Region_of_Interest_Flow_Json_Extraction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(100, 90), (100, 400), (555, 70), (573, 728)]\n"
     ]
    }
   ],
   "source": [
    "import json\n",
    "import os\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import PIL.ImageDraw as ImageDraw\n",
    "import PIL.Image as Image\n",
    "from skimage.measure import compare_ssim\n",
    "import imutils\n",
    "import cv2\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "json_file = open(\"/home/ankit/Downloads/atg.json\", \"r\", encoding=\"utf-8\")\n",
    "sh_json= json.load(json_file)\n",
    "json_file.close()\n",
    "\n",
    "dots=[]\n",
    "image = Image.new(\"RGB\", (640, 480))\n",
    "draw = ImageDraw.Draw(image)\n",
    "\n",
    "for person in sh_json:\n",
    "    y=len(sh_json['valid_vertices'])\n",
    "for x in range(0,y):\n",
    "    k=(sh_json['valid_vertices'])\n",
    "    dots.append((k[x][0],k[x][1]))\n",
    "print(dots)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "\n",
    "# original image\n",
    "# -1 loads as-is so if it will be 3 or 4 channel as the original\n",
    "image = cv2.imread('/home/ankit/Desktop/Barbi.jpg',-1)\n",
    "# mask defaulting to black for 3-channel and transparent for 4-channel\n",
    "# (of course replace corners with yours)\n",
    "mask = np.zeros(image.shape, dtype=np.uint8)\n",
    "#roi_corners = np.array([[(10,10), (300,300), (10,300)]], dtype=np.int32)\n",
    "roi_corners = np.array([dots], np.int32)\n",
    "# fill the ROI so it doesn't get wiped out when the mask is applied\n",
    "channel_count = image.shape[2]  # i.e. 3 or 4 depending on your image\n",
    "ignore_mask_color = (255,)*channel_count\n",
    "cv2.fillPoly(mask, roi_corners, ignore_mask_color)\n",
    "# from Masterfool: use cv2.fillConvexPoly if you know it's convex\n",
    "\n",
    "# apply the mask\n",
    "masked_image = cv2.bitwise_and(image, mask)\n",
    "# save the result\n",
    "cv2.imwrite('image_masked.png', masked_image)\n",
    "cv2.imshow('image_masked.png',masked_image)\n",
    "cv2.waitKey(0)\n",
    "cv2.destroyAllWindows()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Python 2/3 compatibility\n",
    "from __future__ import print_function\n",
    "\n",
    "import numpy as np\n",
    "import cv2 as cv\n",
    "import video\n",
    "\n",
    "\n",
    "def draw_flow(points, flow, step=7.5):\n",
    "    h,  w = points.shape[:2]\n",
    "    y, x = np.mgrid[step/2:h:step, step/2:w:step].reshape(2,-1).astype(int) # mesh-grid gives ndarrays all of the same dimensions\n",
    "    fx, fy = flow[y,x].T\n",
    "    points1 = np.vstack([x, y, x+fx, y+fy]).T.reshape(-1, 2, 2) # vstack is use for to collect the array and make stack of array in vertical form (row wise)\n",
    "    points1 = np.int32(points1 + 0.5)\n",
    "    vis = cv.cvtColor(points, cv.COLOR_GRAY2BGR)\n",
    "    \n",
    "    #mask = np.zeros(image.shape, dtype=np.uint8)\n",
    "    #channel_count = image.shape[2]  # i.e. 3 or 4 depending on your image\n",
    "    #ignore_mask_color = (255,)*channel_count\n",
    "    #roi_corners = np.array([dots], np.int32)\n",
    "    #cv2.fillPoly(mask, roi_corners, ignore_mask_color)\n",
    "    # masked_image = cv2.bitwise_and(image, mask)\n",
    "\n",
    "    #cv2.imwrite('image_masked.png', masked_image)\n",
    "    #cv2.imshow('image',masked_image)\n",
    "    #cv2.waitKey(0)\n",
    "    #cv2.destroyAllWindows()\n",
    "    cv.polylines(vis, points1, True, (0, 100,500))\n",
    "    for (x1, y1), (_x2, _y2) in points1:\n",
    "        cv.circle(vis, (x1, y1), 1, (0, 255, 0), -1)\n",
    "    return vis \n",
    "\n",
    "def draw_hsv(flow):\n",
    "    h, w = flow.shape[:2]\n",
    "    fx, fy = flow[:,:,0], flow[:,:,1]\n",
    "    ang = np.arctan2(fy, fx) + np.pi\n",
    "    v = np.sqrt(fx*fx+fy*fy)\n",
    "    hsv = np.zeros((h, w, 3), np.uint8)\n",
    "    hsv[...,0] = ang*(180/np.pi/2)\n",
    "    hsv[...,1] = 255\n",
    "    hsv[...,2] = np.minimum(v*4, 255)\n",
    "    bgr = cv.cvtColor(hsv, cv.COLOR_HSV2BGR)\n",
    "    return bgr\n",
    "\n",
    "\n",
    "def warp_flow(img, flow):\n",
    "    h, w = flow.shape[:2]\n",
    "    flow = -flow\n",
    "    flow[:,:,0] += np.arange(w)\n",
    "    flow[:,:,1] += np.arange(h)[:,np.newaxis]\n",
    "    res = cv.remap(img, flow, None, cv.INTER_LINEAR)\n",
    "    return res\n",
    "\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    import sys  \n",
    "    \n",
    "    cam = cv2.VideoCapture('/home/ankit/Downloads/opencv-master /samples/data/vtest.avi')\n",
    "    cam.set(cv2.CAP_PROP_FRAME_WIDTH, 1920)\n",
    "    cam.set(cv2.CAP_PROP_FRAME_HEIGHT, 1080)\n",
    "    codec = cv2.VideoWriter_fourcc(*'XVID') \n",
    "    output1 = cv2.VideoWriter('/home/ankit/Downloads/opencv-master /samples/python/just.avi', codec, 60.0,(1920,1080))\n",
    "    \n",
    "    ret, prev = cam.read() \n",
    "    mask2 = np.zeros(prev.shape, dtype=np.uint8)\n",
    "    roi_corners2 = np.array([dots], np.int32)\n",
    "    channel_count2 = prev.shape[2]\n",
    "    ignore_mask_color2 = (255,)*channel_count2\n",
    "    cv2.fillPoly(mask2, roi_corners2, ignore_mask_color2)\n",
    "    masked_image2 = cv2.bitwise_and(prev, mask2)\n",
    "    # save the result\n",
    "    #cv2.imwrite('image_masked.png', masked_image)\n",
    "    prevgray = cv2.cvtColor(masked_image2, cv2.COLOR_BGR2GRAY)\n",
    "\n",
    "    show_hsv = False\n",
    "    show_glitch = False\n",
    "    cur_glitch = prev.copy()\n",
    "    \n",
    "    while(cam.isOpened()):\n",
    "        ret, prev = cam.read()\n",
    "        mask1 = np.zeros(prev.shape, dtype=np.uint8)\n",
    "        roi_corners1 = np.array([dots], np.int32)\n",
    "        channel_count1 = prev.shape[2]\n",
    "        ignore_mask_color1 = (255,)*channel_count1\n",
    "        cv2.fillPoly(mask1, roi_corners1, ignore_mask_color1)\n",
    "        masked_image1 = cv2.bitwise_and(prev, mask1)\n",
    "        # save the result\n",
    "        #cv2.imwrite('image_masked.png', masked_image)\n",
    "        gray = cv.cvtColor(masked_image1, cv.COLOR_BGR2GRAY) \n",
    "        flow = cv.calcOpticalFlowFarneback(prevgray, gray, None, 0.5, 3, 15, 3, 5, 1.2, 0) \n",
    "        prevgray = gray\n",
    "        cv.imshow('flow', draw_flow(gray, flow))                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              \n",
    "        \n",
    "        if show_hsv:\n",
    "            cv.imshow('flow HSV', draw_hsv(flow))\n",
    "        if show_glitch:\n",
    "            cur_glitch = warp_flow(cur_glitch, flow)\n",
    "            cv.imshow('glitch', cur_glitch)\n",
    "        ch = cv.waitKey(1)\n",
    "        if ch == 27:\n",
    "            break\n",
    "        if ch == ord('1'):\n",
    "            show_hsv = not show_hsv\n",
    "            print('HSV flow visualization is', ['off', 'on'][show_hsv])\n",
    "        if ch == ord('2'):\n",
    "            show_glitch = not show_glitch\n",
    "            if show_glitch:\n",
    "                cur_glitch = img.copy()\n",
    "            print('glitch is', ['off', 'on'][show_glitch])\n",
    "\n",
    "    cam.release()\n",
    "    output1.release()\n",
    "    cv.destroyAllWindows()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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