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https://github.com/blakeblackshear/frigate.git
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track objects and add config for tracked objects
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Blake Blackshear
parent
0c6717090c
commit
cc2abe93a6
@ -6,7 +6,7 @@ import prctl
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import numpy as np
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from edgetpu.detection.engine import DetectionEngine
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from frigate.util import tonumpyarray, LABELS, PATH_TO_CKPT
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from frigate.util import tonumpyarray, LABELS, PATH_TO_CKPT, calculate_region
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class PreppedQueueProcessor(threading.Thread):
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def __init__(self, cameras, prepped_frame_queue, fps, queue_full):
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@ -57,8 +57,12 @@ class RegionRequester(threading.Thread):
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# make a copy of the frame_time
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frame_time = self.camera.frame_time.value
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# grab the current tracked objects
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tracked_objects = self.camera.object_tracker.tracked_objects.values()
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with self.camera.regions_in_process_lock:
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self.camera.regions_in_process[frame_time] = len(self.camera.config['regions'])
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self.camera.regions_in_process[frame_time] += len(tracked_objects)
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for index, region in enumerate(self.camera.config['regions']):
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self.camera.resize_queue.put({
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@ -70,6 +74,24 @@ class RegionRequester(threading.Thread):
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'y_offset': region['y_offset']
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})
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# request a region for tracked objects
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for tracked_object in tracked_objects:
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box = tracked_object['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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self.camera.resize_queue.put({
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'camera_name': self.camera.name,
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'frame_time': frame_time,
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'region_id': -1,
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'size': size,
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'x_offset': x_offset,
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'y_offset': y_offset
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})
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class RegionPrepper(threading.Thread):
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def __init__(self, frame_cache, resize_request_queue, prepped_frame_queue):
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threading.Thread.__init__(self)
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@ -50,14 +50,14 @@ class DetectedObjectsProcessor(threading.Thread):
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objects = frame['detected_objects']
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# print(f"Processing objects for: {frame['size']} {frame['x_offset']} {frame['y_offset']}")
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# if len(objects) == 0:
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# continue
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for raw_obj in objects:
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name = str(LABELS[raw_obj.label_id])
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if not name in self.camera.objects_to_track:
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continue
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obj = {
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'name': str(LABELS[raw_obj.label_id]),
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'name': name,
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'score': float(raw_obj.score),
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'box': {
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'xmin': int((raw_obj.bounding_box[0][0] * frame['size']) + frame['x_offset']),
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@ -75,9 +75,6 @@ class DetectedObjectsProcessor(threading.Thread):
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'region_id': frame['region_id']
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}
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if not obj['name'] == 'bicycle':
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continue
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# if the object is within 5 pixels of the region border, and the region is not on the edge
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# consider the object to be clipped
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obj['clipped'] = False
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@ -245,15 +242,14 @@ class ObjectTracker(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: track objects
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frame_time = self.camera.refined_frame_queue.get()
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self.match_and_update(self.camera.detected_objects[frame_time])
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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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id = f"{str(obj.frame_time)}-{index}"
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id = f"{str(obj['frame_time'])}-{index}"
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self.tracked_objects[id] = obj
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self.disappeared[id] = 0
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@ -262,10 +258,12 @@ class ObjectTracker(threading.Thread):
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del self.tracked_objects[id]
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def update(self, id, new_obj):
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new_obj.detections = self.tracked_objects[id].detections
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new_obj.detections.append({
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})
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self.tracked_objects[id]['centroid'] = new_obj['centroid']
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self.tracked_objects[id]['box'] = new_obj['box']
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self.tracked_objects[id]['region'] = new_obj['region']
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self.tracked_objects[id]['score'] = new_obj['score']
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self.tracked_objects[id]['name'] = new_obj['name']
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# TODO: am i missing anything? history?
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def match_and_update(self, new_objects):
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# check to see if the list of input bounding box rectangles
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@ -290,16 +288,16 @@ class ObjectTracker(threading.Thread):
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for obj in new_objects:
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centroid_x = int((obj['box']['xmin']+obj['box']['xmax']) / 2.0)
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centroid_y = int((obj['box']['ymin']+obj['box']['ymax']) / 2.0)
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obj.centroid = (centroid_x, centroid_y)
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obj['centroid'] = (centroid_x, centroid_y)
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if len(self.tracked_objects) == 0:
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for index, obj in enumerate(new_objects):
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self.register(index, obj)
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return
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new_centroids = np.array([o.centroid for o in new_objects])
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new_centroids = np.array([o['centroid'] for o in new_objects])
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current_ids = list(self.tracked_objects.keys())
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current_centroids = np.array([o.centroid for o in self.tracked_objects])
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current_centroids = np.array([o['centroid'] for o in self.tracked_objects.values()])
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# compute the distance between each pair of tracked
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# centroids and new centroids, respectively -- our
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@ -376,110 +374,6 @@ class ObjectTracker(threading.Thread):
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for col in unusedCols:
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self.register(col, new_objects[col])
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# -------------
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# # initialize an array of input centroids for the current frame
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# inputCentroids = np.zeros((len(rects), 2), dtype="int")
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# # loop over the bounding box rectangles
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# for (i, (startX, startY, endX, endY)) in enumerate(rects):
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# # use the bounding box coordinates to derive the centroid
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# cX = int((startX + endX) / 2.0)
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# cY = int((startY + endY) / 2.0)
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# inputCentroids[i] = (cX, cY)
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# # if we are currently not tracking any objects take the input
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# # centroids and register each of them
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# if len(self.objects) == 0:
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# for i in range(0, len(inputCentroids)):
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# self.register(inputCentroids[i])
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# # otherwise, are are currently tracking objects so we need to
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# # try to match the input centroids to existing object
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# # centroids
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# else:
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# # grab the set of object IDs and corresponding centroids
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# objectIDs = list(self.objects.keys())
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# objectCentroids = list(self.objects.values())
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# # compute the distance between each pair of object
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# # centroids and input centroids, respectively -- our
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# # goal will be to match an input centroid to an existing
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# # object centroid
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# D = dist.cdist(np.array(objectCentroids), inputCentroids)
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# # in order to perform this matching we must (1) find the
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# # smallest value in each row and then (2) sort the row
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# # indexes based on their minimum values so that the row
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# # with the smallest value is at the *front* of the index
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# # list
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# rows = D.min(axis=1).argsort()
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# # next, we perform a similar process on the columns by
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# # finding the smallest value in each column and then
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# # sorting using the previously computed row index list
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# cols = D.argmin(axis=1)[rows]
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# # in order to determine if we need to update, register,
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# # or deregister an object we need to keep track of which
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# # of the rows and column indexes we have already examined
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# usedRows = set()
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# usedCols = set()
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# # loop over the combination of the (row, column) index
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# # tuples
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# for (row, col) in zip(rows, cols):
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# # if we have already examined either the row or
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# # column value before, ignore it
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# # val
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# if row in usedRows or col in usedCols:
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# continue
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# # otherwise, grab the object ID for the current row,
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# # set its new centroid, and reset the disappeared
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# # counter
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# objectID = objectIDs[row]
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# self.objects[objectID] = inputCentroids[col]
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# self.disappeared[objectID] = 0
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# # indicate that we have examined each of the row and
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# # column indexes, respectively
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# usedRows.add(row)
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# usedCols.add(col)
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# # compute both the row and column index we have NOT yet
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# # examined
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# unusedRows = set(range(0, D.shape[0])).difference(usedRows)
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# unusedCols = set(range(0, D.shape[1])).difference(usedCols)
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# # in the event that the number of object centroids is
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# # equal or greater than the number of input centroids
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# # we need to check and see if some of these objects have
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# # potentially disappeared
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# if D.shape[0] >= D.shape[1]:
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# # loop over the unused row indexes
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# for row in unusedRows:
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# # grab the object ID for the corresponding row
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# # index and increment the disappeared counter
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# objectID = objectIDs[row]
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# self.disappeared[objectID] += 1
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# # check to see if the number of consecutive
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# # frames the object has been marked "disappeared"
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# # for warrants deregistering the object
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# if self.disappeared[objectID] > self.maxDisappeared:
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# self.deregister(objectID)
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# # otherwise, if the number of input centroids is greater
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# # than the number of existing object centroids we need to
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# # register each new input centroid as a trackable object
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# else:
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# for col in unusedCols:
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# self.register(inputCentroids[col])
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# # return the set of trackable objects
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# return self.objects
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# Maintains the frame and object with the highest score
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class BestFrames(threading.Thread):
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def __init__(self, objects_parsed, recent_frames, detected_objects):
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@ -114,7 +114,6 @@ class Camera:
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self.name = name
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self.config = config
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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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@ -172,21 +171,13 @@ class Camera:
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self.capture_thread = None
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self.fps = EventsPerSecond()
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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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# combine tracked objects lists
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self.objects_to_track = set().union(global_objects_config.get('track', ['person', 'car', 'truck']), camera_objects_config.get('track', []))
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# merge object filters
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objects_with_config = set().union(global_objects_config.get('filters', {}).keys(), camera_objects_config.get('filters', {}).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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self.object_filters = {**global_objects_config.get(obj, {}), **camera_objects_config.get(obj, {})}
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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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@ -311,12 +302,12 @@ class Camera:
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color, 2)
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# draw the bounding boxes on the screen
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for obj in self.detected_objects[frame_time]:
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for id, obj in self.object_tracker.tracked_objects.items():
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# for obj in detected_objects[frame_time]:
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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), 1)
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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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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']} {id}")
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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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