| # Copyright © 2020-2021 Arm Ltd and Contributors. All rights reserved. |
| # SPDX-License-Identifier: MIT |
| |
| """ |
| Object detection demo that takes a video file, runs inference on each frame producing |
| bounding boxes and labels around detected objects, and saves the processed video. |
| """ |
| |
| import os |
| import sys |
| script_dir = os.path.dirname(__file__) |
| sys.path.insert(1, os.path.join(script_dir, '..', 'common')) |
| |
| import cv2 |
| from tqdm import tqdm |
| 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_file_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, video_writer, frame_count = init_video_file_capture(args.video_file_path, args.output_video_file_path) |
| |
| executor = ArmnnNetworkExecutor(args.model_file_path, args.preferred_backends) |
| process_output, resize_factor = get_model_processing(args.model_name, video, executor.input_binding_info) |
| labels = dict_labels(args.label_path, include_rgb=True) |
| |
| for _ in tqdm(frame_count, desc='Processing frames'): |
| frame_present, frame = video.read() |
| if not frame_present: |
| continue |
| model_name = args.model_name |
| 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) |
| output_result = executor.run(input_tensors) |
| detections = process_output(output_result) |
| draw_bounding_boxes(frame, detections, resize_factor, labels) |
| video_writer.write(frame) |
| print('Finished processing frames') |
| video.release(), video_writer.release() |
| |
| |
| if __name__ == '__main__': |
| parser = ArgumentParser() |
| parser.add_argument('--video_file_path', required=True, type=str, |
| help='Path to the video file to run object detection on') |
| 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('--output_video_file_path', type=str, |
| help='Path to the output video file with detections added in') |
| 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) |