python - Object Detection using Tensorflow -


i following tensorflow object detection tutorial oxford-iiit pets dataset: https://github.com/tensorflow/models/blob/master/object_detection/g3doc/running_pets.md

i have generated "frozen_inference_graph.pb" latest checkpoint. how can test inference graph - "frozen_inference_graph.pb" , pet labels - "pet_label_map.pbtxt" on image.

i have tried using jupytor notebook nothing gets detected in image. have used following python code detecting "dog" , "cat" nothing gets detected. python code given below:

import os import cv2 import time import argparse import multiprocessing import numpy np import tensorflow tf  utils import fps, webcamvideostream multiprocessing import queue, pool object_detection.utils import label_map_util object_detection.utils import visualization_utils vis_util  path_to_ckpt = os.path.join('frozen_inference_graph.pb')  path_to_labels = os.path.join('pet_label_map.pbtxt')  num_classes = 37  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 model expects images 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 part of image particular object detected.     boxes = detection_graph.get_tensor_by_name('detection_boxes:0')      # each score represent how level of confidence each of objects.     # score shown on result image, 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 results of 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 image_np   def worker(input_q, output_q):     # load (frozen) tensorflow model memory.     detection_graph = tf.graph()     detection_graph.as_default():         od_graph_def = tf.graphdef()         tf.gfile.gfile(path_to_ckpt, 'rb') 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)     frame = input_q.get()     output_q.put(detect_objects(frame, sess, detection_graph))      sess.close()   if __name__ == '__main__':     parser = argparse.argumentparser()     parser.add_argument('-src', '--source', dest='video_source', type=int,                         default=0, help='device index of camera.')     parser.add_argument('-wd', '--width', dest='width', type=int,                         default=20, help='width of frames in video stream.')     parser.add_argument('-ht', '--height', dest='height', type=int,                         default=20, help='height of frames in video stream.')     parser.add_argument('-num-w', '--num-workers', dest='num_workers', type=int,                         default=2, help='number of workers.')     parser.add_argument('-q-size', '--queue-size', dest='queue_size', type=int,                         default=5, help='size of queue.')     args = parser.parse_args()      logger = multiprocessing.log_to_stderr()     logger.setlevel(multiprocessing.subdebug)      input_q = queue(maxsize=args.queue_size)     output_q = queue(maxsize=args.queue_size)     pool = pool(args.num_workers, worker, (input_q, output_q))       frame = cv2.imread("image2.jpg");      input_q.put(frame)       cv2.imshow('video', output_q.get())       cv2.waitkey(0)     cv2.destroyallwindows()

any appreciated related running inference graph on actual image or debugging if nothing gets detected.

what outputs of boxes, scores , classes? can print them? if numbers them, maybe need change few lines in code visualize results.

for test, can use:

        vis_util.save_image_array_as_png(image,'./outputimg.png')         #print(image.shape)         print('image saved')         img=mpimg.imread('./outputimg.png')         imgplot = plt.imshow(img)         plt.show()

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