CV- Real time object color detection using OpenCV.ipynb

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    "###########   Revision    ##########\n",
    "##### generate your own images and apply bitwise operations\n",
    "\n",
    "import cv2\n",
    "import numpy as np\n",
    "\n",
    "# Draw 2 images and put rectangle on both\n",
    "img1 = np.zeros((250, 500, 3), np.uint8)\n",
    "img1 = cv2.rectangle(img1,(200, 0), (300, 100), (255, 255, 255), -1)\n",
    "\n",
    "img2 = np.full((250, 500, 3), 255, np.uint8)\n",
    "img2 = cv2.rectangle(img2,(0, 0), (250, 250), (0, 0, 0), -1)\n",
    "\n",
    "#Apply bitwise operations\n",
    "bitAnd = cv2.bitwise_and(img2, img1)\n",
    "\n",
    "bitOr = cv2.bitwise_or(img2, img1)\n",
    "\n",
    "bitXor = cv2.bitwise_xor(img1, img2)\n",
    "\n",
    "#bitNot1 = cv2.bitwise_not(img1)\n",
    "\n",
    "#bitNot2 = cv2.bitwise_not(img2)\n",
    "\n",
    "cv2.imshow(\"img1\", img1)\n",
    "\n",
    "cv2.imshow(\"img2\", img2)\n",
    "\n",
    "cv2.imshow('bitAnd', bitAnd)\n",
    "\n",
    "cv2.imshow('bitOr', bitOr)\n",
    "\n",
    "cv2.imshow('bitXor', bitXor)\n",
    "\n",
    "#cv2.imshow('bitNot1', bitNot1)\n",
    "\n",
    "#cv2.imshow('bitNot2', bitNot2)\n",
    "\n",
    "cv2.waitKey(0)\n",
    "\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54bf3a32",
   "metadata": {},
   "source": [
    "How to detect a monochromatic colour object using python and OpenCV. \n",
    "\n",
    "Monochromatic color means light of a single wavelength.\n",
    "\n",
    "it’s highly recommended to convert the colors from RGB format to HSV. \n",
    "\n",
    "HSV stands for hue, saturation, and value. Hue signifies the color itself, technically it denotes the angle in the HSV color model. \n",
    "\n",
    "Imagine a cylinder, it has a height, a radius, and a curved surface area. Take the initial position of the radius as a reference and move the radius anticlockwise to an angle θ, i.e. θ is the angle between the current position of the radius with its initial position. If θ is 0 degrees then it denotes the color red, if θ is 120 degrees then it denotes green, and so on.  The length of the radius is the saturation of the color and the height signifies the value or V in HSV.\n",
    "\n",
    "The main idea is to convert the input RGB image (BGR in the case of OpenCV because that’s how images are formatted in this module) to HSV format, which will make it easier for us to mask the specific color out of the frame. That is, whatever color in the provided HSV range will be given a value of 255 and others will be simply 0, and as a result, every object with color in the specified range will change to white leaving the rest of the image i.e. background black."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "0019f28c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "frame = cv2.imread('HSV.jpg') \n",
    "cv2.imshow('HSV Color',frame)\n",
    "cv2.waitKey(0)\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e13b78ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "  \n",
    "cap = cv2.VideoCapture('cart.mp4')\n",
    "  \n",
    "while 1:\n",
    "    ret,frame =cap.read() \n",
    "#     frame = cv2.imread('Colored Balls.png')    \n",
    "    # ret will return a true value if the frame exists otherwise False\n",
    "    into_hsv =cv2.cvtColor(frame,cv2.COLOR_BGR2HSV)\n",
    "    # changing the color format from BGr to HSV \n",
    "    # This will be used to create the mask\n",
    "    \n",
    "    \"\"\"\n",
    "    After that specify the lower and upper limit of the color blue ( or any color\n",
    "    you prefer). By creating two NumPy arrays with the desired lower and upper \n",
    "    limit as [H, S, V].  These two NumPy arrays will be used as arguments in the \n",
    "    thresholding function i.e. the cv2.inRange() function. Which takes three \n",
    "    arguments, image source, lower limit, and the upper limit.\n",
    "    \"\"\"\n",
    "    L_limit=np.array([98,50,50]) # setting the blue lower limit\n",
    "    U_limit=np.array([139,255,255]) # setting the blue upper limit\n",
    "          \n",
    "    \"\"\"\n",
    "    cv2.inRange() function sets all the values that lie within the range to\n",
    "    255 and the rest to 0.  The output of this function will be our mask.\n",
    "    \"\"\"\n",
    "    b_mask=cv2.inRange(into_hsv,L_limit,U_limit)\n",
    "    # creating the mask using inRange() function\n",
    "    # this will produce an image where the color of the objects\n",
    "    # falling in the range will turn white and rest will be black\n",
    "    blue=cv2.bitwise_and(frame,frame,mask=b_mask)\n",
    "    # this will give the color to mask.\n",
    "    cv2.imshow('Original',frame) # to display the original frame\n",
    "    cv2.imshow('masked Image', b_mask)\n",
    "    cv2.imshow('Blue Detector',blue) # to display the blue object output\n",
    "  \n",
    "    if cv2.waitKey(1) & 0xff == 27: \n",
    "           break\n",
    "       \n",
    "    # this function will be triggered when the ESC key is pressed\n",
    "    # and the while loop will terminate and so will the program\n",
    "# cap.release()\n",
    "  \n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5b8b32f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#import os\n",
    "#os.chdir(R\"C:\\Users\\Downloads\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6f92873b",
   "metadata": {},
   "outputs": [],
   "source": [
    "cv2.imshow('Blue Detector',into_hsv) \n",
    "cv2.waitKey(0)\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8cf14309",
   "metadata": {},
   "outputs": [
    {
     "ename": "error",
     "evalue": "OpenCV(4.7.0) D:\\a\\opencv-python\\opencv-python\\opencv\\modules\\imgproc\\src\\color.cpp:182: error: (-215:Assertion failed) !_src.empty() in function 'cv::cvtColor'\n",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31merror\u001b[0m                                     Traceback (most recent call last)",
      "\u001b[1;32mC:\\Users\\ANASGR~1\\AppData\\Local\\Temp/ipykernel_15552/991279517.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     10\u001b[0m \u001b[1;32mwhile\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     11\u001b[0m     \u001b[0m_\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mframe\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcap\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 12\u001b[1;33m     \u001b[0mhsv_frame\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcv2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcvtColor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mframe\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcv2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mCOLOR_BGR2HSV\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     13\u001b[0m     \u001b[0mheight\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mwidth\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mframe\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     14\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31merror\u001b[0m: OpenCV(4.7.0) D:\\a\\opencv-python\\opencv-python\\opencv\\modules\\imgproc\\src\\color.cpp:182: error: (-215:Assertion failed) !_src.empty() in function 'cv::cvtColor'\n"
     ]
    }
   ],
   "source": [
    "#Color-Recognition-by-opencv\n",
    "# Extra informatioin\n",
    "import cv2\n",
    "\n",
    "# cap = cv2.VideoCapture(0)\n",
    "cap = cv2.VideoCapture('cart.mp4')\n",
    "cap.set(cv2.CAP_PROP_FRAME_WIDTH, 1280)\n",
    "cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 720)\n",
    "\n",
    "while True:\n",
    "    _, frame = cap.read()\n",
    "    hsv_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)\n",
    "    height, width, _ = frame.shape\n",
    "\n",
    "    cx = int(width / 2)\n",
    "    cy = int(height / 2)\n",
    "\n",
    "    # Pick pixel value\n",
    "    pixel_center = hsv_frame[cy, cx]\n",
    "    hue_value = pixel_center[0]\n",
    "\n",
    "    color = \"Undefined\"\n",
    "    if hue_value < 5:\n",
    "        color = \"RED\"\n",
    "    elif hue_value < 22:\n",
    "        color = \"ORANGE\"\n",
    "    elif hue_value < 33:\n",
    "        color = \"YELLOW\"\n",
    "    elif hue_value < 78:\n",
    "        color = \"GREEN\"\n",
    "    elif hue_value < 131:\n",
    "        color = \"BLUE\"\n",
    "    elif hue_value < 170:\n",
    "        color = \"VIOLET\"\n",
    "    else:\n",
    "        color = \"RED\"\n",
    "\n",
    "    pixel_center_bgr = frame[cy, cx]\n",
    "    b, g, r = int(pixel_center_bgr[0]), int(pixel_center_bgr[1]), int(pixel_center_bgr[2])\n",
    "\n",
    "    cv2.rectangle(frame, (cx - 220, 10), (cx + 200, 120), (255, 255, 255), -1)\n",
    "    cv2.putText(frame, color, (cx - 200, 100), 0, 3, (b, g, r), 5)\n",
    "    cv2.circle(frame, (cx, cy), 5, (25, 25, 25), 3)\n",
    "    cv2.imshow(\"Frame\", frame)\n",
    "    #out.write(frame) #save your video\n",
    "    if cv2.waitKey(25) & 0xFF == ord('q'):\n",
    "            break\n",
    "\n",
    "# cap.release()\n",
    "\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1928e54a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "L_limit=np.array([98,50,50])\n",
    "print (L_limit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bf7eea35",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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