def _gen():
detected = {} # FIXME need to gc
counter = 0
while True:
img = onnx.last_img.copy()
detections = []
if inference:
height, width = img.shape[0], img.shape[1]
predictions = onnx.last_prediction
for prediction in predictions:
tag = prediction["tagName"]
if onnx.has_aoi:
draw_aoi(img, onnx.aoi_info)
(x1, y1), (x2, y2) = parse_bbox(prediction, width, height)
detections.append(
Detection(tag, x1, y1, x2, y2,
prediction["probability"]))
# print(x1, y1, x2, y2)
if prediction["probability"] > onnx.threshold:
if onnx.has_aoi:
if not is_inside_aoi(x1, y1, x2, y2,
onnx.aoi_info):
continue
# img = cv2.rectangle(
# img, (x1, y1), (x2, y2), (255, 255, 255), 1)
# img = draw_confidence_level(img, prediction)
# objs = mot_tracker.update(np.array(detections))
t0 = time.time()
scenario.update(detections)
print("update", time.time() - t0)
t0 = time.time()
scenario.draw_counter(img)
print("draw", time.time() - t0)
t0 = time.time()
scenario.draw_constraint(img)
print("draw c", time.time() - t0)
scenario.draw_objs(img)
# counter, objs, counted = tracker.update(detections)
# print(objs)
time.sleep(0.02)
# print(img.shape)
# img = cv2.line(img, (int(170/2), int(680/2)), (int(1487/2), int(815/2)), (0, 255, 255), 5)
# img = tracker.draw_counter(img)
# img = tracker.draw_line(img)
yield (b"--frame\r\n"
b"Content-Type: image/jpeg\r\n\r\n" +
cv2.imencode(".jpg", img)[1].tobytes() + b"\r\n")
def predict(self, image):
width = self.IMG_WIDTH
ratio = self.IMG_WIDTH / image.shape[1]
height = int(image.shape[0] * ratio + 0.000001)
if height >= self.IMG_HEIGHT:
height = self.IMG_HEIGHT
ratio = self.IMG_HEIGHT / image.shape[0]
width = int(image.shape[1] * ratio + 0.000001)
image = cv2.resize(image, (width, height))
# prediction
self.mutex.acquire()
predictions, inf_time = self.model.Score(image)
#print('predictions', predictions, flush=True)
self.mutex.release()
# check whether it's the tag we want
predictions = list(
p for p in predictions if p['tagName'] in self.model.parts)
# check whether it's inside aoi (if has)
if self.has_aoi:
_predictions = []
for p in predictions:
(x1, y1), (x2, y2) = parse_bbox(p, width, height)
if is_inside_aoi(x1, y1, x2, y2, self.aoi_info):
_predictions.append(p)
predictions = _predictions
# update detection status before filter out by threshold
self.update_detection_status(predictions)
if self.is_retrain:
self.process_retrain_image(predictions, image)
if self.iothub_is_send:
self.process_send_message_to_iothub(predictions)
# check whether it's larger than threshold
predictions = list(
p for p in predictions if p['probability'] >= self.threshold)
# update last_prediction_count
_last_prediction_count = {}
for p in predictions:
tag = p['tagName']
if tag not in _last_prediction_count:
_last_prediction_count[tag] = 1
else:
_last_prediction_count[tag] += 1
self.last_prediction_count = _last_prediction_count
# update the buffer
# no need to copy since resize already did it
self.last_img = image
self.last_prediction = predictions
# FIXME support more scenarios
# Update Tracker / Scenario
_detections = []
for prediction in predictions:
tag = prediction['tagName']
#if prediction['probability'] > 0.5:
(x1, y1), (x2, y2) = parse_bbox(prediction, width, height)
_detections.append(
Detection(tag, x1, y1, x2, y2, prediction['probability']))
if self.scenario:
self.scenario.update(_detections)
self.draw_img()
if self.model.send_video_to_cloud:
self.precess_send_signal_to_lva()
if self.scenario:
#print('drawing...', flush=True)
#print(self.scenario, flush=True)
self.last_drawn_img = self.scenario.draw_counter(
self.last_drawn_img)
# FIXME close this
# self.scenario.draw_constraint(self.last_drawn_img)
if self.get_mode() == 'DD':
self.scenario.draw_objs(self.last_drawn_img)
# update avg inference time (moving avg)
inf_time_ms = inf_time * 1000
self.average_inference_time = 1/16*inf_time_ms + 15/16*self.average_inference_time
def predict(self, image):
width = self.IMG_WIDTH
ratio = self.IMG_WIDTH / image.shape[1]
height = int(image.shape[0] * ratio + 0.000001)
if height >= self.IMG_HEIGHT:
height = self.IMG_HEIGHT
ratio = self.IMG_HEIGHT / image.shape[0]
width = int(image.shape[1] * ratio + 0.000001)
# prediction
# self.mutex.acquire()
# predictions, inf_time = self.model.Score(image)
if ':7777/predict' in self.model.endpoint.lower():
image = cv2.resize(image, (width, height))
data = image.tobytes()
res = requests.post(self.model.endpoint, data=data)
if res.json()[1] == 200:
lva_prediction = json.loads(res.json()[0])['inferences']
inf_time = json.loads(res.json()[0])['inf_time']
predictions = lva_to_customvision_format(lva_prediction)
else:
logger.warning('No inference result')
predictions = []
inf_time = 0
else:
image = cv2.resize(image, (416, 416)) # for yolo enpoint testing
str_encode = cv2.imencode('.jpg', image)[1].tostring()
f4 = BytesIO(str_encode)
f5 = BufferedReader(f4)
s = time.time()
res = requests.post(self.model.endpoint, data=f5)
inf_time = time.time() - s
if res.status_code == 200:
lva_prediction = res.json()['inferences']
predictions = lva_to_customvision_format(lva_prediction)
else:
logger.warning('No inference result')
predictions = []
logger.warning('request prediction time: {}'.format(inf_time))
# print('predictions', predictions, flush=True)
# self.mutex.release()
# check whether it's the tag we want
predictions = list(
p for p in predictions if p["tagName"] in self.model.parts)
# check whether it's inside aoi (if has)
if self.has_aoi:
_predictions = []
for p in predictions:
(x1, y1), (x2, y2) = parse_bbox(p, width, height)
if is_inside_aoi(x1, y1, x2, y2, self.aoi_info):
_predictions.append(p)
predictions = _predictions
# update detection status before filter out by threshold
self.update_detection_status(predictions)
if self.is_retrain:
self.process_retrain_image(predictions, image)
# check whether it's larger than threshold
predictions = list(
p for p in predictions if p["probability"] >= self.threshold)
# update last_prediction_count
_last_prediction_count = {}
for p in predictions:
tag = p["tagName"]
if tag not in _last_prediction_count:
_last_prediction_count[tag] = 1
else:
_last_prediction_count[tag] += 1
self.last_prediction_count = _last_prediction_count
# update the buffer
# no need to copy since resize already did it
self.last_img = image
self.last_prediction = predictions
# FIXME support more scenarios
# Update Tracker / Scenario
_detections = []
for prediction in predictions:
tag = prediction["tagName"]
# if prediction['probability'] > 0.5:
(x1, y1), (x2, y2) = parse_bbox(prediction, width, height)
_detections.append(
Detection(tag, x1, y1, x2, y2, prediction["probability"])
)
if self.scenario:
self.scenario.update(_detections)
self.draw_img()
if self.scenario:
if (self.get_mode() == 'ES' and self.use_zone == True) or (self.get_mode() in ['DD', 'PD', 'PC'] and self.use_line == True):
self.scenario.draw_counter(self.last_drawn_img)
if self.get_mode() == "DD":
self.scenario.draw_objs(self.last_drawn_img)
if self.get_mode() == 'PD' and self.use_tracker is True:
self.scenario.draw_objs(self.last_drawn_img)
if self.iothub_is_send:
if self.get_mode() == 'ES':
if self.scenario.has_new_event:
self.process_send_message_to_iothub(predictions)
else:
self.process_send_message_to_iothub(predictions)
if self.send_video_to_cloud:
if self.get_mode() == 'ES':
if self.scenario.has_new_event:
self.precess_send_signal_to_lva()
else:
self.precess_send_signal_to_lva()
# update avg inference time (moving avg)
inf_time_ms = inf_time * 1000
self.average_inference_time = (
1 / 16 * inf_time_ms + 15 / 16 * self.average_inference_time
)