def process_video_signalstate(
video_file_path, output_dir_path, class_name_map, model_name,
model_signature_name, model_server_host, model_input_size,
return_code_queue, log_queue, log_level, ffmpeg_path, ffprobe_path,
do_crop, crop_width, crop_height, crop_x, crop_y,
do_extract_timestamps, timestamp_max_width, timestamp_height,
timestamp_x, timestamp_y, do_deinterlace, num_channels, batch_size,
do_smooth_probs, smoothing_factor, do_binarize_probs,
do_write_bbox_reports, do_write_event_reports, max_threads,
processor_mode):
configure_logger(log_level, log_queue)
interrupt_queue = Queue()
# Create a output subdirectory for the current mode
output_dir_path = path.join(output_dir_path, processor_mode)
output_files = []
def interrupt_handler(signal_number, _):
logging.warning('received interrupt signal {}.'.format(signal_number))
interrupt_queue.put_nowait('_')
# TODO: cancel timestamp/report generation when an interrupt is signalled
# logging.debug('instructing inference pipeline to halt.')
# child_interrupt_queue.put_nowait('_')
signal.signal(signal.SIGINT, interrupt_handler)
video_file_name = path.basename(video_file_path)
video_file_name, _ = path.splitext(video_file_name)
logging.info('preparing to signalstate analyze {}'.format(video_file_path))
try:
start = time()
# For signal state, we use duration as num_frames, as we will only grab one frame per second
frame_width, frame_height, num_frames, duration = IO.get_video_dimensions(
video_file_path, ffprobe_path)
num_frames = duration
end = time() - start
processing_duration = IO.get_processing_duration(
end, 'read video dimensions in')
logging.info(processing_duration)
except Exception as e:
logging.error('encountered an unexpected error while fetching video '
'dimensions')
logging.error(e)
logging.debug(
'will exit with code: exception and value get_video_dimensions')
log_queue.put(None)
log_queue.close()
return_code_queue.put({
'return_code': 'exception',
'return_value': 'get_video_dimensions'
})
return_code_queue.close()
return
try:
do_crop = should_crop(frame_width, frame_height, do_crop, crop_width,
crop_height, crop_x, crop_y)
except Exception as e:
logging.error(e)
logging.debug('will exit with code: exception and value should_crop')
log_queue.put(None)
log_queue.close()
return_code_queue.put({
'return_code': 'exception',
'return_value': 'should_crop'
})
return_code_queue.close()
return
logging.debug('Constructing ffmpeg command')
ffmpeg_command = [ffmpeg_path, '-i', video_file_path]
if do_deinterlace:
ffmpeg_command.append('-deinterlace')
ffmpeg_command.extend([
'-vcodec', 'rawvideo', '-pix_fmt', 'rgb24', '-vsync', 'vfr',
'-hide_banner', '-loglevel', '0', '-r', '1', '-f', 'image2pipe',
'pipe:1'
])
try:
do_extract_timestamps = should_extract_timestamps(
frame_width, frame_height, do_extract_timestamps,
timestamp_max_width, timestamp_height, timestamp_x, timestamp_y)
except Exception as e:
logging.error(e)
logging.debug(
'will exit with code: exception and value should_extract_timestamps'
)
log_queue.put(None)
log_queue.close()
return_code_queue.put({
'return_code': 'exception',
'return_value': 'should_extract_timestamps'
})
return_code_queue.close()
return
frame_shape = [frame_height, frame_width, num_channels]
logging.debug('FFmpeg output frame shape == {}'.format(frame_shape))
analyzer = SignalVideoAnalyzer(
frame_shape, num_frames, len(class_name_map), batch_size, model_name,
model_signature_name, model_server_host, model_input_size,
do_extract_timestamps, timestamp_x, timestamp_y, timestamp_height,
timestamp_max_width, do_crop, crop_x, crop_y, crop_width, crop_height,
ffmpeg_command, max_threads)
try:
start = time()
num_analyzed_frames, frame_map_array, timestamp_array = analyzer.run()
end = time()
analysis_duration = end - start
processing_duration = IO.get_processing_duration(
analysis_duration,
'processed {} frames in'.format(num_analyzed_frames))
logging.info(processing_duration)
except InterruptedError as ae:
logging.error(ae)
logging.debug(
'will exit with code: interrupt and value: analyze_video')
log_queue.put(None)
log_queue.close()
return_code_queue.put({
'return_code': 'interrupt',
'return_value': 'analyze_video'
})
return_code_queue.close()
return
except AssertionError as ae:
logging.error(ae)
logging.debug(
'will exit with code: assertion error and value: analyze_video')
log_queue.put(None)
log_queue.close()
return_code_queue.put({
'return_code': 'assertion error',
'return_value': 'analyze_video'
})
return_code_queue.close()
return
except Exception as e:
logging.error(
'encountered an unexpected error while analyzing {}'.format(
video_file_name))
logging.error(e)
logging.debug(
'will exit with code: exception and value: analyze_video')
log_queue.put(None)
log_queue.close()
return_code_queue.put({
'return_code': 'exception',
'return_value': 'analyze_video'
})
return_code_queue.close()
return
logging.debug('converting timestamp images to strings')
if do_extract_timestamps:
try:
start = time()
timestamp_object = Timestamp(timestamp_height, timestamp_max_width)
timestamp_strings, qa_flags = \
timestamp_object.stringify_timestamps(timestamp_array)
end = time() - start
processing_duration = IO.get_processing_duration(
end, 'timestamp strings converted in')
logging.info(processing_duration)
except Exception as e:
logging.error('encountered an unexpected error while converting '
'timestamp image crops to strings'.format(
os.getpid()))
logging.error(e)
logging.debug(
'will exit with code: exception and value: stringify_timestamps'
)
log_queue.put(None)
log_queue.close()
return_code_queue.put({
'return_code': 'exception',
'return_value': 'stringify_timestamps'
})
return_code_queue.close()
return
else:
timestamp_strings = None
qa_flags = None
logging.debug('attempting to generate reports')
if do_write_bbox_reports:
json_data = []
for frame_num, frame_map in enumerate(frame_map_array, start=0):
if timestamp_strings is not None:
timestamp = timestamp_strings[frame_num]
else:
timestamp = None
for i in range(0, frame_map['num_detections']):
class_name = class_name_map[frame_map['detection_classes'][i]]
bbox = frame_map['detection_boxes'][i]
json_data.append({
'frame_num':
int(frame_num),
'video_name':
video_file_name,
'timestamp':
int(timestamp),
'class_name':
class_name,
'detection_boxes':
bbox.tolist(),
'detection_score':
float(frame_map['detection_scores'][i])
})
bbox_rep = IO.write_json(video_file_name + 'BBOX', output_dir_path,
json_data)
output_files.append(bbox_rep)
try:
start = time()
frame_numbers = list(range(1, len(frame_map_array) + 1))
if timestamp_strings is not None:
timestamp_strings = timestamp_strings.astype(np.int32)
# Process our raw predictions into a list of bounding boxes and frame data
detections = []
for frame_num, frame_map in enumerate(frame_map_array, start=0):
if timestamp_strings is not None:
timestamp = timestamp_strings[frame_num]
else:
timestamp = None
for i in range(0, frame_map['num_detections']):
class_name = class_name_map[frame_map['detection_classes'][i]]
bbox = frame_map['detection_boxes'][i]
xtl = bbox[1] * frame_width
ytl = bbox[0] * frame_height
xbr = bbox[3] * frame_width
ybr = bbox[2] * frame_height
detections.append({'frame_num': frame_num, 'timestamp': timestamp, 'classification': class_name, \
'xtl': xtl, 'ytl': ytl, 'xbr': xbr, 'ybr': ybr})
if len(detections) > 0:
logging.info('{} signal state detections were found in {}'.format(
len(detections), video_file_name))
if do_write_event_reports:
evt_rep = IO.write_signalstate_report(video_file_name,
output_dir_path,
detections)
output_files.append(evt_rep)
else:
logging.info('No signal state events were found in {}'.format(
video_file_name))
end = time() - start
processing_duration = IO.get_processing_duration(
end, 'generated event reports in')
logging.info(processing_duration)
except Exception as e:
logging.error(
'encountered an unexpected error while generating event report.')
logging.error(e)
logging.debug(
'will exit with code: exception and value: write_event_report')
log_queue.put(None)
log_queue.close()
return_code_queue.put({
'return_code': 'exception',
'return_value': 'write_event_report'
})
return_code_queue.close()
return
logging.debug('will exit with code: success and value: {}'.format(
num_analyzed_frames))
log_queue.put(None)
log_queue.close()
return_code_queue.put({
'return_code': 'success',
'return_value': num_analyzed_frames,
'analysis_duration': analysis_duration,
'output_locations': str(output_files)
})
return_code_queue.close()
def process_video(video_file_path, output_dir_path, class_name_map, model_name,
model_signature_name, model_server_host, model_input_size,
return_code_queue, log_queue, log_level, ffmpeg_path,
ffprobe_path, do_crop, crop_width, crop_height, crop_x,
crop_y, do_extract_timestamps, timestamp_max_width,
timestamp_height, timestamp_x, timestamp_y, do_deinterlace,
num_channels, batch_size, do_smooth_probs, smoothing_factor,
do_binarize_probs, do_write_inference_reports,
do_write_event_reports, max_threads, processor_mode):
configure_logger(log_level, log_queue)
interrupt_queue = Queue()
# Create a output subdirectory for the current mode
output_dir_path = path.join(output_dir_path, processor_mode)
def interrupt_handler(signal_number, _):
logging.warning('received interrupt signal {}.'.format(signal_number))
interrupt_queue.put_nowait('_')
# TODO: cancel timestamp/report generation when an interrupt is signalled
# logging.debug('instructing inference pipeline to halt.')
# child_interrupt_queue.put_nowait('_')
signal.signal(signal.SIGINT, interrupt_handler)
video_file_name = path.basename(video_file_path)
video_file_name, _ = path.splitext(video_file_name)
logging.info('preparing to analyze {}'.format(video_file_path))
output_files = []
try:
start = time()
frame_width, frame_height, num_frames, _ = IO.get_video_dimensions(
video_file_path, ffprobe_path)
end = time() - start
processing_duration = IO.get_processing_duration(
end, 'read video dimensions in')
logging.info(processing_duration)
except Exception as e:
logging.error('encountered an unexpected error while fetching video '
'dimensions')
logging.error(e)
logging.debug(
'will exit with code: exception and value get_video_dimensions')
log_queue.put(None)
log_queue.close()
return_code_queue.put({
'return_code': 'exception',
'return_value': 'get_video_dimensions'
})
return_code_queue.close()
return
try:
do_crop = should_crop(frame_width, frame_height, do_crop, crop_width,
crop_height, crop_x, crop_y)
except Exception as e:
logging.error(e)
logging.debug('will exit with code: exception and value should_crop')
log_queue.put(None)
log_queue.close()
return_code_queue.put({
'return_code': 'exception',
'return_value': 'should_crop'
})
return_code_queue.close()
return
logging.debug('Constructing ffmpeg command')
ffmpeg_command = [ffmpeg_path, '-i', video_file_path]
if do_deinterlace:
ffmpeg_command.append('-deinterlace')
ffmpeg_command.extend([
'-vcodec', 'rawvideo', '-pix_fmt', 'rgb24', '-vsync', 'vfr',
'-hide_banner', '-loglevel', '0', '-f', 'image2pipe', 'pipe:1'
])
try:
do_extract_timestamps = should_extract_timestamps(
frame_width, frame_height, do_extract_timestamps,
timestamp_max_width, timestamp_height, timestamp_x, timestamp_y)
except Exception as e:
logging.error(e)
logging.debug(
'will exit with code: exception and value should_extract_timestamps'
)
log_queue.put(None)
log_queue.close()
return_code_queue.put({
'return_code': 'exception',
'return_value': 'should_extract_timestamps'
})
return_code_queue.close()
return
frame_shape = [frame_height, frame_width, num_channels]
logging.debug('FFmpeg output frame shape == {}'.format(frame_shape))
#TODO parameterize tf serving values
analyzer = VideoAnalyzer(frame_shape, num_frames, len(class_name_map),
batch_size, model_name, model_signature_name,
model_server_host, model_input_size,
do_extract_timestamps, timestamp_x, timestamp_y,
timestamp_height, timestamp_max_width, do_crop,
crop_x, crop_y, crop_width, crop_height,
ffmpeg_command, max_threads)
try:
start = time()
num_analyzed_frames, probability_array, timestamp_array = analyzer.run(
)
end = time()
analysis_duration = end - start
processing_duration = IO.get_processing_duration(
analysis_duration,
'processed {} frames in'.format(num_analyzed_frames))
logging.info(processing_duration)
if num_analyzed_frames != num_frames:
if interrupt_queue.empty():
raise AssertionError('num_analyzed_frames ({}) != num_frames '
'({})'.format(num_analyzed_frames,
num_frames))
else:
raise InterruptedError(
'num_analyzed_frames ({}) != num_frames '
'({})'.format(num_analyzed_frames, num_frames))
except InterruptedError as ae:
logging.error(ae)
logging.debug(
'will exit with code: interrupt and value: analyze_video')
log_queue.put(None)
log_queue.close()
return_code_queue.put({
'return_code': 'interrupt',
'return_value': 'analyze_video'
})
return_code_queue.close()
return
except AssertionError as ae:
logging.error(ae)
logging.debug(
'will exit with code: assertion error and value: analyze_video')
log_queue.put(None)
log_queue.close()
return_code_queue.put({
'return_code': 'assertion error',
'return_value': 'analyze_video'
})
return_code_queue.close()
return
except Exception as e:
logging.error(
'encountered an unexpected error while analyzing {}'.format(
video_file_name))
logging.error(e)
logging.debug(
'will exit with code: exception and value: analyze_video')
log_queue.put(None)
log_queue.close()
return_code_queue.put({
'return_code': 'exception',
'return_value': 'analyze_video'
})
return_code_queue.close()
return
logging.debug('converting timestamp images to strings')
if do_extract_timestamps:
try:
start = time()
timestamp_object = Timestamp(timestamp_height, timestamp_max_width)
timestamp_strings, qa_flags = \
timestamp_object.stringify_timestamps(timestamp_array)
end = time() - start
processing_duration = IO.get_processing_duration(
end, 'timestamp strings converted in')
logging.info(processing_duration)
except Exception as e:
logging.error('encountered an unexpected error while converting '
'timestamp image crops to strings'.format(
os.getpid()))
logging.error(e)
logging.debug(
'will exit with code: exception and value: stringify_timestamps'
)
log_queue.put(None)
log_queue.close()
return_code_queue.put({
'return_code': 'exception',
'return_value': 'stringify_timestamps'
})
return_code_queue.close()
return
else:
timestamp_strings = None
qa_flags = None
logging.debug('attempting to generate reports')
if do_write_inference_reports:
try:
start = time()
inf_report = IO.write_inference_report(
video_file_name, output_dir_path, analyzer.prob_array,
class_name_map, timestamp_strings, qa_flags, do_smooth_probs,
smoothing_factor, do_binarize_probs)
output_files.append(inf_report)
end = time() - start
processing_duration = IO.get_processing_duration(
end, 'generated inference reports in')
logging.info(processing_duration)
except Exception as e:
logging.error(
'encountered an unexpected error while generating inference report.'
)
logging.error(e)
logging.debug(
'will exit with code: exception and value: write_inference_report'
)
log_queue.put(None)
log_queue.close()
return_code_queue.put({
'return_code': 'exception',
'return_value': 'write_inference_report'
})
return_code_queue.close()
return
try:
start = time()
if do_smooth_probs:
probability_array = IO.smooth_probs(probability_array,
smoothing_factor)
frame_numbers = list(range(1, len(probability_array) + 1))
if timestamp_strings is not None:
timestamp_strings = timestamp_strings.astype(np.int32)
trip = Trip(frame_numbers, timestamp_strings, qa_flags,
probability_array, class_name_map)
if processor_mode == "weather":
if len(trip.feature_sequence) > 0:
logging.info('{} weather events were found in {}'.format(
len(trip.feature_sequence), video_file_name))
if do_write_event_reports:
weather_rep = IO.write_weather_report(
video_file_name, output_dir_path,
trip.feature_sequence)
output_files.append(weather_rep)
else:
events = trip.find_work_zone_events()
if len(events) > 0:
logging.info('{} work zone events were found in {}'.format(
len(events), video_file_name))
if do_write_event_reports:
event_rep = IO.write_event_report(video_file_name,
output_dir_path, events)
output_files.append(event_rep)
else:
logging.info('No work zone events were found in {}'.format(
video_file_name))
end = time() - start
processing_duration = IO.get_processing_duration(
end, 'generated event reports in')
logging.info(processing_duration)
except Exception as e:
logging.error(
'encountered an unexpected error while generating event report.')
logging.error(e)
logging.debug(
'will exit with code: exception and value: write_event_report')
log_queue.put(None)
log_queue.close()
return_code_queue.put({
'return_code': 'exception',
'return_value': 'write_event_report'
})
return_code_queue.close()
return
logging.debug('will exit with code: success and value: {}'.format(
num_analyzed_frames))
log_queue.put(None)
log_queue.close()
return_code_queue.put({
'return_code': 'success',
'return_value': num_analyzed_frames,
'analysis_duration': analysis_duration,
'output_locations': str(output_files)
})
return_code_queue.close()
def process_video(video_file_path, output_dir_path, class_name_map,
model_input_size, device_id_queue, return_code_queue,
log_queue, log_level, device_type, logical_device_count,
physical_device_count, ffmpeg_path, ffprobe_path, model_path,
node_name_map, gpu_memory_fraction, do_crop, crop_width,
crop_height, crop_x, crop_y, do_extract_timestamps,
timestamp_max_width, timestamp_height, timestamp_x,
timestamp_y, do_deinterlace, num_channels, batch_size,
do_smooth_probs, smoothing_factor, do_binarize_probs):
configure_logger(log_level, log_queue)
interrupt_queue = Queue()
child_interrupt_queue = Queue()
def interrupt_handler(signal_number, _):
logging.warning('received interrupt signal {}.'.format(signal_number))
interrupt_queue.put_nowait('_')
# TODO: cancel timestamp/report generation when an interrupt is signalled
logging.debug('instructing inference pipeline to halt.')
child_interrupt_queue.put_nowait('_')
signal.signal(signal.SIGINT, interrupt_handler)
video_file_name = path.basename(video_file_path)
video_file_name, _ = path.splitext(video_file_name)
logging.info('preparing to analyze {}'.format(video_file_path))
try:
start = time()
frame_width, frame_height, num_frames = IO.get_video_dimensions(
video_file_path, ffprobe_path)
end = time() - start
processing_duration = IO.get_processing_duration(
end, 'read video dimensions in')
logging.info(processing_duration)
except Exception as e:
logging.error('encountered an unexpected error while fetching video '
'dimensions')
logging.error(e)
logging.debug(
'will exit with code: exception and value get_video_dimensions')
log_queue.put(None)
log_queue.close()
return_code_queue.put({
'return_code': 'exception',
'return_value': 'get_video_dimensions'
})
return_code_queue.close()
return
try:
do_crop = should_crop(frame_width, frame_height, do_crop, crop_width,
crop_height, crop_x, crop_y)
except Exception as e:
logging.error(e)
logging.debug('will exit with code: exception and value should_crop')
log_queue.put(None)
log_queue.close()
return_code_queue.put({
'return_code': 'exception',
'return_value': 'should_crop'
})
return_code_queue.close()
return
logging.debug('Constructing ffmpeg command')
ffmpeg_command = [ffmpeg_path, '-i', video_file_path]
if do_deinterlace:
ffmpeg_command.append('-deinterlace')
ffmpeg_command.extend([
'-vcodec', 'rawvideo', '-pix_fmt', 'rgb24', '-vsync', 'vfr',
'-hide_banner', '-loglevel', '0', '-f', 'image2pipe', 'pipe:1'
])
try:
do_extract_timestamps = should_extract_timestamps(
frame_width, frame_height, do_extract_timestamps,
timestamp_max_width, timestamp_height, timestamp_x, timestamp_y)
except Exception as e:
logging.error(e)
logging.debug(
'will exit with code: exception and value should_extract_timestamps'
)
log_queue.put(None)
log_queue.close()
return_code_queue.put({
'return_code': 'exception',
'return_value': 'should_extract_timestamps'
})
return_code_queue.close()
return
frame_shape = [frame_height, frame_width, num_channels]
logging.debug('FFmpeg output frame shape == {}'.format(frame_shape))
def release_device_id(device_id, device_id_queue):
try:
logging.debug(
'attempting to unset CUDA_VISIBLE_DEVICES environment variable.'
)
os.environ.pop('CUDA_VISIBLE_DEVICES')
except KeyError as ke:
logging.warning(ke)
logging.debug('released {} device with id {}'.format(
device_type, device_id))
device_id_queue.put(device_id)
device_id_queue.close()
result_queue = Queue(1)
analyzer = VideoAnalyzer(
frame_shape, num_frames, len(class_name_map), batch_size,
model_input_size, model_path, device_type, logical_device_count,
os.cpu_count(), node_name_map, gpu_memory_fraction,
do_extract_timestamps, timestamp_x, timestamp_y, timestamp_height,
timestamp_max_width, do_crop, crop_x, crop_y, crop_width, crop_height,
ffmpeg_command, child_interrupt_queue, result_queue, video_file_name)
device_id = device_id_queue.get()
logging.debug('acquired {} device with id {}'.format(
device_type, device_id))
try:
_ = child_interrupt_queue.get_nowait()
release_device_id(device_id, device_id_queue)
logging.debug(
'will exit with code: interrupt and value: process_video')
log_queue.put(None)
log_queue.close()
return_code_queue.put({
'return_code': 'interrupt',
'return_value': 'process_video'
})
return_code_queue.close()
return
except:
pass
if device_type == 'gpu':
mapped_device_id = str(int(device_id) % physical_device_count)
logging.debug(
'mapped logical device_id {} to physical device_id {}'.format(
device_id, mapped_device_id))
logging.debug('setting CUDA_VISIBLE_DEVICES environment variable to '
'{}.'.format(mapped_device_id))
os.environ['CUDA_VISIBLE_DEVICES'] = mapped_device_id
else:
logging.debug(
'Setting CUDA_VISIBLE_DEVICES environment variable to None.')
os.environ['CUDA_VISIBLE_DEVICES'] = ''
try:
start = time()
analyzer.start()
num_analyzed_frames, probability_array, timestamp_array = result_queue.get(
)
analyzer.terminate()
result_queue.close()
end = time()
analysis_duration = end - start
processing_duration = IO.get_processing_duration(
analysis_duration,
'processed {} frames in'.format(num_analyzed_frames))
logging.info(processing_duration)
analyzer.join(timeout=15)
try:
os.kill(analyzer.pid, signal.SIGKILL)
logging.debug(
'analyzer process {} remained alive following join timeout '
'and had to be killed'.format(analyzer.pid))
except:
pass
release_device_id(device_id, device_id_queue)
if num_analyzed_frames != num_frames:
if interrupt_queue.empty():
raise AssertionError('num_analyzed_frames ({}) != num_frames '
'({})'.format(num_analyzed_frames,
num_frames))
else:
raise InterruptedError(
'num_analyzed_frames ({}) != num_frames '
'({})'.format(num_analyzed_frames, num_frames))
except InterruptedError as ae:
logging.error(ae)
release_device_id(device_id, device_id_queue)
logging.debug(
'will exit with code: interrupt and value: analyze_video')
log_queue.put(None)
log_queue.close()
return_code_queue.put({
'return_code': 'interrupt',
'return_value': 'analyze_video'
})
return_code_queue.close()
return
except AssertionError as ae:
logging.error(ae)
release_device_id(device_id, device_id_queue)
logging.debug(
'will exit with code: assertion error and value: analyze_video')
log_queue.put(None)
log_queue.close()
return_code_queue.put({
'return_code': 'assertion error',
'return_value': 'analyze_video'
})
return_code_queue.close()
return
except Exception as e:
logging.error(
'encountered an unexpected error while analyzing {}'.format(
video_file_name))
logging.error(e)
release_device_id(device_id, device_id_queue)
logging.debug(
'will exit with code: exception and value: analyze_video')
log_queue.put(None)
log_queue.close()
return_code_queue.put({
'return_code': 'exception',
'return_value': 'analyze_video'
})
return_code_queue.close()
return
logging.debug('converting timestamp images to strings')
if do_extract_timestamps:
try:
start = time()
timestamp_object = Timestamp(timestamp_height, timestamp_max_width)
timestamp_strings, qa_flags = \
timestamp_object.stringify_timestamps(timestamp_array)
end = time() - start
processing_duration = IO.get_processing_duration(
end, 'timestamp strings converted in')
logging.info(processing_duration)
except Exception as e:
logging.error('encountered an unexpected error while converting '
'timestamp image crops to strings'.format(
os.getpid()))
logging.error(e)
logging.debug(
'will exit with code: exception and value: stringify_timestamps'
)
log_queue.put(None)
log_queue.close()
return_code_queue.put({
'return_code': 'exception',
'return_value': 'stringify_timestamps'
})
return_code_queue.close()
return
else:
timestamp_strings = None
qa_flags = None
logging.debug('attempting to generate reports')
try:
start = time()
IO.write_inference_report(video_file_name, output_dir_path,
probability_array, class_name_map,
timestamp_strings, qa_flags, do_smooth_probs,
smoothing_factor, do_binarize_probs)
end = time() - start
processing_duration = IO.get_processing_duration(
end, 'generated inference reports in')
logging.info(processing_duration)
except Exception as e:
logging.error(
'encountered an unexpected error while generating inference report.'
)
logging.error(e)
logging.debug(
'will exit with code: exception and value: write_inference_report')
log_queue.put(None)
log_queue.close()
return_code_queue.put({
'return_code': 'exception',
'return_value': 'write_inference_report'
})
return_code_queue.close()
return
try:
start = time()
if do_smooth_probs:
probability_array = IO.smooth_probs(probability_array,
smoothing_factor)
frame_numbers = [i + 1 for i in range(len(probability_array))]
if timestamp_strings is not None:
timestamp_strings = timestamp_strings.astype(np.int32)
trip = Trip(frame_numbers, timestamp_strings, qa_flags,
probability_array, class_name_map)
work_zone_events = trip.find_work_zone_events()
if len(work_zone_events) > 0:
logging.info('{} work zone events were found in {}'.format(
len(work_zone_events), video_file_name))
IO.write_event_report(video_file_name, output_dir_path,
work_zone_events)
else:
logging.info(
'No work zone events were found in {}'.format(video_file_name))
end = time() - start
processing_duration = IO.get_processing_duration(
end, 'generated event reports in')
logging.info(processing_duration)
except Exception as e:
logging.error(
'encountered an unexpected error while generating event report.')
logging.error(e)
logging.debug(
'will exit with code: exception and value: write_event_report')
log_queue.put(None)
log_queue.close()
return_code_queue.put({
'return_code': 'exception',
'return_value': 'write_event_report'
})
return_code_queue.close()
return
logging.debug('will exit with code: success and value: {}'.format(
num_analyzed_frames))
log_queue.put(None)
log_queue.close()
return_code_queue.put({
'return_code': 'success',
'return_value': num_analyzed_frames,
'analysis_duration': analysis_duration
})
return_code_queue.close()