Object_detection_webcam.ipynb

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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import io\n",
    "import os\n",
    "import scipy.misc\n",
    "import numpy as np\n",
    "import six\n",
    "import time\n",
    "import cv2\n",
    "\n",
    "from six import BytesIO\n",
    "\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import tensorflow as tf\n",
    "from object_detection.utils import visualization_utils as viz_utils\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "category_index = {\n",
    "    1: {'id': 1, 'name': 'Mercedes'},\n",
    "    2: {'id': 2, 'name': 'Red Bull Racing'},\n",
    "    3: {'id': 3, 'name': 'Racing Point'},\n",
    "    4: {'id': 4, 'name': 'McLaren'},\n",
    "    5: {'id': 5, 'name': 'Renault'},\n",
    "    6: {'id': 6, 'name': 'Ferrari'},\n",
    "    7: {'id': 7, 'name': 'accident'},\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "start_time = time.time()\n",
    "tf.keras.backend.clear_session()\n",
    "detect_fn = tf.saved_model.load('C:/Users/Ricky K Nguyen/Desktop/Capstone/inference_graph/saved_model')\n",
    "end_time = time.time()\n",
    "elapsed_time = end_time - start_time\n",
    "print('Elapsed time: ' + str(elapsed_time) + 's')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Select your webcam\n",
    "video = cv2.VideoCapture(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "while True:    \n",
    "    ret, image_np = video.read()\n",
    "    input_tensor = np.expand_dims(image_np, 0)\n",
    "    detections = detect_fn(input_tensor)\n",
    "    \n",
    "    plt.rcParams['figure.figsize'] = [42, 21]\n",
    "    label_id_offset = 1\n",
    "    image_np_with_detections = image_np.copy()\n",
    "    viz_utils.visualize_boxes_and_labels_on_image_array(image_np_with_detections,\n",
    "                                                        detections['detection_boxes'][0].numpy(),\n",
    "                                                        detections['detection_classes'][0].numpy().astype(np.int32),\n",
    "                                                        detections['detection_scores'][0].numpy(),\n",
    "                                                        category_index,\n",
    "                                                        use_normalized_coordinates=True,\n",
    "                                                        max_boxes_to_draw=200,\n",
    "                                                        min_score_thresh=.40,\n",
    "                                                        agnostic_mode=False)\n",
    "    cv2.imshow('object detection', cv2.resize(image_np_with_detections, (900,600)))\n",
    "    if cv2.waitKey(25) & 0xFF == ord('q'):\n",
    "        cv2.destroyAllWindows()\n",
    "        break"
   ]
  }
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