From 0514eeac03fd520bab885db541158656f90ecf44 Mon Sep 17 00:00:00 2001 From: blakeblackshear Date: Wed, 27 Mar 2019 20:44:57 -0500 Subject: [PATCH] switch to a thread for object detection --- detect_objects.py | 37 ++----------- frigate/object_detection.py | 101 ++++++++++-------------------------- 2 files changed, 29 insertions(+), 109 deletions(-) diff --git a/detect_objects.py b/detect_objects.py index cd1da04cf..121fd9002 100644 --- a/detect_objects.py +++ b/detect_objects.py @@ -75,22 +75,12 @@ def main(): frame_lock = mp.Lock() # Condition for notifying that a new frame is ready frame_ready = mp.Condition() - # Shared memory array for passing prepped frame to tensorflow - prepped_frame_array = mp.Array(ctypes.c_uint8, 300*300*3) - # create shared value for storing the frame_time - prepped_frame_time = mp.Value('d', 0.0) - # Event for notifying that object detection needs a new frame - prepped_frame_grabbed = mp.Event() - # Event for notifying that new frame is ready for detection - prepped_frame_ready = mp.Event() # Condition for notifying that objects were parsed objects_parsed = mp.Condition() # Queue for detected objects - object_queue = mp.Queue() + object_queue = queue.Queue() # Queue for prepped frames prepped_frame_queue = queue.Queue(len(regions)*2) - # Array for passing original region box to compute object bounding box - prepped_frame_box = mp.Array(ctypes.c_uint16, 3) # shape current frame so it can be treated as an image frame_arr = tonumpyarray(shared_arr).reshape(frame_shape) @@ -113,28 +103,11 @@ def main(): )) prepped_queue_processor = PreppedQueueProcessor( - prepped_frame_array, - prepped_frame_time, - prepped_frame_ready, - prepped_frame_grabbed, - prepped_frame_box, - prepped_frame_queue + prepped_frame_queue, + object_queue ) prepped_queue_processor.start() - # create a process for object detection - # if the coprocessor is doing the work, can this run as a thread - # since it is waiting for IO? - detection_process = mp.Process(target=detect_objects, args=( - prepped_frame_array, - prepped_frame_time, - prepped_frame_ready, - prepped_frame_grabbed, - prepped_frame_box, - object_queue, DEBUG - )) - detection_process.daemon = True - # start a thread to store recent motion frames for processing frame_tracker = FrameTracker(frame_arr, shared_frame_time, frame_ready, frame_lock, recent_frames) @@ -176,9 +149,6 @@ def main(): # start the object detection prep threads for detection_prep_thread in detection_prep_threads: detection_prep_thread.start() - - detection_process.start() - print("detection_process pid ", detection_process.pid) # create a flask app that encodes frames a mjpeg on demand app = Flask(__name__) @@ -237,7 +207,6 @@ def main(): capture_process.join() for detection_prep_thread in detection_prep_threads: detection_prep_thread.join() - detection_process.join() frame_tracker.join() best_person_frame.join() object_parser.join() diff --git a/frigate/object_detection.py b/frigate/object_detection.py index 00f1360a6..d4a9c14e8 100644 --- a/frigate/object_detection.py +++ b/frigate/object_detection.py @@ -21,89 +21,40 @@ def ReadLabelFile(file_path): ret[int(pair[0])] = pair[1].strip() return ret -def detect_objects(prepped_frame_array, prepped_frame_time, - prepped_frame_ready, prepped_frame_grabbed, - prepped_frame_box, object_queue, debug): - prepped_frame_np = tonumpyarray(prepped_frame_array) - - # Load the edgetpu engine and labels - engine = DetectionEngine(PATH_TO_CKPT) - labels = ReadLabelFile(PATH_TO_LABELS) - - frame_time = 0.0 - region_box = [0,0,0] - while True: - # wait until a frame is ready - prepped_frame_ready.wait() - - prepped_frame_copy = prepped_frame_np.copy() - frame_time = prepped_frame_time.value - region_box[:] = prepped_frame_box - - prepped_frame_grabbed.set() - # print("Grabbed " + str(region_box[1]) + "," + str(region_box[2])) - - # Actual detection. - objects = engine.DetectWithInputTensor(prepped_frame_copy, threshold=0.5, top_k=3) - # time.sleep(0.1) - # objects = [] - # print(engine.get_inference_time()) - # put detected objects in the queue - if objects: - for obj in objects: - box = obj.bounding_box.flatten().tolist() - object_queue.put({ - 'frame_time': frame_time, - 'name': str(labels[obj.label_id]), - 'score': float(obj.score), - 'xmin': int((box[0] * region_box[0]) + region_box[1]), - 'ymin': int((box[1] * region_box[0]) + region_box[2]), - 'xmax': int((box[2] * region_box[0]) + region_box[1]), - 'ymax': int((box[3] * region_box[0]) + region_box[2]) - }) - # else: - # object_queue.put({ - # 'frame_time': frame_time, - # 'name': 'dummy', - # 'score': 0.99, - # 'xmin': int(0 + region_box[1]), - # 'ymin': int(0 + region_box[2]), - # 'xmax': int(10 + region_box[1]), - # 'ymax': int(10 + region_box[2]) - # }) - class PreppedQueueProcessor(threading.Thread): - def __init__(self, prepped_frame_array, - prepped_frame_time, - prepped_frame_ready, - prepped_frame_grabbed, - prepped_frame_box, - prepped_frame_queue): + def __init__(self, prepped_frame_queue, object_queue): threading.Thread.__init__(self) - self.prepped_frame_array = prepped_frame_array - self.prepped_frame_time = prepped_frame_time - self.prepped_frame_ready = prepped_frame_ready - self.prepped_frame_grabbed = prepped_frame_grabbed - self.prepped_frame_box = prepped_frame_box self.prepped_frame_queue = prepped_frame_queue + self.object_queue = object_queue + + # Load the edgetpu engine and labels + self.engine = DetectionEngine(PATH_TO_CKPT) + self.labels = ReadLabelFile(PATH_TO_LABELS) def run(self): - prepped_frame_np = tonumpyarray(self.prepped_frame_array) # process queue... while True: frame = self.prepped_frame_queue.get() # print(self.prepped_frame_queue.qsize()) - prepped_frame_np[:] = frame['frame'] - self.prepped_frame_time.value = frame['frame_time'] - self.prepped_frame_box[0] = frame['region_size'] - self.prepped_frame_box[1] = frame['region_x_offset'] - self.prepped_frame_box[2] = frame['region_y_offset'] - # print("Passed " + str(frame['region_x_offset']) + "," + str(frame['region_x_offset'])) - self.prepped_frame_ready.set() - self.prepped_frame_grabbed.wait() - self.prepped_frame_grabbed.clear() - self.prepped_frame_ready.clear() + # Actual detection. + objects = self.engine.DetectWithInputTensor(frame['frame'], threshold=0.5, top_k=3) + # time.sleep(0.1) + # objects = [] + # print(engine.get_inference_time()) + # put detected objects in the queue + if objects: + for obj in objects: + box = obj.bounding_box.flatten().tolist() + self.object_queue.put({ + 'frame_time': frame['frame_time'], + 'name': str(self.labels[obj.label_id]), + 'score': float(obj.score), + 'xmin': int((box[0] * frame['region_size']) + frame['region_x_offset']), + 'ymin': int((box[1] * frame['region_size']) + frame['region_y_offset']), + 'xmax': int((box[2] * frame['region_size']) + frame['region_x_offset']), + 'ymax': int((box[3] * frame['region_size']) + frame['region_y_offset']) + }) # should this be a region class? @@ -156,5 +107,5 @@ class FramePrepper(threading.Thread): 'region_x_offset': self.region_x_offset, 'region_y_offset': self.region_y_offset }) - # else: - # print("queue full. moving on") + else: + print("queue full. moving on")