def frame_callback(frame: VideoFrame):
with frame.data() as mat:
for roi in frame.regions():
labels = []
for tensor in roi:
data = tensor.data()
if "landmarks" in tensor.model_name():
lm_color = (255, 0, 0)
for i in range(0, len(data), 2):
x = int(roi.meta().x + roi.meta().w * data[i])
y = int(roi.meta().y + roi.meta().h * data[i + 1])
cv2.circle(mat, (x, y), int(1 + 0.02 * roi.meta().w), lm_color, -1)
if "gender" in tensor.model_name() and "prob" in tensor.layer_name():
if data[1] > 0.5:
labels.append("M")
else:
labels.append("F")
elif "age" in tensor.layer_name():
labels.append(str(int(data[0] * 100)))
elif "EmoNet" in tensor.model_name():
emotions = ["neutral", "happy", "sad", "surprise", "anger"]
index = numpy.argmax(data)
labels.append(emotions[index])
if labels:
label = " ".join(labels)
cv2.putText(mat, label, (roi.meta().x, roi.meta().y + roi.meta().h + 30), cv2.FONT_HERSHEY_SIMPLEX,
1, (0, 0, 255), 2)
def frame_callback(frame: VideoFrame):
with frame.data() as mat:
for roi in frame.regions():
labels = []
rect = roi.rect()
for tensor in roi.tensors():
data = tensor.data()
if "align_fc3" == tensor.layer_name():
lm_color = (255, 0, 0)
for i in range(0, len(data), 2):
x = int(rect.x + rect.w * data[i])
y = int(rect.y + rect.h * data[i + 1])
cv2.circle(mat, (x, y), int(1 + 0.02 * rect.w),
lm_color, -1)
if "prob" == tensor.layer_name():
if data[1] > 0.5:
labels.append("M")
else:
labels.append("F")
if "age_conv3" == tensor.layer_name():
labels.append(str(int(data[0] * 100)))
if "prob_emotion" == tensor.layer_name():
emotions = ["neutral", "happy", "sad", "surprise", "anger"]
index = numpy.argmax(data)
labels.append(emotions[index])
if labels:
label = " ".join(labels)
cv2.putText(mat, label, (rect.x, rect.y + rect.h + 30),
cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)
def frame_callback(frame: VideoFrame):
event_list = None
if True:
for i, detection in enumerate(frame.regions()):
#print("number of tensors: ", len(detection.tensors()), " label: ", detection.label(), " roi_type: ", detection.meta().get_roi_type())
# , " object_id : ", detection.object_id()
for j, tensor in enumerate(detection.tensors()):
if "detection" in tensor.name():
# bbbox = (tensor["x_min"], tensor["y_min"], tensor["x_max"], tensor["y_max"])
# print(i, detection.meta().get_roi_type(), bbbox, tensor["confidence"])
pass
elif "object_id" in tensor.name():
# print(i, tensor["id"])
pass
elif "ocr" in tensor.name():
#print(i, "MONOTOSH: ", tensor)
pass
else:
print(tensor.name())
if event_list is not None:
#event publish logic
#with frame.data() as mat:
# cv2.imwrite("dump.jpg", mat)
pass
def draw_conf(frame: VideoFrame):
with frame.data() as img:
for roi in frame.regions():
rect = roi.rect()
conf = roi.confidence()
if rect:
cv2.putText(img, f'{conf:.2f}', (rect.x, rect.y + rect.h + 30),
cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)
def log_age(self, frame: VideoFrame) -> bool:
for roi in frame.regions():
for tensor in roi.tensors():
if tensor.name() == 'detection':
continue
layer_name = tensor.layer_name()
if 'age_conv3' == layer_name:
self.log_file.write(tensor.label() + "\n")
continue
return True
def process_frame(self, frame: VideoFrame) -> bool:
timestamp = int(round(time.time()*1000))
events = []
new_counts = Counter()
for detection in frame.regions():
new_counts[detection.meta().get_roi_type()] += 1
for key, count in new_counts.items():
if key in self.item_count:
if count > self.item_count[key]:
for x in range(0, count-self.item_count[key]):
events.append({'event_time': timestamp,
'roi_action': 'ENTER',
'object_id': key})
elif count < self.item_count[key]:
for x in range(0, self.item_count[key]-count):
events.append({'event_time': timestamp,
'roi_action': 'DEPART',
'object_id': key})
else:
for x in range(0, count):
events.append({'event_time': timestamp,
'roi_action': 'ENTER',
'object_id': key})
for key, count in self.item_count.items():
if key not in new_counts:
for x in range(0, count):
events.append({'event_time': timestamp,
'roi_action': 'DEPART',
'object_id': key})
if events:
frame.add_message(json.dumps(events))
self.item_count = new_counts
if self.item_count['bottle'] <= 1:
frame.add_region(0, 0, 0, 0, 0, region_tensor=REGION_TENSOR)
else:
frame.add_region(0, 0, 0, 0, 0, region_tensor=VideoFrame.create_labels_structure(
["Bottle Count: " + str(self.item_count['bottle'])]))
if (self.item_count['person'] > 0) and self.redact_person:
with frame.data() as contents:
contents[:] = 0
self.remove_regions(frame)
frame.add_region(0, 0, 0, 0, 1, region_tensor=REGION_TENSOR)
return True
def process_frame(frame: VideoFrame) -> bool:
for roi in frame.regions():
for tensor in roi:
if tensor.name() == 'detection':
continue
layer_name = tensor.layer_name()
data = tensor.data()
if 'age' in layer_name:
tensor.set_label(str(int(data[0] * 100)))
if 'gender' in tensor.model_name() and 'prob' in layer_name:
tensor.set_label(" M " if data[1] > 0.5 else " F ")
if 'EmoNet' in layer_name:
emotions = ["neutral", "happy", "sad", "surprise", "anger"]
tensor.set_label(emotions[data.index(max(data))])
return True
def process_frame(frame: VideoFrame) -> bool:
for roi in frame.regions():
for tensor in roi.tensors():
if tensor.name() == 'detection':
continue
layer_name = tensor.layer_name()
data = tensor.data()
if 'age_conv3' == layer_name:
tensor.set_label(str(int(data[0] * 100)))
continue
if 'prob' == layer_name:
tensor.set_label(" M " if data[1] > 0.5 else " F ")
continue
if 'prob_emotion' == layer_name:
emotions = ["neutral", "happy", "sad", "surprise", "anger"]
tensor.set_label(emotions[data.index(max(data))])
continue
return True
def remove_regions(self, frame: VideoFrame):
for _ in range(len(frame.regions())):
frame.pop_region()