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merged shared memory objects and regions and set color of bounding box based on motion

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blakeblackshear 2019-02-10 14:25:17 -06:00
parent 30c223711d
commit f3376a83d6
1 changed files with 25 additions and 29 deletions
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54
detect_objects.py
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@ -157,22 +157,7 @@ def main():
'size': int(region_parts[0]), 'size': int(region_parts[0]),
'x_offset': int(region_parts[1]), 'x_offset': int(region_parts[1]),
'y_offset': int(region_parts[2]), 'y_offset': int(region_parts[2]),
'min_object_size': int(region_parts[3]) 'min_object_size': int(region_parts[3]),
})
# capture a single frame and check the frame shape so the correct array
# size can be allocated in memory
video = cv2.VideoCapture(RTSP_URL)
ret, frame = video.read()
if ret:
frame_shape = frame.shape
else:
print("Unable to capture video stream")
exit(1)
video.release()
shared_memory_objects = []
for region in regions:
shared_memory_objects.append({
# shared value for signaling to the capture process that we are ready for the next frame # shared value for signaling to the capture process that we are ready for the next frame
# (1 for ready 0 for not ready) # (1 for ready 0 for not ready)
'ready_for_frame': mp.Value('i', 1), 'ready_for_frame': mp.Value('i', 1),
@ -184,6 +169,16 @@ def main():
# memory. probably something to do with the size of the memory block # memory. probably something to do with the size of the memory block
'output_array': mp.Array(ctypes.c_double, 6*10) 'output_array': mp.Array(ctypes.c_double, 6*10)
}) })
# capture a single frame and check the frame shape so the correct array
# size can be allocated in memory
video = cv2.VideoCapture(RTSP_URL)
ret, frame = video.read()
if ret:
frame_shape = frame.shape
else:
print("Unable to capture video stream")
exit(1)
video.release()
# compute the flattened array length from the array shape # compute the flattened array length from the array shape
flat_array_length = frame_shape[0] * frame_shape[1] * frame_shape[2] flat_array_length = frame_shape[0] * frame_shape[1] * frame_shape[2]
@ -194,15 +189,16 @@ def main():
# shape current frame so it can be treated as an image # shape current frame so it can be treated as an image
frame_arr = tonumpyarray(shared_arr).reshape(frame_shape) frame_arr = tonumpyarray(shared_arr).reshape(frame_shape)
capture_process = mp.Process(target=fetch_frames, args=(shared_arr, shared_frame_time, [obj['ready_for_frame'] for obj in shared_memory_objects], frame_shape)) capture_process = mp.Process(target=fetch_frames, args=(shared_arr,
shared_frame_time, [region['ready_for_frame'] for region in regions], frame_shape))
capture_process.daemon = True capture_process.daemon = True
detection_processes = [] detection_processes = []
for index, region in enumerate(regions): for index, region in enumerate(regions):
detection_process = mp.Process(target=process_frames, args=(shared_arr, detection_process = mp.Process(target=process_frames, args=(shared_arr,
shared_memory_objects[index]['output_array'], region['output_array'],
shared_frame_time, shared_frame_time,
shared_memory_objects[index]['motion_detected'], region['motion_detected'],
frame_shape, frame_shape,
region['size'], region['x_offset'], region['y_offset'])) region['size'], region['x_offset'], region['y_offset']))
detection_process.daemon = True detection_process.daemon = True
@ -212,20 +208,20 @@ def main():
for index, region in enumerate(regions): for index, region in enumerate(regions):
motion_process = mp.Process(target=detect_motion, args=(shared_arr, motion_process = mp.Process(target=detect_motion, args=(shared_arr,
shared_frame_time, shared_frame_time,
shared_memory_objects[index]['ready_for_frame'], region['ready_for_frame'],
shared_memory_objects[index]['motion_detected'], region['motion_detected'],
frame_shape, frame_shape,
region['size'], region['x_offset'], region['y_offset'], region['size'], region['x_offset'], region['y_offset'],
region['min_object_size'])) region['min_object_size']))
motion_process.daemon = True motion_process.daemon = True
motion_processes.append(motion_process) motion_processes.append(motion_process)
object_parser = ObjectParser([obj['output_array'] for obj in shared_memory_objects]) object_parser = ObjectParser([region['output_array'] for region in regions])
object_parser.start() object_parser.start()
mqtt_publisher = MqttPublisher(MQTT_HOST, MQTT_MOTION_TOPIC, MQTT_OBJECT_TOPIC, mqtt_publisher = MqttPublisher(MQTT_HOST, MQTT_MOTION_TOPIC, MQTT_OBJECT_TOPIC,
MQTT_OBJECT_CLASSES.split(','), MQTT_OBJECT_CLASSES.split(','),
[obj['motion_detected'] for obj in shared_memory_objects]) [region['motion_detected'] for region in regions])
mqtt_publisher.start() mqtt_publisher.start()
capture_process.start() capture_process.start()
@ -268,15 +264,15 @@ def main():
use_normalized_coordinates=False) use_normalized_coordinates=False)
for region in regions: for region in regions:
color = (255,255,255)
if region['motion_detected'].value == 1:
color = (0,255,0)
cv2.rectangle(frame, (region['x_offset'], region['y_offset']), cv2.rectangle(frame, (region['x_offset'], region['y_offset']),
(region['x_offset']+region['size'], region['y_offset']+region['size']), (region['x_offset']+region['size'], region['y_offset']+region['size']),
(255,255,255), 2) color, 2)
motion_status = 'No Motion' cv2.putText(frame, datetime.datetime.now().strftime("%H:%M:%S"), (1125, 20),
if any(obj['motion_detected'].value == 1 for obj in shared_memory_objects): cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 0, 255), 2)
motion_status = 'Motion'
cv2.putText(frame, motion_status, (10, 20),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
# convert back to BGR # convert back to BGR
frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR) frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
# encode the image into a jpg # encode the image into a jpg