real_time.ipynb

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    "# USAGE\n",
    "# python detect.py --images images\n",
    "\n",
    "# import the necessary packages\n",
    "from __future__ import print_function\n",
    "from imutils.object_detection import non_max_suppression\n",
    "from imutils import paths\n",
    "import numpy as np\n",
    "import argparse\n",
    "import imutils\n",
    "import cv2\n",
    "\n",
    "# construct the argument parse and parse the arguments\n",
    "#ap = argparse.ArgumentParser()\n",
    "#ap.add_argument(\"-i\", \"--images\", required=True, help=\"path to images directory\")\n",
    "#args = vars(ap.parse_args())\n",
    "\n",
    "# initialize the HOG descriptor/person detector\n",
    "hog = cv2.HOGDescriptor()\n",
    "hog.setSVMDetector(cv2.HOGDescriptor_getDefaultPeopleDetector())\n",
    "\n",
    "# loop over the image paths\n",
    "#imagePaths = list(paths.list_images(args[\"images\"]))\n",
    "\n",
    "#for imagePath in imagePaths:\n",
    "\n",
    "cap = cv2.VideoCapture(0)\n",
    "# cap = cv2.VideoCapture('People.mkv')\n",
    "\n",
    "while True:\n",
    "    r, image = cap.read()\n",
    "    # load the image and resize it to (1) reduce detection time\n",
    "    # and (2) improve detection accuracy\n",
    "    #image = cv2.imread(frame)\n",
    "    image = imutils.resize(image, width=min(400, image.shape[1]))\n",
    "    orig = image.copy()\n",
    "\n",
    "    # detect people in the image\n",
    "    (rects, weights) = hog.detectMultiScale(image, winStride=(4, 4),\n",
    "        padding=(8, 8), scale=1.05)\n",
    "\n",
    "    # draw the original bounding boxes\n",
    "    for (x, y, w, h) in rects:\n",
    "        cv2.rectangle(orig, (x, y), (x + w, y + h), (0, 0, 255), 2)\n",
    "\n",
    "    # apply non-maxima suppression to the bounding boxes using a\n",
    "    # fairly large overlap threshold to try to maintain overlapping\n",
    "    # boxes that are still people\n",
    "    rects = np.array([[x, y, x + w, y + h] for (x, y, w, h) in rects])\n",
    "    pick = non_max_suppression(rects, probs=None, overlapThresh=0.65)\n",
    "\n",
    "    # draw the final bounding boxes\n",
    "    for (xA, yA, xB, yB) in pick:\n",
    "        cv2.rectangle(image, (xA, yA), (xB, yB), (0, 255, 0), 2)\n",
    "\n",
    "    # show some information on the number of bounding boxes\n",
    "    #filename = imagePath[imagePath.rfind(\"/\") + 1:]\n",
    "    #print(\"[INFO] {}: {} original boxes, {} after suppression\".format(\n",
    "    #filename, len(rects), len(pick)))\n",
    "\n",
    "    # show the output images\n",
    "    #cv2.imshow(\"Before NMS\", orig)\n",
    "    cv2.imshow(\"After NMS\", image)\n",
    "    #cv2.waitKey(0)\n",
    "    k = cv2.waitKey(1)\n",
    "    if k & 0xFF == ord(\"q\"): # Exit condition\n",
    "        break"
   ]
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   "cell_type": "code",
   "execution_count": null,
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    "cap = cv2.VideoCapture(0)\n",
    "\n",
    "while True:\n",
    "    r, image = cap.read()\n",
    "    \n",
    "\n",
    "    # show the output images\n",
    "    #cv2.imshow(\"Before NMS\", orig)\n",
    "    cv2.imshow(\"After NMS\", image)\n",
    "    #cv2.waitKey(0)\n",
    "    k = cv2.waitKey(1)\n",
    "    if k & 0xFF == ord(\"q\"): # Exit condition\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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