def process_frame(frame: VideoFrame,
threshold: float = DETECT_THRESHOLD) -> bool:
width = frame.video_info().width
height = frame.video_info().height
for tensor in frame.tensors():
dims = tensor.dims()
data = tensor.data()
object_size = dims[-1]
for i in range(dims[-2]):
image_id = data[i * object_size + 0]
confidence = data[i * object_size + 2]
x_min = int(data[i * object_size + 3] * width + 0.5)
y_min = int(data[i * object_size + 4] * height + 0.5)
x_max = int(data[i * object_size + 5] * width + 0.5)
y_max = int(data[i * object_size + 6] * height + 0.5)
if image_id != 0:
break
if confidence < threshold:
continue
frame.add_region(x_min,
y_min,
x_max - x_min,
y_max - y_min,
1,
region_tensor=REGION_TENSOR)
return True
def process_frame(self,
frame: VideoFrame,
_: float = DETECT_THRESHOLD) -> bool:
while self.json_objects:
metadata_pts = self.json_objects[0][
"timestamp"] + self.offset_timestamp
timestamp_difference = abs(frame.video_meta().buffer.pts -
metadata_pts)
# A margin of error of 1000 nanoseconds
# If the difference is greater than the margin of error:
# If frame has a higher pts then the timestamp at the head of the list,
# pop the head of the list for being outdated
# If frame has a lower pts then the timestamp at the head of the list,
# its still possible for the timestamp to come up, so break
# Otherwise, assume this timestamp at the head of the list is accurate to that frame
if timestamp_difference > 1000:
if (frame.video_meta().buffer.pts - metadata_pts) > 0:
self.json_objects.pop(0)
continue
break
detected_objects = self.json_objects[0]["objects"]
for indv_object in detected_objects:
frame.add_region(
indv_object["detection"]["bounding_box"]["x_min"],
indv_object["detection"]["bounding_box"]["y_min"],
indv_object["detection"]["bounding_box"]["x_max"] - \
indv_object["detection"]["bounding_box"]["x_min"],
indv_object["detection"]["bounding_box"]["y_max"] - \
indv_object["detection"]["bounding_box"]["y_min"],
indv_object["detection"]["label"],
indv_object["detection"]["confidence"],
True)
self.json_objects.pop(0)
break
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
