inference.py

import numpy as np
import os
import six.moves.urllib as urllib
import sys
import tarfile
import tensorflow as tf
import zipfile

from collections import defaultdict
from io import StringIO
from matplotlib import pyplot as plt
from PIL import Image
from settings import Config

# This is needed since the notebook is stored in the object_detection folder.
sys.path.append(Config.PATH_TO_RESEARCH_FOLDER)
from object_detection.utils import ops as utils_ops
from object_detection.utils import label_map_util
from object_detection.utils import visualization_utils as vis_util

def run_inference(image_path, output_filename):

    PATH_TO_CKPT = Config.PATH_FROZEN_INFERENCE_GRAPH
    PATH_TO_LABELS = 'label_map.pbtxt'
    NUM_CLASSES = 1

    ## Load a (frozen) Tensorflow model into memory.
    detection_graph = tf.Graph()
    with detection_graph.as_default():
        od_graph_def = tf.GraphDef()
        with tf.gfile.GFile(PATH_TO_CKPT, 'rb') as fid:
            serialized_graph = fid.read()
        od_graph_def.ParseFromString(serialized_graph)
        tf.import_graph_def(od_graph_def, name='')


    label_map = label_map_util.load_labelmap(PATH_TO_LABELS)
    categories = label_map_util.convert_label_map_to_categories(label_map, max_num_classes=NUM_CLASSES, use_display_name=True)
    category_index = label_map_util.create_category_index(categories)


    def load_image_into_numpy_array(image):
        (im_width, im_height) = image.size
        return np.array(image.getdata()).reshape(
            (im_height, im_width, 3)).astype(np.uint8)


      # add path to the images to the TEST_IMAGE_PATHS.
    PATH_TO_TEST_IMAGES_DIR = 'object_detection/test_images'
    TEST_IMAGE_PATHS = [ os.path.join(PATH_TO_TEST_IMAGES_DIR, 'image{}.jpg'.format(i)) for i in range(10, 11) ]

    # Size, in inches, of the output images.
    IMAGE_SIZE = (10, 8)


    def run_inference_for_single_image(image, graph):
        with graph.as_default():
            with tf.Session() as sess:
                # Get handles to input and output tensors
                ops = tf.get_default_graph().get_operations()
                all_tensor_names = {output.name for op in ops for output in op.outputs}
                tensor_dict = {}
                for key in [
                      'num_detections', 'detection_boxes', 'detection_scores',
                      'detection_classes', 'detection_masks'
                      ]:
                    tensor_name = key + ':0'
                    if tensor_name in all_tensor_names:
                        tensor_dict[key] = tf.get_default_graph().get_tensor_by_name(tensor_name)
                if 'detection_masks' in tensor_dict:
                    # The following processing is only for single image
                    detection_boxes = tf.squeeze(tensor_dict['detection_boxes'], [0])
                    detection_masks = tf.squeeze(tensor_dict['detection_masks'], [0])
                    # Reframe is required to translate mask from box coordinates to image coordinates and fit the image size.
                    real_num_detection = tf.cast(tensor_dict['num_detections'][0], tf.int32)
                    detection_boxes = tf.slice(detection_boxes, [0, 0], [real_num_detection, -1])
                    detection_masks = tf.slice(detection_masks, [0, 0, 0], [real_num_detection, -1, -1])
                    detection_masks_reframed = utils_ops.reframe_box_masks_to_image_masks(
                            detection_masks, detection_boxes, image.shape[0], image.shape[1])
                    detection_masks_reframed = tf.cast(
                            tf.greater(detection_masks_reframed, 0.5), tf.uint8)
                    # Follow the convention by adding back the batch dimension
                    tensor_dict['detection_masks'] = tf.expand_dims(
                            detection_masks_reframed, 0)

                image_tensor = tf.get_default_graph().get_tensor_by_name('image_tensor:0')

                # Run inference
                output_dict = sess.run(tensor_dict, feed_dict={image_tensor: np.expand_dims(image, 0)})

                # all outputs are float32 numpy arrays, so convert types as appropriate
                output_dict['num_detections'] = int(output_dict['num_detections'][0])
                output_dict['detection_classes'] = output_dict[
                        'detection_classes'][0].astype(np.uint8)
                output_dict['detection_boxes'] = output_dict['detection_boxes'][0]
                output_dict['detection_scores'] = output_dict['detection_scores'][0]
                if 'detection_masks' in output_dict:
                    output_dict['detection_masks'] = output_dict['detection_masks'][0]
            return output_dict

    image = Image.open(image_path)
    # the array based representation of the image will be used later in order to prepare the
    # result image with boxes and labels on it.
    image_np = load_image_into_numpy_array(image)
    # Expand dimensions since the model expects images to have shape: [1, None, None, 3]
    image_np_expanded = np.expand_dims(image_np, axis=0)
    # Actual detection.
    output_dict = run_inference_for_single_image(image_np, detection_graph)
    # Visualization of the results of a detection.
    vis_util.visualize_boxes_and_labels_on_image_array( image_np,
            output_dict['detection_boxes'],
            output_dict['detection_classes'],
            output_dict['detection_scores'],
            category_index,
            instance_masks=output_dict.get('detection_masks'),
            use_normalized_coordinates=True,
            line_thickness=8)

    #print(output_dict['detection_scores'])
    final_score = np.squeeze(output_dict['detection_scores'])
    count = 0
    for i in range(100):
      if final_score[i] > 0.5:
          count = count + 1
    print "coconut count = %d" % count
    plt.figure(figsize=IMAGE_SIZE)
    plt.imshow(image_np)
    #print(image.filename)
    plt.savefig('static/out/'+output_filename, bbox_inches='tight')
    output_dict = None
    return count

if __name__ == '__main__':
    run_inference(sys.argv[1], sys.argv[2])