MLECO-2079 Adding the python KWS example
Signed-off-by: Eanna O Cathain <eanna.ocathain@arm.com>
Change-Id: Ie1463aaeb5e3cade22df8f560ae99a8e1c4a9c17
diff --git a/python/pyarmnn/examples/object_detection/run_video_file.py b/python/pyarmnn/examples/object_detection/run_video_file.py
index e31b779..52f19d2 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)