#!/usr/bin/env python """ Copyright (C) 2018-2019 Intel Corporation Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. """ from __future__ import print_function import sys import os from argparse import ArgumentParser, SUPPRESS import cv2 import time import logging as log from openvino.inference_engine import IENetwork, IECore def build_argparser(): parser = ArgumentParser(add_help=False) args = parser.add_argument_group('Options') args.add_argument('-h', '--help', action='help', default=SUPPRESS, help='Show this help message and exit.') args.add_argument("-m", "--model", help="Required. Path to an .xml file with a trained model.", required=True, type=str) args.add_argument("-i", "--input", help="Required. Path to video file or image. 'cam' for capturing video stream from camera", required=True, type=str) args.add_argument("-l", "--cpu_extension", help="Optional. Required for CPU custom layers. Absolute path to a shared library with the " "kernels implementations.", type=str, default=None) args.add_argument("-d", "--device", help="Optional. Specify the target device to infer on; CPU, GPU, FPGA, HDDL or MYRIAD is " "acceptable. The demo will look for a suitable plugin for device specified. " "Default value is CPU", default="CPU", type=str) args.add_argument("--labels", help="Optional. Path to labels mapping file", default=None, type=str) args.add_argument("-pt", "--prob_threshold", help="Optional. Probability threshold for detections filtering", default=0.5, type=float) args.add_argument("--no_show", help="Optional. Don't show output", action='store_true') return parser def main(): log.basicConfig(format="[ %(levelname)s ] %(message)s", level=log.INFO, stream=sys.stdout) args = build_argparser().parse_args() model_xml = args.model model_bin = os.path.splitext(model_xml)[0] + ".bin" log.info("Creating Inference Engine...") ie = IECore() if args.cpu_extension and 'CPU' in args.device: ie.add_extension(args.cpu_extension, "CPU") # Read IR log.info("Loading network files:\n\t{}\n\t{}".format(model_xml, model_bin)) net = IENetwork(model=model_xml, weights=model_bin) if "CPU" in args.device: supported_layers = ie.query_network(net, "CPU") not_supported_layers = [l for l in net.layers.keys() if l not in supported_layers] if len(not_supported_layers) != 0: log.error("Following layers are not supported by the plugin for specified device {}:\n {}". format(args.device, ', '.join(not_supported_layers))) log.error("Please try to specify cpu extensions library path in sample's command line parameters using -l " "or --cpu_extension command line argument") sys.exit(1) img_info_input_blob = None feed_dict = {} for blob_name in net.inputs: if len(net.inputs[blob_name].shape) == 4: input_blob = blob_name elif len(net.inputs[blob_name].shape) == 2: img_info_input_blob = blob_name else: raise RuntimeError("Unsupported {}D input layer '{}'. Only 2D and 4D input layers are supported" .format(len(net.inputs[blob_name].shape), blob_name)) assert len(net.outputs) == 1, "Demo supports only single output topologies" out_blob = next(iter(net.outputs)) log.info("Loading IR to the plugin...") exec_net = ie.load_network(network=net, num_requests=2, device_name=args.device) # Read and pre-process input image n, c, h, w = net.inputs[input_blob].shape if img_info_input_blob: feed_dict[img_info_input_blob] = [h, w, 1] if args.input == 'cam': input_stream = 0 else: input_stream = args.input cap = cv2.VideoCapture(input_stream) # print(cv2.VideoCapture.get(cv2.CAP_PROP_FPS)) assert cap.isOpened(), "Can't open " + input_stream if args.labels: with open(args.labels, 'r') as f: labels_map = [x.strip() for x in f] else: labels_map = None cur_request_id = 0 next_request_id = 1 log.info("Starting inference in async mode...") is_async_mode = True render_time = 0 if is_async_mode: ret, frame = cap.read() frame_h, frame_w = frame.shape[:2] print("To close the application, press 'CTRL+C' here or switch to the output window and press ESC key") print("To switch between sync/async modes, press TAB key in the output window") while cap.isOpened(): if is_async_mode: ret, next_frame = cap.read() else: ret, frame = cap.read() if ret: frame_h, frame_w = frame.shape[:2] if not ret: break # abandons the last frame in case of async_mode # Main sync point: # in the truly Async mode we start the NEXT infer request, while waiting for the CURRENT to complete # in the regular mode we start the CURRENT request and immediately wait for it's completion inf_start = time.time() if is_async_mode: in_frame = cv2.resize(next_frame, (w, h)) in_frame = in_frame.transpose((2, 0, 1)) # Change data layout from HWC to CHW in_frame = in_frame.reshape((n, c, h, w)) feed_dict[input_blob] = in_frame exec_net.start_async(request_id=next_request_id, inputs=feed_dict) else: in_frame = cv2.resize(frame, (w, h)) in_frame = in_frame.transpose((2, 0, 1)) # Change data layout from HWC to CHW in_frame = in_frame.reshape((n, c, h, w)) feed_dict[input_blob] = in_frame exec_net.start_async(request_id=cur_request_id, inputs=feed_dict) if exec_net.requests[cur_request_id].wait(-1) == 0: inf_end = time.time() det_time = inf_end - inf_start # Parse detection results of the current request res = exec_net.requests[cur_request_id].outputs[out_blob] for obj in res[0][0]: # Draw only objects when probability more than specified threshold if obj[2] > args.prob_threshold: # print(obj[2]) xmin = int(obj[3] * frame_w) ymin = int(obj[4] * frame_h) xmax = int(obj[5] * frame_w) ymax = int(obj[6] * frame_h) class_id = int(obj[1]) # 绘制透明矩形框 alphaReserve = 0.8 BChannel = 255 GChannel = 0 RChannel = 0 # print(class_id) if class_id == 1: frame[ymin:ymax, xmin:xmax, 0] = frame[ymin:ymax, xmin:xmax, 0] * alphaReserve + BChannel * ( 1 - alphaReserve) frame[ymin:ymax, xmin:xmax, 1] = frame[ymin:ymax, xmin:xmax, 1] * alphaReserve + GChannel * ( 1 - alphaReserve) frame[ymin:ymax, xmin:xmax, 2] = frame[ymin:ymax, xmin:xmax, 2] * alphaReserve + RChannel * ( 1 - alphaReserve) else: frame[ymin:ymax, xmin:xmax, 0] = frame[ymin:ymax, xmin:xmax, 0] * alphaReserve + 0 * ( 1 - alphaReserve) frame[ymin:ymax, xmin:xmax, 1] = frame[ymin:ymax, xmin:xmax, 1] * alphaReserve + 255 * ( 1 - alphaReserve) frame[ymin:ymax, xmin:xmax, 2] = frame[ymin:ymax, xmin:xmax, 2] * alphaReserve + RChannel * ( 1 - alphaReserve) # Draw box and label\class_id color = (min(class_id * 12.5, 255), min(class_id * 7, 255), min(class_id * 5, 255)) cv2.rectangle(frame, (xmin, ymin), (xmax, ymax), color, 2) det_label = labels_map[class_id] if labels_map else str(class_id) #cv2.putText(frame, det_label + ' ' + str(round(obj[2] * 100, 1)) + ' %', (xmin, ymin - 7), # cv2.FONT_HERSHEY_COMPLEX, 0.6, color, 1) # Draw performance stats inf_time_message = "Inference time: N\A for async mode" if is_async_mode else \ "Inference time: {:.3f} ms".format(det_time * 1000) FPS = "FPS:{}".format(int(1/det_time)) render_time_message = "OpenCV rendering time: {:.3f} ms".format(render_time * 1000) async_mode_message = "Async mode is on. Processing request {}".format(cur_request_id) if is_async_mode else \ "Async mode is off. Processing request {}".format(cur_request_id) cv2.putText(frame, inf_time_message, (15, 15), cv2.FONT_HERSHEY_COMPLEX, 0.5, (255, 255, 255), 1) cv2.putText(frame, render_time_message, (15, 30), cv2.FONT_HERSHEY_COMPLEX, 0.5, (255, 255, 255), 1) cv2.putText(frame, async_mode_message, (10, int(frame_h - 20)), cv2.FONT_HERSHEY_COMPLEX, 0.5, (10, 10, 200), 1) cv2.putText(frame, FPS, (15, 50), cv2.FONT_HERSHEY_COMPLEX, 0.5, (255, 255, 255), 1) # render_start = time.time() if not args.no_show: cv2.imshow("Detection Results", frame) render_end = time.time() render_time = render_end - render_start if is_async_mode: cur_request_id, next_request_id = next_request_id, cur_request_id frame = next_frame frame_h, frame_w = frame.shape[:2] if not args.no_show: key = cv2.waitKey(1) if key == 27: break if (27 == key): is_async_mode = not is_async_mode log.info("Switched to {} mode".format("async" if is_async_mode else "sync")) cv2.destroyAllWindows() if __name__ == '__main__': sys.exit(main() or 0)