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implement filtering and switch to NMS with OpenCV
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Blake Blackshear
parent
5d0c12fbd4
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7b1da388d9
@ -90,38 +90,6 @@ class DetectedObjectsProcessor(threading.Thread):
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# Compute the area
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obj['area'] = (obj['box']['xmax']-obj['box']['xmin'])*(obj['box']['ymax']-obj['box']['ymin'])
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# find the matching region
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# region = self.camera.regions[frame['region_id']]
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# object_name = obj['name']
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# TODO: move all this to wherever we manage "tracked objects"
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# if object_name in region['objects']:
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# obj_settings = region['objects'][object_name]
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# # if the min area is larger than the
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# # detected object, don't add it to detected objects
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# if obj_settings.get('min_area',-1) > obj['area']:
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# continue
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# # if the detected object is larger than the
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# # max area, don't add it to detected objects
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# if obj_settings.get('max_area', region['size']**2) < obj['area']:
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# continue
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# # if the score is lower than the threshold, skip
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# if obj_settings.get('threshold', 0) > obj['score']:
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# continue
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# # compute the coordinates of the object and make sure
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# # the location isnt outside the bounds of the image (can happen from rounding)
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# y_location = min(int(obj['ymax']), len(self.mask)-1)
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# x_location = min(int((obj['xmax']-obj['xmin'])/2.0)+obj['xmin'], len(self.mask[0])-1)
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# # if the object is in a masked location, don't add it to detected objects
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# if self.camera.mask[y_location][x_location] == [0]:
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# continue
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self.camera.detected_objects[frame['frame_time']].append(obj)
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with self.camera.regions_in_process_lock:
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@ -143,47 +111,38 @@ class RegionRefiner(threading.Thread):
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def run(self):
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prctl.set_name(self.__class__.__name__)
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while True:
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# TODO: I need to process the frames in order for tracking...
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frame_time = self.camera.finished_frame_queue.get()
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detected_objects = self.camera.detected_objects[frame_time].copy()
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# print(f"{frame_time} finished")
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object_groups = []
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# apply non-maxima suppression to suppress weak, overlapping bounding boxes
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boxes = [(o['box']['xmin'], o['box']['ymin'], o['box']['xmax']-o['box']['xmin'], o['box']['ymax']-o['box']['ymin'])
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for o in detected_objects]
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confidences = [o['score'] for o in detected_objects]
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idxs = cv2.dnn.NMSBoxes(boxes, confidences, 0.5, 0.4)
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# group all the duplicate objects together
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# TODO: should I be grouping by object type too? also, the order can determine how well they group...
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for new_obj in self.camera.detected_objects[frame_time]:
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matching_group = self.find_group(new_obj, object_groups)
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if matching_group is None:
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object_groups.append([new_obj])
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else:
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object_groups[matching_group].append(new_obj)
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# just keep the unclipped objects
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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} - NMS reduced objects from {len(detected_objects)} to {len(idxs)}")
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# print(f"{frame_time} found {len(object_groups)} groups")
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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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# if no unclipped objects, we need to look again
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if len(unclipped_objects) == 0:
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# print(f"{frame_time} no unclipped objects in group")
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# get selected objects
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selected_objects = []
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for index in idxs:
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obj = detected_objects[index[0]]
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selected_objects.append(obj)
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if obj['clipped']:
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box = obj['box']
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# calculate a new region that will hopefully get the entire object
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(size, x_offset, y_offset) = calculate_region(self.camera.frame_shape,
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box['xmin'], box['ymin'],
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box['xmax'], box['ymax'])
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# print(f"{frame_time} new region: {size} {x_offset} {y_offset}")
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with self.camera.regions_in_process_lock:
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if not frame_time in self.camera.regions_in_process:
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self.camera.regions_in_process[frame_time] = 1
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else:
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self.camera.regions_in_process[frame_time] += 1
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xmin = min([obj['box']['xmin'] for obj in group])
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ymin = min([obj['box']['ymin'] for obj in group])
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xmax = max([obj['box']['xmax'] for obj in group])
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ymax = max([obj['box']['ymax'] for obj in group])
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# calculate a new region that will hopefully get the entire object
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(size, x_offset, y_offset) = calculate_region(self.camera.frame_shape,
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xmin, ymin,
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xmax, ymax)
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# print(f"{frame_time} new region: {size} {x_offset} {y_offset}")
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# add it to the queue
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self.camera.resize_queue.put({
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@ -201,26 +160,14 @@ class RegionRefiner(threading.Thread):
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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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# remove the clipped objects
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self.camera.detected_objects[frame_time] = [o for o in selected_objects if not o['clipped']]
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continue
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# dedupe the unclipped objects
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deduped_objects = []
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for obj in self.camera.detected_objects[frame_time]:
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duplicate = None
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for index, deduped_obj in enumerate(deduped_objects):
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# if the IOU is more than 0.7, consider it a duplicate
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if self.has_overlap(obj, deduped_obj, .5):
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duplicate = index
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break
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# get the higher scoring object
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if duplicate is None:
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deduped_objects.append(obj)
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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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# filter objects based on camera settings
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selected_objects = [o for o in selected_objects if not self.filtered(o)]
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self.camera.detected_objects[frame_time] = deduped_objects
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self.camera.detected_objects[frame_time] = selected_objects
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with self.camera.objects_parsed:
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self.camera.objects_parsed.notify_all()
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@ -232,6 +179,37 @@ class RegionRefiner(threading.Thread):
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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.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 filtered(self, obj):
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object_name = obj['name']
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if object_name in self.camera.object_filters:
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obj_settings = self.camera.object_filters[object_name]
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# if the min area is larger than the
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# detected object, don't add it to detected objects
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if obj_settings.get('min_area',-1) > obj['area']:
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return True
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# if the detected object is larger than the
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# max area, don't add it to detected objects
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if obj_settings.get('max_area', self.camera.frame_shape[0]*self.camera.frame_shape[1]) < obj['area']:
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return True
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# if the score is lower than the threshold, skip
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if obj_settings.get('threshold', 0) > obj['score']:
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return True
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# compute the coordinates of the object and make sure
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# the location isnt outside the bounds of the image (can happen from rounding)
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y_location = min(int(obj['ymax']), len(self.camera.mask)-1)
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x_location = min(int((obj['xmax']-obj['xmin'])/2.0)+obj['xmin'], len(self.camera.mask[0])-1)
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# if the object is in a masked location, don't add it to detected objects
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if self.camera.mask[y_location][x_location] == [0]:
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return True
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return False
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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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@ -172,17 +172,21 @@ class Camera:
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self.capture_thread = None
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self.fps = EventsPerSecond()
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# for each region, merge the object config
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self.detection_prep_threads = []
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for region in self.config['regions']:
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region_objects = region.get('objects', {})
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# build objects config for region
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objects_with_config = set().union(global_objects_config.keys(), camera_objects_config.keys(), region_objects.keys())
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merged_objects_config = defaultdict(lambda: {})
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for obj in objects_with_config:
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merged_objects_config[obj] = {**global_objects_config.get(obj,{}), **camera_objects_config.get(obj, {}), **region_objects.get(obj, {})}
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# merge object filter config
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objects_with_config = set().union(global_objects_config.keys(), camera_objects_config.keys())
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for obj in objects_with_config:
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self.object_filters = {**global_objects_config.get(obj,{}), **camera_objects_config.get(obj, {})}
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# # for each region, merge the object config
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# for region in self.config['regions']:
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# region_objects = region.get('objects', {})
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# # build objects config for region
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# objects_with_config = set().union(global_objects_config.keys(), camera_objects_config.keys(), region_objects.keys())
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# merged_objects_config = defaultdict(lambda: {})
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# for obj in objects_with_config:
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# merged_objects_config[obj] = {**global_objects_config.get(obj,{}), **camera_objects_config.get(obj, {}), **region_objects.get(obj, {})}
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region['objects'] = merged_objects_config
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# region['objects'] = merged_objects_config
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# start a thread to queue resize requests for regions
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self.region_requester = RegionRequester(self)
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@ -275,9 +279,6 @@ class Camera:
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def start(self):
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self.start_or_restart_capture()
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# start the object detection prep threads
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for detection_prep_thread in self.detection_prep_threads:
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detection_prep_thread.start()
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self.watchdog.start()
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def join(self):
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