1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
|
# Copyright © 2020 Arm Ltd and Contributors. All rights reserved.
# SPDX-License-Identifier: MIT
"""
Object detection demo that takes a video stream from a device, runs inference
on each frame producing bounding boxes and labels around detected objects,
and displays a window with the latest processed frame.
"""
import os
import sys
script_dir = os.path.dirname(__file__)
sys.path.insert(1, os.path.join(script_dir, '..', 'common'))
import cv2
from argparse import ArgumentParser
from ssd import ssd_processing, ssd_resize_factor
from yolo import yolo_processing, yolo_resize_factor
from utils import dict_labels
from cv_utils import init_video_stream_capture, preprocess, draw_bounding_boxes
from network_executor import ArmnnNetworkExecutor
def get_model_processing(model_name: str, video: cv2.VideoCapture, input_binding_info: tuple):
"""
Gets model-specific information such as model labels and decoding and processing functions.
The user can include their own network and functions by adding another statement.
Args:
model_name: Name of type of supported model.
video: Video capture object, contains information about data source.
input_binding_info: Contains shape of model input layer, used for scaling bounding boxes.
Returns:
Model labels, decoding and processing functions.
"""
if model_name == 'ssd_mobilenet_v1':
return ssd_processing, ssd_resize_factor(video)
elif model_name == 'yolo_v3_tiny':
return yolo_processing, yolo_resize_factor(video, input_binding_info)
else:
raise ValueError(f'{model_name} is not a valid model name')
def main(args):
video = init_video_stream_capture(args.video_source)
executor = ArmnnNetworkExecutor(args.model_file_path, args.preferred_backends)
model_name = args.model_name
process_output, resize_factor = get_model_processing(args.model_name, video, executor.input_binding_info)
labels = dict_labels(args.label_path, include_rgb=True)
while True:
frame_present, frame = video.read()
frame = cv2.flip(frame, 1) # Horizontally flip the frame
if not frame_present:
raise RuntimeError('Error reading frame from video stream')
if model_name == "ssd_mobilenet_v1":
input_tensors = preprocess(frame, executor.input_binding_info, True)
else:
input_tensors = preprocess(frame, executor.input_binding_info, False)
print("Running inference...")
output_result = executor.run(input_tensors)
detections = process_output(output_result)
draw_bounding_boxes(frame, detections, resize_factor, labels)
cv2.imshow('PyArmNN Object Detection Demo', frame)
if cv2.waitKey(1) == 27:
print('\nExit key activated. Closing video...')
break
video.release(), cv2.destroyAllWindows()
if __name__ == '__main__':
parser = ArgumentParser()
parser.add_argument('--video_source', type=int, default=0,
help='Device index to access video stream. Defaults to primary device camera at index 0')
parser.add_argument('--model_file_path', required=True, type=str,
help='Path to the Object Detection model to use')
parser.add_argument('--model_name', required=True, type=str,
help='The name of the model being used. Accepted options: ssd_mobilenet_v1, yolo_v3_tiny')
parser.add_argument('--label_path', required=True, type=str,
help='Path to the labelset for the provided model file')
parser.add_argument('--preferred_backends', type=str, nargs='+', default=['CpuAcc', 'CpuRef'],
help='Takes the preferred backends in preference order, separated by whitespace, '
'for example: CpuAcc GpuAcc CpuRef. Accepted options: [CpuAcc, CpuRef, GpuAcc]. '
'Defaults to [CpuAcc, CpuRef]')
args = parser.parse_args()
main(args)
|
