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integrate object detection with motion detection

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blakeblackshear 2019-02-09 10:17:07 -06:00
parent 53c9a7368d
commit 80dbed8055
1 changed files with 34 additions and 26 deletions
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detect_objects.py
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@ -25,8 +25,9 @@ PATH_TO_LABELS = '/label_map.pbtext'
# TODO: make dynamic?
NUM_CLASSES = 90
REGIONS = "350,0,300:400,350,250:400,750,250"
#REGIONS = os.getenv('REGIONS')
# REGIONS = "350,0,300:400,350,250:400,750,250"
# REGIONS = "400,350,250"
REGIONS = os.getenv('REGIONS')
DETECTED_OBJECTS = []
@ -121,8 +122,6 @@ def main():
shared_memory_objects = []
for region in regions:
shared_memory_objects.append({
# create shared value for storing the time the frame was captured
'frame_time': mp.Value('d', 0.0),
# shared value for signaling to the capture process that we are ready for the next frame
# (1 for ready 0 for not ready)
'ready_for_frame': mp.Value('i', 1),
@ -139,17 +138,19 @@ def main():
flat_array_length = frame_shape[0] * frame_shape[1] * frame_shape[2]
# create shared array for storing the full frame image data
shared_arr = mp.Array(ctypes.c_uint16, flat_array_length)
# create shared value for storing the frame_time
shared_frame_time = mp.Value('d', 0.0)
# shape current frame so it can be treated as an image
frame_arr = tonumpyarray(shared_arr).reshape(frame_shape)
capture_process = mp.Process(target=fetch_frames, args=(shared_arr, [obj['frame_time'] for obj in shared_memory_objects], 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.daemon = True
detection_processes = []
for index, region in enumerate(regions):
detection_process = mp.Process(target=process_frames, args=(shared_arr,
shared_memory_objects[index]['output_array'],
shared_memory_objects[index]['frame_time'],
shared_frame_time,
shared_memory_objects[index]['motion_detected'],
frame_shape,
region['size'], region['x_offset'], region['y_offset']))
@ -158,8 +159,9 @@ def main():
motion_processes = []
for index, region in enumerate(regions):
motion_process = mp.Process(target=detect_motion, args=(shared_arr,
shared_memory_objects[index]['frame_time'],
motion_process = mp.Process(target=detect_motion, args=(shared_arr,
shared_frame_time,
shared_memory_objects[index]['ready_for_frame'],
shared_memory_objects[index]['motion_detected'],
frame_shape,
region['size'], region['x_offset'], region['y_offset']))
@ -197,7 +199,7 @@ def main():
# convert to RGB for drawing
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
# draw the bounding boxes on the screen
for obj in DETECTED_OBJECTS:
for obj in detected_objects:
vis_util.draw_bounding_box_on_image_array(frame,
obj['ymin'],
obj['xmin'],
@ -212,6 +214,12 @@ def main():
cv2.rectangle(frame, (region['x_offset'], region['y_offset']),
(region['x_offset']+region['size'], region['y_offset']+region['size']),
(255,255,255), 2)
motion_status = 'No Motion'
if any(obj['motion_detected'].value == 1 for obj in shared_memory_objects):
motion_status = 'Motion'
cv2.putText(frame, motion_status, (10, 20),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
# convert back to BGR
frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
# encode the image into a jpg
@ -234,7 +242,7 @@ def tonumpyarray(mp_arr):
# fetch the frames as fast a possible, only decoding the frames when the
# detection_process has consumed the current frame
def fetch_frames(shared_arr, shared_frame_times, frame_shape):
def fetch_frames(shared_arr, shared_frame_time, ready_for_frame_flags, frame_shape):
# convert shared memory array into numpy and shape into image array
arr = tonumpyarray(shared_arr).reshape(frame_shape)
@ -249,16 +257,17 @@ def fetch_frames(shared_arr, shared_frame_times, frame_shape):
# snapshot the time the frame was grabbed
frame_time = datetime.datetime.now()
if ret:
# if the detection_process is ready for the next frame decode it
# if the anyone is ready for the next frame decode it
# otherwise skip this frame and move onto the next one
if all(shared_frame_time.value == 0.0 for shared_frame_time in shared_frame_times):
if any(flag.value == 1 for flag in ready_for_frame_flags):
# go ahead and decode the current frame
ret, frame = video.retrieve()
if ret:
arr[:] = frame
shared_frame_time.value = frame_time.timestamp()
# signal to the detection_processes by setting the shared_frame_time
for shared_frame_time in shared_frame_times:
shared_frame_time.value = frame_time.timestamp()
for flag in ready_for_frame_flags:
flag.value = 0
else:
# sleep a little to reduce CPU usage
time.sleep(0.01)
@ -325,22 +334,22 @@ def process_frames(shared_arr, shared_output_arr, shared_frame_time, shared_moti
shared_output_arr[:] = objects + [0.0] * (60-len(objects))
# do the actual object detection
def detect_motion(shared_arr, shared_frame_time, shared_motion, frame_shape, region_size, region_x_offset, region_y_offset):
def detect_motion(shared_arr, shared_frame_time, ready_for_frame, shared_motion, frame_shape, region_size, region_x_offset, region_y_offset):
# shape shared input array into frame for processing
arr = tonumpyarray(shared_arr).reshape(frame_shape)
no_frames_available = -1
avg_frame = None
last_motion = -1
frame_time = 0.0
while True:
now = datetime.datetime.now().timestamp()
# if it has been 30 seconds since the last motion, clear the flag
if last_motion > 0 and (now - last_motion) > 30:
last_motion = -1
shared_motion.value = 0
print("motion cleared")
# if there isnt a frame ready for processing
if shared_frame_time.value == 0.0:
if shared_frame_time.value == frame_time:
# save the first time there were no frames available
if no_frames_available == -1:
no_frames_available = now
@ -352,6 +361,8 @@ def detect_motion(shared_arr, shared_frame_time, shared_motion, frame_shape, reg
else:
# rest a little bit to avoid maxing out the CPU
time.sleep(0.01)
if ready_for_frame.value == 0:
ready_for_frame.value = 1
continue
# we got a valid frame, so reset the timer
@ -360,21 +371,19 @@ def detect_motion(shared_arr, shared_frame_time, shared_motion, frame_shape, reg
# if the frame is more than 0.5 second old, discard it
if (now - shared_frame_time.value) > 0.5:
# signal that we need a new frame
shared_frame_time.value = 0.0
ready_for_frame.value = 1
# rest a little bit to avoid maxing out the CPU
time.sleep(0.01)
continue
# make a copy of the cropped frame
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().astype('uint8')
frame_time = shared_frame_time.value
# signal that the frame has been used so a new one will be ready
shared_frame_time.value = 0.0
ready_for_frame.value = 1
# convert to grayscale
gray = cv2.cvtColor(cropped_frame, cv2.COLOR_BGR2GRAY)
# convert to uint8
gray = (gray/256).astype('uint8')
# apply gaussian blur
gray = cv2.GaussianBlur(gray, (21, 21), 0)
@ -400,15 +409,14 @@ def detect_motion(shared_arr, shared_frame_time, shared_motion, frame_shape, reg
if cv2.contourArea(c) < 50:
continue
print("motion_detected")
last_motion = now
shared_motion.value = 1
# compute the bounding box for the contour, draw it on the frame,
# and update the text
(x, y, w, h) = cv2.boundingRect(c)
cv2.rectangle(cropped_frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
cv2.imwrite("motion%d.png" % frame_time, cropped_frame)
# (x, y, w, h) = cv2.boundingRect(c)
# cv2.rectangle(cropped_frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
# cv2.imwrite("motion%d.jpg" % frame_time, cropped_frame)
if __name__ == '__main__':
mp.freeze_support()
main()