385 lines
19 KiB
Plaintext
385 lines
19 KiB
Plaintext
|
{
|
||
|
|
"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
|
||
|
|
}
|