def main(args, detector):
video, video_writer, frame_count = init_video_file_capture(
args.file, 'detector_demo')
if not os.path.exists(args.labels[0]):
labels = args.labels
else:
labels = load_labels(args.labels[0])
frame_num = len(frame_count)
times = []
for _ in tqdm(frame_count, desc='Processing frames'):
frame_present, frame = video.read()
if not frame_present:
continue
start_time = time.time()
results = detection_network.run(frame)
elapsed_ms = (time.time() - start_time) * 1000
detections = decode_yolov3(netout=results, threshold=args.threshold)
draw_bounding_boxes(frame, detections, labels)
times.append(elapsed_ms)
video_writer.write(frame)
print('Finished processing frames')
video.release(), video_writer.release()
print("Average time(ms): ", sum(times) // frame_num)
print("FPS: ",
1000.0 / (sum(times) // frame_num)) # FPS = 1 / time to process loop
def detect(self, original_image):
start_time = time.time()
results = self.run(original_image)
elapsed_ms = (time.time() - start_time) * 1000
detections = decode_yolov3(netout = results, threshold = self._threshold)
draw_bounding_boxes(original_image, detections, self.labels)
fps = 1 / elapsed_ms*1000
print("Estimated frames per second : {0:.2f} Inference time: {1:.2f}".format(fps, elapsed_ms))
return cv2.imencode('.jpg', original_image)[1].tobytes()
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()
def main(args):
video = init_video_stream_capture(args.video_source)
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)
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, executor.input_binding_info)
print("Running inference...")
output_result = executor.run(input_tensors)
detections = process_output(output_result)
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()
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()
def main(args):
enable_profile = args.profiling_enabled == "true"
action_profiler = Profiling(enable_profile)
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 = init_video_stream_capture(args.video_source)
action_profiler.profiling_stop_and_print_us("Video initialization")
model_name = args.model_name
process_output, resize_factor = get_model_processing(
args.model_name, video, executor.get_shape())
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")
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')
action_profiler.profiling_start()
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)
output_result = executor.run([input_data])
if not enable_profile:
print("Running inference...")
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)
cv2.imshow('PyArmNN Object Detection Demo', frame)
if cv2.waitKey(1) == 27:
print('\nExit key activated. Closing video...')
break
video.release(), cv2.destroyAllWindows()