• Home
  • Line#
  • Scopes#
  • Navigate#
  • Raw
  • Download
1# Copyright © 2020 Arm Ltd and Contributors. All rights reserved.
2# SPDX-License-Identifier: MIT
3
4"""
5Object detection demo that takes a video stream from a device, runs inference
6on each frame producing bounding boxes and labels around detected objects,
7and displays a window with the latest processed frame.
8"""
9
10import os
11import sys
12script_dir = os.path.dirname(__file__)
13sys.path.insert(1, os.path.join(script_dir, '..', 'common'))
14
15import cv2
16from argparse import ArgumentParser
17
18from ssd import ssd_processing, ssd_resize_factor
19from yolo import yolo_processing, yolo_resize_factor
20from utils import dict_labels
21from cv_utils import init_video_stream_capture, preprocess, draw_bounding_boxes
22from network_executor import ArmnnNetworkExecutor
23
24
25def get_model_processing(model_name: str, video: cv2.VideoCapture, input_binding_info: tuple):
26    """
27    Gets model-specific information such as model labels and decoding and processing functions.
28    The user can include their own network and functions by adding another statement.
29
30    Args:
31        model_name: Name of type of supported model.
32        video: Video capture object, contains information about data source.
33        input_binding_info: Contains shape of model input layer, used for scaling bounding boxes.
34
35    Returns:
36        Model labels, decoding and processing functions.
37    """
38    if model_name == 'ssd_mobilenet_v1':
39        return ssd_processing, ssd_resize_factor(video)
40    elif model_name == 'yolo_v3_tiny':
41        return yolo_processing, yolo_resize_factor(video, input_binding_info)
42    else:
43        raise ValueError(f'{model_name} is not a valid model name')
44
45
46def main(args):
47    video = init_video_stream_capture(args.video_source)
48    executor = ArmnnNetworkExecutor(args.model_file_path, args.preferred_backends)
49
50    process_output, resize_factor = get_model_processing(args.model_name, video, executor.input_binding_info)
51    labels = dict_labels(args.label_path, include_rgb=True)
52
53    while True:
54        frame_present, frame = video.read()
55        frame = cv2.flip(frame, 1)  # Horizontally flip the frame
56        if not frame_present:
57            raise RuntimeError('Error reading frame from video stream')
58        input_tensors = preprocess(frame, executor.input_binding_info)
59        print("Running inference...")
60        output_result = executor.run(input_tensors)
61        detections = process_output(output_result)
62        draw_bounding_boxes(frame, detections, resize_factor, labels)
63        cv2.imshow('PyArmNN Object Detection Demo', frame)
64        if cv2.waitKey(1) == 27:
65            print('\nExit key activated. Closing video...')
66            break
67    video.release(), cv2.destroyAllWindows()
68
69
70if __name__ == '__main__':
71    parser = ArgumentParser()
72    parser.add_argument('--video_source', type=int, default=0,
73                        help='Device index to access video stream. Defaults to primary device camera at index 0')
74    parser.add_argument('--model_file_path', required=True, type=str,
75                        help='Path to the Object Detection model to use')
76    parser.add_argument('--model_name', required=True, type=str,
77                        help='The name of the model being used. Accepted options: ssd_mobilenet_v1, yolo_v3_tiny')
78    parser.add_argument('--label_path', required=True, type=str,
79                        help='Path to the labelset for the provided model file')
80    parser.add_argument('--preferred_backends', type=str, nargs='+', default=['CpuAcc', 'CpuRef'],
81                        help='Takes the preferred backends in preference order, separated by whitespace, '
82                             'for example: CpuAcc GpuAcc CpuRef. Accepted options: [CpuAcc, CpuRef, GpuAcc]. '
83                             'Defaults to [CpuAcc, CpuRef]')
84    args = parser.parse_args()
85    main(args)
86