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https://github.com/blakeblackshear/frigate.git
synced 2024-11-21 19:07:46 +01:00
looping over all regions with motion. ugly, but working
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blakeblackshear
parent
c406fda288
commit
7d3027e056
@ -52,11 +52,12 @@ def main():
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'mask': region_mask,
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# Event for motion detection signaling
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'motion_detected': mp.Event(),
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# create shared array for storing 10 detected objects
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# note: this must be a double even though the value you are storing
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# is a float. otherwise it stops updating the value in shared
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# memory. probably something to do with the size of the memory block
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'output_array': mp.Array(ctypes.c_double, 6*10)
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# array for prepped frame with shape (1, 300, 300, 3)
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'prepped_frame_array': mp.Array(ctypes.c_uint8, 300*300*3),
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# shared value for storing the prepped_frame_time
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'prepped_frame_time': mp.Value('d', 0.0),
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# Lock to control access to the prepped frame
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'prepped_frame_lock': mp.Lock()
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})
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# capture a single frame and check the frame shape so the correct array
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# size can be allocated in memory
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@ -85,16 +86,6 @@ def main():
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objects_parsed = mp.Condition()
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# Queue for detected objects
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object_queue = mp.Queue()
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# array for prepped frame with shape (1, 300, 300, 3)
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prepped_frame_array = mp.Array(ctypes.c_uint8, 300*300*3)
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# shared value for storing the prepped_frame_time
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prepped_frame_time = mp.Value('d', 0.0)
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# Condition for notifying that a new prepped frame is ready
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prepped_frame_ready = mp.Condition()
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# Lock to control access to the prepped frame
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prepped_frame_lock = mp.Lock()
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# array for prepped frame box [x1, y1, x2, y2]
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prepped_frame_box = mp.Array(ctypes.c_uint16, 4)
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# shape current frame so it can be treated as an image
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frame_arr = tonumpyarray(shared_arr).reshape(frame_shape)
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@ -116,8 +107,8 @@ def main():
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region['motion_detected'],
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frame_shape,
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region['size'], region['x_offset'], region['y_offset'],
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prepped_frame_array, prepped_frame_time, prepped_frame_ready,
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prepped_frame_lock, prepped_frame_box))
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region['prepped_frame_array'], region['prepped_frame_time'],
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region['prepped_frame_lock']))
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detection_prep_process.daemon = True
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detection_prep_processes.append(detection_prep_process)
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@ -135,9 +126,12 @@ def main():
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# create a process for object detection
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detection_process = mp.Process(target=detect_objects, args=(
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prepped_frame_array, prepped_frame_time,
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prepped_frame_lock, prepped_frame_ready,
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prepped_frame_box, object_queue, DEBUG
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[region['prepped_frame_array'] for region in regions],
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[region['prepped_frame_time'] for region in regions],
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[region['prepped_frame_lock'] for region in regions],
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[[region['size'], region['x_offset'], region['y_offset']] for region in regions],
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motion_changed, [region['motion_detected'] for region in regions],
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object_queue, DEBUG
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))
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detection_process.daemon = True
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@ -19,48 +19,64 @@ def ReadLabelFile(file_path):
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ret[int(pair[0])] = pair[1].strip()
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return ret
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def detect_objects(prepped_frame_array, prepped_frame_time, prepped_frame_lock,
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prepped_frame_ready, prepped_frame_box, object_queue, debug):
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prepped_frame_np = tonumpyarray(prepped_frame_array)
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def detect_objects(prepped_frame_arrays, prepped_frame_times, prepped_frame_locks,
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prepped_frame_boxes, motion_changed, motion_regions, object_queue, debug):
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prepped_frame_nps = [tonumpyarray(prepped_frame_array) for prepped_frame_array in prepped_frame_arrays]
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# Load the edgetpu engine and labels
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engine = DetectionEngine(PATH_TO_CKPT)
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labels = ReadLabelFile(PATH_TO_LABELS)
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frame_time = 0.0
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region_box = [0,0,0,0]
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region_box = [0,0,0]
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while True:
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with prepped_frame_ready:
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prepped_frame_ready.wait()
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# while there is motion
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while len([r for r in motion_regions if r.is_set()]) > 0:
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# make a copy of the cropped frame
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with prepped_frame_lock:
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prepped_frame_copy = prepped_frame_np.copy()
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frame_time = prepped_frame_time.value
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region_box[:] = prepped_frame_box
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# loop over all the motion regions and look for objects
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for i, motion_region in enumerate(motion_regions):
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# skip the region if no motion
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if not motion_region.is_set():
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continue
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# Actual detection.
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objects = engine.DetectWithInputTensor(prepped_frame_copy, threshold=0.5, top_k=3)
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# print(engine.get_inference_time())
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# put detected objects in the queue
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if objects:
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# assumes square
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region_size = region_box[2]-region_box[0]
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for obj in objects:
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box = obj.bounding_box.flatten().tolist()
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object_queue.put({
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'frame_time': frame_time,
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'name': str(labels[obj.label_id]),
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'score': float(obj.score),
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'xmin': int((box[0] * region_size) + region_box[0]),
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'ymin': int((box[1] * region_size) + region_box[1]),
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'xmax': int((box[2] * region_size) + region_box[0]),
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'ymax': int((box[3] * region_size) + region_box[1])
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})
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# make a copy of the cropped frame
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with prepped_frame_locks[i]:
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prepped_frame_copy = prepped_frame_nps[i].copy()
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frame_time = prepped_frame_times[i].value
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region_box[:] = prepped_frame_boxes[i]
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# Actual detection.
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objects = engine.DetectWithInputTensor(prepped_frame_copy, threshold=0.5, top_k=3)
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# print(engine.get_inference_time())
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# put detected objects in the queue
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if objects:
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for obj in objects:
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box = obj.bounding_box.flatten().tolist()
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object_queue.put({
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'frame_time': frame_time,
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'name': str(labels[obj.label_id]),
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'score': float(obj.score),
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'xmin': int((box[0] * region_box[0]) + region_box[1]),
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'ymin': int((box[1] * region_box[0]) + region_box[2]),
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'xmax': int((box[2] * region_box[0]) + region_box[1]),
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'ymax': int((box[3] * region_box[0]) + region_box[2])
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})
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else:
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object_queue.put({
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'frame_time': frame_time,
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'name': 'dummy',
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'score': 0.99,
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'xmin': int(0 + region_box[1]),
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'ymin': int(0 + region_box[2]),
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'xmax': int(10 + region_box[1]),
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'ymax': int(10 + region_box[2])
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})
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# wait for the global motion flag to change
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with motion_changed:
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motion_changed.wait()
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def prep_for_detection(shared_whole_frame_array, shared_frame_time, frame_lock, frame_ready,
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motion_detected, frame_shape, region_size, region_x_offset, region_y_offset,
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prepped_frame_array, prepped_frame_time, prepped_frame_ready, prepped_frame_lock,
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prepped_frame_box):
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prepped_frame_array, prepped_frame_time, prepped_frame_lock):
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# shape shared input array into frame for processing
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shared_whole_frame = tonumpyarray(shared_whole_frame_array).reshape(frame_shape)
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@ -94,9 +110,4 @@ def prep_for_detection(shared_whole_frame_array, shared_frame_time, frame_lock,
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# copy the prepped frame to the shared output array
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with prepped_frame_lock:
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shared_prepped_frame[:] = frame_expanded
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prepped_frame_time = frame_time
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prepped_frame_box[:] = [region_x_offset, region_y_offset, region_x_offset+region_size, region_y_offset+region_size]
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# signal that a prepped frame is ready
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with prepped_frame_ready:
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prepped_frame_ready.notify_all()
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prepped_frame_time.value = frame_time
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