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track objects and add config for tracked objects

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Blake Blackshear 2020-01-04 18:13:53 -06:00
parent 0c6717090c
commit cc2abe93a6
3 changed files with 48 additions and 141 deletions
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24
frigate/object_detection.py
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@ -6,7 +6,7 @@ import prctl
import numpy as np import numpy as np
from edgetpu.detection.engine import DetectionEngine from edgetpu.detection.engine import DetectionEngine
from frigate.util import tonumpyarray, LABELS, PATH_TO_CKPT from frigate.util import tonumpyarray, LABELS, PATH_TO_CKPT, calculate_region
class PreppedQueueProcessor(threading.Thread): class PreppedQueueProcessor(threading.Thread):
def __init__(self, cameras, prepped_frame_queue, fps, queue_full): def __init__(self, cameras, prepped_frame_queue, fps, queue_full):
@ -57,8 +57,12 @@ class RegionRequester(threading.Thread):
# make a copy of the frame_time # make a copy of the frame_time
frame_time = self.camera.frame_time.value frame_time = self.camera.frame_time.value
# grab the current tracked objects
tracked_objects = self.camera.object_tracker.tracked_objects.values()
with self.camera.regions_in_process_lock: with self.camera.regions_in_process_lock:
self.camera.regions_in_process[frame_time] = len(self.camera.config['regions']) self.camera.regions_in_process[frame_time] = len(self.camera.config['regions'])
self.camera.regions_in_process[frame_time] += len(tracked_objects)
for index, region in enumerate(self.camera.config['regions']): for index, region in enumerate(self.camera.config['regions']):
self.camera.resize_queue.put({ self.camera.resize_queue.put({
@ -70,6 +74,24 @@ class RegionRequester(threading.Thread):
'y_offset': region['y_offset'] 'y_offset': region['y_offset']
}) })
# request a region for tracked objects
for tracked_object in tracked_objects:
box = tracked_object['box']
# calculate a new region that will hopefully get the entire object
(size, x_offset, y_offset) = calculate_region(self.camera.frame_shape,
box['xmin'], box['ymin'],
box['xmax'], box['ymax'])
self.camera.resize_queue.put({
'camera_name': self.camera.name,
'frame_time': frame_time,
'region_id': -1,
'size': size,
'x_offset': x_offset,
'y_offset': y_offset
})
class RegionPrepper(threading.Thread): class RegionPrepper(threading.Thread):
def __init__(self, frame_cache, resize_request_queue, prepped_frame_queue): def __init__(self, frame_cache, resize_request_queue, prepped_frame_queue):
threading.Thread.__init__(self) threading.Thread.__init__(self)

140
frigate/objects.py
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@ -50,14 +50,14 @@ class DetectedObjectsProcessor(threading.Thread):
objects = frame['detected_objects'] objects = frame['detected_objects']
# print(f"Processing objects for: {frame['size']} {frame['x_offset']} {frame['y_offset']}")
# if len(objects) == 0:
# continue
for raw_obj in objects: for raw_obj in objects:
name = str(LABELS[raw_obj.label_id])
if not name in self.camera.objects_to_track:
continue
obj = { obj = {
'name': str(LABELS[raw_obj.label_id]), 'name': name,
'score': float(raw_obj.score), 'score': float(raw_obj.score),
'box': { 'box': {
'xmin': int((raw_obj.bounding_box[0][0] * frame['size']) + frame['x_offset']), 'xmin': int((raw_obj.bounding_box[0][0] * frame['size']) + frame['x_offset']),
@ -75,9 +75,6 @@ class DetectedObjectsProcessor(threading.Thread):
'region_id': frame['region_id'] 'region_id': frame['region_id']
} }
if not obj['name'] == 'bicycle':
continue
# if the object is within 5 pixels of the region border, and the region is not on the edge # if the object is within 5 pixels of the region border, and the region is not on the edge
# consider the object to be clipped # consider the object to be clipped
obj['clipped'] = False obj['clipped'] = False
@ -245,15 +242,14 @@ class ObjectTracker(threading.Thread):
def run(self): def run(self):
prctl.set_name(self.__class__.__name__) prctl.set_name(self.__class__.__name__)
while True: while True:
# TODO: track objects
frame_time = self.camera.refined_frame_queue.get() frame_time = self.camera.refined_frame_queue.get()
self.match_and_update(self.camera.detected_objects[frame_time])
# f = open(f"/debug/{str(frame_time)}.jpg", 'wb') # f = open(f"/debug/{str(frame_time)}.jpg", 'wb')
# f.write(self.camera.frame_with_objects(frame_time)) # f.write(self.camera.frame_with_objects(frame_time))
# f.close() # f.close()
def register(self, index, obj): def register(self, index, obj):
id = f"{str(obj.frame_time)}-{index}" id = f"{str(obj['frame_time'])}-{index}"
self.tracked_objects[id] = obj self.tracked_objects[id] = obj
self.disappeared[id] = 0 self.disappeared[id] = 0
@ -262,10 +258,12 @@ class ObjectTracker(threading.Thread):
del self.tracked_objects[id] del self.tracked_objects[id]
def update(self, id, new_obj): def update(self, id, new_obj):
new_obj.detections = self.tracked_objects[id].detections self.tracked_objects[id]['centroid'] = new_obj['centroid']
new_obj.detections.append({ self.tracked_objects[id]['box'] = new_obj['box']
self.tracked_objects[id]['region'] = new_obj['region']
}) self.tracked_objects[id]['score'] = new_obj['score']
self.tracked_objects[id]['name'] = new_obj['name']
# TODO: am i missing anything? history?
def match_and_update(self, new_objects): def match_and_update(self, new_objects):
# check to see if the list of input bounding box rectangles # check to see if the list of input bounding box rectangles
@ -290,16 +288,16 @@ class ObjectTracker(threading.Thread):
for obj in new_objects: for obj in new_objects:
centroid_x = int((obj['box']['xmin']+obj['box']['xmax']) / 2.0) centroid_x = int((obj['box']['xmin']+obj['box']['xmax']) / 2.0)
centroid_y = int((obj['box']['ymin']+obj['box']['ymax']) / 2.0) centroid_y = int((obj['box']['ymin']+obj['box']['ymax']) / 2.0)
obj.centroid = (centroid_x, centroid_y) obj['centroid'] = (centroid_x, centroid_y)
if len(self.tracked_objects) == 0: if len(self.tracked_objects) == 0:
for index, obj in enumerate(new_objects): for index, obj in enumerate(new_objects):
self.register(index, obj) self.register(index, obj)
return return
new_centroids = np.array([o.centroid for o in new_objects]) new_centroids = np.array([o['centroid'] for o in new_objects])
current_ids = list(self.tracked_objects.keys()) current_ids = list(self.tracked_objects.keys())
current_centroids = np.array([o.centroid for o in self.tracked_objects]) current_centroids = np.array([o['centroid'] for o in self.tracked_objects.values()])
# compute the distance between each pair of tracked # compute the distance between each pair of tracked
# centroids and new centroids, respectively -- our # centroids and new centroids, respectively -- our
@ -376,110 +374,6 @@ class ObjectTracker(threading.Thread):
for col in unusedCols: for col in unusedCols:
self.register(col, new_objects[col]) self.register(col, new_objects[col])
# -------------
# # initialize an array of input centroids for the current frame
# inputCentroids = np.zeros((len(rects), 2), dtype="int")
# # loop over the bounding box rectangles
# for (i, (startX, startY, endX, endY)) in enumerate(rects):
# # use the bounding box coordinates to derive the centroid
# cX = int((startX + endX) / 2.0)
# cY = int((startY + endY) / 2.0)
# inputCentroids[i] = (cX, cY)
# # if we are currently not tracking any objects take the input
# # centroids and register each of them
# if len(self.objects) == 0:
# for i in range(0, len(inputCentroids)):
# self.register(inputCentroids[i])
# # otherwise, are are currently tracking objects so we need to
# # try to match the input centroids to existing object
# # centroids
# else:
# # grab the set of object IDs and corresponding centroids
# objectIDs = list(self.objects.keys())
# objectCentroids = list(self.objects.values())
# # compute the distance between each pair of object
# # centroids and input centroids, respectively -- our
# # goal will be to match an input centroid to an existing
# # object centroid
# D = dist.cdist(np.array(objectCentroids), inputCentroids)
# # in order to perform this matching we must (1) find the
# # smallest value in each row and then (2) sort the row
# # indexes based on their minimum values so that the row
# # with the smallest value is at the *front* of the index
# # list
# rows = D.min(axis=1).argsort()
# # next, we perform a similar process on the columns by
# # finding the smallest value in each column and then
# # sorting using the previously computed row index list
# cols = D.argmin(axis=1)[rows]
# # in order to determine if we need to update, register,
# # or deregister an object we need to keep track of which
# # of the rows and column indexes we have already examined
# usedRows = set()
# usedCols = set()
# # loop over the combination of the (row, column) index
# # tuples
# for (row, col) in zip(rows, cols):
# # if we have already examined either the row or
# # column value before, ignore it
# # val
# if row in usedRows or col in usedCols:
# continue
# # otherwise, grab the object ID for the current row,
# # set its new centroid, and reset the disappeared
# # counter
# objectID = objectIDs[row]
# self.objects[objectID] = inputCentroids[col]
# self.disappeared[objectID] = 0
# # indicate that we have examined each of the row and
# # column indexes, respectively
# usedRows.add(row)
# usedCols.add(col)
# # compute both the row and column index we have NOT yet
# # examined
# unusedRows = set(range(0, D.shape[0])).difference(usedRows)
# unusedCols = set(range(0, D.shape[1])).difference(usedCols)
# # in the event that the number of object centroids is
# # equal or greater than the number of input centroids
# # we need to check and see if some of these objects have
# # potentially disappeared
# if D.shape[0] >= D.shape[1]:
# # loop over the unused row indexes
# for row in unusedRows:
# # grab the object ID for the corresponding row
# # index and increment the disappeared counter
# objectID = objectIDs[row]
# self.disappeared[objectID] += 1
# # check to see if the number of consecutive
# # frames the object has been marked "disappeared"
# # for warrants deregistering the object
# if self.disappeared[objectID] > self.maxDisappeared:
# self.deregister(objectID)
# # otherwise, if the number of input centroids is greater
# # than the number of existing object centroids we need to
# # register each new input centroid as a trackable object
# else:
# for col in unusedCols:
# self.register(inputCentroids[col])
# # return the set of trackable objects
# return self.objects
# Maintains the frame and object with the highest score # Maintains the frame and object with the highest score
class BestFrames(threading.Thread): class BestFrames(threading.Thread):
def __init__(self, objects_parsed, recent_frames, detected_objects): def __init__(self, objects_parsed, recent_frames, detected_objects):

25
frigate/video.py
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@ -114,7 +114,6 @@ class Camera:
self.name = name self.name = name
self.config = config self.config = config
self.detected_objects = defaultdict(lambda: []) self.detected_objects = defaultdict(lambda: [])
self.tracked_objects = []
self.frame_cache = {} self.frame_cache = {}
self.last_processed_frame = None self.last_processed_frame = None
# queue for re-assembling frames in order # queue for re-assembling frames in order
@ -172,21 +171,13 @@ class Camera:
self.capture_thread = None self.capture_thread = None
self.fps = EventsPerSecond() self.fps = EventsPerSecond()
# merge object filter config # combine tracked objects lists
objects_with_config = set().union(global_objects_config.keys(), camera_objects_config.keys()) self.objects_to_track = set().union(global_objects_config.get('track', ['person', 'car', 'truck']), camera_objects_config.get('track', []))
# merge object filters
objects_with_config = set().union(global_objects_config.get('filters', {}).keys(), camera_objects_config.get('filters', {}).keys())
for obj in objects_with_config: for obj in objects_with_config:
self.object_filters = {**global_objects_config.get(obj,{}), **camera_objects_config.get(obj, {})} self.object_filters = {**global_objects_config.get(obj, {}), **camera_objects_config.get(obj, {})}
# # for each region, merge the object config
# for region in self.config['regions']:
# region_objects = region.get('objects', {})
# # build objects config for region
# objects_with_config = set().union(global_objects_config.keys(), camera_objects_config.keys(), region_objects.keys())
# merged_objects_config = defaultdict(lambda: {})
# for obj in objects_with_config:
# merged_objects_config[obj] = {**global_objects_config.get(obj,{}), **camera_objects_config.get(obj, {}), **region_objects.get(obj, {})}
# region['objects'] = merged_objects_config
# start a thread to queue resize requests for regions # start a thread to queue resize requests for regions
self.region_requester = RegionRequester(self) self.region_requester = RegionRequester(self)
@ -311,12 +302,12 @@ class Camera:
color, 2) color, 2)
# draw the bounding boxes on the screen # draw the bounding boxes on the screen
for obj in self.detected_objects[frame_time]: for id, obj in self.object_tracker.tracked_objects.items():
# for obj in detected_objects[frame_time]: # for obj in detected_objects[frame_time]:
cv2.rectangle(frame, (obj['region']['xmin'], obj['region']['ymin']), cv2.rectangle(frame, (obj['region']['xmin'], obj['region']['ymin']),
(obj['region']['xmax'], obj['region']['ymax']), (obj['region']['xmax'], obj['region']['ymax']),
(0,255,0), 1) (0,255,0), 1)
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']}") 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}")
# print a timestamp # print a timestamp
time_to_show = datetime.datetime.fromtimestamp(frame_time).strftime("%m/%d/%Y %H:%M:%S") time_to_show = datetime.datetime.fromtimestamp(frame_time).strftime("%m/%d/%Y %H:%M:%S")