Pre-process

PP1_notebook.ipynb

+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "aab590e1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import cv2\n",
+    "from glob import glob\n",
+    "import os\n",
+    "import math\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "e0adeb34",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cap = cv2.VideoCapture(0)\n",
+    "frameRate = cap.get(5)\n",
+    "\n",
+    "count = 0\n",
+    "\n",
+    "files = glob('D:/dataset/temp/*')\n",
+    "for f in files:\n",
+    "    os.remove(f)\n",
+    "    \n",
+    "while (True):\n",
+    "    frameId = cap.get(1)\n",
+    "    ret, frame = cap.read()\n",
+    "    \n",
+    "    #cv2.imshow('frame', frame)\n",
+    "    \n",
+    "    # Display original image\n",
+    "    cv2.imshow('Original', frame)\n",
+    "\n",
+    "    # Convert to graycsale\n",
+    "    img_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n",
+    "    # Convert to HSV\n",
+    "    img_hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)\n",
+    "    # Blur the image for better edge detection\n",
+    "    img_blur1 = cv2.GaussianBlur(img_gray, (3,3), 0) \n",
+    "    # Blur the image for better edge detection\n",
+    "    img_blur2 = cv2.GaussianBlur(img_hsv, (3,3), 0)\n",
+    "\n",
+    "    # Sobel Edge Detection\n",
+    "    sobelx1 = cv2.Sobel(src=img_blur1, ddepth=cv2.CV_64F, dx=1, dy=0, ksize=5) # Sobel Edge Detection on the X axis\n",
+    "    sobely1 = cv2.Sobel(src=img_blur1, ddepth=cv2.CV_64F, dx=0, dy=1, ksize=5) # Sobel Edge Detection on the Y axis\n",
+    "    sobelxy1 = cv2.Sobel(src=img_blur1, ddepth=cv2.CV_64F, dx=1, dy=1, ksize=5) # Combined X and Y Sobel Edge Detection\n",
+    "    \n",
+    "    # Sobel Edge Detection\n",
+    "    sobelx2 = cv2.Sobel(src=img_blur2, ddepth=cv2.CV_64F, dx=1, dy=0, ksize=5) # Sobel Edge Detection on the X axis\n",
+    "    sobely2 = cv2.Sobel(src=img_blur2, ddepth=cv2.CV_64F, dx=0, dy=1, ksize=5) # Sobel Edge Detection on the Y axis\n",
+    "    sobelxy2 = cv2.Sobel(src=img_blur2, ddepth=cv2.CV_64F, dx=1, dy=1, ksize=5) # Combined X and Y Sobel Edge Detection\n",
+    "    \n",
+    "    # Display Sobel Edge Detection Images\n",
+    "    #cv2.imshow('Sobel X', sobelx)\n",
+    "    #cv2.waitKey(0)\n",
+    "    cv2.imshow('Sobel Y1', sobely1)\n",
+    "    cv2.imshow('Sobel Y2', sobely2)\n",
+    "    #cv2.waitKey(0)\n",
+    "    #cv2.imshow('Sobel X Y using Sobel() function', sobelxy)\n",
+    "    #cv2.waitKey(0)\n",
+    "\n",
+    "    if (cv2.waitKey(1) & 0xFF == ord('q')):\n",
+    "        break\n",
+    "    if (ret != True):\n",
+    "        break\n",
+    "    if(frameId % math.floor(frameRate) == 0):\n",
+    "        filename = 'D:/dataset/temp/' + \"_frame%d.jpg\" % count;count+=1\n",
+    "        cv2.imwrite(filename, sobely)\n",
+    "\n",
+    "cap.release()\n",
+    "cv2.destroyAllWindows()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "e08c53fc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(1280, 720)\n",
+      "(1280, 720)\n"
+     ]
+    }
+   ],
+   "source": [
+    "img_array=[]\n",
+    "sizes = (0,0)\n",
+    "for filename in glob('D:/dataset/temp/*.jpg'):\n",
+    "    img = cv2.imread(filename)\n",
+    "    height, width, layers = img.shape\n",
+    "    size = (width,height)\n",
+    "    print(size)\n",
+    "    sizes = size\n",
+    "    print(sizes)\n",
+    "    img_array.append(img)\n",
+    "\n",
+    "out = cv2.VideoWriter('D:/dataset/project.mp4',cv2.VideoWriter_fourcc(*'DIVX'), 15, sizes)\n",
+    " \n",
+    "for i in range(len(img_array)):\n",
+    "    out.write(img_array[i])\n",
+    "out.release()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "01052d28",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cap = cv2.VideoCapture(0)\n",
+    "frameRate = cap.get(5)\n",
+    "\n",
+    "count = 0\n",
+    "\n",
+    "files = glob('D:/dataset/temp/*')\n",
+    "for f in files:\n",
+    "    os.remove(f)\n",
+    "    \n",
+    "while (True):\n",
+    "    frameId = cap.get(1)\n",
+    "    ret, frame = cap.read()\n",
+    "    \n",
+    "    #cv2.imshow('frame', frame)\n",
+    "    \n",
+    "    # Display original image\n",
+    "    cv2.imshow('Original', frame)\n",
+    "\n",
+    "    # Convert to graycsale\n",
+    "    img_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n",
+    "    # Blur the image for better edge detection\n",
+    "    img_blur = cv2.GaussianBlur(img_gray, (3,3), 0) \n",
+    "\n",
+    "    # Canny Edge Detection\n",
+    "    edges = cv2.Canny(image=img_blur, threshold1=100, threshold2=200) # Canny Edge Detection\n",
+    "    # Display Canny Edge Detection Image\n",
+    "    cv2.imshow('Canny Edge Detection', edges)\n",
+    "\n",
+    "    if (cv2.waitKey(1) & 0xFF == ord('q')):\n",
+    "        break\n",
+    "    if (ret != True):\n",
+    "        break\n",
+    "    if(frameId % math.floor(frameRate) == 0):\n",
+    "        filename = 'D:/dataset/temp/' + \"_frame%d.jpg\" % count;count+=1\n",
+    "        #cv2.imwrite(filename, sobelxy)\n",
+    "        \n",
+    "cap.release()\n",
+    "cv2.destroyAllWindows()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4d9df1a1",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.9.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}