def main(args):
# Read command line args
audio_file = args.audio_file_path
model = ModelParams(args.model_file_path)
labels = dict_labels(args.labels_file_path)
# Create the ArmNN inference runner
network = ArmnnNetworkExecutor(model.path, args.preferred_backends)
audio_capture = AudioCapture(model)
buffer = audio_capture.from_audio_file(audio_file)
# Create the preprocessor
mfcc_params = MFCCParams(sampling_freq=16000,
num_fbank_bins=128,
mel_lo_freq=0,
mel_hi_freq=8000,
num_mfcc_feats=13,
frame_len=512,
use_htk_method=False,
n_FFT=512)
mfcc = MFCC(mfcc_params)
preprocessor = Preprocessor(mfcc, model_input_size=296, stride=160)
text = ""
current_r_context = ""
is_first_window = True
print("Processing Audio Frames...")
for audio_data in buffer:
# Prepare the input Tensors
input_tensors = prepare_input_tensors(audio_data,
network.input_binding_info,
preprocessor)
# Run inference
output_result = network.run(input_tensors)
# Slice and Decode the text, and store the right context
current_r_context, text = decode_text(is_first_window, labels,
output_result)
is_first_window = False
display_text(text)
print(current_r_context, flush=True)
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, 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)
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)
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()