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use events to signal when motion is detected

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blakeblackshear 2019-02-17 12:12:48 -06:00
parent 8525f05f29
commit 957bd2adeb
1 changed files with 28 additions and 55 deletions
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69
detect_objects.py
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@ -153,7 +153,7 @@ class MqttPublisher(threading.Thread):
# send message for motion # send message for motion
motion_status = 'OFF' motion_status = 'OFF'
if any(obj.value == 1 for obj in self.motion_flags): if any(obj.is_set() for obj in self.motion_flags):
motion_status = 'ON' motion_status = 'ON'
if motion_status != last_motion: if motion_status != last_motion:
@ -172,11 +172,8 @@ def main():
'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]),
# shared value for signaling to the capture process that we are ready for the next frame # Event for motion detection signaling
# (1 for ready 0 for not ready) 'motion_detected': mp.Event(),
'ready_for_frame': mp.Value('i', 1),
# shared value for motion detection signal (1 for motion 0 for no motion)
'motion_detected': mp.Value('i', 0),
# create shared array for storing 10 detected objects # create shared array for storing 10 detected objects
# note: this must be a double even though the value you are storing # note: this must be a double even though the value you are storing
# is a float. otherwise it stops updating the value in shared # is a float. otherwise it stops updating the value in shared
@ -212,18 +209,18 @@ def main():
capture_process.daemon = True capture_process.daemon = True
detection_processes = [] detection_processes = []
for index, region in enumerate(regions): motion_processes = []
for region in regions:
detection_process = mp.Process(target=process_frames, args=(shared_arr, detection_process = mp.Process(target=process_frames, args=(shared_arr,
region['output_array'], region['output_array'],
shared_frame_time, shared_frame_time,
frame_lock, frame_ready,
region['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
detection_processes.append(detection_process) detection_processes.append(detection_process)
motion_processes = []
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,
frame_lock, frame_ready, frame_lock, frame_ready,
@ -267,9 +264,8 @@ def main():
# make a copy of the current detected objects # make a copy of the current detected objects
detected_objects = DETECTED_OBJECTS.copy() detected_objects = DETECTED_OBJECTS.copy()
# lock and make a copy of the current frame # lock and make a copy of the current frame
frame_lock.aquire() with frame_lock:
frame = frame_arr.copy() frame = frame_arr.copy()
frame_lock.release()
# convert to RGB for drawing # convert to RGB for drawing
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
# draw the bounding boxes on the screen # draw the bounding boxes on the screen
@ -286,7 +282,7 @@ def main():
for region in regions: for region in regions:
color = (255,255,255) color = (255,255,255)
if region['motion_detected'].value == 1: if region['motion_detected'].is_set():
color = (0,255,0) 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']),
@ -336,10 +332,9 @@ def fetch_frames(shared_arr, shared_frame_time, frame_lock, frame_ready, frame_s
ret, frame = video.retrieve() ret, frame = video.retrieve()
if ret: if ret:
# Lock access and update frame # Lock access and update frame
frame_lock.acquire() with frame_lock:
arr[:] = frame arr[:] = frame
shared_frame_time.value = frame_time.timestamp() shared_frame_time.value = frame_time.timestamp()
frame_lock.release()
# Notify with the condition that a new frame is ready # Notify with the condition that a new frame is ready
with frame_ready: with frame_ready:
frame_ready.notify_all() frame_ready.notify_all()
@ -347,7 +342,7 @@ def fetch_frames(shared_arr, shared_frame_time, frame_lock, frame_ready, frame_s
video.release() video.release()
# do the actual object detection # do the actual object detection
def process_frames(shared_arr, shared_output_arr, shared_frame_time, shared_motion, frame_shape, region_size, region_x_offset, region_y_offset): def process_frames(shared_arr, shared_output_arr, shared_frame_time, frame_lock, frame_ready, motion_detected, frame_shape, region_size, region_x_offset, region_y_offset):
debug = True debug = True
# shape shared input array into frame for processing # shape shared input array into frame for processing
arr = tonumpyarray(shared_arr).reshape(frame_shape) arr = tonumpyarray(shared_arr).reshape(frame_shape)
@ -362,40 +357,20 @@ def process_frames(shared_arr, shared_output_arr, shared_frame_time, shared_moti
tf.import_graph_def(od_graph_def, name='') tf.import_graph_def(od_graph_def, name='')
sess = tf.Session(graph=detection_graph) sess = tf.Session(graph=detection_graph)
no_frames_available = -1
frame_time = 0.0 frame_time = 0.0
while True: while True:
now = datetime.datetime.now().timestamp() now = datetime.datetime.now().timestamp()
# if there is no motion detected
if shared_motion.value == 0:
time.sleep(0.1)
continue
# if there isnt a new frame ready for processing # wait until motion is detected
if shared_frame_time.value == frame_time: motion_detected.wait()
# save the first time there were no frames available
if no_frames_available == -1:
no_frames_available = now
# if there havent been any frames available in 30 seconds,
# sleep to avoid using so much cpu if the camera feed is down
if no_frames_available > 0 and (now - no_frames_available) > 30:
time.sleep(1)
print("sleeping because no frames have been available in a while")
else:
# rest a little bit to avoid maxing out the CPU
time.sleep(0.1)
continue
# we got a valid frame, so reset the timer with frame_ready:
no_frames_available = -1 # if there isnt a frame ready for processing or it is old, wait for a signal
if shared_frame_time.value == frame_time or (now - shared_frame_time.value) > 0.5:
# if the frame is more than 0.5 second old, ignore it frame_ready.wait()
if (now - shared_frame_time.value) > 0.5:
# rest a little bit to avoid maxing out the CPU
time.sleep(0.1)
continue
# make a copy of the cropped frame # make a copy of the cropped frame
with frame_lock:
cropped_frame = arr[region_y_offset:region_y_offset+region_size, region_x_offset:region_x_offset+region_size].copy() cropped_frame = arr[region_y_offset:region_y_offset+region_size, region_x_offset:region_x_offset+region_size].copy()
frame_time = shared_frame_time.value frame_time = shared_frame_time.value
@ -407,11 +382,10 @@ def process_frames(shared_arr, shared_output_arr, shared_frame_time, shared_moti
shared_output_arr[:] = objects + [0.0] * (60-len(objects)) shared_output_arr[:] = objects + [0.0] * (60-len(objects))
# do the actual motion detection # do the actual motion detection
def detect_motion(shared_arr, shared_frame_time, frame_lock, frame_ready, shared_motion, frame_shape, region_size, region_x_offset, region_y_offset, min_motion_area, debug): def detect_motion(shared_arr, shared_frame_time, frame_lock, frame_ready, motion_detected, frame_shape, region_size, region_x_offset, region_y_offset, min_motion_area, debug):
# shape shared input array into frame for processing # shape shared input array into frame for processing
arr = tonumpyarray(shared_arr).reshape(frame_shape) arr = tonumpyarray(shared_arr).reshape(frame_shape)
no_frames_available = -1
avg_frame = None avg_frame = None
last_motion = -1 last_motion = -1
frame_time = 0.0 frame_time = 0.0
@ -421,7 +395,7 @@ def detect_motion(shared_arr, shared_frame_time, frame_lock, frame_ready, shared
# if it has been long enough since the last motion, clear the flag # if it has been long enough since the last motion, clear the flag
if last_motion > 0 and (now - last_motion) > 2: if last_motion > 0 and (now - last_motion) > 2:
last_motion = -1 last_motion = -1
shared_motion.value = 0 motion_detected.clear()
with frame_ready: with frame_ready:
# if there isnt a frame ready for processing or it is old, wait for a signal # if there isnt a frame ready for processing or it is old, wait for a signal
@ -429,10 +403,9 @@ def detect_motion(shared_arr, shared_frame_time, frame_lock, frame_ready, shared
frame_ready.wait() frame_ready.wait()
# lock and make a copy of the cropped frame # lock and make a copy of the cropped frame
frame_lock.acquire() with frame_lock:
cropped_frame = arr[region_y_offset:region_y_offset+region_size, region_x_offset:region_x_offset+region_size].copy().astype('uint8') cropped_frame = arr[region_y_offset:region_y_offset+region_size, region_x_offset:region_x_offset+region_size].copy().astype('uint8')
frame_time = shared_frame_time.value frame_time = shared_frame_time.value
frame_lock.release()
# convert to grayscale # convert to grayscale
gray = cv2.cvtColor(cropped_frame, cv2.COLOR_BGR2GRAY) gray = cv2.cvtColor(cropped_frame, cv2.COLOR_BGR2GRAY)
@ -480,7 +453,7 @@ def detect_motion(shared_arr, shared_frame_time, frame_lock, frame_ready, shared
motion_frames += 1 motion_frames += 1
# if there have been enough consecutive motion frames, report motion # if there have been enough consecutive motion frames, report motion
if motion_frames >= 3: if motion_frames >= 3:
shared_motion.value = 1 motion_detected.set()
last_motion = now last_motion = now
else: else:
motion_frames = 0 motion_frames = 0