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https://github.com/blakeblackshear/frigate.git
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fixing a few things
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Blake Blackshear
parent
5b4c6e50bc
commit
02efb6f415
@ -133,9 +133,6 @@ class DetectedObjectsProcessor(threading.Thread):
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# print(f"{frame['frame_time']} no remaining regions")
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self.camera.finished_frame_queue.put(frame['frame_time'])
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with self.camera.objects_parsed:
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self.camera.objects_parsed.notify_all()
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# Thread that checks finished frames for clipped objects and sends back
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# for processing if needed
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class RegionRefiner(threading.Thread):
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@ -166,7 +163,7 @@ class RegionRefiner(threading.Thread):
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self.camera.detected_objects[frame_time] = [obj for obj in self.camera.detected_objects[frame_time] if obj['clipped'] == False]
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# print(f"{frame_time} found {len(object_groups)} groups")
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clipped_object = False
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look_again = False
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# find the largest unclipped object in each group
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for group in object_groups:
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unclipped_objects = [obj for obj in group if obj['clipped'] == False]
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@ -198,10 +195,12 @@ class RegionRefiner(threading.Thread):
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'y_offset': y_offset
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})
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self.camera.dynamic_region_fps.update()
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clipped_object = True
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look_again = True
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# TODO: zoom in on unclipped low confidence objects
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# else: ...
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# if we found a clipped object, then this frame is not ready for processing
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if clipped_object:
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# if we are looking again, then this frame is not ready for processing
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if look_again:
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continue
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# dedupe the unclipped objects
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@ -220,14 +219,19 @@ class RegionRefiner(threading.Thread):
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else:
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if deduped_objects[duplicate]['score'] < obj['score']:
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deduped_objects[duplicate] = obj
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self.camera.detected_objects[frame_time] = deduped_objects
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with self.camera.objects_parsed:
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self.camera.objects_parsed.notify_all()
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# print(f"{frame_time} is actually finished")
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# keep adding frames to the refined queue as long as they are finished
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with self.camera.regions_in_process_lock:
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while self.camera.frame_queue.qsize() > 0 and self.camera.frame_queue.queue[0] not in self.camera.regions_in_process:
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self.camera.refined_frame_queue.put(self.camera.frame_queue.get())
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self.camera.last_processed_frame = self.camera.frame_queue.get()
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self.camera.refined_frame_queue.put(self.camera.last_processed_frame)
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def has_overlap(self, new_obj, obj, overlap=.7):
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# compute intersection rectangle with existing object and new objects region
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@ -265,9 +269,9 @@ class ObjectTracker(threading.Thread):
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while True:
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# TODO: track objects
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frame_time = self.camera.refined_frame_queue.get()
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f = open(f"/debug/{str(frame_time)}.jpg", 'wb')
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f.write(self.camera.frame_with_objects(frame_time))
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f.close()
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# f = open(f"/debug/{str(frame_time)}.jpg", 'wb')
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# f.write(self.camera.frame_with_objects(frame_time))
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# f.close()
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def register(self, index, obj):
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@ -116,6 +116,7 @@ class Camera:
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self.detected_objects = defaultdict(lambda: [])
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self.tracked_objects = []
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self.frame_cache = {}
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self.last_processed_frame = None
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# queue for re-assembling frames in order
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self.frame_queue = queue.Queue()
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# track how many regions have been requested for a frame so we know when a frame is complete
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@ -332,45 +333,11 @@ class Camera:
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return jpg.tobytes()
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def get_current_frame_with_objects(self):
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# lock and make a copy of the current frame
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with self.frame_lock:
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frame = self.current_frame.copy()
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frame_time = self.frame_time.value
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frame_time = self.last_processed_frame
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if frame_time == self.cached_frame_with_objects['frame_time']:
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return self.cached_frame_with_objects['frame_bytes']
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# make a copy of the current detected objects
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detected_objects = self.detected_objects.copy()
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# draw the bounding boxes on the screen
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for obj in [obj for frame_list in detected_objects.values() for obj in frame_list]:
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# for obj in detected_objects[frame_time]:
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draw_box_with_label(frame, obj['box']['xmin'], obj['box']['ymin'], obj['box']['xmax'], obj['box']['ymax'], obj['name'], f"{int(obj['score']*100)}% {obj['area']} {obj['clipped']}")
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cv2.rectangle(frame, (obj['region']['xmin'], obj['region']['ymin']),
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(obj['region']['xmax'], obj['region']['ymax']),
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(0,255,0), 2)
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for region in self.regions:
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color = (255,255,255)
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cv2.rectangle(frame, (region['x_offset'], region['y_offset']),
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(region['x_offset']+region['size'], region['y_offset']+region['size']),
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color, 2)
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# print a timestamp
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time_to_show = datetime.datetime.fromtimestamp(frame_time).strftime("%m/%d/%Y %H:%M:%S")
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cv2.putText(frame, time_to_show, (10, 30), cv2.FONT_HERSHEY_SIMPLEX, fontScale=.8, color=(255, 255, 255), thickness=2)
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# print fps
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cv2.putText(frame, str(self.fps.eps())+'FPS', (10, 60), cv2.FONT_HERSHEY_SIMPLEX, fontScale=.8, color=(255, 255, 255), thickness=2)
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# convert to BGR
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frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
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# encode the image into a jpg
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ret, jpg = cv2.imencode('.jpg', frame)
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frame_bytes = jpg.tobytes()
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frame_bytes = self.frame_with_objects(frame_time)
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self.cached_frame_with_objects = {
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'frame_bytes': frame_bytes,
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