local_inference_test-Copy1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Run inference test\n",
    "Test with images in repository `object_detection_demo/test` directory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-16T07:14:11.491377Z",
     "start_time": "2019-02-16T07:14:11.475446Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['./test\\\\IMG_20181126_192443_958.jpg', './test\\\\IMG_20190312_131424132125.jpg', './test\\\\labimage715.jpg']\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import glob\n",
    "\n",
    "# Path to frozen detection graph. This is the actual model that is used for the object detection.\n",
    "PATH_TO_CKPT = './models/frozen_inference_graph.pb'\n",
    "\n",
    "# List of the strings that is used to add correct label for each box.\n",
    "PATH_TO_LABELS = './models/label_map.pbtxt'\n",
    "\n",
    "# If you want to test the code with your images, just add images files to the PATH_TO_TEST_IMAGES_DIR (./test).\n",
    "PATH_TO_TEST_IMAGES_DIR = './test'\n",
    "\n",
    "assert os.path.isfile(PATH_TO_CKPT)\n",
    "assert os.path.isfile(PATH_TO_LABELS)\n",
    "TEST_IMAGE_PATHS = glob.glob(os.path.join(PATH_TO_TEST_IMAGES_DIR, \"*.*\"))\n",
    "assert len(TEST_IMAGE_PATHS) > 0, 'No image found in `{}`.'.format(PATH_TO_TEST_IMAGES_DIR)\n",
    "print(TEST_IMAGE_PATHS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
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    "ExecuteTime": {
     "end_time": "2019-02-16T07:14:27.590771Z",
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   "source": [
    "# %cd /content/models/research/object_detection\n",
    "\n",
    "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",
    "from collections import defaultdict\n",
    "from io import StringIO\n",
    "from matplotlib import pyplot as plt\n",
    "from PIL import Image\n",
    "\n",
    "# This is needed since the notebook is stored in the object_detection folder.\n",
    "sys.path.append(\"..\")\n",
    "from object_detection.utils import ops as utils_ops\n",
    "\n",
    "\n",
    "# This is needed to display the images.\n",
    "%matplotlib inline\n",
    "\n",
    "\n",
    "from object_detection.utils import label_map_util\n",
    "\n",
    "from object_detection.utils import visualization_utils as vis_util\n",
    "\n",
    "NUM_CLASSES = 37\n",
    "\n",
    "detection_graph = tf.Graph()\n",
    "with detection_graph.as_default():\n",
    "    od_graph_def = tf.GraphDef()\n",
    "    with tf.gfile.GFile(PATH_TO_CKPT, 'rb') as fid:\n",
    "        serialized_graph = fid.read()\n",
    "        od_graph_def.ParseFromString(serialized_graph)\n",
    "        tf.import_graph_def(od_graph_def, name='')\n",
    "\n",
    "\n",
    "label_map = label_map_util.load_labelmap(PATH_TO_LABELS)\n",
    "categories = label_map_util.convert_label_map_to_categories(\n",
    "    label_map, max_num_classes=NUM_CLASSES, use_display_name=True)\n",
    "category_index = label_map_util.create_category_index(categories)\n",
    "\n",
    "\n",
    "def load_image_into_numpy_array(image):\n",
    "    (im_width, im_height) = image.size\n",
    "    return np.array(image.getdata()).reshape(\n",
    "        (im_height, im_width, 3)).astype(np.uint8)\n",
    "\n",
    "\n",
    "# Size, in inches, of the output images.\n",
    "IMAGE_SIZE = (12, 8)\n",
    "\n",
    "\n",
    "def run_inference_for_single_image(image, graph):\n",
    "    with graph.as_default():\n",
    "        with tf.Session() as sess:\n",
    "            # Get handles to input and output tensors\n",
    "            ops = tf.get_default_graph().get_operations()\n",
    "            all_tensor_names = {\n",
    "                output.name for op in ops for output in op.outputs}\n",
    "            tensor_dict = {}\n",
    "            for key in [\n",
    "                'num_detections', 'detection_boxes', 'detection_scores',\n",
    "                'detection_classes', 'detection_masks'\n",
    "            ]:\n",
    "                tensor_name = key + ':0'\n",
    "                if tensor_name in all_tensor_names:\n",
    "                    tensor_dict[key] = tf.get_default_graph().get_tensor_by_name(\n",
    "                        tensor_name)\n",
    "            if 'detection_masks' in tensor_dict:\n",
    "                # The following processing is only for single image\n",
    "                detection_boxes = tf.squeeze(\n",
    "                    tensor_dict['detection_boxes'], [0])\n",
    "                detection_masks = tf.squeeze(\n",
    "                    tensor_dict['detection_masks'], [0])\n",
    "                # Reframe is required to translate mask from box coordinates to image coordinates and fit the image size.\n",
    "                real_num_detection = tf.cast(\n",
    "                    tensor_dict['num_detections'][0], tf.int32)\n",
    "                detection_boxes = tf.slice(detection_boxes, [0, 0], [\n",
    "                                           real_num_detection, -1])\n",
    "                detection_masks = tf.slice(detection_masks, [0, 0, 0], [\n",
    "                                           real_num_detection, -1, -1])\n",
    "                detection_masks_reframed = utils_ops.reframe_box_masks_to_image_masks(\n",
    "                    detection_masks, detection_boxes, image.shape[0], image.shape[1])\n",
    "                detection_masks_reframed = tf.cast(\n",
    "                    tf.greater(detection_masks_reframed, 0.5), tf.uint8)\n",
    "                # Follow the convention by adding back the batch dimension\n",
    "                tensor_dict['detection_masks'] = tf.expand_dims(\n",
    "                    detection_masks_reframed, 0)\n",
    "            image_tensor = tf.get_default_graph().get_tensor_by_name('image_tensor:0')\n",
    "\n",
    "            # Run inference\n",
    "            output_dict = sess.run(tensor_dict,\n",
    "                                   feed_dict={image_tensor: np.expand_dims(image, 0)})\n",
    "\n",
    "            # all outputs are float32 numpy arrays, so convert types as appropriate\n",
    "            output_dict['num_detections'] = int(\n",
    "                output_dict['num_detections'][0])\n",
    "            output_dict['detection_classes'] = output_dict[\n",
    "                'detection_classes'][0].astype(np.uint8)\n",
    "            output_dict['detection_boxes'] = output_dict['detection_boxes'][0]\n",
    "            output_dict['detection_scores'] = output_dict['detection_scores'][0]\n",
    "            if 'detection_masks' in output_dict:\n",
    "                output_dict['detection_masks'] = output_dict['detection_masks'][0]\n",
    "    return output_dict\n",
    "\n",
    "\n",
    "for image_path in TEST_IMAGE_PATHS:\n",
    "    image = Image.open(image_path)\n",
    "    # the array based representation of the image will be used later in order to prepare the\n",
    "    # result image with boxes and labels on it.\n",
    "    image_np = load_image_into_numpy_array(image)\n",
    "    # Expand dimensions since the model expects images to have shape: [1, None, None, 3]\n",
    "    image_np_expanded = np.expand_dims(image_np, axis=0)\n",
    "    # Actual detection.\n",
    "    output_dict = run_inference_for_single_image(image_np, detection_graph)\n",
    "    # Visualization of the results of a detection.\n",
    "    vis_util.visualize_boxes_and_labels_on_image_array(\n",
    "        image_np,\n",
    "        output_dict['detection_boxes'],\n",
    "        output_dict['detection_classes'],\n",
    "        output_dict['detection_scores'],\n",
    "        category_index,\n",
    "        instance_masks=output_dict.get('detection_masks'),\n",
    "        use_normalized_coordinates=True,\n",
    "        line_thickness=8)\n",
    "    plt.figure(figsize=IMAGE_SIZE)\n",
    "    plt.imshow(image_np)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Benchmark TensorFlow inference speed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-16T07:14:51.938810Z",
     "start_time": "2019-02-16T07:14:48.818696Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "average(sec):0.237,fps:4.21\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "\n",
    "\n",
    "def run_inference_benchmark(image, graph, trial=20, gpu=True):\n",
    "    \"\"\"Run TensorFlow inference benchmark.\n",
    "\n",
    "    Arguments:\n",
    "        image {np.array} -- Input image as an Numpy array.\n",
    "        graph {tf.Graph} -- TensorFlow graph object.\n",
    "\n",
    "    Keyword Arguments:\n",
    "        trial {int} -- Number of inference to run for averaging. (default: {20})\n",
    "        gpu {bool} -- Use Nvidia GPU when available. (default: {True})\n",
    "\n",
    "    Returns:\n",
    "        int -- Frame per seconds benchmark result.\n",
    "    \"\"\"\n",
    "\n",
    "    with graph.as_default():\n",
    "        if gpu:\n",
    "            config = tf.ConfigProto()\n",
    "        else:\n",
    "            config = tf.ConfigProto(device_count={\"GPU\": 0})\n",
    "        with tf.Session(config=config) as sess:\n",
    "            # Get handles to input and output tensors\n",
    "            ops = tf.get_default_graph().get_operations()\n",
    "            all_tensor_names = {output.name for op in ops for output in op.outputs}\n",
    "            tensor_dict = {}\n",
    "            for key in [\n",
    "                \"num_detections\",\n",
    "                \"detection_boxes\",\n",
    "                \"detection_scores\",\n",
    "                \"detection_classes\",\n",
    "                \"detection_masks\",\n",
    "            ]:\n",
    "                tensor_name = key + \":0\"\n",
    "                if tensor_name in all_tensor_names:\n",
    "                    tensor_dict[key] = tf.get_default_graph().get_tensor_by_name(\n",
    "                        tensor_name\n",
    "                    )\n",
    "            if \"detection_masks\" in tensor_dict:\n",
    "                # The following processing is only for single image\n",
    "                detection_boxes = tf.squeeze(tensor_dict[\"detection_boxes\"], [0])\n",
    "                detection_masks = tf.squeeze(tensor_dict[\"detection_masks\"], [0])\n",
    "                # Reframe is required to translate mask from box coordinates to image coordinates and fit the image size.\n",
    "                real_num_detection = tf.cast(\n",
    "                    tensor_dict[\"num_detections\"][0], tf.int32\n",
    "                )\n",
    "                detection_boxes = tf.slice(\n",
    "                    detection_boxes, [0, 0], [real_num_detection, -1]\n",
    "                )\n",
    "                detection_masks = tf.slice(\n",
    "                    detection_masks, [0, 0, 0], [real_num_detection, -1, -1]\n",
    "                )\n",
    "                detection_masks_reframed = utils_ops.reframe_box_masks_to_image_masks(\n",
    "                    detection_masks, detection_boxes, image.shape[0], image.shape[1]\n",
    "                )\n",
    "                detection_masks_reframed = tf.cast(\n",
    "                    tf.greater(detection_masks_reframed, 0.5), tf.uint8\n",
    "                )\n",
    "                # Follow the convention by adding back the batch dimension\n",
    "                tensor_dict[\"detection_masks\"] = tf.expand_dims(\n",
    "                    detection_masks_reframed, 0\n",
    "                )\n",
    "            image_tensor = tf.get_default_graph().get_tensor_by_name(\n",
    "                \"image_tensor:0\"\n",
    "            )\n",
    "\n",
    "            # Run inference\n",
    "            times = []\n",
    "            # Kick start the first inference which takes longer and followings.\n",
    "            output_dict = sess.run(\n",
    "                tensor_dict, feed_dict={image_tensor: np.expand_dims(image, 0)}\n",
    "            )\n",
    "            for i in range(trial):\n",
    "                start_time = time.time()\n",
    "                output_dict = sess.run(\n",
    "                    tensor_dict, feed_dict={image_tensor: np.expand_dims(image, 0)}\n",
    "                )\n",
    "                delta = time.time() - start_time\n",
    "                times.append(delta)\n",
    "            mean_delta = np.array(times).mean()\n",
    "            fps = 1 / mean_delta\n",
    "            print(\"average(sec):{:.3f},fps:{:.2f}\".format(mean_delta, fps))\n",
    "\n",
    "    return fps\n",
    "\n",
    "image = Image.open(TEST_IMAGE_PATHS[-1])\n",
    "# the array based representation of the image will be used later in order to prepare the\n",
    "# result image with boxes and labels on it.\n",
    "image_np = load_image_into_numpy_array(image)\n",
    "# Expand dimensions since the model expects images to have shape: [1, None, None, 3]\n",
    "image_np_expanded = np.expand_dims(image_np, axis=0)\n",
    "# Actual detection benchmark.\n",
    "fps = run_inference_benchmark(image_np, detection_graph, trial=20, gpu=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
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