object_detection_test.py

import json
from json import encoder
import os
import sys

# Location of the pre-compiled dependencies
sys.path.append("/models/research")

# Now that the script knows where to look, we can safely import our objects
#import cv2
import numpy as np
import tensorflow as tf
#from matplotlib import pyplot as plt
from PIL import Image
from object_detection.utils import label_map_util
from object_detection.utils import visualization_utils as vis_util


# Path to frozen detection graph. This is the actual model that is used for the object detection.
MODEL_NAME = '/ssd_mobilenet_v1_coco_11_06_2017'
PATH_TO_CKPT = os.path.join(MODEL_NAME, 'frozen_inference_graph.pb')

# List of the strings that is used to add correct label for each box.
PATH_TO_LABELS = os.path.join('/models/research/object_detection', 'data', 'mscoco_label_map.pbtxt')

NUM_CLASSES = 90

# Loading label map
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 detect_objects(image_np, sess, detection_graph):
    # Expand dimensions since the model expects images to have shape: [1, None, None, 3]
    image_np_expanded = np.expand_dims(image_np, axis=0)
    image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')

    # Each box represents a part of the image where a particular object was detected.
    boxes = detection_graph.get_tensor_by_name('detection_boxes:0')

    # Each score represent the level of confidence for each of the objects.
    # Score is shown on the result image, together with the class label.
    scores = detection_graph.get_tensor_by_name('detection_scores:0')
    classes = detection_graph.get_tensor_by_name('detection_classes:0')
    num_detections = detection_graph.get_tensor_by_name('num_detections:0')

    # Actual detection.
    (boxes, scores, classes, num_detections) = sess.run(
        [boxes, scores, classes, num_detections],
        feed_dict={image_tensor: image_np_expanded})

    # Visualization of the results of a detection.
    vis_util.visualize_boxes_and_labels_on_image_array(
        image_np,
        np.squeeze(boxes),
        np.squeeze(classes).astype(np.int32),
        np.squeeze(scores),
        category_index,
        use_normalized_coordinates=True,
        line_thickness=8)
    return scores, classes, image_np


if __name__ == '__main__':
    # Load image
    image = Image.open("/models/research/object_detection/test_images/image1.jpg")
    (im_width, im_height) = image.size
    image_np = np.array(image.getdata()).reshape(
        (im_height, im_width, 3)).astype(np.uint8)

    # 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='')

        sess = tf.Session(graph=detection_graph)

    # Detect objects
    scores, classes, image_with_labels = detect_objects(image_np, sess, detection_graph)
    #print("\n".join("{0:<20s}: {1:.1f}%".format(category_index[c]['name'], s*100.) for (c, s) in zip(classes[0], scores[0])))
    #plt.imshow(image_with_labels)
    #plt.show()

    sess.close()

    encoder.FLOAT_REPR = lambda f: format(f, '.4f')
    encoder.c_make_encoder = None
    result = [{'class': category_index[c]['name'], 'score': float(s)} for (c, s) in zip(classes[0], scores[0])]
    print json.dumps(result)