def main(_):
args = parse_args()
cfg = load_module(args.path_to_config)
infer(cfg, args.input_type, args.input, args.conf_threshold,
args.dump_to_json, args.show, args.dump_output_video,
args.path_to_output_video)
def main():
log.basicConfig(format="[ %(levelname)s ] %(message)s", level=log.INFO, stream=sys.stdout)
args = build_argparser().parse_args()
cfg = load_module(args.path_to_config)
exec_net, plugin, input_blob, out_blob, shape = load_ir_model(args.model, args.device,
args.plugin_dir, args.cpu_extension)
n_batch, channels, height, width = shape
cur_request_id = 0
while 1:
frame = cv2.imread(args.input_image)
img_to_display = frame.copy()
in_frame = cv2.resize(frame, (width, height))
in_frame = in_frame.transpose((2, 0, 1)) # Change data layout from HWC to CHW
in_frame = in_frame.reshape((n_batch, channels, height, width))
exec_net.start_async(request_id=cur_request_id, inputs={input_blob: in_frame})
if exec_net.requests[cur_request_id].wait(-1) == 0:
# Parse detection results of the current request
lp_code = exec_net.requests[cur_request_id].outputs[out_blob]
lp_number = decode_ie_output(lp_code, cfg.r_vocab)
img_to_display = display_license_plate(lp_number, img_to_display)
cv2.imshow('License Plate', img_to_display)
key = cv2.waitKey(0)
if key == 27:
break
del exec_net
del plugin
def main(_): args = parse_args() cfg = load_module(args.path_to_config) validate(cfg)
def main(_): args = parse_args() cfg = load_module(args.path_to_config) eval_loop(cfg)
def main(_):
args = parse_args()
cfg = load_module(args.path_to_config)
infer(cfg, args.input_type, args.input, args.conf_threshold, args.dump_to_json, args.show,
args.dump_output_video, args.path_to_output_video)
def main(_):
args = parse_args()
cfg = load_module(args.path_to_config)
export(cfg, args.mo, 1, 'FP32') #set batch_size and precision
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
with tf.Graph().as_default() as graph:
tf.import_graph_def(graph_def)
return graph
if __name__ == "__main__":
path = "weight/graph.pb.frozen"
image_path = "data/000005.png"
image_path = "E:/deepLearning/Synthetic_Chinese_License_Plates/crops/000191.png"
config_path = "chinese_lp/config.py"
graph = load_graph(path)
config = load_module(config_path)
image = cv2.imread(image_path)
img = cv2.resize(image, (94, 24))
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img = np.float32(img)
img = np.multiply(img, 1.0 / 255.0)
input = graph.get_tensor_by_name("import/input:0")
output = graph.get_tensor_by_name("import/d_predictions:0")
with tf.Session(graph=graph) as sess:
results = sess.run(output, feed_dict={input: [img]})
print(results)
print(config.r_vocab)
def main(_):
args = parse_args()
cfg = load_module(args.path_to_config)
export(cfg, args.mo)
def main(_): args = parse_args() cfg = load_module(args.path_to_config) train(cfg)
def main(_): args = parse_args() cfg = load_module(args.path_to_config) export(cfg, args.mo)
def main(_): args = parse_args() cfg = load_module(args.path_to_config) infer(cfg)
def main(_):
args = parse_args()
cfg = load_module(args.path_to_config)
eval_loop(cfg)
