def inference(self, pl):
t0 = time.time()
logger.debug('payload size: {}'.format(len(pl)))
logger.debug('payload type: {}'.format(type(pl)))
jpg_json = payload.deserialize_payload(pl.decode('utf-8'))
jpg_bytes = payload.destringify_jpg(jpg_json['bytes'])
logger.debug('destringify_jpg: {} ms'.format(time.time() - t0))
t1 = time.time()
bgr_array = payload.jpg2bgr(jpg_bytes)
logger.debug('jpg2bgr: {} ms'.format(time.time() - t1))
t2 = time.time()
image_data = self.engine.process_input(bgr_array)
output = self.engine.inference(image_data)
model_outputs = self.engine.process_output(output)
logger.debug('Result: {}'.format(model_outputs))
logger.debug('Detection takes {} ms'.format(time.time() - t2))
classes = self.engine.classes
labels = self.engine.labels
logger.debug('draw = {}'.format(self.draw))
if self.draw is False:
self.result_hook(self.generalize_result(jpg_json, model_outputs))
else:
self.result_hook(
draw_bb(bgr_array,
self.generalize_result(jpg_json, model_outputs),
generate_class_color(class_num=classes), labels))
def inference(self, pl):
jpg_json = payload.deserialize_payload(pl.decode('utf-8'))
jpg_bytes = payload.destringify_jpg(jpg_json['bytes'])
bgr_array = payload.jpg2bgr(jpg_bytes)
image_data = self.engine.process_input(bgr_array)
output = self.engine.inference(image_data)
model_outputs = self.engine.process_output(output)
classes = self.engine.classes
labels = self.engine.labels
if self.draw is False:
self.result_hook(self.generalize_result(jpg_json, model_outputs))
else:
self.result_hook(
draw_bb(bgr_array,
self.generalize_result(jpg_json, model_outputs),
generate_class_color(class_num=classes), labels))