2)
|
||||
cv2.polylines(frame_color, [points], isClosed=False, color=color, thickness=thickness)
|
||||
cv2.rectangle(frame_color, (x1, y1), (x2, y2), color, thickness)
|
||||
text = "ID: {} {}".format(track_id, class_name)
|
||||
cv2.putText(frame_color, text, (x1, y1 - 5), font, font_scale, color, thickness)
|
||||
|
||||
if cy > threshold - 5 and cy < threshold + 5 and cx < 670:
|
||||
down[track_id] = x1, y1, x2, y2
|
||||
|
||||
if cy > threshold - 5 and cy < threshold + 5 and cx > 670:
|
||||
up[track_id] = x1, y1, x2, y2
|
||||
|
||||
up_text = "Kanan:{}".format(len(list(up.keys())))
|
||||
down_text = "Kiri:{}".format(len(list(down.keys())))
|
||||
kenderaan_kanan = len(list(up.keys()))
|
||||
kenderaan_kiri = len(list(down.keys()))
|
||||
cv2.putText(frame_color, up_text, (1150, threshold - 5), font, 0.8, color_red, thickness)
|
||||
cv2.putText(frame_color, down_text, (0, threshold - 5), font, 0.8, color_red, thickness)
|
||||
|
||||
# Background subtraction dan deteksi kontur
|
||||
grey = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) # Konversi frame ke citra grayscale
|
||||
blur = cv2.GaussianBlur(grey, (3, 3), 5) # Reduksi noise menggunakan Gaussian Blur
|
||||
img_sub = subtracao.apply(blur) # Background subtraction
|
||||
dilat = cv2.dilate(img_sub, np.ones((5, 5))) # Dilasi untuk meningkatkan ketebalan objek
|
||||
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5)) # Kernel untuk operasi morfologi
|
||||
dilatada = cv2.morphologyEx(dilat, cv2.MORPH_CLOSE, kernel) # Operasi closing untuk mengisi lubang kecil pada objek
|
||||
dilatada = cv2.morphologyEx(dilatada, cv2.MORPH_CLOSE, kernel) # Operasi closing tambahan
|
||||
contorno, h = cv2.findContours(dilatada, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) # Deteksi kontur objek
|
||||
frame_bw = cv2.cvtColor(dilatada, cv2.COLOR_GRAY2BGR) # Konversi frame grayscale ke BGR
|
||||
|
||||
# displays images and transformations and resize to 1280x720
|
||||
# cv2.imshow("countours", image)
|
||||
# cv2.moveWindow("countours", 0, 0)
|
||||
if stat == 'color':
|
||||
frame_to_encode = frame_color
|
||||
# frame_to_encode = frame
|
||||
# resize to 1280x720
|
||||
frame_to_encode = cv2.resize(image, (1280, 720))
|
||||
|
||||
|
||||
# cv2.imshow("fgmask", fgmask)
|
||||
# cv2.moveWindow("fgmask", int(width * ratio), 0)
|
||||
elif stat == 'grayscale':
|
||||
frame_to_encode = frame_gray
|
||||
elif stat == 'original':
|
||||
frame_to_encode = frame
|
||||
else: # Assuming 'detectar' state
|
||||
frame_to_encode = frame_bw
|
||||
# frame_to_encode = gray
|
||||
frame_to_encode = cv2.resize(gray, (1280, 720))
|
||||
|
||||
# cv2.imshow("closing", closing)
|
||||
# cv2.moveWindow("closing", width, 0)
|
||||
elif stat == 'detectar':
|
||||
# frame_to_encode = closing
|
||||
frame_to_encode = cv2.resize(closing, (1280, 720))
|
||||
else :
|
||||
# frame_to_encode = opening
|
||||
frame_to_encode = cv2.resize(frame, (1280, 720))
|
||||
|
||||
_, buffer = cv2.imencode('.jpg', frame_to_encode)
|
||||
frame_bytes = buffer.tobytes()
|
||||
|
||||
yield (b'--frame\r\n'
|
||||
b'Content-Type: image/jpeg\r\n\r\n' + frame_bytes + b'\r\n')
|
||||
except Exception as e:
|
||||
print("Terjadi kesalahan:", str(e))
|
||||
continue
|
||||
|
||||
jumlah_kenderaan = kenderaan_kiri + kenderaan_kanan
|
||||
|
||||
# cv2.imshow("opening", opening)
|
||||
# cv2.moveWindow("opening", 0, int(height * ratio))
|
||||
|
||||
# cv2.imshow("dilation", dilation)
|
||||
# cv2.moveWindow("dilation", int(width * ratio), int(height * ratio))
|
||||
|
||||
# cv2.imshow("binary", bins)
|
||||
# cv2.moveWindow("binary", width, int(height * ratio))
|
||||
|
||||
# video.write(image) # save the current image to video file from earlier
|
||||
|
||||
# adds to framecount
|
||||
framenumber = framenumber + 1
|
||||
|
||||
k = cv2.waitKey(int(1000/fps)) & 0xff # int(1000/fps) is normal speed since waitkey is in ms
|
||||
if k == 27:
|
||||
break
|
||||
|
||||
else: # if video is finished then break loop
|
||||
|
||||
break
|
||||
|
||||
cap.release()
|
||||
|
||||
|
||||
|
||||
|
||||
def update_video_list():
|
||||
global video_list
|
||||
# add "video/" to the video_list and only take video extensions
|
||||
|
|
@ -155,7 +515,7 @@ def video_feed():
|
|||
stat = request.args.get('stat', 'color') # Default to 'color' if state is not specified
|
||||
# Return the response with the generator function
|
||||
print("ini semua variable:", video, threshold, stat)
|
||||
return Response(generate_frames(video, threshold, stat), mimetype='multipart/x-mixed-replace; boundary=frame')
|
||||
return Response(generate_frames2(video, threshold, stat), mimetype='multipart/x-mixed-replace; boundary=frame')
|
||||
|
||||
|
||||
@app.route('/video_list')
|
||||
|
|
|
|||
|
|
@ -0,0 +1,194 @@
|
|||
from flask import Flask, render_template, Response, request,jsonify,send_from_directory
|
||||
import cv2
|
||||
import imutils
|
||||
import numpy as np
|
||||
from ultralytics import YOLO
|
||||
from collections import defaultdict
|
||||
import os
|
||||
|
||||
app = Flask(__name__, static_folder='assets')
|
||||
|
||||
video_list = []
|
||||
|
||||
color = (0, 255, 0)
|
||||
color_red = (0, 0, 255)
|
||||
thickness = 2
|
||||
|
||||
font = cv2.FONT_HERSHEY_SIMPLEX
|
||||
font_scale = 0.5
|
||||
|
||||
# Background subtraction menggunakan MOG2
|
||||
subtracao = cv2.createBackgroundSubtractorMOG2()
|
||||
|
||||
jumlah_kenderaan = 0
|
||||
kenderaan_kiri = 0
|
||||
kenderaan_kanan = 0
|
||||
|
||||
|
||||
|
||||
# Define the generate_frames function with parameters for video, threshold, and state
|
||||
def generate_frames(video, threshold, stat):
|
||||
model_path = "models/yolov8n.pt"
|
||||
cap = cv2.VideoCapture(video)
|
||||
model = YOLO(model_path)
|
||||
|
||||
vehicle_ids = [2, 3, 5, 7]
|
||||
track_history = defaultdict(lambda: [])
|
||||
|
||||
up = {}
|
||||
down = {}
|
||||
|
||||
global jumlah_kenderaan
|
||||
global kenderaan_kiri
|
||||
global kenderaan_kanan
|
||||
|
||||
jumlah_kenderaan = 0
|
||||
kenderaan_kiri = 0
|
||||
kenderaan_kanan = 0
|
||||
|
||||
while True:
|
||||
ret, frame = cap.read()
|
||||
if not ret:
|
||||
break
|
||||
|
||||
|
||||
try:
|
||||
frame = imutils.resize(frame, width=1280, height=720)
|
||||
# freame_original = frame.copy()
|
||||
frame_color = frame.copy()
|
||||
frame_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
|
||||
frame_gray = cv2.cvtColor(frame_gray, cv2.COLOR_GRAY2BGR)
|
||||
frame_bw = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
|
||||
|
||||
results = model.track(frame_color, persist=True, verbose=False)[0]
|
||||
bboxes = np.array(results.boxes.data.tolist(), dtype="int")
|
||||
|
||||
# Gambar garis pembatas untuk menghitung jumlah kendaraan yang melewati garis
|
||||
cv2.line(frame_color, (0, threshold), (1280, threshold), color, thickness)
|
||||
text_position = (620, threshold - 5) # Adjust the Y coordinate to place the text just above the line
|
||||
cv2.putText(frame_color, "Pembatas Jalan", text_position, font, 0.7, color_red, thickness)
|
||||
|
||||
|
||||
for box in bboxes:
|
||||
x1, y1, x2, y2, track_id, score, class_id = box
|
||||
cx = int((x1 + x2) / 2)
|
||||
cy = int((y1 + y2) / 2)
|
||||
if class_id in vehicle_ids:
|
||||
class_name = results.names[int(class_id)].upper()
|
||||
|
||||
track = track_history[track_id]
|
||||
track.append((cx, cy))
|
||||
if len(track) > 20:
|
||||
track.pop(0)
|
||||
|
||||
points = np.hstack(track).astype("int32").reshape(-1, 1, 2)
|
||||
cv2.polylines(frame_color, [points], isClosed=False, color=color, thickness=thickness)
|
||||
cv2.rectangle(frame_color, (x1, y1), (x2, y2), color, thickness)
|
||||
text = "ID: {} {}".format(track_id, class_name)
|
||||
cv2.putText(frame_color, text, (x1, y1 - 5), font, font_scale, color, thickness)
|
||||
|
||||
if cy > threshold - 5 and cy < threshold + 5 and cx < 670:
|
||||
down[track_id] = x1, y1, x2, y2
|
||||
|
||||
if cy > threshold - 5 and cy < threshold + 5 and cx > 670:
|
||||
up[track_id] = x1, y1, x2, y2
|
||||
|
||||
up_text = "Kanan:{}".format(len(list(up.keys())))
|
||||
down_text = "Kiri:{}".format(len(list(down.keys())))
|
||||
kenderaan_kanan = len(list(up.keys()))
|
||||
kenderaan_kiri = len(list(down.keys()))
|
||||
cv2.putText(frame_color, up_text, (1150, threshold - 5), font, 0.8, color_red, thickness)
|
||||
cv2.putText(frame_color, down_text, (0, threshold - 5), font, 0.8, color_red, thickness)
|
||||
|
||||
# Background subtraction dan deteksi kontur
|
||||
grey = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) # Konversi frame ke citra grayscale
|
||||
blur = cv2.GaussianBlur(grey, (3, 3), 5) # Reduksi noise menggunakan Gaussian Blur
|
||||
img_sub = subtracao.apply(blur) # Background subtraction
|
||||
dilat = cv2.dilate(img_sub, np.ones((5, 5))) # Dilasi untuk meningkatkan ketebalan objek
|
||||
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5)) # Kernel untuk operasi morfologi
|
||||
dilatada = cv2.morphologyEx(dilat, cv2.MORPH_CLOSE, kernel) # Operasi closing untuk mengisi lubang kecil pada objek
|
||||
dilatada = cv2.morphologyEx(dilatada, cv2.MORPH_CLOSE, kernel) # Operasi closing tambahan
|
||||
contorno, h = cv2.findContours(dilatada, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) # Deteksi kontur objek
|
||||
frame_bw = cv2.cvtColor(dilatada, cv2.COLOR_GRAY2BGR) # Konversi frame grayscale ke BGR
|
||||
|
||||
if stat == 'color':
|
||||
frame_to_encode = frame_color
|
||||
elif stat == 'grayscale':
|
||||
frame_to_encode = frame_gray
|
||||
elif stat == 'original':
|
||||
frame_to_encode = frame
|
||||
else: # Assuming 'detectar' state
|
||||
frame_to_encode = frame_bw
|
||||
|
||||
_, buffer = cv2.imencode('.jpg', frame_to_encode)
|
||||
frame_bytes = buffer.tobytes()
|
||||
|
||||
yield (b'--frame\r\n'
|
||||
b'Content-Type: image/jpeg\r\n\r\n' + frame_bytes + b'\r\n')
|
||||
except Exception as e:
|
||||
print("Terjadi kesalahan:", str(e))
|
||||
continue
|
||||
|
||||
jumlah_kenderaan = kenderaan_kiri + kenderaan_kanan
|
||||
|
||||
|
||||
cap.release()
|
||||
|
||||
def update_video_list():
|
||||
global video_list
|
||||
# add "video/" to the video_list and only take video extensions
|
||||
video_list = [f"video/{f}" for f in os.listdir("video") if f.endswith(".mp4")]
|
||||
|
||||
@app.route('/')
|
||||
def index():
|
||||
update_video_list()
|
||||
print("video_list:", video_list)
|
||||
video = request.args.get('video', 'video/video.mp4')
|
||||
threshold = int(request.args.get('threshold', 450))
|
||||
# Pass the video file path and threshold value to the template
|
||||
return render_template('index.html', video=video, threshold=threshold, video_list=video_list)
|
||||
|
||||
def video_feed():
|
||||
# Get the video file path, threshold value, and state from the URL parameters
|
||||
video = request.args.get('video')
|
||||
threshold = int(request.args.get('threshold', 450))
|
||||
stat = request.args.get('stat', 'color') # Default to 'color' if state is not specified
|
||||
# Return the response with the generator function
|
||||
print("ini semua variable:", video, threshold, stat)
|
||||
return Response(generate_frames(video, threshold, stat), mimetype='multipart/x-mixed-replace; boundary=frame')
|
||||
|
||||
|
||||
@app.route('/video_list')
|
||||
def video_list():
|
||||
update_video_list()
|
||||
return render_template('video_list.html', video_list=video_list)
|
||||
|
||||
@app.route('/videos/<path:video>')
|
||||
def video(video):
|
||||
return send_from_directory('', video)
|
||||
|
||||
# Add route for the video feed
|
||||
app.add_url_rule('/video_feed', 'video_feed', video_feed)
|
||||
|
||||
@app.route('/check_jumlah_kenderaan', methods=['GET'])
|
||||
def check_jumlah_kenderaan():
|
||||
global jumlah_kenderaan
|
||||
global kenderaan_kiri
|
||||
global kenderaan_kanan
|
||||
return jsonify({'jumlah_kenderaan': jumlah_kenderaan, 'kenderaan_kiri': kenderaan_kiri, 'kenderaan_kanan': kenderaan_kanan})
|
||||
|
||||
UPLOAD_FOLDER = 'video'
|
||||
@app.route('/upload', methods=['POST'])
|
||||
def upload_file():
|
||||
file = request.files['file']
|
||||
|
||||
if file.filename == '':
|
||||
return jsonify({'status': False, 'message': 'No file selected'})
|
||||
|
||||
if file:
|
||||
filename = file.filename
|
||||
file.save(os.path.join(UPLOAD_FOLDER, filename))
|
||||
return jsonify({'status': True, 'message': 'File uploaded successfully', 'filename': filename})
|
||||
|
||||
if __name__ == "__main__":
|
||||
app.run(debug=True)
|
||||
2
main.py
2
main.py
|
|
@ -14,7 +14,7 @@ font = cv2.FONT_HERSHEY_SIMPLEX
|
|||
font_scale = 0.5
|
||||
|
||||
# video_path = "inference/test.mp4"
|
||||
video_path = "video3.mp4"
|
||||
video_path = "video/videonya.mp4"
|
||||
model_path = "models/yolov8n.pt"
|
||||
|
||||
cap = cv2.VideoCapture(video_path) #videoyu okumak için
|
||||
|
|
|
|||
|
|
@ -0,0 +1,333 @@
|
|||
import numpy as np
|
||||
import cv2
|
||||
import pandas as pd
|
||||
|
||||
cap = cv2.VideoCapture('video/video.mp4')
|
||||
frames_count, fps, width, height = cap.get(cv2.CAP_PROP_FRAME_COUNT), cap.get(cv2.CAP_PROP_FPS), cap.get(
|
||||
cv2.CAP_PROP_FRAME_WIDTH), cap.get(cv2.CAP_PROP_FRAME_HEIGHT)
|
||||
width = int(width)
|
||||
height = int(height)
|
||||
print(frames_count, fps, width, height)
|
||||
|
||||
# membuat data frame pandas dengan jumlah baris sama dengan jumlah frame
|
||||
df = pd.DataFrame(index=range(int(frames_count)))
|
||||
df.index.name = "Frame" # frame dalam bahasa indonesia
|
||||
|
||||
framenumber = 0 # mencatat frame saat ini
|
||||
carscrossedup = 0 # mencatat mobil yang melintasi atas
|
||||
carscrosseddown = 0 # mencatat mobil yang melintasi bawah
|
||||
carids = [] # list kosong untuk menambah id mobil
|
||||
caridscrossed = [] # list kosong untuk menambah id mobil yang telah melintasi
|
||||
totalcars = 0 # mencatat total mobil
|
||||
|
||||
fgbg = cv2.createBackgroundSubtractorMOG2() # membuat subtractor latar belakang MOG2
|
||||
|
||||
# informasi untuk memulai menyimpan file video
|
||||
ret, frame = cap.read() # impor gambar
|
||||
ratio = .5 # rasio pengubah ukuran
|
||||
image = cv2.resize(frame, (0, 0), None, ratio, ratio) # ubah ukuran gambar
|
||||
width2, height2, channels = image.shape
|
||||
# video = cv2.VideoWriter('penghitung_kendaraan.avi', cv2.VideoWriter_fourcc('M', 'J', 'P', 'G'), fps, (height2, width2), 1)
|
||||
|
||||
while True:
|
||||
|
||||
ret, frame = cap.read() # impor gambar
|
||||
|
||||
if ret: # jika ada frame lanjutkan kode
|
||||
|
||||
image = cv2.resize(frame, (0, 0), None, ratio, ratio) # ubah ukuran gambar
|
||||
|
||||
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) # konversi gambar ke warna abu-abu
|
||||
|
||||
fgmask = fgbg.apply(gray) # menggunakan pengurangan latar belakang MOG2
|
||||
|
||||
# menerapkan tingkat kesulitan pada fgmask untuk mencoba mengisolasi mobil
|
||||
# perlu mencoba berbagai pengaturan hingga mobil mudah diidentifikasi
|
||||
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5)) # membuat kernel untuk operasi morfologi
|
||||
closing = cv2.morphologyEx(fgmask, cv2.MORPH_CLOSE, kernel)
|
||||
opening = cv2.morphologyEx(closing, cv2.MORPH_OPEN, kernel)
|
||||
dilation = cv2.dilate(opening, kernel)
|
||||
retvalbin, bins = cv2.threshold(dilation, 220, 255, cv2.THRESH_BINARY) # menghapus shadow
|
||||
|
||||
# membuat kontur
|
||||
contours, hierarchy = cv2.findContours(bins, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)[-2:]
|
||||
|
||||
# menggunakan konveks hull untuk membuat poligon kait dengan kontur
|
||||
hull = [cv2.convexHull(c) for c in contours]
|
||||
|
||||
# menggambar kontur
|
||||
cv2.drawContours(image, hull, -1, (0, 255, 0), 3)
|
||||
|
||||
# garis dibuat untuk menghentikan penghitungan kontur, diperlukan karena mobil jauh menjadi kontur satu
|
||||
lineypos = 100
|
||||
cv2.line(image, (0, lineypos), (width, lineypos), (255, 0, 0), 5)
|
||||
|
||||
# garis y posisi dibuat untuk menghitung kontur
|
||||
lineypos2 = 125
|
||||
cv2.line(image, (0, lineypos2), (width, lineypos2), (0, 255, 0), 5)
|
||||
|
||||
# area minimal untuk kontur agar tidak dihitung sebagai rumit
|
||||
minarea = 400
|
||||
|
||||
# area maksimal untuk kontur, dapat cukup besar untuk bus
|
||||
maxarea = 40000
|
||||
|
||||
# vektor untuk x dan y lokasi tengah kontur dalam frame saat ini
|
||||
cxx = np.zeros(len(contours))
|
||||
cyy = np.zeros(len(contours))
|
||||
|
||||
for i in range(len(contours)): # melakukan iterasi pada semua kontur dalam frame saat ini
|
||||
|
||||
# menggunakan hierarki untuk hanya menghitung kontur induk (kontur yang tidak berada dalam kontur lain)
|
||||
if hierarchy[0, i, 3] == -1:
|
||||
|
||||
area = cv2.contourArea(contours[i]) # menghitung luas kontur
|
||||
|
||||
if minarea < area < maxarea: # menggunakan area sebagai garis pembatas untuk kontur
|
||||
|
||||
# menghitung centroid dari kontur
|
||||
cnt = contours[i]
|
||||
M = cv2.moments(cnt)
|
||||
cx = int(M['m10'] / M['m00'])
|
||||
cy = int(M['m01'] / M['m00'])
|
||||
|
||||
if cy > lineypos: # menghapus kontur yang berada di atas garis (y dimulai dari atas)
|
||||
|
||||
# mengambil titik koordinat untuk membuat kotak lingkaran
|
||||
x, y, w, h = cv2.boundingRect(cnt)
|
||||
|
||||
# membuat kotak lingkaran dari kontur
|
||||
cv2.rectangle(image, (x, y), (x + w, y + h), (255, 0, 0), 2)
|
||||
|
||||
# Menambahkan teks centroid untuk memverifikasi pada tahap selanjutnya
|
||||
cv2.putText(image, str(cx) + "," + str(cy), (cx + 10, cy + 10), cv2.FONT_HERSHEY_SIMPLEX,
|
||||
0.3, (0, 0, 255), 1)
|
||||
|
||||
cv2.drawMarker(image, (cx, cy), (0, 0, 255), cv2.MARKER_STAR, markerSize=5, thickness=1,
|
||||
line_type=cv2.LINE_AA)
|
||||
|
||||
# menambahkan centroid yang telah memenuhi kriteria ke dalam list centroid
|
||||
cxx[i] = cx
|
||||
cyy[i] = cy
|
||||
|
||||
# menghapus nol dalam vector centroid yang tidak dihitung (centroid yang tidak dikirim ke dataframe)
|
||||
cxx = cxx[cxx != 0]
|
||||
cyy = cyy[cyy != 0]
|
||||
|
||||
# list kosong untuk nanti mencatat indeks centroid yang dikirim ke dataframe
|
||||
minx_index2 = []
|
||||
miny_index2 = []
|
||||
|
||||
# jumlah maksimum yang diizinkan untuk centroid dalam frame saat ini untuk dikaitkan dengan centroid dari frame sebelumnya
|
||||
maxrad = 25
|
||||
|
||||
# bagian berikut mengelola centroid dan mengasignasinya ke id mobil lama atau id mobil baru
|
||||
|
||||
# jika terdapat centroid dalam area yang ditentukan
|
||||
if len(cxx): # jika ada centroid dalam area yang ditentukan
|
||||
|
||||
if not carids: # jika daftar carids kosong
|
||||
|
||||
for i in range(len(cxx)): # melakukan loop sebanyak centroid yang ada
|
||||
|
||||
carids.append(i) # menambahkan id mobil ke dalam daftar kosong
|
||||
df[str(carids[i])] = "" # menambahkan kolom ke dalam dataframe berdasarkan id mobil
|
||||
|
||||
# mengisi nilai centroid pada frame saat ini dan id mobil yang sesuai
|
||||
df.at[int(framenumber), str(carids[i])] = [cxx[i], cyy[i]]
|
||||
|
||||
totalcars = carids[i] + 1 # menambahkan 1 pada jumlah mobil
|
||||
|
||||
else: # jika sudah ada id mobil
|
||||
|
||||
dx = np.zeros((len(cxx), len(carids))) # array untuk menghitung deltanya
|
||||
dy = np.zeros((len(cyy), len(carids))) # array untuk menghitung deltanya
|
||||
|
||||
for i in range(len(cxx)): # melakukan loop sebanyak centroid yang ada
|
||||
|
||||
for j in range(len(carids)): # melakukan loop sebanyak id mobil yang ada
|
||||
|
||||
# mengambil centroid dari frame sebelumnya untuk id mobil tertentu
|
||||
oldcxcy = df.iloc[int(framenumber - 1)][str(carids[j])]
|
||||
|
||||
# mengambil centroid dari frame sekarang yang tidak selalu sesuai dengan centroid dari frame sebelumnya
|
||||
curcxcy = np.array([cxx[i], cyy[i]])
|
||||
|
||||
if not oldcxcy: # jika centroid dari frame sebelumnya kosong karena mobil keluar layar
|
||||
|
||||
continue # lanjutkan ke id mobil selanjutnya
|
||||
|
||||
else: # hitung deltanya untuk dibandingkan dengan centroid dari frame sekarang
|
||||
|
||||
dx[i, j] = oldcxcy[0] - curcxcy[0]
|
||||
dy[i, j] = oldcxcy[1] - curcxcy[1]
|
||||
|
||||
for j in range(len(carids)): # melakukan loop sebanyak id mobil yang ada
|
||||
|
||||
jumlahjumlah = np.abs(dx[:, j]) + np.abs(dy[:, j]) # menghitung jumlah delta wrt id mobil tertentu
|
||||
|
||||
# mencari indeks id mobil yang memiliki nilai minimum dan ini indeks yang tepat
|
||||
indeksindextrue = np.argmin(np.abs(jumlahjumlah))
|
||||
minx_index = indeksindextrue
|
||||
miny_index = indeksindextrue
|
||||
|
||||
# mengambil nilai delta untuk id mobil yang dipilih
|
||||
deltadeltadx = dx[minx_index, j]
|
||||
deltadeltady = dy[miny_index, j]
|
||||
|
||||
if deltadeltadx == 0 and deltadeltady == 0 and np.all(dx[:, j] == 0) and np.all(dy[:, j] == 0):
|
||||
# periksa apakah nilai minimum adalah 0 dan periksa apakah semua delta adalah nol karena ini adalah kumpulan kosong
|
||||
# delta dapat berupa nol jika centroid tidak berpindah
|
||||
|
||||
continue # lanjutkan ke id mobil selanjutnya
|
||||
|
||||
else:
|
||||
|
||||
# jika nilai delta kurang dari radius maksimum maka tambahkan centroid ke id mobil yang sesuai
|
||||
if np.abs(deltadeltadx) < maxrad and np.abs(deltadeltady) < maxrad:
|
||||
|
||||
# menambahkan centroid ke id mobil yang sudah ada
|
||||
df.at[int(framenumber), str(carids[j])] = [cxx[minx_index], cyy[miny_index]]
|
||||
minx_index2.append(minx_index) # menambahkan indeks centroid yang sudah ditambahkan ke id mobil lain
|
||||
miny_index2.append(miny_index)
|
||||
|
||||
for i in range(len(cxx)): # melakukan loop sebanyak centroid yang ada
|
||||
|
||||
# jika centroid tidak ada dalam list minindex maka mobil baru perlu ditambahkan
|
||||
if i not in minx_index2 and miny_index2:
|
||||
|
||||
df[str(totalcars)] = "" # membuat kolom baru untuk mobil baru yang tercatat
|
||||
totalcars = totalcars + 1 # menambahkan jumlah mobil yang tercatat
|
||||
t = totalcars - 1 # t adalah placeholder untuk jumlah mobil
|
||||
carids.append(t) # menambahkan id mobil ke list id mobil
|
||||
df.at[int(framenumber), str(t)] = [cxx[i], cyy[i]] # menambahkan centroid ke mobil yang sudah ada
|
||||
|
||||
elif curcxcy[0] and not oldcxcy and not minx_index2 and not miny_index2:
|
||||
# jika centroid saat ini ada namun centroid sebelumnya tidak ada
|
||||
# mobil baru perlu ditambahkan jika minindex2 kosong
|
||||
|
||||
df[str(totalcars)] = "" # membuat kolom baru untuk mobil baru yang tercatat
|
||||
totalcars = totalcars + 1 # menambahkan jumlah mobil yang tercatat
|
||||
t = totalcars - 1 # t adalah placeholder untuk jumlah mobil
|
||||
carids.append(t) # menambahkan id mobil ke list id mobil
|
||||
df.at[int(framenumber), str(t)] = [cxx[i], cyy[i]] # menambahkan centroid ke mobil yang sudah ada
|
||||
|
||||
# Bagian di bawah menglabel centroid yang ada di layar
|
||||
|
||||
currentcars = 0 # mobil yang ada di layar
|
||||
currentcarsindex = [] # indeks id mobil yang ada di layar
|
||||
|
||||
for i in range(len(carids)): # melakukan loops sebanyak jumlah id mobil
|
||||
|
||||
# memeriksa frame saat ini untuk mengetahui id mobil yang sedang aktif
|
||||
# dengan memeriksa adanya centroid pada frame saat ini untuk id mobil tertentu
|
||||
if df.at[int(framenumber), str(carids[i])] != '':
|
||||
|
||||
currentcars = currentcars + 1 # menambahkan mobil yang ada di layar
|
||||
currentcarsindex.append(i) # menambahkan id mobil yang ada di layar
|
||||
|
||||
for i in range(currentcars): # melakukan loops sebanyak jumlah mobil yang ada di layar
|
||||
|
||||
# mengambil centroid untuk id mobil tertentu pada frame saat ini
|
||||
curcent = df.iloc[int(framenumber)][str(carids[currentcarsindex[i]])]
|
||||
|
||||
# mengambil centroid untuk id mobil tertentu pada frame sebelumnya
|
||||
oldcent = df.iloc[int(framenumber - 1)][str(carids[currentcarsindex[i]])]
|
||||
|
||||
if curcent: # jika ada centroid pada frame saat ini
|
||||
|
||||
# Teks di layar untuk centroid saat ini
|
||||
cv2.putText(image, "Centroid" + str(curcent[0]) + "," + str(curcent[1]),
|
||||
(int(curcent[0]), int(curcent[1])), cv2.FONT_HERSHEY_SIMPLEX, .5, (0, 255, 255), 2)
|
||||
|
||||
cv2.putText(image, "ID:" + str(carids[currentcarsindex[i]]), (int(curcent[0]), int(curcent[1] - 15)),
|
||||
cv2.FONT_HERSHEY_SIMPLEX, .5, (0, 255, 255), 2)
|
||||
|
||||
cv2.drawMarker(image, (int(curcent[0]), int(curcent[1])), (0, 0, 255), cv2.MARKER_STAR, markerSize=5,
|
||||
thickness=1, line_type=cv2.LINE_AA)
|
||||
|
||||
# Periksa apakah centroid lama ada
|
||||
# Tambahkan kotak radius dari centroid lama ke centroid saat ini untuk visualisasi
|
||||
if oldcent:
|
||||
xmulai = oldcent[0] - maxrad
|
||||
ymulai = oldcent[1] - maxrad
|
||||
xakhir = oldcent[0] + maxrad
|
||||
yakhir = oldcent[1] + maxrad
|
||||
cv2.rectangle(image, (int(xmulai), int(ymulai)), (int(xakhir), int(yakhir)), (0, 125, 0), 1)
|
||||
|
||||
# Periksa apakah centroid lama di bawah garis dan centroid baru di atas garis
|
||||
# Untuk menghitung mobil dan memastikan mobil tidak dihitung dua kali
|
||||
if oldcent[1] >= lineypos2 and curcent[1] <= lineypos2 and carids[
|
||||
currentcarsindex[i]] not in caridscrossed:
|
||||
|
||||
carscrossedup = carscrossedup + 1
|
||||
cv2.line(image, (0, lineypos2), (width, lineypos2), (0, 0, 255), 5)
|
||||
caridscrossed.append(
|
||||
currentcarsindex[i]) # Tambahkan id mobil ke daftar mobil yang dihitung untuk mencegah penghitungan dua kali
|
||||
|
||||
# Periksa apakah centroid lama di atas garis dan centroid baru di bawah garis
|
||||
# Untuk menghitung mobil dan memastikan mobil tidak dihitung dua kali
|
||||
elif oldcent[1] <= lineypos2 and curcent[1] >= lineypos2 and carids[
|
||||
currentcarsindex[i]] not in caridscrossed:
|
||||
|
||||
carscrosseddown = carscrosseddown + 1
|
||||
cv2.line(image, (0, lineypos2), (width, lineypos2), (0, 0, 125), 5)
|
||||
caridscrossed.append(currentcarsindex[i])
|
||||
|
||||
# menampilkan jumlah mobil yang melintasi atas
|
||||
cv2.putText(image, "Mobil yang Melintasi Atas: " + str(carscrossedup), (0, 15), cv2.FONT_HERSHEY_SIMPLEX, .5, (255, 255, 255),
|
||||
1)
|
||||
|
||||
# menampilkan jumlah mobil yang melintasi bawah
|
||||
cv2.putText(image, "Mobil yang Melintasi Bawah: " + str(carscrosseddown), (0, 30), cv2.FONT_HERSHEY_SIMPLEX, .5,
|
||||
(255, 255, 255), 1)
|
||||
|
||||
# # menampilkan jumlah total mobil yang terdeteksi
|
||||
# cv2.putText(image, "Total Mobil yang Terdeteksi: " + str(len(carids)), (0, 60), cv2.FONT_HERSHEY_SIMPLEX, .5,
|
||||
# (255, 255, 255), 1)
|
||||
|
||||
# menampilkan frame saat ini dan total frame
|
||||
cv2.putText(image, "Frame: " + str(framenumber) + ' dari ' + str(frames_count), (0, 45), cv2.FONT_HERSHEY_SIMPLEX,
|
||||
.5, (255, 255, 255), 1)
|
||||
|
||||
# menampilkan waktu yang sudah berlalu dan total waktu
|
||||
cv2.putText(image, 'Waktu: ' + str(round(framenumber / fps, 2)) + ' detik dari ' + str(round(frames_count / fps, 2))
|
||||
+ ' detik', (0, 60), cv2.FONT_HERSHEY_SIMPLEX, .5, (255, 255, 255), 1)
|
||||
|
||||
# menampilkan images dan transformasi
|
||||
cv2.imshow("Output", image)
|
||||
cv2.moveWindow("Output", 0, 0)
|
||||
|
||||
cv2.imshow("gray", gray)
|
||||
cv2.moveWindow("gray", int(width * ratio), 0)
|
||||
|
||||
cv2.imshow("closing", closing)
|
||||
cv2.moveWindow("closing", width, 0)
|
||||
|
||||
# cv2.imshow("opening", opening)
|
||||
# cv2.moveWindow("opening", 0, int(height * ratio))
|
||||
|
||||
# cv2.imshow("dilation", dilation)
|
||||
# cv2.moveWindow("dilation", int(width * ratio), int(height * ratio))
|
||||
|
||||
# cv2.imshow("binary", bins)
|
||||
# cv2.moveWindow("binary", width, int(height * ratio))
|
||||
|
||||
|
||||
# adds to framecount
|
||||
framenumber = framenumber + 1
|
||||
|
||||
# Menunggu key dari user dalam milidetik, fps adalah frame per detik, dan 0xff adalah binary
|
||||
# bahasa indonesia: Menunggu key dari user dalam milidetik
|
||||
k = cv2.waitKey(int(1000/fps)) & 0xff
|
||||
if k == 27: # bahasa indonesia: Jika key nya adalah 27 (ESC) maka break loop
|
||||
break
|
||||
|
||||
else: # bahasa indonesia: Jika video selesai maka break loop
|
||||
|
||||
|
||||
break
|
||||
|
||||
cap.release()
|
||||
cv2.destroyAllWindows()
|
||||
|
||||
|
|
@ -108,11 +108,11 @@
|
|||
{% endfor %}
|
||||
</select>
|
||||
</div>
|
||||
<div class="form-group">
|
||||
<!-- <div class="form-group">
|
||||
<label for="video">Threshold</label>
|
||||
<input type="text" class="form-control" id="threshold" name="threshold"
|
||||
value="{{ threshold }}">
|
||||
</div>
|
||||
</div> -->
|
||||
<div class="form-group text-center">
|
||||
<button type="button" class="btn btn-primary" onclick="olah_video()">Proses
|
||||
Video</button>
|
||||
|
|
@ -224,7 +224,8 @@
|
|||
<script>
|
||||
function olah_video() {
|
||||
var video = document.getElementById('video').value;
|
||||
var threshold = document.getElementById('threshold').value;
|
||||
// var threshold = document.getElementById('threshold').value;
|
||||
var threshold = 450;
|
||||
window.location.href = '/?video=' + video + '&threshold=' + threshold;
|
||||
}
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue