From 9b30890b15aea456b34eb2cec0205d1d01e9e4fd Mon Sep 17 00:00:00 2001 From: kicap Date: Mon, 6 May 2024 08:26:54 +0800 Subject: [PATCH] added new process the video --- .DS_Store | Bin 6148 -> 6148 bytes .../Pengujian baru-checkpoint.ipynb | 384 +++++++++++++ .../Pengujian lama-checkpoint.ipynb | 344 +++++++++++ ...oint.ipynb => Pengujian1-checkpoint.ipynb} | 54 +- .../Pengujian2-checkpoint.ipynb | 6 + Pengujian baru.ipynb | 384 +++++++++++++ Pengujian lama.ipynb | 344 +++++++++++ Pengujian.ipynb | 322 ----------- app.py | 532 +++++++++++++++--- ini app sebelumnya.py | 194 +++++++ main.py | 2 +- main3.py | 333 +++++++++++ templates/index.html | 7 +- 13 files changed, 2479 insertions(+), 427 deletions(-) create mode 100644 .ipynb_checkpoints/Pengujian baru-checkpoint.ipynb create mode 100644 .ipynb_checkpoints/Pengujian lama-checkpoint.ipynb rename .ipynb_checkpoints/{Pengujian-checkpoint.ipynb => Pengujian1-checkpoint.ipynb} (83%) create mode 100644 .ipynb_checkpoints/Pengujian2-checkpoint.ipynb create mode 100644 Pengujian baru.ipynb create mode 100644 Pengujian lama.ipynb delete mode 100644 Pengujian.ipynb create mode 100644 ini app sebelumnya.py create mode 100644 main3.py diff --git a/.DS_Store b/.DS_Store index d603a1d5ee79fc9efc99daa1bb432cfedb9630f3..c474d2330b718e87917975dec443bb8bfef5ccc0 100644 GIT binary patch delta 47 zcmZoMXfc@J&nUPtU^g?P;AS2cab|8-hBAgsh7^WWhWyE1?DCUmv*%1~@Y~GJ@s}R} DC%_E= delta 108 zcmZoMXfc@J&nUDpU^g?P&}JSMab|U4h7yJ%hD3%mAk1V)W{3yke1=knJfL_gP(}|Z uSH_SzS&v;_nG?ifNCArH!^Ayv@{^Nt@{<@C7zBWL{bW1#>dovNfB69;i5QLm diff --git a/.ipynb_checkpoints/Pengujian baru-checkpoint.ipynb b/.ipynb_checkpoints/Pengujian baru-checkpoint.ipynb new file mode 100644 index 0000000..823d857 --- /dev/null +++ b/.ipynb_checkpoints/Pengujian baru-checkpoint.ipynb @@ -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 +} diff --git a/.ipynb_checkpoints/Pengujian lama-checkpoint.ipynb b/.ipynb_checkpoints/Pengujian lama-checkpoint.ipynb new file mode 100644 index 0000000..fa45c66 --- /dev/null +++ b/.ipynb_checkpoints/Pengujian lama-checkpoint.ipynb @@ -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 +} diff --git a/.ipynb_checkpoints/Pengujian-checkpoint.ipynb b/.ipynb_checkpoints/Pengujian1-checkpoint.ipynb similarity index 83% rename from .ipynb_checkpoints/Pengujian-checkpoint.ipynb rename to .ipynb_checkpoints/Pengujian1-checkpoint.ipynb index 7baf0fd..87c3e23 100644 --- a/.ipynb_checkpoints/Pengujian-checkpoint.ipynb +++ b/.ipynb_checkpoints/Pengujian1-checkpoint.ipynb @@ -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": [], diff --git a/.ipynb_checkpoints/Pengujian2-checkpoint.ipynb b/.ipynb_checkpoints/Pengujian2-checkpoint.ipynb new file mode 100644 index 0000000..363fcab --- /dev/null +++ b/.ipynb_checkpoints/Pengujian2-checkpoint.ipynb @@ -0,0 +1,6 @@ +{ + "cells": [], + "metadata": {}, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/Pengujian baru.ipynb b/Pengujian baru.ipynb new file mode 100644 index 0000000..823d857 --- /dev/null +++ b/Pengujian baru.ipynb @@ -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 +} diff --git a/Pengujian lama.ipynb b/Pengujian lama.ipynb new file mode 100644 index 0000000..fa45c66 --- /dev/null +++ b/Pengujian lama.ipynb @@ -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 +} diff --git a/Pengujian.ipynb b/Pengujian.ipynb deleted file mode 100644 index 11b1983..0000000 --- a/Pengujian.ipynb +++ /dev/null @@ -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..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", - "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", - "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, 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\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/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", - "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", - "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 -} diff --git a/app.py b/app.py index 3d7277d..7f189c9 100644 --- a/app.py +++ b/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') diff --git a/ini app sebelumnya.py b/ini app sebelumnya.py new file mode 100644 index 0000000..3d7277d --- /dev/null +++ b/ini app sebelumnya.py @@ -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/') +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) diff --git a/main.py b/main.py index 0a2a16a..06d06c3 100644 --- a/main.py +++ b/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 diff --git a/main3.py b/main3.py new file mode 100644 index 0000000..e648b7c --- /dev/null +++ b/main3.py @@ -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() + diff --git a/templates/index.html b/templates/index.html index 2a75bfd..ab04ffe 100644 --- a/templates/index.html +++ b/templates/index.html @@ -108,11 +108,11 @@ {% endfor %} -
+
@@ -224,7 +224,8 @@