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Photo_Video_Colorization/B_W_Video_Colorization.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# B W Video Colorization\n",
+    "**Based on \"Black and white image colorization with OpenCV and Deep Learning\", by Dr. Adrian Rosebrok:**<br>\n",
+    "https://www.pyimagesearch.com/2019/02/25/black-and-white-image-colorization-with-opencv-and-deep-learning/"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Page to Download videos from a URL:**<br>\n",
+    "https://www.vidpaw.com"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-02-28T21:11:40.626663Z",
+     "start_time": "2019-02-28T21:11:40.356811Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# import the necessary packages\n",
+    "from imutils.video import VideoStream\n",
+    "import numpy as np\n",
+    "import imutils\n",
+    "import time\n",
+    "import cv2\n",
+    "import os\n",
+    "from os.path import isfile, join"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-02-28T21:11:41.469064Z",
+     "start_time": "2019-02-28T21:11:41.466880Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "VIDEO = \"rio_32.mp4\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-03-01T14:45:53.314182Z",
+     "start_time": "2019-03-01T14:45:53.304779Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# define paths\n",
+    "prototxt = \"./model/colorization_deploy_v2.prototxt\"\n",
+    "model = \"./model/colorization_release_v2.caffemodel\"\n",
+    "points = \"./model/pts_in_hull.npy\"\n",
+    "video =  \"./input_video/\"+VIDEO\n",
+    "width = 500"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-02-28T21:11:42.886701Z",
+     "start_time": "2019-02-28T21:11:42.872697Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "vs = cv2.VideoCapture(video)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-02-28T21:11:44.019619Z",
+     "start_time": "2019-02-28T21:11:43.626657Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# load our serialized black and white colorizer model and cluster\n",
+    "# center points from disk\n",
+    "\n",
+    "net = cv2.dnn.readNetFromCaffe(prototxt,model)\n",
+    "pts = np.load(points)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-02-28T21:11:44.307565Z",
+     "start_time": "2019-02-28T21:11:44.303192Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# add the cluster centers as 1x1 convolutions to the model\n",
+    "class8 = net.getLayerId(\"class8_ab\")\n",
+    "conv8 = net.getLayerId(\"conv8_313_rh\")\n",
+    "pts = pts.transpose().reshape(2, 313, 1, 1)\n",
+    "net.getLayer(class8).blobs = [pts.astype(\"float32\")]\n",
+    "net.getLayer(conv8).blobs = [np.full([1, 313], 2.606, dtype=\"float32\")]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-03-01T00:21:57.201564Z",
+     "start_time": "2019-02-28T21:11:46.105962Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# loop over frames from the video stream\n",
+    "count = 0\n",
+    "success = True\n",
+    "while success:\n",
+    "\t# grab the next frame and handle if we are reading from either\n",
+    "\t# VideoCapture or VideoStream\n",
+    "\tsuccess, frame = vs.read()\n",
+    "\n",
+    "\t# if we are viewing a video and we did not grab a frame then we\n",
+    "\t# have reached the end of the video\n",
+    "\tif frame is None:\n",
+    "\t\tbreak\n",
+    "\n",
+    "\t# resize the input frame, scale the pixel intensities to the\n",
+    "\t# range [0, 1], and then convert the frame from the BGR to Lab\n",
+    "\t# color space\n",
+    "\tframe = imutils.resize(frame, 500)\n",
+    "\tframe = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n",
+    "\tframe = cv2.cvtColor(frame, cv2.COLOR_GRAY2RGB)\n",
+    "\tscaled = frame.astype(\"float32\") / 255.0\n",
+    "\tlab = cv2.cvtColor(scaled, cv2.COLOR_RGB2LAB)\n",
+    "\n",
+    "\t# resize the Lab frame to 224x224 (the dimensions the colorization\n",
+    "\t# network accepts), split channels, extract the 'L' channel, and\n",
+    "\t# then perform mean centering\n",
+    "\tresized = cv2.resize(lab, (224, 224))\n",
+    "\tL = cv2.split(resized)[0]\n",
+    "\tL -= 50\n",
+    "\n",
+    "\t# pass the L channel through the network which will *predict* the\n",
+    "\t# 'a' and 'b' channel values\n",
+    "\tnet.setInput(cv2.dnn.blobFromImage(L))\n",
+    "\tab = net.forward()[0, :, :, :].transpose((1, 2, 0))\n",
+    "\n",
+    "\t# resize the predicted 'ab' volume to the same dimensions as our\n",
+    "\t# input frame, then grab the 'L' channel from the *original* input\n",
+    "\t# frame (not the resized one) and concatenate the original 'L'\n",
+    "\t# channel with the predicted 'ab' channels\n",
+    "\tab = cv2.resize(ab, (frame.shape[1], frame.shape[0]))\n",
+    "\tL = cv2.split(lab)[0]\n",
+    "\tcolorized = np.concatenate((L[:, :, np.newaxis], ab), axis=2)\n",
+    "\n",
+    "\t# convert the output frame from the Lab color space to RGB, clip\n",
+    "\t# any values that fall outside the range [0, 1], and then convert\n",
+    "\t# to an 8-bit unsigned integer ([0, 255] range)\n",
+    "\tcolorized = cv2.cvtColor(colorized, cv2.COLOR_LAB2BGR)\n",
+    "\tcolorized = np.clip(colorized, 0, 1)\n",
+    "\tcolorized = (255 * colorized).astype(\"uint8\")\n",
+    "\n",
+    "\t# show the original and final colorized frames\n",
+    "\tcv2.imshow(\"Original\", frame)\n",
+    "\tcv2.imshow(\"Colorized\", colorized)\n",
+    "    \n",
+    "\tcv2.imwrite(\"./colorized_video_frames/frame%d.jpg\" % count, colorized)\n",
+    "\tcount += 1\n",
+    "\tkey = cv2.waitKey(1) & 0xFF\n",
+    "\n",
+    "\t# if the `q` key was pressed, break from the loop\n",
+    "\tif key == ord(\"q\"):\n",
+    "\t\tbreak\n",
+    "\n",
+    "vs.release()\n",
+    "\n",
+    "# close any open windows\n",
+    "cv2.destroyAllWindows()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-03-01T13:07:01.946157Z",
+     "start_time": "2019-03-01T13:07:01.918797Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def convert_frames_to_video(pathIn, pathOut, fps):\n",
+    "    frame_array = []\n",
+    "    files = [f for f in os.listdir(pathIn) if isfile(join(pathIn, f))]\n",
+    " \n",
+    "    #for sorting the file names properly\n",
+    "    files.sort(key = lambda x: int(x[5:-4]))\n",
+    " \n",
+    "    for i in range(len(files)):\n",
+    "        filename=pathIn + files[i]\n",
+    "        #reading each files\n",
+    "        img = cv2.imread(filename)\n",
+    "        height, width, layers = img.shape\n",
+    "        size = (width,height)\n",
+    "        print(filename)\n",
+    "        #inserting the frames into an image array\n",
+    "        frame_array.append(img)\n",
+    " \n",
+    "    out = cv2.VideoWriter(pathOut,cv2.VideoWriter_fourcc(*'MJPG'), fps, size)\n",
+    " \n",
+    "    for i in range(len(frame_array)):\n",
+    "        # writing to a image array\n",
+    "        out.write(frame_array[i])\n",
+    "    out.release()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-03-01T13:07:04.540130Z",
+     "start_time": "2019-03-01T13:07:04.306801Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "B_W_Photo_Colorization.ipynb           \u001b[34mcolorized_video_frames\u001b[m\u001b[m/\r\n",
+      "B_W_Photo_Colorization_function.ipynb  \u001b[34mcolorized_videos\u001b[m\u001b[m/\r\n",
+      "B_W_Video_Colorization.ipynb           \u001b[34minput_images\u001b[m\u001b[m/\r\n",
+      "Paper_Colorization.pdf                 \u001b[34minput_video\u001b[m\u001b[m/\r\n",
+      "\u001b[34mbw-colorization\u001b[m\u001b[m/                       \u001b[34mresults\u001b[m\u001b[m/\r\n",
+      "\u001b[34mcolorized_images\u001b[m\u001b[m/\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "ls"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-03-01T13:14:20.569886Z",
+     "start_time": "2019-03-01T13:07:07.504777Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "pathIn= './colorized_video_frames/'\n",
+    "pathOut = './colorized_videos/video.avi'\n",
+    "fps = 30.0\n",
+    "convert_frames_to_video(pathIn, pathOut, fps)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
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