MLECO-1253 Adding ASR sample application using the PyArmNN api
Change-Id: I450b23800ca316a5bfd4608c8559cf4f11271c21
Signed-off-by: Éanna Ó Catháin <eanna.ocathain@arm.com>
diff --git a/python/pyarmnn/examples/object_detection/run_video_file.py b/python/pyarmnn/examples/object_detection/run_video_file.py
index 4f06eb1..fc3e214 100644
--- a/python/pyarmnn/examples/object_detection/run_video_file.py
+++ b/python/pyarmnn/examples/object_detection/run_video_file.py
@@ -7,55 +7,19 @@
"""
import os
+import sys
+script_dir = os.path.dirname(__file__)
+sys.path.insert(1, os.path.join(script_dir, '..', 'common'))
+
import cv2
-import pyarmnn as ann
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 create_video_writer, create_network, dict_labels, preprocess, execute_network, draw_bounding_boxes
-
-
-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', 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()
-
-
-def init_video(video_path: str, output_path: str):
- """
- Creates a video capture object from a video file.
-
- Args:
- video_path: User-specified video file path.
- output_path: Optional path to save the processed video.
-
- Returns:
- Video capture object to capture frames, video writer object to write processed
- frames to file, plus total frame count of video source to iterate through.
- """
- if not os.path.exists(video_path):
- raise FileNotFoundError(f'Video file not found for: {video_path}')
- video = cv2.VideoCapture(video_path)
- if not video.isOpened:
- raise RuntimeError(f'Failed to open video capture from file: {video_path}')
-
- video_writer = create_video_writer(video, video_path, output_path)
- iter_frame_count = range(int(video.get(cv2.CAP_PROP_FRAME_COUNT)))
- return video, video_writer, iter_frame_count
+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):
@@ -72,30 +36,29 @@
Model labels, decoding and processing functions.
"""
if model_name == 'ssd_mobilenet_v1':
- labels = os.path.join('ssd_labels.txt')
+ labels = os.path.join(script_dir, 'ssd_labels.txt')
return labels, ssd_processing, ssd_resize_factor(video)
elif model_name == 'yolo_v3_tiny':
- labels = os.path.join('yolo_labels.txt')
+ labels = os.path.join(script_dir, 'yolo_labels.txt')
return labels, 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(args.video_file_path, args.output_video_file_path)
- net_id, runtime, input_binding_info, output_binding_info = create_network(args.model_file_path,
- args.preferred_backends)
- output_tensors = ann.make_output_tensors(output_binding_info)
- labels, process_output, resize_factor = get_model_processing(args.model_name, video, input_binding_info)
- labels = dict_labels(labels if args.label_path is None else args.label_path)
+ 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)
+ labels, process_output, resize_factor = get_model_processing(args.model_name, video, executor.input_binding_info)
+ labels = dict_labels(labels if args.label_path is None else 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, input_binding_info)
- inference_output = execute_network(input_tensors, output_tensors, runtime, net_id)
- detections = process_output(inference_output)
+ 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')
@@ -103,4 +66,20 @@
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', 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)