Image_segmentation_video_stream.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Image segmentation from a local camera demo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/envs/MaskRCNN/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: compiletime version 3.5 of module 'tensorflow.python.framework.fast_tensor_util' does not match runtime version 3.6\n",
      "  return f(*args, **kwds)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import os\n",
    "import six.moves.urllib as urllib\n",
    "import sys\n",
    "import tarfile\n",
    "import tensorflow as tf\n",
    "import zipfile\n",
    "\n",
    "import random\n",
    "import math\n",
    "import skimage.io\n",
    "import matplotlib\n",
    "\n",
    "%matplotlib inline \n",
    "\n",
    "# Root directory of the project\n",
    "ROOT_DIR = os.getcwd()\n",
    "\n",
    "from collections import defaultdict\n",
    "from io import StringIO\n",
    "from matplotlib import pyplot as plt\n",
    "from PIL import Image\n",
    "from random import randint\n",
    "from __future__ import division"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2 \n",
    "cap = cv2.VideoCapture(0)\n",
    "#cap.set(cv2.cv2.CV_CAP_PROP_FPS, 60)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Env setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This is needed to display the images.\n",
    "%matplotlib inline\n",
    "\n",
    "# This is needed since the notebook is stored in the object_detection folder.\n",
    "sys.path.append(\"..\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model preparation "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Helper code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "# Import Mask RCNN # Import \n",
    "sys.path.append(ROOT_DIR)  # To find local version of the library\n",
    "from mrcnn import utils\n",
    "import mrcnn.model as modellib\n",
    "from mrcnn import visualize\n",
    "# Import SUN config\n",
    "sys.path.append(os.path.join(ROOT_DIR, \"samples/sun/\"))  # To find local version\n",
    "import sun\n",
    "\n",
    "# Directory to save logs and trained model\n",
    "MODEL_DIR = os.path.join(ROOT_DIR)\n",
    "\n",
    "# Local path to trained weights file\n",
    "SUN_MODEL_PATH = os.path.join(ROOT_DIR, \"mask_rcnn_sun.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Configurations:\n",
      "BACKBONE                       resnet101\n",
      "BACKBONE_STRIDES               [4, 8, 16, 32, 64]\n",
      "BATCH_SIZE                     1\n",
      "BBOX_STD_DEV                   [0.1 0.1 0.2 0.2]\n",
      "COMPUTE_BACKBONE_SHAPE         None\n",
      "DETECTION_MAX_INSTANCES        100\n",
      "DETECTION_MIN_CONFIDENCE       0.9\n",
      "DETECTION_NMS_THRESHOLD        0.3\n",
      "FPN_CLASSIF_FC_LAYERS_SIZE     1024\n",
      "GPU_COUNT                      1\n",
      "GRADIENT_CLIP_NORM             5.0\n",
      "IMAGES_PER_GPU                 1\n",
      "IMAGE_MAX_DIM                  1024\n",
      "IMAGE_META_SIZE                26\n",
      "IMAGE_MIN_DIM                  800\n",
      "IMAGE_MIN_SCALE                0\n",
      "IMAGE_RESIZE_MODE              square\n",
      "IMAGE_SHAPE                    [1024 1024    3]\n",
      "LEARNING_MOMENTUM              0.9\n",
      "LEARNING_RATE                  0.001\n",
      "LOSS_WEIGHTS                   {'rpn_class_loss': 1.0, 'rpn_bbox_loss': 1.0, 'mrcnn_class_loss': 1.0, 'mrcnn_bbox_loss': 1.0, 'mrcnn_mask_loss': 1.0}\n",
      "MASK_POOL_SIZE                 14\n",
      "MASK_SHAPE                     [28, 28]\n",
      "MAX_GT_INSTANCES               100\n",
      "MEAN_PIXEL                     [123.7 116.8 103.9]\n",
      "MINI_MASK_SHAPE                (56, 56)\n",
      "NAME                           sun\n",
      "NUM_CLASSES                    14\n",
      "POOL_SIZE                      7\n",
      "POST_NMS_ROIS_INFERENCE        1000\n",
      "POST_NMS_ROIS_TRAINING         2000\n",
      "ROI_POSITIVE_RATIO             0.33\n",
      "RPN_ANCHOR_RATIOS              [0.5, 1, 2]\n",
      "RPN_ANCHOR_SCALES              (32, 64, 128, 256, 512)\n",
      "RPN_ANCHOR_STRIDE              1\n",
      "RPN_BBOX_STD_DEV               [0.1 0.1 0.2 0.2]\n",
      "RPN_NMS_THRESHOLD              0.7\n",
      "RPN_TRAIN_ANCHORS_PER_IMAGE    256\n",
      "STEPS_PER_EPOCH                100\n",
      "TOP_DOWN_PYRAMID_SIZE          256\n",
      "TRAIN_BN                       False\n",
      "TRAIN_ROIS_PER_IMAGE           200\n",
      "USE_MINI_MASK                  True\n",
      "USE_RPN_ROIS                   True\n",
      "VALIDATION_STEPS               50\n",
      "WEIGHT_DECAY                   0.0001\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "class InferenceConfig(sun.SunConfig):\n",
    "    # Set batch size to 1 since we'll be running inference on\n",
    "    # one image at a time. Batch size = GPU_COUNT * IMAGES_PER_GPU\n",
    "    GPU_COUNT = 1\n",
    "    IMAGES_PER_GPU = 1\n",
    "\n",
    "config = InferenceConfig()\n",
    "config.display()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Image segmentation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create model object in inference mode.\n",
    "model = modellib.MaskRCNN(mode = \"inference\", model_dir=MODEL_DIR, config=config)\n",
    "\n",
    "# Load weights trained on MS-SUN\n",
    "model.load_weights(SUN_MODEL_PATH, by_name=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<mrcnn.model.MaskRCNN at 0x1206d07f0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "class_names = ['BG', 'bed', 'books', 'ceiling', 'chair', 'floor',\n",
    "               'furniture', 'objects', 'picture', 'sofa', 'table',\n",
    "               'tv', 'wall', 'window']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def  random_colors(N):\n",
    "    np.random.seed(1)\n",
    "    colors = [tuple(255 * np.random.rand(3)) for _ in range(N)]\n",
    "    return colors\n",
    "\n",
    "def apply_mask(image, mask, color, alpha=0.5):\n",
    "    \"\"\"apply mask to image\"\"\"\n",
    "    for n, c in enumerate(color):\n",
    "        image[:, :, n] = np.where(\n",
    "            mask == 1,\n",
    "            image[:, :, n] * (1 - alpha) + alpha * c,\n",
    "            image[:, :, n]\n",
    "        )\n",
    "    return image\n",
    "\n",
    "def display_instances(image, boxes, masks, ids, names, scores):\n",
    "    \"\"\"\n",
    "        take the image and results and apply the mask, box, and Label\n",
    "    \"\"\"\n",
    "    n_instances = boxes.shape[0]\n",
    "    colors = random_colors(n_instances)\n",
    "\n",
    "    if not n_instances:\n",
    "        print('NO INSTANCES TO DISPLAY')\n",
    "    else:\n",
    "        assert boxes.shape[0] == masks.shape[-1] == ids.shape[0]\n",
    "\n",
    "    for i, color in enumerate(colors):\n",
    "        if not np.any(boxes[i]):\n",
    "            continue\n",
    "\n",
    "        y1, x1, y2, x2 = boxes[i]\n",
    "        label = names[ids[i]]\n",
    "        score = scores[i] if scores is not None else None\n",
    "        caption = '{} {:.2f}'.format(label, score) if score else label\n",
    "        mask = masks[:, :, i]\n",
    "\n",
    "        image = apply_mask(image, mask, color)\n",
    "        image = cv2.rectangle(image, (x1, y1), (x2, y2), color, 2)\n",
    "        image = cv2.putText(image, caption, (x1, y1), cv2.FONT_HERSHEY_COMPLEX, 0.7, color, 2)\n",
    "\n",
    "    return image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing 1 images\n",
      "image                    shape: (720, 1280, 3)        min:    0.00000  max:  255.00000  uint8\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/envs/MaskRCNN/lib/python3.6/site-packages/skimage/transform/_warps.py:110: UserWarning: Anti-aliasing will be enabled by default in skimage 0.15 to avoid aliasing artifacts when down-sampling images.\n",
      "  warn(\"Anti-aliasing will be enabled by default in skimage 0.15 to \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "molded_images            shape: (1, 1024, 1024, 3)    min: -123.70000  max:  151.10000  float64\n",
      "image_metas              shape: (1, 26)               min:    0.00000  max: 1280.00000  float64\n",
      "anchors                  shape: (1, 261888, 4)        min:   -0.35390  max:    1.29134  float32\n",
      "NO INSTANCES TO DISPLAY\n",
      "Processing 1 images\n",
      "image                    shape: (720, 1280, 3)        min:    0.00000  max:  255.00000  uint8\n",
      "molded_images            shape: (1, 1024, 1024, 3)    min: -123.70000  max:  151.10000  float64\n",
      "image_metas              shape: (1, 26)               min:    0.00000  max: 1280.00000  float64\n",
      "anchors                  shape: (1, 261888, 4)        min:   -0.35390  max:    1.29134  float32\n",
      "NO INSTANCES TO DISPLAY\n",
      "Processing 1 images\n",
      "image                    shape: (720, 1280, 3)        min:    0.00000  max:  255.00000  uint8\n",
      "molded_images            shape: (1, 1024, 1024, 3)    min: -123.70000  max:  151.10000  float64\n",
      "image_metas              shape: (1, 26)               min:    0.00000  max: 1280.00000  float64\n",
      "anchors                  shape: (1, 261888, 4)        min:   -0.35390  max:    1.29134  float32\n",
      "NO INSTANCES TO DISPLAY\n",
      "Processing 1 images\n",
      "image                    shape: (720, 1280, 3)        min:    0.00000  max:  255.00000  uint8\n",
      "molded_images            shape: (1, 1024, 1024, 3)    min: -123.70000  max:  151.10000  float64\n",
      "image_metas              shape: (1, 26)               min:    0.00000  max: 1280.00000  float64\n",
      "anchors                  shape: (1, 261888, 4)        min:   -0.35390  max:    1.29134  float32\n",
      "NO INSTANCES TO DISPLAY\n",
      "Processing 1 images\n",
      "image                    shape: (720, 1280, 3)        min:    0.00000  max:  255.00000  uint8\n",
      "molded_images            shape: (1, 1024, 1024, 3)    min: -123.70000  max:  151.10000  float64\n",
      "image_metas              shape: (1, 26)               min:    0.00000  max: 1280.00000  float64\n",
      "anchors                  shape: (1, 261888, 4)        min:   -0.35390  max:    1.29134  float32\n",
      "NO INSTANCES TO DISPLAY\n",
      "Processing 1 images\n",
      "image                    shape: (720, 1280, 3)        min:    0.00000  max:  255.00000  uint8\n",
      "molded_images            shape: (1, 1024, 1024, 3)    min: -123.70000  max:  151.10000  float64\n",
      "image_metas              shape: (1, 26)               min:    0.00000  max: 1280.00000  float64\n",
      "anchors                  shape: (1, 261888, 4)        min:   -0.35390  max:    1.29134  float32\n",
      "NO INSTANCES TO DISPLAY\n",
      "Processing 1 images\n",
      "image                    shape: (720, 1280, 3)        min:    0.00000  max:  255.00000  uint8\n",
      "molded_images            shape: (1, 1024, 1024, 3)    min: -123.70000  max:  151.10000  float64\n",
      "image_metas              shape: (1, 26)               min:    0.00000  max: 1280.00000  float64\n",
      "anchors                  shape: (1, 261888, 4)        min:   -0.35390  max:    1.29134  float32\n",
      "NO INSTANCES TO DISPLAY\n",
      "Processing 1 images\n",
      "image                    shape: (720, 1280, 3)        min:    0.00000  max:  255.00000  uint8\n",
      "molded_images            shape: (1, 1024, 1024, 3)    min: -123.70000  max:  151.10000  float64\n",
      "image_metas              shape: (1, 26)               min:    0.00000  max: 1280.00000  float64\n",
      "anchors                  shape: (1, 261888, 4)        min:   -0.35390  max:    1.29134  float32\n",
      "NO INSTANCES TO DISPLAY\n",
      "Processing 1 images\n",
      "image                    shape: (720, 1280, 3)        min:    0.00000  max:  255.00000  uint8\n",
      "molded_images            shape: (1, 1024, 1024, 3)    min: -123.70000  max:  151.10000  float64\n",
      "image_metas              shape: (1, 26)               min:    0.00000  max: 1280.00000  float64\n",
      "anchors                  shape: (1, 261888, 4)        min:   -0.35390  max:    1.29134  float32\n",
      "Processing 1 images\n",
      "image                    shape: (720, 1280, 3)        min:    0.00000  max:  255.00000  uint8\n",
      "molded_images            shape: (1, 1024, 1024, 3)    min: -123.70000  max:  151.10000  float64\n",
      "image_metas              shape: (1, 26)               min:    0.00000  max: 1280.00000  float64\n",
      "anchors                  shape: (1, 261888, 4)        min:   -0.35390  max:    1.29134  float32\n",
      "Processing 1 images\n",
      "image                    shape: (720, 1280, 3)        min:    0.00000  max:  255.00000  uint8\n",
      "molded_images            shape: (1, 1024, 1024, 3)    min: -123.70000  max:  151.10000  float64\n",
      "image_metas              shape: (1, 26)               min:    0.00000  max: 1280.00000  float64\n",
      "anchors                  shape: (1, 261888, 4)        min:   -0.35390  max:    1.29134  float32\n",
      "Processing 1 images\n",
      "image                    shape: (720, 1280, 3)        min:    0.00000  max:  255.00000  uint8\n",
      "molded_images            shape: (1, 1024, 1024, 3)    min: -123.70000  max:  151.10000  float64\n",
      "image_metas              shape: (1, 26)               min:    0.00000  max: 1280.00000  float64\n",
      "anchors                  shape: (1, 261888, 4)        min:   -0.35390  max:    1.29134  float32\n",
      "Processing 1 images\n",
      "image                    shape: (720, 1280, 3)        min:    0.00000  max:  255.00000  uint8\n",
      "molded_images            shape: (1, 1024, 1024, 3)    min: -123.70000  max:  151.10000  float64\n",
      "image_metas              shape: (1, 26)               min:    0.00000  max: 1280.00000  float64\n",
      "anchors                  shape: (1, 261888, 4)        min:   -0.35390  max:    1.29134  float32\n",
      "Processing 1 images\n",
      "image                    shape: (720, 1280, 3)        min:    0.00000  max:  255.00000  uint8\n",
      "molded_images            shape: (1, 1024, 1024, 3)    min: -123.70000  max:  151.10000  float64\n",
      "image_metas              shape: (1, 26)               min:    0.00000  max: 1280.00000  float64\n",
      "anchors                  shape: (1, 261888, 4)        min:   -0.35390  max:    1.29134  float32\n",
      "Processing 1 images\n",
      "image                    shape: (720, 1280, 3)        min:    0.00000  max:  255.00000  uint8\n",
      "molded_images            shape: (1, 1024, 1024, 3)    min: -123.70000  max:  151.10000  float64\n",
      "image_metas              shape: (1, 26)               min:    0.00000  max: 1280.00000  float64\n",
      "anchors                  shape: (1, 261888, 4)        min:   -0.35390  max:    1.29134  float32\n",
      "Processing 1 images\n",
      "image                    shape: (720, 1280, 3)        min:    0.00000  max:  255.00000  uint8\n",
      "molded_images            shape: (1, 1024, 1024, 3)    min: -123.70000  max:  151.10000  float64\n",
      "image_metas              shape: (1, 26)               min:    0.00000  max: 1280.00000  float64\n",
      "anchors                  shape: (1, 261888, 4)        min:   -0.35390  max:    1.29134  float32\n",
      "Processing 1 images\n",
      "image                    shape: (720, 1280, 3)        min:    0.00000  max:  255.00000  uint8\n",
      "molded_images            shape: (1, 1024, 1024, 3)    min: -123.70000  max:  151.10000  float64\n",
      "image_metas              shape: (1, 26)               min:    0.00000  max: 1280.00000  float64\n",
      "anchors                  shape: (1, 261888, 4)        min:   -0.35390  max:    1.29134  float32\n",
      "Processing 1 images\n",
      "image                    shape: (720, 1280, 3)        min:    0.00000  max:  255.00000  uint8\n",
      "molded_images            shape: (1, 1024, 1024, 3)    min: -123.70000  max:  151.10000  float64\n",
      "image_metas              shape: (1, 26)               min:    0.00000  max: 1280.00000  float64\n",
      "anchors                  shape: (1, 261888, 4)        min:   -0.35390  max:    1.29134  float32\n",
      "Processing 1 images\n",
      "image                    shape: (720, 1280, 3)        min:    0.00000  max:  255.00000  uint8\n",
      "molded_images            shape: (1, 1024, 1024, 3)    min: -123.70000  max:  151.10000  float64\n",
      "image_metas              shape: (1, 26)               min:    0.00000  max: 1280.00000  float64\n",
      "anchors                  shape: (1, 261888, 4)        min:   -0.35390  max:    1.29134  float32\n",
      "Processing 1 images\n",
      "image                    shape: (720, 1280, 3)        min:    0.00000  max:  255.00000  uint8\n",
      "molded_images            shape: (1, 1024, 1024, 3)    min: -123.70000  max:  151.10000  float64\n",
      "image_metas              shape: (1, 26)               min:    0.00000  max: 1280.00000  float64\n",
      "anchors                  shape: (1, 261888, 4)        min:   -0.35390  max:    1.29134  float32\n",
      "Processing 1 images\n",
      "image                    shape: (720, 1280, 3)        min:    0.00000  max:  255.00000  uint8\n",
      "molded_images            shape: (1, 1024, 1024, 3)    min: -123.70000  max:  151.10000  float64\n",
      "image_metas              shape: (1, 26)               min:    0.00000  max: 1280.00000  float64\n",
      "anchors                  shape: (1, 261888, 4)        min:   -0.35390  max:    1.29134  float32\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing 1 images\n",
      "image                    shape: (720, 1280, 3)        min:    0.00000  max:  255.00000  uint8\n",
      "molded_images            shape: (1, 1024, 1024, 3)    min: -123.70000  max:  151.10000  float64\n",
      "image_metas              shape: (1, 26)               min:    0.00000  max: 1280.00000  float64\n",
      "anchors                  shape: (1, 261888, 4)        min:   -0.35390  max:    1.29134  float32\n",
      "Processing 1 images\n",
      "image                    shape: (720, 1280, 3)        min:    0.00000  max:  255.00000  uint8\n",
      "molded_images            shape: (1, 1024, 1024, 3)    min: -123.70000  max:  151.10000  float64\n",
      "image_metas              shape: (1, 26)               min:    0.00000  max: 1280.00000  float64\n",
      "anchors                  shape: (1, 261888, 4)        min:   -0.35390  max:    1.29134  float32\n",
      "NO INSTANCES TO DISPLAY\n",
      "Processing 1 images\n",
      "image                    shape: (720, 1280, 3)        min:    0.00000  max:  255.00000  uint8\n",
      "molded_images            shape: (1, 1024, 1024, 3)    min: -123.70000  max:  151.10000  float64\n",
      "image_metas              shape: (1, 26)               min:    0.00000  max: 1280.00000  float64\n",
      "anchors                  shape: (1, 261888, 4)        min:   -0.35390  max:    1.29134  float32\n",
      "NO INSTANCES TO DISPLAY\n",
      "Processing 1 images\n",
      "image                    shape: (720, 1280, 3)        min:    0.00000  max:  255.00000  uint8\n",
      "molded_images            shape: (1, 1024, 1024, 3)    min: -123.70000  max:  151.10000  float64\n",
      "image_metas              shape: (1, 26)               min:    0.00000  max: 1280.00000  float64\n",
      "anchors                  shape: (1, 261888, 4)        min:   -0.35390  max:    1.29134  float32\n",
      "Processing 1 images\n",
      "image                    shape: (720, 1280, 3)        min:    0.00000  max:  255.00000  uint8\n",
      "molded_images            shape: (1, 1024, 1024, 3)    min: -123.70000  max:  151.10000  float64\n",
      "image_metas              shape: (1, 26)               min:    0.00000  max: 1280.00000  float64\n",
      "anchors                  shape: (1, 261888, 4)        min:   -0.35390  max:    1.29134  float32\n",
      "Processing 1 images\n",
      "image                    shape: (720, 1280, 3)        min:    0.00000  max:  255.00000  uint8\n",
      "molded_images            shape: (1, 1024, 1024, 3)    min: -123.70000  max:  151.10000  float64\n",
      "image_metas              shape: (1, 26)               min:    0.00000  max: 1280.00000  float64\n",
      "anchors                  shape: (1, 261888, 4)        min:   -0.35390  max:    1.29134  float32\n",
      "Processing 1 images\n",
      "image                    shape: (720, 1280, 3)        min:    0.00000  max:  255.00000  uint8\n",
      "molded_images            shape: (1, 1024, 1024, 3)    min: -123.70000  max:  151.10000  float64\n",
      "image_metas              shape: (1, 26)               min:    0.00000  max: 1280.00000  float64\n",
      "anchors                  shape: (1, 261888, 4)        min:   -0.35390  max:    1.29134  float32\n",
      "NO INSTANCES TO DISPLAY\n",
      "Processing 1 images\n",
      "image                    shape: (720, 1280, 3)        min:    0.00000  max:  255.00000  uint8\n",
      "molded_images            shape: (1, 1024, 1024, 3)    min: -123.70000  max:  151.10000  float64\n",
      "image_metas              shape: (1, 26)               min:    0.00000  max: 1280.00000  float64\n",
      "anchors                  shape: (1, 261888, 4)        min:   -0.35390  max:    1.29134  float32\n",
      "Processing 1 images\n",
      "image                    shape: (720, 1280, 3)        min:    0.00000  max:  255.00000  uint8\n",
      "molded_images            shape: (1, 1024, 1024, 3)    min: -123.70000  max:  151.10000  float64\n",
      "image_metas              shape: (1, 26)               min:    0.00000  max: 1280.00000  float64\n",
      "anchors                  shape: (1, 261888, 4)        min:   -0.35390  max:    1.29134  float32\n",
      "Processing 1 images\n",
      "image                    shape: (720, 1280, 3)        min:    0.00000  max:  255.00000  uint8\n",
      "molded_images            shape: (1, 1024, 1024, 3)    min: -123.70000  max:  151.10000  float64\n",
      "image_metas              shape: (1, 26)               min:    0.00000  max: 1280.00000  float64\n",
      "anchors                  shape: (1, 261888, 4)        min:   -0.35390  max:    1.29134  float32\n",
      "Processing 1 images\n",
      "image                    shape: (720, 1280, 3)        min:    0.00000  max:  255.00000  uint8\n",
      "molded_images            shape: (1, 1024, 1024, 3)    min: -123.70000  max:  151.10000  float64\n",
      "image_metas              shape: (1, 26)               min:    0.00000  max: 1280.00000  float64\n",
      "anchors                  shape: (1, 261888, 4)        min:   -0.35390  max:    1.29134  float32\n",
      "Processing 1 images\n",
      "image                    shape: (720, 1280, 3)        min:    0.00000  max:  255.00000  uint8\n",
      "molded_images            shape: (1, 1024, 1024, 3)    min: -123.70000  max:  151.10000  float64\n",
      "image_metas              shape: (1, 26)               min:    0.00000  max: 1280.00000  float64\n",
      "anchors                  shape: (1, 261888, 4)        min:   -0.35390  max:    1.29134  float32\n",
      "Processing 1 images\n",
      "image                    shape: (720, 1280, 3)        min:    0.00000  max:  255.00000  uint8\n",
      "molded_images            shape: (1, 1024, 1024, 3)    min: -123.70000  max:  151.10000  float64\n",
      "image_metas              shape: (1, 26)               min:    0.00000  max: 1280.00000  float64\n",
      "anchors                  shape: (1, 261888, 4)        min:   -0.35390  max:    1.29134  float32\n",
      "Processing 1 images\n",
      "image                    shape: (720, 1280, 3)        min:    0.00000  max:  255.00000  uint8\n",
      "molded_images            shape: (1, 1024, 1024, 3)    min: -123.70000  max:  151.10000  float64\n",
      "image_metas              shape: (1, 26)               min:    0.00000  max: 1280.00000  float64\n",
      "anchors                  shape: (1, 261888, 4)        min:   -0.35390  max:    1.29134  float32\n",
      "Processing 1 images\n",
      "image                    shape: (720, 1280, 3)        min:    0.00000  max:  255.00000  uint8\n",
      "molded_images            shape: (1, 1024, 1024, 3)    min: -123.70000  max:  151.10000  float64\n",
      "image_metas              shape: (1, 26)               min:    0.00000  max: 1280.00000  float64\n",
      "anchors                  shape: (1, 261888, 4)        min:   -0.35390  max:    1.29134  float32\n",
      "Processing 1 images\n",
      "image                    shape: (720, 1280, 3)        min:    0.00000  max:  255.00000  uint8\n",
      "molded_images            shape: (1, 1024, 1024, 3)    min: -123.70000  max:  151.10000  float64\n",
      "image_metas              shape: (1, 26)               min:    0.00000  max: 1280.00000  float64\n",
      "anchors                  shape: (1, 261888, 4)        min:   -0.35390  max:    1.29134  float32\n",
      "Processing 1 images\n",
      "image                    shape: (720, 1280, 3)        min:    0.00000  max:  255.00000  uint8\n",
      "molded_images            shape: (1, 1024, 1024, 3)    min: -123.70000  max:  151.10000  float64\n",
      "image_metas              shape: (1, 26)               min:    0.00000  max: 1280.00000  float64\n",
      "anchors                  shape: (1, 261888, 4)        min:   -0.35390  max:    1.29134  float32\n",
      "NO INSTANCES TO DISPLAY\n",
      "Processing 1 images\n",
      "image                    shape: (720, 1280, 3)        min:    0.00000  max:  255.00000  uint8\n",
      "molded_images            shape: (1, 1024, 1024, 3)    min: -123.70000  max:  151.10000  float64\n",
      "image_metas              shape: (1, 26)               min:    0.00000  max: 1280.00000  float64\n",
      "anchors                  shape: (1, 261888, 4)        min:   -0.35390  max:    1.29134  float32\n",
      "NO INSTANCES TO DISPLAY\n",
      "Processing 1 images\n",
      "image                    shape: (720, 1280, 3)        min:    0.00000  max:  255.00000  uint8\n",
      "molded_images            shape: (1, 1024, 1024, 3)    min: -123.70000  max:  151.10000  float64\n",
      "image_metas              shape: (1, 26)               min:    0.00000  max: 1280.00000  float64\n",
      "anchors                  shape: (1, 261888, 4)        min:   -0.35390  max:    1.29134  float32\n",
      "NO INSTANCES TO DISPLAY\n",
      "Processing 1 images\n",
      "image                    shape: (720, 1280, 3)        min:    0.00000  max:  255.00000  uint8\n",
      "molded_images            shape: (1, 1024, 1024, 3)    min: -123.70000  max:  151.10000  float64\n",
      "image_metas              shape: (1, 26)               min:    0.00000  max: 1280.00000  float64\n",
      "anchors                  shape: (1, 261888, 4)        min:   -0.35390  max:    1.29134  float32\n",
      "Processing 1 images\n",
      "image                    shape: (720, 1280, 3)        min:    0.00000  max:  255.00000  uint8\n",
      "molded_images            shape: (1, 1024, 1024, 3)    min: -123.70000  max:  151.10000  float64\n",
      "image_metas              shape: (1, 26)               min:    0.00000  max: 1280.00000  float64\n",
      "anchors                  shape: (1, 261888, 4)        min:   -0.35390  max:    1.29134  float32\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NO INSTANCES TO DISPLAY\n",
      "Processing 1 images\n",
      "image                    shape: (720, 1280, 3)        min:    0.00000  max:  255.00000  uint8\n",
      "molded_images            shape: (1, 1024, 1024, 3)    min: -123.70000  max:  151.10000  float64\n",
      "image_metas              shape: (1, 26)               min:    0.00000  max: 1280.00000  float64\n",
      "anchors                  shape: (1, 261888, 4)        min:   -0.35390  max:    1.29134  float32\n",
      "Processing 1 images\n",
      "image                    shape: (720, 1280, 3)        min:    0.00000  max:  255.00000  uint8\n",
      "molded_images            shape: (1, 1024, 1024, 3)    min: -123.70000  max:  151.10000  float64\n",
      "image_metas              shape: (1, 26)               min:    0.00000  max: 1280.00000  float64\n",
      "anchors                  shape: (1, 261888, 4)        min:   -0.35390  max:    1.29134  float32\n",
      "Processing 1 images\n",
      "image                    shape: (720, 1280, 3)        min:    0.00000  max:  255.00000  uint8\n",
      "molded_images            shape: (1, 1024, 1024, 3)    min: -123.70000  max:  151.10000  float64\n",
      "image_metas              shape: (1, 26)               min:    0.00000  max: 1280.00000  float64\n",
      "anchors                  shape: (1, 261888, 4)        min:   -0.35390  max:    1.29134  float32\n",
      "NO INSTANCES TO DISPLAY\n",
      "Processing 1 images\n",
      "image                    shape: (720, 1280, 3)        min:    0.00000  max:  255.00000  uint8\n",
      "molded_images            shape: (1, 1024, 1024, 3)    min: -123.70000  max:  151.10000  float64\n",
      "image_metas              shape: (1, 26)               min:    0.00000  max: 1280.00000  float64\n",
      "anchors                  shape: (1, 261888, 4)        min:   -0.35390  max:    1.29134  float32\n",
      "Processing 1 images\n",
      "image                    shape: (720, 1280, 3)        min:    0.00000  max:  255.00000  uint8\n",
      "molded_images            shape: (1, 1024, 1024, 3)    min: -123.70000  max:  151.10000  float64\n",
      "image_metas              shape: (1, 26)               min:    0.00000  max: 1280.00000  float64\n",
      "anchors                  shape: (1, 261888, 4)        min:   -0.35390  max:    1.29134  float32\n",
      "Processing 1 images\n",
      "image                    shape: (720, 1280, 3)        min:    0.00000  max:  255.00000  uint8\n",
      "molded_images            shape: (1, 1024, 1024, 3)    min: -123.70000  max:  151.10000  float64\n",
      "image_metas              shape: (1, 26)               min:    0.00000  max: 1280.00000  float64\n",
      "anchors                  shape: (1, 261888, 4)        min:   -0.35390  max:    1.29134  float32\n",
      "Processing 1 images\n",
      "image                    shape: (720, 1280, 3)        min:    0.00000  max:  255.00000  uint8\n",
      "molded_images            shape: (1, 1024, 1024, 3)    min: -123.70000  max:  151.10000  float64\n",
      "image_metas              shape: (1, 26)               min:    0.00000  max: 1280.00000  float64\n",
      "anchors                  shape: (1, 261888, 4)        min:   -0.35390  max:    1.29134  float32\n",
      "NO INSTANCES TO DISPLAY\n",
      "Processing 1 images\n",
      "image                    shape: (720, 1280, 3)        min:    0.00000  max:  255.00000  uint8\n",
      "molded_images            shape: (1, 1024, 1024, 3)    min: -123.70000  max:  151.10000  float64\n",
      "image_metas              shape: (1, 26)               min:    0.00000  max: 1280.00000  float64\n",
      "anchors                  shape: (1, 261888, 4)        min:   -0.35390  max:    1.29134  float32\n",
      "Processing 1 images\n",
      "image                    shape: (720, 1280, 3)        min:    0.00000  max:  255.00000  uint8\n",
      "molded_images            shape: (1, 1024, 1024, 3)    min: -123.70000  max:  151.10000  float64\n",
      "image_metas              shape: (1, 26)               min:    0.00000  max: 1280.00000  float64\n",
      "anchors                  shape: (1, 261888, 4)        min:   -0.35390  max:    1.29134  float32\n",
      "Processing 1 images\n",
      "image                    shape: (720, 1280, 3)        min:    0.00000  max:  255.00000  uint8\n",
      "molded_images            shape: (1, 1024, 1024, 3)    min: -123.70000  max:  151.10000  float64\n",
      "image_metas              shape: (1, 26)               min:    0.00000  max: 1280.00000  float64\n",
      "anchors                  shape: (1, 261888, 4)        min:   -0.35390  max:    1.29134  float32\n",
      "Processing 1 images\n",
      "image                    shape: (720, 1280, 3)        min:    0.00000  max:  255.00000  uint8\n",
      "molded_images            shape: (1, 1024, 1024, 3)    min: -123.70000  max:  151.10000  float64\n",
      "image_metas              shape: (1, 26)               min:    0.00000  max: 1280.00000  float64\n",
      "anchors                  shape: (1, 261888, 4)        min:   -0.35390  max:    1.29134  float32\n",
      "NO INSTANCES TO DISPLAY\n",
      "Processing 1 images\n",
      "image                    shape: (720, 1280, 3)        min:    0.00000  max:  255.00000  uint8\n",
      "molded_images            shape: (1, 1024, 1024, 3)    min: -123.70000  max:  151.10000  float64\n",
      "image_metas              shape: (1, 26)               min:    0.00000  max: 1280.00000  float64\n",
      "anchors                  shape: (1, 261888, 4)        min:   -0.35390  max:    1.29134  float32\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-10-9f7d40eacc30>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m     \u001b[0mret\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mframe\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcap\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m     \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdetect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mframe\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m     \u001b[0;31m# Visualize results\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Documents/GitHub/Mask_RCNN/mrcnn/model.py\u001b[0m in \u001b[0;36mdetect\u001b[0;34m(self, images, verbose)\u001b[0m\n\u001b[1;32m   2529\u001b[0m         \u001b[0;31m# Run object detection\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2530\u001b[0m         \u001b[0mdetections\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmrcnn_mask\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m \u001b[0;34m=\u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2531\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeras_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmolded_images\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimage_metas\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0manchors\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2532\u001b[0m         \u001b[0;31m# Process detections\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2533\u001b[0m         \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/envs/MaskRCNN/lib/python3.6/site-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mpredict\u001b[0;34m(self, x, batch_size, verbose, steps)\u001b[0m\n\u001b[1;32m   1165\u001b[0m                                             \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1166\u001b[0m                                             \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mverbose\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1167\u001b[0;31m                                             steps=steps)\n\u001b[0m\u001b[1;32m   1168\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1169\u001b[0m     def train_on_batch(self, x, y,\n",
      "\u001b[0;32m/anaconda3/envs/MaskRCNN/lib/python3.6/site-packages/keras/engine/training_arrays.py\u001b[0m in \u001b[0;36mpredict_loop\u001b[0;34m(model, f, ins, batch_size, verbose, steps)\u001b[0m\n\u001b[1;32m    292\u001b[0m                 \u001b[0mins_batch\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mins_batch\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtoarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    293\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 294\u001b[0;31m             \u001b[0mbatch_outs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mins_batch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    295\u001b[0m             \u001b[0mbatch_outs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mto_list\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch_outs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    296\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mbatch_index\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/envs/MaskRCNN/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m   2664\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_legacy_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2665\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2666\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2667\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2668\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mpy_any\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mis_tensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/envs/MaskRCNN/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py\u001b[0m in \u001b[0;36m_call\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m   2634\u001b[0m                                 \u001b[0msymbol_vals\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2635\u001b[0m                                 session)\n\u001b[0;32m-> 2636\u001b[0;31m         \u001b[0mfetched\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_callable_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0marray_vals\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2637\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mfetched\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2638\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/envs/MaskRCNN/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1380\u001b[0m           ret = tf_session.TF_SessionRunCallable(\n\u001b[1;32m   1381\u001b[0m               \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_handle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstatus\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1382\u001b[0;31m               run_metadata_ptr)\n\u001b[0m\u001b[1;32m   1383\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1384\u001b[0m           \u001b[0mproto_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "while True:\n",
    "    ret, frame = cap.read()\n",
    "\n",
    "    results = model.detect([frame], verbose=1)\n",
    "\n",
    "    # Visualize results\n",
    "    r = results[0]\n",
    "    frame = display_instances(frame, r['rois'], r['masks'], r['class_ids'], class_names, r['scores'])\n",
    "    cv2.imshow('image segmentation', cv2.resize(frame, (800,600)))\n",
    "\n",
    "    key = cv2.waitKey(delay=1)\n",
    "    if key == ord('q'):\n",
    "        break\n",
    "\n",
    "device.stop()\n",
    "device.close()\n",
    "\n",
    "sys.exit(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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