detect_image.py

"""detect_image.py

This script is used to test my trained egohands (hand detector) models.  It is modified from the following example from TensorFlow Object Detection API:

https://github.com/tensorflow/models/blob/master/research/object_detection/object_detection_tutorial.ipynb
"""


import sys

import numpy as np
import cv2
import tensorflow as tf

from object_detection.utils import label_map_util
from object_detection.utils import visualization_utils as vis_util


PATH_TO_FROZEN_GRAPH = 'model_exported/frozen_inference_graph.pb'
PATH_TO_LABELS = 'data/egohands_label_map.pbtxt'
OUTPUT_PATH = 'detection_output.jpg'


def detect_image(image_path):
    # load label map
    category_index = label_map_util.create_category_index_from_labelmap(
        PATH_TO_LABELS)

    # load detection graph
    detection_graph = tf.Graph()
    with detection_graph.as_default():
        od_graph_def = tf.GraphDef()
        with tf.gfile.GFile(PATH_TO_FROZEN_GRAPH, 'rb') as fid:
            serialized_graph = fid.read()
            od_graph_def.ParseFromString(serialized_graph)
            tf.import_graph_def(od_graph_def, name='')

    # define input/output tensors
    image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')
    detection_boxes = detection_graph.get_tensor_by_name('detection_boxes:0')
    detection_scores = detection_graph.get_tensor_by_name('detection_scores:0')
    detection_classes = detection_graph.get_tensor_by_name('detection_classes:0')
    num_detections = detection_graph.get_tensor_by_name('num_detections:0')

    # load input image
    img = cv2.imread(image_path)
    if img is None:
        sys.exit('failed to load image: %s' % image_path)
    img = img[..., ::-1]  # BGR to RGB

    # run inference
    with detection_graph.as_default():
        with tf.Session() as sess:
            boxes, scores, classes, _ = sess.run(
                [detection_boxes, detection_scores, detection_classes, num_detections],
                feed_dict={image_tensor: np.expand_dims(img, 0)})

    # draw the results of the detection
    vis_util.visualize_boxes_and_labels_on_image_array(
        img,
        np.squeeze(boxes),
        np.squeeze(classes).astype(np.int32),
        np.squeeze(scores),
        category_index,
        use_normalized_coordinates=True,
        line_thickness=6,
        min_score_thresh=0.3)

    # save the output image
    img = img[..., ::-1]  # RGB to BGR
    cv2.imwrite(OUTPUT_PATH, img)

    print('Output has been written to %s\n' % OUTPUT_PATH)


def main():
    if len(sys.argv) != 2:
        sys.exit('Usage: %s <image_path>' % sys.argv[0])
    detect_image(image_path=sys.argv[1])


if __name__ == '__main__':
    main()