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looping over all regions with motion. ugly, but working

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blakeblackshear
2019-03-20 07:11:38 -05:00
parent c406fda288
commit 7d3027e056
2 changed files with 62 additions and 57 deletions
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85
frigate/object_detection.py
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@@ -19,48 +19,64 @@ def ReadLabelFile(file_path):
ret[int(pair[0])] = pair[1].strip()
return ret
def detect_objects(prepped_frame_array, prepped_frame_time, prepped_frame_lock,
prepped_frame_ready, prepped_frame_box, object_queue, debug):
prepped_frame_np = tonumpyarray(prepped_frame_array)
def detect_objects(prepped_frame_arrays, prepped_frame_times, prepped_frame_locks,
prepped_frame_boxes, motion_changed, motion_regions, object_queue, debug):
prepped_frame_nps = [tonumpyarray(prepped_frame_array) for prepped_frame_array in prepped_frame_arrays]
# Load the edgetpu engine and labels
engine = DetectionEngine(PATH_TO_CKPT)
labels = ReadLabelFile(PATH_TO_LABELS)
frame_time = 0.0
region_box = [0,0,0,0]
region_box = [0,0,0]
while True:
with prepped_frame_ready:
prepped_frame_ready.wait()
# while there is motion
while len([r for r in motion_regions if r.is_set()]) > 0:
# make a copy of the cropped frame
with prepped_frame_lock:
prepped_frame_copy = prepped_frame_np.copy()
frame_time = prepped_frame_time.value
region_box[:] = prepped_frame_box
# loop over all the motion regions and look for objects
for i, motion_region in enumerate(motion_regions):
# skip the region if no motion
if not motion_region.is_set():
continue
# Actual detection.
objects = engine.DetectWithInputTensor(prepped_frame_copy, threshold=0.5, top_k=3)
# print(engine.get_inference_time())
# put detected objects in the queue
if objects:
# assumes square
region_size = region_box[2]-region_box[0]
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_size) + region_box[0]),
'ymin': int((box[1] * region_size) + region_box[1]),
'xmax': int((box[2] * region_size) + region_box[0]),
'ymax': int((box[3] * region_size) + region_box[1])
})
# make a copy of the cropped frame
with prepped_frame_locks[i]:
prepped_frame_copy = prepped_frame_nps[i].copy()
frame_time = prepped_frame_times[i].value
region_box[:] = prepped_frame_boxes[i]
# Actual detection.
objects = engine.DetectWithInputTensor(prepped_frame_copy, threshold=0.5, top_k=3)
# 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])
})
# wait for the global motion flag to change
with motion_changed:
motion_changed.wait()
def prep_for_detection(shared_whole_frame_array, shared_frame_time, frame_lock, frame_ready,
motion_detected, frame_shape, region_size, region_x_offset, region_y_offset,
prepped_frame_array, prepped_frame_time, prepped_frame_ready, prepped_frame_lock,
prepped_frame_box):
prepped_frame_array, prepped_frame_time, prepped_frame_lock):
# shape shared input array into frame for processing
shared_whole_frame = tonumpyarray(shared_whole_frame_array).reshape(frame_shape)
@@ -94,9 +110,4 @@ def prep_for_detection(shared_whole_frame_array, shared_frame_time, frame_lock,
# copy the prepped frame to the shared output array
with prepped_frame_lock:
shared_prepped_frame[:] = frame_expanded
prepped_frame_time = frame_time
prepped_frame_box[:] = [region_x_offset, region_y_offset, region_x_offset+region_size, region_y_offset+region_size]
# signal that a prepped frame is ready
with prepped_frame_ready:
prepped_frame_ready.notify_all()
prepped_frame_time.value = frame_time