# Copyright © 2020-2022 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, Profiling from cv_utils import init_video_file_capture, preprocess, draw_bounding_boxes import style_transfer def get_model_processing(model_name: str, video: cv2.VideoCapture, input_data_shape: 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_data_shape: Contains shape of model input layer. 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_data_shape) else: raise ValueError(f'{model_name} is not a valid model name') def main(args): enable_profile = args.profiling_enabled == "true" action_profiler = Profiling(enable_profile) overall_profiler = Profiling(enable_profile) overall_profiler.profiling_start() action_profiler.profiling_start() if args.tflite_delegate_path is not None: from network_executor_tflite import TFLiteNetworkExecutor as NetworkExecutor exec_input_args = (args.model_file_path, args.preferred_backends, args.tflite_delegate_path) else: from network_executor import ArmnnNetworkExecutor as NetworkExecutor exec_input_args = (args.model_file_path, args.preferred_backends) executor = NetworkExecutor(*exec_input_args) action_profiler.profiling_stop_and_print_us("Executor initialization") action_profiler.profiling_start() video, video_writer, frame_count = init_video_file_capture(args.video_file_path, args.output_video_file_path) process_output, resize_factor = get_model_processing(args.model_name, video, executor.get_shape()) action_profiler.profiling_stop_and_print_us("Video initialization") labels = dict_labels(args.label_path, include_rgb=True) if all(element is not None for element in [args.style_predict_model_file_path, args.style_transfer_model_file_path, args.style_image_path, args.style_transfer_class]): style_image = cv2.imread(args.style_image_path) action_profiler.profiling_start() style_transfer_executor = style_transfer.StyleTransfer(args.style_predict_model_file_path, args.style_transfer_model_file_path, style_image, args.preferred_backends, args.tflite_delegate_path) action_profiler.profiling_stop_and_print_us("Style Transfer Executor initialization") 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_data = preprocess(frame, executor.get_data_type(), executor.get_shape(), True) else: input_data = preprocess(frame, executor.get_data_type(), executor.get_shape(), False) action_profiler.profiling_start() output_result = executor.run([input_data]) action_profiler.profiling_stop_and_print_us("Running inference") detections = process_output(output_result) if all(element is not None for element in [args.style_predict_model_file_path, args.style_transfer_model_file_path, args.style_image_path, args.style_transfer_class]): action_profiler.profiling_start() frame = style_transfer.create_stylized_detection(style_transfer_executor, args.style_transfer_class, frame, detections, resize_factor, labels) action_profiler.profiling_stop_and_print_us("Running Style Transfer") else: draw_bounding_boxes(frame, detections, resize_factor, labels) video_writer.write(frame) print('Finished processing frames') overall_profiler.profiling_stop_and_print_us("Total compute time") 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]') parser.add_argument('--tflite_delegate_path', type=str, help='Enter TensorFlow Lite Delegate file path (.so file). If not entered,' 'will use armnn executor') parser.add_argument('--profiling_enabled', type=str, help='[OPTIONAL] Enabling this option will print important ML related milestones timing' 'information in micro-seconds. By default, this option is disabled.' 'Accepted options are true/false.') parser.add_argument('--style_predict_model_file_path', type=str, help='Path to the style prediction model to use') parser.add_argument('--style_transfer_model_file_path', type=str, help='Path to the style transfer model to use') parser.add_argument('--style_image_path', type=str, help='Path to the style image to create stylized frames') parser.add_argument('--style_transfer_class', type=str, help='A class to transform its style') args = parser.parse_args() main(args)