From f42f56870c6201a876f025a423eb5540d7438e83 Mon Sep 17 00:00:00 2001 From: alexander Date: Fri, 16 Jul 2021 11:30:56 +0100 Subject: MLECO-2079 Adding the python KWS example Signed-off-by: Eanna O Cathain Change-Id: Ie1463aaeb5e3cade22df8f560ae99a8e1c4a9c17 --- .../examples/object_detection/run_video_file.py | 170 +++++++++++---------- 1 file changed, 87 insertions(+), 83 deletions(-) (limited to 'python/pyarmnn/examples/object_detection/run_video_file.py') diff --git a/python/pyarmnn/examples/object_detection/run_video_file.py b/python/pyarmnn/examples/object_detection/run_video_file.py index e31b779458..52f19d2c15 100644 --- a/python/pyarmnn/examples/object_detection/run_video_file.py +++ b/python/pyarmnn/examples/object_detection/run_video_file.py @@ -1,83 +1,87 @@ -# Copyright © 2020 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 - input_tensors = preprocess(frame, executor.input_binding_info) - 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) +# 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) -- cgit v1.2.1