Lecture 9.4 - Camshift Object Tracking.ipynb

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    "## Camshift Object Tracking"
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    "import numpy as np\n",
    "import cv2\n",
    "\n",
    "# Initialize webcam\n",
    "cap = cv2.VideoCapture(0)\n",
    "\n",
    "# take first frame of the video\n",
    "ret, frame = cap.read()\n",
    "\n",
    "# setup default location of window\n",
    "r, h, c, w = 240, 100, 400, 160 \n",
    "track_window = (c, r, w, h)\n",
    "\n",
    "# Crop region of interest for tracking\n",
    "roi = frame[r:r+h, c:c+w]\n",
    "\n",
    "# Convert cropped window to HSV color space\n",
    "hsv_roi =  cv2.cvtColor(roi, cv2.COLOR_BGR2HSV)\n",
    "\n",
    "# Create a mask between the HSV bounds\n",
    "lower_purple = np.array([130,60,60])\n",
    "upper_purple = np.array([175,255,255])\n",
    "mask = cv2.inRange(hsv_roi, lower_purple, upper_purple)\n",
    "\n",
    "# Obtain the color histogram of the ROI\n",
    "roi_hist = cv2.calcHist([hsv_roi], [0], mask, [180], [0,180])\n",
    "\n",
    "# Normalize values to lie between the range 0, 255\n",
    "cv2.normalize(roi_hist, roi_hist, 0, 255, cv2.NORM_MINMAX)\n",
    "\n",
    "# Setup the termination criteria\n",
    "# We stop calculating the centroid shift after ten iterations \n",
    "# or if the centroid has moved at least 1 pixel\n",
    "term_crit = ( cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 1 )\n",
    "\n",
    "while True:\n",
    "    \n",
    "    # Read webcam frame\n",
    "    ret, frame = cap.read()\n",
    "\n",
    "    if ret == True:\n",
    "        # Convert to HSV\n",
    "        hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)\n",
    "        \n",
    "        # Calculate the histogram back projection \n",
    "        # Each pixel's value is it's probability\n",
    "        dst = cv2.calcBackProject([hsv],[0],roi_hist,[0,180],1)\n",
    "\n",
    "        # apply Camshift to get the new location\n",
    "        ret, track_window = cv2.CamShift(dst, track_window, term_crit)\n",
    "\n",
    "        # Draw it on image \n",
    "        # We use polylines to represent Adaptive box \n",
    "        pts = cv2.boxPoints(ret)\n",
    "        pts = np.int0(pts)\n",
    "        img2 = cv2.polylines(frame,[pts],True, 255,2)\n",
    "        \n",
    "        cv2.imshow('Camshift Tracking', img2)\n",
    "        \n",
    "        if cv2.waitKey(1) == 13: #13 is the Enter Key\n",
    "            break\n",
    "\n",
    "    else:\n",
    "        break\n",
    "\n",
    "cv2.destroyAllWindows()\n",
    "cap.release()"
   ]
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