def run_inference_for_images_per_image(graph, image_folder, np_boxes_path,
score_threshold):
frame_lists = util.get_frames_paths(image_folder, gap=2)
image_height, image_width = 0, 0
with graph.as_default():
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'
]:
tensor_name = key + ':0'
if tensor_name in all_tensor_names:
tensor_dict[key] = tf.get_default_graph().get_tensor_by_name(
tensor_name)
image_tensor = tf.get_default_graph().get_tensor_by_name(
'image_tensor:0')
with tf.Session() as sess:
path_box_lists = []
for i, frame_path in enumerate(frame_lists):
image = util.data_preprocessing(frame_path, target_size=640)
image = np.expand_dims(image, axis=0)
# Run inference
output_dict = sess.run(tensor_dict,
feed_dict={image_tensor: image})
# 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.int8)
output_dict['detection_boxes'] = output_dict[
'detection_boxes'][0]
output_dict['detection_scores'] = output_dict[
'detection_scores'][0]
# print(output_dict)
for score, box, _class in zip(
output_dict['detection_scores'],
output_dict['detection_boxes'],
output_dict['detection_classes']):
if score >= score_threshold:
path_box_lists.append([
frame_path, box[0], box[1], box[2], box[3], _class
])
print(i)
sess.close()
np.save(np_boxes_path, path_box_lists)
print('finish boxes detection!')
def run_inference_get_feature(graph,image_folder):
frame_lists=util.get_frames_paths(image_folder,gap=2)
with graph.as_default():
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)
image_tensor=tf.get_default_graph().get_tensor_by_name('image_tensor:0')
# return output_dict
print('output_dict[\'detection_boxes\'] shape is {}'.format(output_dict['detection_boxes'].shape))
print('output_dict[\'detection_scores\'] shape is {}'.format(output_dict['detection_scores'].shape))
category_index = label_map_util.create_category_index_from_labelmap(PATH_TO_LABELS, use_display_name=True)
image=vis_util.visualize_boxes_and_labels_on_image_array(
image,
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=3,min_score_thresh=0.3)
plt.imsave(output_image_path,image)
sess.close()
if __name__=='__main__':
args=arg_parse()
os.environ['CUDA_VISIBLE_DEVICES']=args.gpu
np_paths_boxes_path = args.box_imgs_npy_path
# print(image_dataset_path)
graph=load_frozen_graph(args.forzen_graph)
frame_lists=util.get_frames_paths(args.dataset_folder,gap=2)
# vis_detection_result(graph,frame_lists[20],'/home/'+args.machine+'/vis_result.jpg')
run_inference_for_images_per_image(graph,image_dataset_path,np_paths_boxes_path,0.5)
frame_lists=util.get_frames_paths(image_folder,gap=2)
with graph.as_default():
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)
image_tensor=tf.get_default_graph().get_tensor_by_name('image_tensor:0')
# with tf.Session() as sess:
#
# pass
if __name__=='__main__':
args=arg_parse()
os.environ['CUDA_VISIBLE_DEVICES']=args.gpu
np_paths_boxes_path = '/home/'+args.machine+'/'+args.dataset+'_'+'img_path_box.npy'
image_dataset_path=prefix+args.dataset+'/training/frames/'
# print(image_dataset_path)
graph=load_frozen_graph()
frame_lists=util.get_frames_paths(image_dataset_path,gap=2)
# vis_detection_result(graph,frame_lists[20],'/home/'+args.machine+'/vis_result.jpg')
run_inference_for_images_per_image(graph,image_dataset_path,np_paths_boxes_path,0.5)