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start the detection process

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This commit is contained in:
blakeblackshear 2019-03-18 07:48:04 -05:00
parent f2c205be99
commit 8ff9a982b6
2 changed files with 26 additions and 13 deletions
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16
detect_objects.py
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@ -108,6 +108,8 @@ def main():
detection_prep_processes = []
motion_processes = []
for region in regions:
# possibly try putting these on threads and putting prepped
# frames in a queue
detection_prep_process = mp.Process(target=prep_for_detection, args=(shared_arr,
shared_frame_time,
frame_lock, frame_ready,
@ -131,6 +133,14 @@ def main():
motion_process.daemon = True
motion_processes.append(motion_process)
# create a process for object detection
detection_process = mp.Process(target=detect_objects, args=(
prepped_frame_array, prepped_frame_time,
prepped_frame_lock, prepped_frame_ready,
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_motion_frames, motion_changed, [region['motion_detected'] for region in regions])
@ -176,11 +186,14 @@ def main():
capture_process.start()
print("capture_process pid ", capture_process.pid)
# start the object detection processes
# start the object detection prep processes
for detection_prep_process in detection_prep_processes:
detection_prep_process.start()
print("detection_prep_process pid ", detection_prep_process.pid)
detection_process.start()
print("detection_process pid ", detection_process.pid)
# start the motion detection processes
# for motion_process in motion_processes:
# motion_process.start()
@ -253,6 +266,7 @@ def main():
detection_prep_process.join()
for motion_process in motion_processes:
motion_process.join()
detection_process.join()
frame_tracker.join()
best_person_frame.join()
object_parser.join()

23
frigate/object_detection.py
View File

@ -21,32 +21,34 @@ def ReadLabelFile(file_path):
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)
# Load the edgetpu engine and labels
engine = DetectionEngine(PATH_TO_CKPT)
labels = ReadLabelFile(PATH_TO_LABELS)
prepped_frame_time = 0.0
frame_time = 0.0
region_box = [0,0,0,0]
while True:
with prepped_frame_ready:
prepped_frame_ready.wait()
# make a copy of the cropped frame
with prepped_frame_lock:
prepped_frame_copy = prepped_frame_array.copy()
prepped_frame_time = prepped_frame_time.value
region_box = prepped_frame_box.value
prepped_frame_copy = prepped_frame_np.copy()
frame_time = prepped_frame_time.value
region_box[:] = prepped_frame_box
# Actual detection.
ans = engine.DetectWithInputTensor(prepped_frame_copy, threshold=0.5, top_k=3)
objects = engine.DetectWithInputTensor(prepped_frame_copy, threshold=0.5, top_k=3)
# print(engine.get_inference_time())
# put detected objects in the queue
if ans:
if objects:
# assumes square
region_size = region_box[3]-region_box[0]
for obj in ans:
for obj in objects:
box = obj.bounding_box.flatten().tolist()
object_queue.append({
'frame_time': prepped_frame_time,
'frame_time': frame_time,
'name': str(labels[obj.label_id]),
'score': float(obj.score),
'xmin': int((box[0] * region_size) + region_box[0]),
@ -74,7 +76,6 @@ def prep_for_detection(shared_whole_frame_array, shared_frame_time, frame_lock,
with frame_ready:
# if there isnt a frame ready for processing or it is old, wait for a new frame
if shared_frame_time.value == frame_time or (now - shared_frame_time.value) > 0.5:
print("waiting...")
frame_ready.wait()
# make a copy of the cropped frame
@ -82,8 +83,6 @@ def prep_for_detection(shared_whole_frame_array, shared_frame_time, frame_lock,
cropped_frame = shared_whole_frame[region_y_offset:region_y_offset+region_size, region_x_offset:region_x_offset+region_size].copy()
frame_time = shared_frame_time.value
print("grabbed frame " + str(frame_time))
# convert to RGB
cropped_frame_rgb = cv2.cvtColor(cropped_frame, cv2.COLOR_BGR2RGB)
# Resize to 300x300 if needed