MLECO-955: Added python object detection example for PyArmNN
Change-Id: I1344c027f4cc70520b7846b34dfbc2abf399d10a
Signed-off-by: Jakub Sujak <jakub.sujak@arm.com>
diff --git a/python/pyarmnn/examples/object_detection/run_video_stream.py b/python/pyarmnn/examples/object_detection/run_video_stream.py
new file mode 100644
index 0000000..94dc6c8
--- /dev/null
+++ b/python/pyarmnn/examples/object_detection/run_video_stream.py
@@ -0,0 +1,102 @@
+# Copyright © 2020 Arm Ltd and Contributors. All rights reserved.
+# SPDX-License-Identifier: MIT
+
+"""
+Object detection demo that takes a video stream from a device, runs inference
+on each frame producing bounding boxes and labels around detected objects,
+and displays a window with the latest processed frame.
+"""
+
+import os
+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_network, dict_labels, preprocess, execute_network, draw_bounding_boxes
+
+
+parser = ArgumentParser()
+parser.add_argument('--video_source', type=int, default=0,
+ help='Device index to access video stream. Defaults to primary device camera at index 0')
+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('--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_source: int):
+ """
+ Creates a video capture object from a device.
+
+ Args:
+ video_source: Device index used to read video stream.
+
+ Returns:
+ Video capture object used to capture frames from a video stream.
+ """
+ video = cv2.VideoCapture(video_source)
+ if not video.isOpened:
+ raise RuntimeError(f'Failed to open video capture for device with index: {video_source}')
+ print('Processing video stream. Press \'Esc\' key to exit the demo.')
+ return video
+
+
+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':
+ labels = os.path.join('ssd_labels.txt')
+ return labels, ssd_processing, ssd_resize_factor(video)
+ elif model_name == 'yolo_v3_tiny':
+ labels = os.path.join('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 = init_video(args.video_source)
+ 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)
+
+ while True:
+ frame_present, frame = video.read()
+ frame = cv2.flip(frame, 1) # Horizontally flip the frame
+ if not frame_present:
+ raise RuntimeError('Error reading frame from video stream')
+ input_tensors = preprocess(frame, input_binding_info)
+ inference_output = execute_network(input_tensors, output_tensors, runtime, net_id)
+ detections = process_output(inference_output)
+ draw_bounding_boxes(frame, detections, resize_factor, labels)
+ cv2.imshow('PyArmNN Object Detection Demo', frame)
+ if cv2.waitKey(1) == 27:
+ print('\nExit key activated. Closing video...')
+ break
+ video.release(), cv2.destroyAllWindows()
+
+
+if __name__ == '__main__':
+ main(args)