computer vision/Day-32-Computer-Visio.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Chapter 11 Computer vision**\n",
    "\n",
    "In our era, we’re trying to train machines to make them do what we’re doing in daily basis. Not with the perfect rate but we are trying to develop machines to help us, makes us better, and faster at some important tasks. Computer vision is one of the most important task we’re working on. Seeing is a gift for human race. We can see, interpret, and analyze everything around us. When we look at a picture with dog chasing a ball eagerly, we can say just like I wrote. We can perceive the emotions in the picture, what is happening at that exact moment, or what can happen just after the shot. Computer vision task is trying to be developed for this purpose. Because sometimes, human vision may be inadequate or delayed. Sometimes instant responses may be required. Like, tumor diagnoses, intervention at the time of crime.\n",
    "\n",
    "**Computer vision is the science that enables computers to analyze and elaborate videos, and images like human brain does.**\n",
    "\n",
    "All idea behind computer vision is what computers can tell from a digital video or an image. It is a field that aims to automate tasks that the human vision can do with. Computer vision operations require some methods, like processing, analyzing, and extraction of the images. We cannot feed the model with direct images obviously. As you know, computers only understand numbers and in order to train the model, we must convert the pictures to matrices or tensors. We can also make changes in the images to make the operations easier.\n",
    "\n",
    "## What is OpenCV library?\n",
    "\n",
    "OpenCV-Python is a library of Python bindings designed to solve computer vision problems. OpenCV supports a wide variety of programming languages like Python, C++, Java, etc. It can process images and videos to identify objects, faces, or even the handwriting of a human.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reading and displaying image\n",
    "Before whatever we want to do to our image, we first need to read our image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# import library\n",
    "import cv2 as cv\n",
    "\n",
    "# read image \n",
    "img = cv.imread(\"resourses/image.jpg\")\n",
    "\n",
    "# show image\n",
    "cv.imshow(\"Pehli_Image\", img)\n",
    "cv.waitKey(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "cv.waitKey(0) will display the window infinitely until any keypress. You can shut the image window by pressing one random key on your keyboard, like ‘q’ or escape key. If you put any other number in waitKey, it will wait that long milliseconds, and shut down itself.\n",
    "\n",
    "cv.destroyAllWindows() simply destroys all the windows we created."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Resizing image "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import library\n",
    "import cv2 as cv\n",
    "\n",
    "# read image \n",
    "img2 = cv.imread(\"resourses/image.jpg\")\n",
    "\n",
    "# resize image\n",
    "img2 = cv.resize(img2, (800,600))\n",
    "\n",
    "# show image\n",
    "cv.imshow(\"Pehli_Image\", img2)\n",
    "cv.imwrite('resourses/resize_image.png', img2)\n",
    "cv.waitKey(0)\n",
    "cv.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## grayscale conversion "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import library\n",
    "import cv2 as cv\n",
    "\n",
    "# read and resize image \n",
    "img3 = cv.imread(\"resourses/image.jpg\")\n",
    "img3 = cv.resize(img3, (800,600))\n",
    "\n",
    "# grayscale conversion\n",
    "gray_img3 = cv.cvtColor(img3, cv.COLOR_BGR2GRAY)\n",
    "\n",
    "# display image\n",
    "cv.imshow(\"orignal_Image\", img3)\n",
    "cv.imshow(\"gray_Image\", gray_img3)\n",
    "\n",
    "# delay code\n",
    "cv.waitKey(0)\n",
    "cv.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Image into Black and white image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import library\n",
    "import cv2 as cv\n",
    "\n",
    "# read and resize image \n",
    "img4 = cv.imread(\"resourses/image.jpg\")\n",
    "img4 = cv.resize(img4, (800,600))\n",
    "\n",
    "# grayscale and Black and white  conversion\n",
    "gray = cv.cvtColor(img4, cv.COLOR_BGR2GRAY)\n",
    "(thresh, binary)= cv.threshold(gray, 127, 255, cv. THRESH_BINARY)\n",
    "\n",
    "# display image\n",
    "cv.imshow('original', img4 )\n",
    "cv.imshow('gray', gray)\n",
    "cv.imshow('Black and White', binary)\n",
    "cv. waitKey(0)\n",
    "cv.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Writing image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# import library\n",
    "import cv2 as cv\n",
    "\n",
    "# read and resize image\n",
    "img5 = cv.imread(\"resourses/image.jpg\")\n",
    "img5 = cv.resize(img5, (800,600))\n",
    "\n",
    "# grayscale and Black and white  conversion\n",
    "gray = cv.cvtColor(img5, cv.COLOR_BGR2GRAY)\n",
    "(thresh, binary)= cv.threshold(gray, 127, 255, cv. THRESH_BINARY)\n",
    "\n",
    "# saving image\n",
    "cv.imwrite('resourses/Image_gray.png', gray)\n",
    "cv.imwrite('resourses/Image_bw.png', binary)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reading video"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import library\n",
    "import cv2 as cv\n",
    "\n",
    "# reading video\n",
    "cap=cv.VideoCapture('resourses/video.mp4')\n",
    "\n",
    "#indicator\n",
    "if (cap.isOpened () == False):\n",
    "    print(\"error in reading video\")\n",
    "\n",
    "    #reading and playing\n",
    "while(cap.isOpened ()):\n",
    "    ret, frame = cap.read()\n",
    "    if ret == True:\n",
    "        cv.imshow(\"Video\", frame)\n",
    "        if cv.waitKey(25) & 0XFF == ord('q'):\n",
    "            break\n",
    "    else:\n",
    "        break\n",
    "\n",
    "cap.release()\n",
    "cv.destroyAllWindows ()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Converting video to gray or Black and white"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import library\n",
    "import cv2 as cv\n",
    "\n",
    "# reading video\n",
    "cap2 = cv.VideoCapture('resourses/video.mp4')\n",
    "\n",
    "#converting video to gray or Black and white\n",
    "while (True):\n",
    "    ret, frame = cap2.read()\n",
    "    grayframe = cv.cvtColor(frame, cv.COLOR_BGR2GRAY)\n",
    "    (thresh, binary) = cv.threshold(grayframe, 127, 255, cv.THRESH_BINARY)\n",
    "    if ret == True:\n",
    "       cv.imshow(\"Video\", grayframe)\n",
    "       if cv.waitKey(1) & 0XFF == ord('q'):\n",
    "            break\n",
    "    else:\n",
    "        break\n",
    "\n",
    "cap.release()\n",
    "cv.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Writing Video"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import library\n",
    "import cv2 as cv\n",
    "\n",
    "# reading video\n",
    "cap=cv.VideoCapture('resourses/video.mp4')\n",
    "\n",
    "\n",
    "# writing format, codec, video writer object and file output\n",
    "frame_width = int(cap.get(3))\n",
    "frame_height = int(cap.get(4))\n",
    "out = cv.VideoWriter(\"resourses/Out_video.avi\", cv.VideoWriter_fourcc('M', \"j\", 'P', 'G'), 10, (frame_width, frame_height))\n",
    "                 \n",
    "#indicator\n",
    "if (cap.isOpened () == False):\n",
    "    print(\"error in reading video\")\n",
    "\n",
    "    #reading and playing\n",
    "while(cap.isOpened ()):\n",
    "    ret, frame = cap.read()\n",
    "    if ret == True:\n",
    "        out.write(frame)\n",
    "        cv.imshow(\"Video\", frame)\n",
    "        if cv.waitKey(25) & 0XFF == ord('q'):\n",
    "            break\n",
    "    else:\n",
    "        break\n",
    "\n",
    "cap.release()\n",
    "cv.destroyAllWindows ()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Capture a webcam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# how to capture a webcam inside python\n",
    "\n",
    "# Step-1 Import libraries\n",
    "import cv2 as cv\n",
    "import numpy as np\n",
    "\n",
    "# Step-2 Read the frames from Camera\n",
    "cap = cv.VideoCapture(0) #webcam no.1\n",
    "\n",
    "# read until the end\n",
    "# Step-3 Display frame by frame\n",
    "while(cap.isOpened()):\n",
    "     #capture frame by frame\n",
    "     ret, frame = cap.read ()\n",
    "     if ret == True:\n",
    "           # to display frame\n",
    "           cv. imshow(\"Frame\", frame)\n",
    "           # to quit with q key\n",
    "           if cv.waitKey(1) & 0XFF == ord('q'):\n",
    "                break\n",
    "     else:\n",
    "           break\n",
    "\n",
    "# Step-4 release or close windows easily\n",
    "cap.release()\n",
    "cv.destroyAllWindows ()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## change color of webcam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import libraries\n",
    "import cv2 as cv\n",
    "import numpy as np\n",
    "\n",
    "# Read the frames from Camera\n",
    "cap = cv.VideoCapture(0)\n",
    "\n",
    "# converting frames from Camera into gray or Black and white\n",
    "while(True):\n",
    "    (ret, frame) = cap.read()\n",
    "    gray_frame = cv.cvtColor(frame, cv.COLOR_BGR2GRAY)\n",
    "    (thresh, binary)= cv.threshold(gray_frame, 127, 255, cv. THRESH_BINARY)\n",
    "\n",
    "    cv.imshow(\"OriginalCam\", frame)\n",
    "    cv.imshow(\"GrayCam\", gray_frame)\n",
    "    cv.imshow(\"BinaryCam\", binary)\n",
    "    if cv.waitKey(1) & 0XFF == ord('q'):\n",
    "        break\n",
    "\n",
    "\n",
    "cap.release()\n",
    "cv.destroyAllWindows ()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Writing videos from cam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Writing videos from cam\n",
    "\n",
    "# Import libraries\n",
    "import cv2 as cv\n",
    "import numpy as np\n",
    "\n",
    "# Read the frames from Camera\n",
    "cap=cv.VideoCapture(0)\n",
    "\n",
    "# writing format, codec, video writer object and file output\n",
    "# Obtain frame size information using get() method\n",
    "frame_width = int(cap.get(3))\n",
    "frame_height = int(cap.get(4))\n",
    "frame_size = (frame_width,frame_height)\n",
    "fps = 15\n",
    "\n",
    "out = cv.VideoWriter(\"resourses/cam_video.avi\", cv.VideoWriter_fourcc('M', \"j\", 'P', 'G'), 15, frame_size)\n",
    "\n",
    "# fourcc: 4-character code of codec, used to compress the frames (fourcc)\n",
    "# fps: Frame rate of the created video stream  \n",
    "     \n",
    "\n",
    "if (cap.isOpened () == False):\n",
    "    print(\"error in reading video\")\n",
    "\n",
    "    #reading and playing\n",
    "while(cap.isOpened ()):\n",
    "    ret, frame = cap.read()\n",
    "    if ret == True:\n",
    "        out.write(frame)\n",
    "        cv.imshow(\"Video\", frame)\n",
    "        if cv.waitKey(25) & 0XFF == ord('q'):\n",
    "            break\n",
    "    else:\n",
    "        break\n",
    "\n",
    "\n",
    "cap.release()\n",
    "out.release()\n",
    "cv.destroyAllWindows ()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setting of camera or video"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Setting of camera or video\n",
    "\n",
    "# Import libraries\n",
    "import cv2 as cv\n",
    "import numpy as np\n",
    "\n",
    "# Read the frames from Camera\n",
    "cap = cv.VideoCapture(0)\n",
    "\n",
    "\n",
    "cap.set(10, 100) # 10 is the key to set brightness\n",
    "cap.set(3, 640) # width key 3\n",
    "cap.set(4, 480) #height key 4\n",
    "\n",
    "\n",
    "while (True):\n",
    "     ret, frame = cap.read()\n",
    "     if ret == True:\n",
    "           cv.imshow(\"frame\", frame)\n",
    "           if cv.waitKey(1) & 0XFF == ord('q'):\n",
    "                break\n",
    "     else:\n",
    "           break\n",
    "\n",
    "\n",
    "cap.release()\n",
    "cv.destroyAllWindows ()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Basic functions in opencv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# basic functions or manipulation in opencv\n",
    "# Import libraries\n",
    "import cv2 as cv\n",
    "\n",
    "# read image \n",
    "img1 = cv.imread(\"resourses/image.jpg\")\n",
    "\n",
    "#resize\n",
    "img = cv.resize(img1, (800,600))\n",
    "\n",
    "# gray\n",
    "gray_img = cv.cvtColor(img, cv.COLOR_BGR2GRAY)\n",
    "\n",
    "# binary\n",
    "(thresh, binary)= cv.threshold(gray_img, 127, 255, cv.THRESH_BINARY)\n",
    "\n",
    "# blurred image\n",
    "blurr_img = cv.GaussianBlur(img, (7,7), 0) \n",
    "\n",
    "# edge detection\n",
    "edge_img = cv.Canny(img, 53,53)\n",
    "\n",
    "# thickness of lines\n",
    "mat_kernel = np.ones((3,3), np.uint8)\n",
    "dilated_img= cv.dilate(edge_img, (mat_kernel), iterations=1)\n",
    "\n",
    "# Make thinner outline\n",
    "ero_img = cv.erode(dilated_img, mat_kernel, iterations=1) \n",
    "\n",
    "#cropping we will use numpy library not open cv\n",
    "#print(\"The size of our image is: \", img.shape)\n",
    "cropped_img = img[0:500, 150:400]\n",
    "\n",
    "\n",
    "cv.imshow(\"Original\", img)\n",
    "cv.imshow(\"Resized\", img)\n",
    "cv.imshow(\"Gray\", gray_img)\n",
    "cv.imshow('Black and White', binary)\n",
    "cv.imshow(\"Blurr\", blurr_img)\n",
    "cv.imshow(\"edge\", edge_img)\n",
    "cv.imshow(\"Dilated\", dilated_img)\n",
    "cv.imshow(\"Erosion\", ero_img)\n",
    "cv.imshow(\"Cropped\", cropped_img)\n",
    "\n",
    "\n",
    "cv.waitKey(0)\n",
    "cv.destroyAllWindows ()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Draw lines, and shapes "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The size of our canvas is:  (600, 600)\n"
     ]
    }
   ],
   "source": [
    "# How to draw lines, and shapes in python\n",
    "# Import libraries\n",
    "import cv2 as cv\n",
    "import numpy as np\n",
    "\n",
    "# Draw a canvas e is for Black\n",
    "img = np.zeros((600,600)) # black\n",
    "img1 = np.ones((600,600))\n",
    "\n",
    "# print size\n",
    "print(\"The size of our canvas is: \", img.shape)\n",
    "# print(img)\n",
    "\n",
    "#adding colors to the canvas\n",
    "colored_img = np.zeros( (600,600, 3), np.uint8)   #color channel addition\n",
    "\n",
    "colored_img[:] = 255,0, 255 #color complete image\n",
    "\n",
    "colored_img[150:230, 180:500] = 255,168, 10 # part of image to be colored\n",
    "\n",
    "#adding line\n",
    "cv.line(colored_img, (0,0), (colored_img.shape[0], colored_img.shape[1]), (255,0,0) ,3) #croosed line\n",
    "cv.line(colored_img, (100,100), (300, 300), (255,255,50) ,3) # part line\n",
    "\n",
    "#adding rectangles\n",
    "#cv.rectangle(colored_img, (200, 200), (300, 600), (255,255,255), 3) # draw\n",
    "cv.rectangle(colored_img, (300, 500), (300, 400), (255,255,255), cv.FILLED) # draw\n",
    "\n",
    "# adding circle\n",
    "cv.circle(colored_img, (400,300), 50, (255,100,0), 5) # draw\n",
    "cv.circle(colored_img, (400,300), 50, (255,100,0), cv.FILLED) # draw\n",
    "\n",
    "#adding text\n",
    "y0, dy, text = 500,50, \"Python ka Chilla \\non Codanics Youtube Channel\"\n",
    "for i, line in enumerate(text.split('\\n')):\n",
    "    y = y0 + i*dy\n",
    "    cv.putText(colored_img, line, (50, y ), cv.FONT_HERSHEY_SIMPLEX, 1, (255,255, 0), 2, cv.LINE_AA, False)\n",
    "\n",
    "\n",
    "# cv.imshow(\"Black\", img)\n",
    "# cv.imshow(\"White\", img1)\n",
    "cv.imshow(\"Colored\", colored_img)\n",
    "\n",
    "cv.waitKey(0)\n",
    "cv.destroyAllWindows ()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Resolution of cam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import libraries\n",
    "import cv2 as cv\n",
    "import numpy as np\n",
    "\n",
    "# Read the frames from Camera\n",
    "cap = cv.VideoCapture(0)\n",
    "\n",
    "\n",
    "#resolutions\n",
    "def hd_resolution():\n",
    "    cap.set (3, 1280)\n",
    "    cap.set(4, 720)\n",
    "def sd_resolution():\n",
    "    cap.set(3, 640)\n",
    "    cap.set(4, 480)\n",
    "def fhd_resolution():\n",
    "    cap.set(3, 1920)\n",
    "    cap.set(4, 1080)\n",
    "\n",
    "cap.set(cv.CAP_PROP_FPS, 30)\n",
    "\n",
    "#sd_resolution()\n",
    "hd_resolution()\n",
    "#fhd_resolution()\n",
    "\n",
    "while(True):\n",
    "    ret, frame = cap.read()\n",
    "    if ret ==True:\n",
    "        cv. imshow(\"Camera\", frame)\n",
    "        if cv.waitKey(1) & 0XFF == ord('q'):\n",
    "             break\n",
    "            \n",
    "    else:\n",
    "       break\n",
    "cap.release()\n",
    "cv.destroyAllWindows ()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Saving HD recording of Cam steaming"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Saving HD recording of Cam steaming\n",
    "\n",
    "import cv2 as cv\n",
    "import numpy as np\n",
    "cap = cv.VideoCapture(0)\n",
    "\n",
    "cap = cv.VideoCapture(0)\n",
    "\n",
    "#resolutions\n",
    "def hd_resolution():\n",
    "    cap.set (3, 1280)\n",
    "    cap.set(4, 720)\n",
    "def sd_resolution():\n",
    "    cap.set(3, 640)\n",
    "    cap.set(4, 480)\n",
    "def fhd_resolution():\n",
    "    cap.set(3, 1920)\n",
    "    cap.set(4, 1080)\n",
    "\n",
    "\n",
    "#sd_resolution()\n",
    "hd_resolution()\n",
    "#fhd_resolution()\n",
    "\n",
    "# Obtain frame size information using get() method\n",
    "frame_width = int(cap.get(3))\n",
    "frame_height = int(cap.get(4))\n",
    "frame_size = (frame_width,frame_height)\n",
    "fps = 30\n",
    "\n",
    "out = cv.VideoWriter(\"resourses/cam_video.avi\", cv.VideoWriter_fourcc('M', \"j\", 'P', 'G'), 30, frame_size)\n",
    "\n",
    "# fourcc: 4-character code of codec, used to compress the frames (fourcc)\n",
    "# fps: Frame rate of the created video stream  \n",
    "     \n",
    "\n",
    "if (cap.isOpened () == False):\n",
    "    print(\"error in reading video\")\n",
    "\n",
    "    #reading and playing\n",
    "while(cap.isOpened ()):\n",
    "    ret, frame = cap.read()\n",
    "    if ret == True:\n",
    "        out.write(frame)\n",
    "        cv.imshow(\"Video\", frame)\n",
    "        if cv.waitKey(25) & 0XFF == ord('q'):\n",
    "            break\n",
    "    else:\n",
    "        break\n",
    "cap.release()\n",
    "out.release()\n",
    "cv.destroyAllWindows ()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Joining two images "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Joining two images\n",
    "\n",
    "import cv2 as cv\n",
    "import numpy as np\n",
    "\n",
    "img = cv.imread(\"resourses/image.jpg\") \n",
    "img = cv.resize(img, (800,600))\n",
    "\n",
    "#stacking same image\n",
    "\n",
    "#1- Horizontal stack\n",
    "hor_img = np.hstack((img, img))\n",
    "\n",
    "# 2- Vertical stack\n",
    "ver_img = np.vstack((img, img)) \n",
    "\n",
    "cv.imshow(\"Hoizontal\", hor_img)\n",
    "\n",
    "cv.imshow(\"vertical\", ver_img)\n",
    "cv.imwrite('resourses/vertical.png', ver_img)\n",
    "cv.imwrite('resourses/Hoizontal.png', hor_img)\n",
    "cv.waitKey(0)\n",
    "cv.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Combine 2 images  with two different sizes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "import numpy as np\n",
    "import cv2 as cv\n",
    "\n",
    "img1 = cv.imread(\"resourses/image.jpg\")\n",
    "img1 = cv.resize(img1, (800,600)) \n",
    "img2 = cv.imread(\"resourses/01.jpg\")  \n",
    "\n",
    "\n",
    "#combine 2 images  with two different sizes\n",
    "h1, w1 = img1.shape[:2]\n",
    "h2, w2 = img2.shape[:2]\n",
    "\n",
    "#create empty martrix (Mat)\n",
    "res = np.zeros(shape=(max(h1, h2), w1 + w2, 3), dtype=np.uint8)\n",
    "\n",
    "# assign BGR values to concatenate images\n",
    "for i in range(res.shape[2]):\n",
    "    # assign img1 colors\n",
    "    res[:h1, :w1, i] = np.ones([img1.shape[0], img1.shape[1]]) * img1[:, :, i]\n",
    "    # assign img2 colors\n",
    "    res[:h2, w1:w1 + w2, i] = np.ones([img2.shape[0], img2.shape[1]]) * img2[:, :, i]\n",
    "\n",
    "output_img = res.astype('uint8')\n",
    "cv.imshow(\"output\", output_img )\n",
    "cv.imwrite('resourses/output.png', output_img)\n",
    "cv.waitKey(0)\n",
    "cv.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Resize + join image + change color"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np, cv2\n",
    "\n",
    "img1 = cv2.imread(\"resourses/image.jpg\", 0)\n",
    "img1 = cv.resize(img1, (800,600)) \n",
    "img2 = cv2.imread(\"resourses/01.jpg\", 0)\n",
    "img2 = cv2.resize(img2, (800,600)) \n",
    "h1, w1 = img1.shape[:2]\n",
    "h2, w2 = img2.shape[:2]\n",
    "vis = np.zeros((max(h1, h2), w1+w2), np.uint8)\n",
    "vis[:h1, :w1] = img1\n",
    "vis[:h2, w1:w1+w2] = img2\n",
    "vis = cv2.cvtColor(vis, cv2.COLOR_GRAY2BGR)\n",
    "\n",
    "cv2.imshow(\"test\", vis)\n",
    "cv.imwrite('resourses/test.png', vis)\n",
    "cv2.waitKey()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Change the perspective of an image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# how to change the perspective of an image\n",
    "\n",
    "import cv2 as cv\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "width, height = 800, 1200\n",
    "img = cv.imread(\"resourses/pass.jpg\")\n",
    "red_point = [114 , 159]\n",
    "green_point = [619 , 143]\n",
    "blue_point =[202 , 626] \n",
    "black_point = [601 , 610]\n",
    "# Create point matrix\n",
    "point_matrix = np.float32([red_point, green_point,blue_point, black_point])\n",
    "\n",
    "# Draw circle for esch point\n",
    "cv.circle(img,(red_point[0], red_point[1]) ,10,(0,8, 255),cv.FILLED)\n",
    "cv.circle(img, (green_point[0],green_point[1]),10, (0 ,255,0), cv.FILLED)\n",
    "cv.circle(img, (blue_point[0],blue_point[1]),16,(255,0,8) ,cv.FILLED)\n",
    "cv.circle(img,(black_point[0] ,black_point[1]), 18, (0,0.0), cv.FILLED)\n",
    "\n",
    "converted_points = np.float32([[0,0], [800,0], [0, height], [width, height]])\n",
    "perspective_transform = cv.getPerspectiveTransform(point_matrix ,converted_points)\n",
    "img_Output = cv.warpPerspective(img,perspective_transform ,(width, height))\n",
    "\n",
    "cv.imshow(\"Original Image\", img)\n",
    "cv.imshow(\"Output Image\", img_Output)\n",
    "plt.subplot(121),plt.imshow(img),plt.title('Input')\n",
    "plt.subplot(122),plt.imshow(img_Output),plt.title('Output')\n",
    "plt.show()\n",
    "cv.waitKey(0)\n",
    "cv.destroyAllWindows ()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Coordinates of an image or video"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# coordinates of an image or video\n",
    "# BGR color codes from an image\n",
    "\n",
    "# Step-1: Import libraries\n",
    "import cv2 as cv\n",
    "import numpy as np\n",
    "\n",
    "# Step-2: define a function\n",
    "def find_coord(event, x, y, flags, params):\n",
    "    if event == cv.EVENT_LBUTTONDOWN:\n",
    "        #left mouse click\n",
    "        print(x, '', y)\n",
    "        #how to define or print on the same image or window\n",
    "        font = cv. FONT_HERSHEY_PLAIN\n",
    "        cv.putText(img, str(x) + ','+ str(y), (x, y), font, 1, (255,0,0), thickness=2)\n",
    "        #show the text on image and img itself\n",
    "        cv.imshow(\"image\", img)\n",
    "    # for color finding\n",
    "    if event == cv.EVENT_RBUTTONDOWN:\n",
    "     print(x, '', y)\n",
    "     font = cv.FONT_HERSHEY_SIMPLEX\n",
    "     b = img[y,x,0]\n",
    "     g = img[y,x,1]\n",
    "     r = img[y,x,2]\n",
    "     cv. putText(img, str(b) + ',' + str(g) + ',' + str(r), (x,y), font, 1, (255,0,0), 2) \n",
    "     cv.imshow('image', img)\n",
    "                                                          \n",
    "\n",
    "# Step-3: final function to read an display\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    #reading an image\n",
    "    img = cv.imread(\"resourses/pass.jpg\", 1)\n",
    "    #display the image\n",
    "    cv.imshow('image', img)\n",
    "    # setting call back function\n",
    "    cv.setMouseCallback(\"image\", find_coord)\n",
    "    cv.waitKey(0)\n",
    "    cv.destroyAllWindows() \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Split video into frames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# split your video into frames or image sequence\n",
    "import cv2 as cv\n",
    "cap = cv.VideoCapture('resourses/video.mp4')\n",
    "frameNr = 0\n",
    "while (True):\n",
    "    success, frame = cap.read()\n",
    "    if success:\n",
    "          cv.imwrite(f\"resourses/frames/frame_{frameNr}.jpg\", frame)\n",
    "    else:\n",
    "          break\n",
    "    frameNr = frameNr+1\n",
    "cap.release()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Color detection using opencv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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    }
   ],
   "source": [
    "import cv2 as cv\n",
    "import numpy as np\n",
    "\n",
    "img = cv.imread(\"resourses/01.jpg\")\n",
    "img = cv.resize(img, (600,500)) \n",
    "def empty(a):\n",
    "    pass\n",
    "\n",
    "cv.namedWindow(\"HSV\")\n",
    "cv.resizeWindow(\"HSV\",640,240)\n",
    "cv.createTrackbar(\"HUE Min\",\"HSV\",0,179,empty)\n",
    "cv.createTrackbar(\"HUE Max\",\"HSV\",179,179,empty)\n",
    "cv.createTrackbar(\"SAT Min\",\"HSV\",0,255,empty)\n",
    "cv.createTrackbar(\"SAT Max\",\"HSV\",255,255,empty)\n",
    "cv.createTrackbar(\"VALUE Min\",\"HSV\",0,255,empty)\n",
    "cv.createTrackbar(\"VALUE Max\",\"HSV\",255,255,empty)\n",
    "\n",
    "while True:\n",
    "\n",
    "    \n",
    "    imgHsv = cv.cvtColor(img,cv.COLOR_BGR2HSV)\n",
    "\n",
    "    h_min = cv.getTrackbarPos(\"HUE Min\",\"HSV\")\n",
    "    h_max = cv.getTrackbarPos(\"HUE Max\", \"HSV\")\n",
    "    s_min = cv.getTrackbarPos(\"SAT Min\", \"HSV\")\n",
    "    s_max = cv.getTrackbarPos(\"SAT Max\", \"HSV\")\n",
    "    v_min = cv.getTrackbarPos(\"VALUE Min\", \"HSV\")\n",
    "    v_max = cv.getTrackbarPos(\"VALUE Max\", \"HSV\")\n",
    "    print(h_min)\n",
    "\n",
    "    lower = np.array([h_min,s_min,v_min])\n",
    "    upper = np.array([h_max,s_max,v_max])\n",
    "    mask = cv.inRange(imgHsv,lower,upper)\n",
    "    result = cv.bitwise_and(img,img, mask = mask)\n",
    "\n",
    "    mask = cv.cvtColor(mask, cv.COLOR_GRAY2BGR)\n",
    "    hStack = np.hstack([img,mask,result])\n",
    "    #cv.imshow('Original', img)\n",
    "    #cv.imshow('HSV Color Space', imgHsv)\n",
    "    #cv.imshow('Mask', mask)\n",
    "    #cv.imshow('Result', result)\n",
    "    cv.imshow('Horizontal Stacking', hStack)\n",
    "    if cv.waitKey(1) & 0xFF == ord('q'):\n",
    "        break\n",
    "\n",
    "cv.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## face detection in images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# face detection in images\n",
    "\n",
    "import cv2 as cv\n",
    "\n",
    "face_cascade = cv.CascadeClassifier('resourses/haarcascade_frontalface_default.xml')\n",
    "\n",
    "img = cv.imread(\"resourses/01.jpg\")\n",
    "\n",
    "img= cv.resize(img, (800, 900))\n",
    "\n",
    "\n",
    "gray_img = cv.cvtColor(img, cv.COLOR_BGR2GRAY)\n",
    "\n",
    "faces = face_cascade.detectMultiScale(gray_img, 1.1, 4)\n",
    "\n",
    "#draw a rectangle\n",
    "\n",
    "for (x, y, w, h) in faces:\n",
    "\n",
    "    cv.rectangle(img, (x,y), (x+w, y+h), (255,0,0), 2)\n",
    "\n",
    "cv.imshow(\"image\", img)\n",
    "cv.imwrite(\"resourses/arsl.png\", img)\n",
    "\n",
    "cv.waitKey(0)\n",
    "cv.destroyAllWindows ()\n",
    "\n"
   ]
  }
 ],
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