Mirrors/blakeblackshear.frigate
1
0
Fork 0
mirror of https://github.com/blakeblackshear/frigate.git synced 2025-10-27 10:52:11 +01:00
Code Issues Projects Releases Wiki Activity

maintain thumbnail frames for tracked objects

Browse Source
This commit is contained in:
Blake Blackshear 2020-11-05 08:39:21 -06:00
parent 8b8f5b5c40
commit 24b703a875
1 changed files with 74 additions and 19 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

93
frigate/object_processing.py
View File

@ -52,6 +52,34 @@ def zone_filtered(obj, object_config):
return False
def on_edge(box, frame_shape):
if (
box[0] == 0 or
box[1] == 0 or
box[2] == frame_shape[1]-1 or
box[3] == frame_shape[0]-1
):
return True
def is_better_thumbnail(current_thumb, new_obj, frame_shape) -> bool:
# larger is better
# cutoff images are less ideal, but they should also be smaller?
# better scores are obviously better too
# if the new_thumb is on an edge, and the current thumb is not
if on_edge(new_obj['box'], frame_shape) and not on_edge(current_thumb['box'], frame_shape):
return False
# if the score is better by more than 5%
if new_obj['score'] > current_thumb['score']+.05:
return True
# if the area is 10% larger
if new_obj['area'] > current_thumb['area']*1.1:
return True
return False
# Maintains the state of a camera
class CameraState():
def __init__(self, name, config, frame_manager):
@ -62,6 +90,7 @@ class CameraState():
self.best_objects = {}
self.object_status = defaultdict(lambda: 'OFF')
self.tracked_objects = {}
self.thumbnail_frames = {}
self.zone_objects = defaultdict(lambda: [])
self._current_frame = np.zeros(self.config.frame_shape_yuv, np.uint8)
self.current_frame_lock = threading.Lock()
@ -138,44 +167,63 @@ class CameraState():
updated_ids = list(set(current_ids).intersection(previous_ids))
for id in new_ids:
self.tracked_objects[id] = tracked_objects[id]
self.tracked_objects[id]['zones'] = []
self.tracked_objects[id]['entered_zones'] = set()
new_obj = self.tracked_objects[id] = tracked_objects[id]
new_obj['zones'] = []
new_obj['entered_zones'] = set()
new_obj['thumbnail'] = {
'frame': new_obj['frame_time'],
'box': new_obj['box'],
'area': new_obj['area'],
'region': new_obj['region'],
'score': new_obj['score']
}
# start the score history
self.tracked_objects[id]['score_history'] = [self.tracked_objects[id]['score']]
new_obj['score_history'] = [self.tracked_objects[id]['score']]
# calculate if this is a false positive
self.tracked_objects[id]['computed_score'] = self.compute_score(self.tracked_objects[id])
self.tracked_objects[id]['top_score'] = self.tracked_objects[id]['computed_score']
self.tracked_objects[id]['false_positive'] = self.false_positive(self.tracked_objects[id])
new_obj['computed_score'] = self.compute_score(self.tracked_objects[id])
new_obj['top_score'] = self.tracked_objects[id]['computed_score']
new_obj['false_positive'] = self.false_positive(self.tracked_objects[id])
# call event handlers
for c in self.callbacks['start']:
c(self.name, tracked_objects[id])
c(self.name, new_obj)
for id in updated_ids:
self.tracked_objects[id].update(tracked_objects[id])
updated_obj = self.tracked_objects[id]
# if the object is not in the current frame, add a 0.0 to the score history
if self.tracked_objects[id]['frame_time'] != self.current_frame_time:
self.tracked_objects[id]['score_history'].append(0.0)
if updated_obj['frame_time'] != self.current_frame_time:
updated_obj['score_history'].append(0.0)
else:
self.tracked_objects[id]['score_history'].append(self.tracked_objects[id]['score'])
updated_obj['score_history'].append(updated_obj['score'])
# only keep the last 10 scores
if len(self.tracked_objects[id]['score_history']) > 10:
self.tracked_objects[id]['score_history'] = self.tracked_objects[id]['score_history'][-10:]
if len(updated_obj['score_history']) > 10:
updated_obj['score_history'] = updated_obj['score_history'][-10:]
# calculate if this is a false positive
computed_score = self.compute_score(self.tracked_objects[id])
self.tracked_objects[id]['computed_score'] = computed_score
if computed_score > self.tracked_objects[id]['top_score']:
self.tracked_objects[id]['top_score'] = computed_score
self.tracked_objects[id]['false_positive'] = self.false_positive(self.tracked_objects[id])
computed_score = self.compute_score(updated_obj)
updated_obj['computed_score'] = computed_score
if computed_score > updated_obj['top_score']:
updated_obj['top_score'] = computed_score
updated_obj['false_positive'] = self.false_positive(updated_obj)
# determine if this frame is a better thumbnail
if is_better_thumbnail(updated_obj['thumbnail'], updated_obj, self.config.frame_shape):
updated_obj['thumbnail'] = {
'frame': updated_obj['frame_time'],
'box': updated_obj['box'],
'area': updated_obj['area'],
'region': updated_obj['region'],
'score': updated_obj['score']
}
# call event handlers
for c in self.callbacks['update']:
c(self.name, self.tracked_objects[id])
c(self.name, updated_obj)
for id in removed_ids:
# publish events to mqtt
@ -201,6 +249,13 @@ class CameraState():
obj['zones'] = current_zones
# update frame storage for thumbnails based on thumbnails for all tracked objects
current_thumb_frames = set([obj['thumbnail']['frame'] for obj in self.tracked_objects.values()])
if self.current_frame_time in current_thumb_frames:
self.thumbnail_frames[self.current_frame_time] = np.copy(current_frame)
thumb_frames_to_delete = [t for t in self.thumbnail_frames.keys() if not t in current_thumb_frames]
for t in thumb_frames_to_delete: del self.thumbnail_frames[t]
# maintain best objects
for obj in self.tracked_objects.values():
object_type = obj['label']