def diffs(self, other):
diff = cv2.absdiff(self.processed, other.processed)
_, diff = cv2.threshold(diff, int(config.get("MIN_CHANGE_THRESHOLD")), 255, cv2.THRESH_BINARY)
# dilate to join broken contours
diff = cv2.dilate(diff, None, iterations=2)
(_, contours, _) = cv2.findContours(diff, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
return [cv2.boundingRect(c) for c in contours if cv2.contourArea(c) >= int(config.get("MIN_CHANGE_AREA"))]
def process(image):
# Discard colour information; irrelevant to motion detection
processed = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# Remove noise
blur_kernel = (int(config.get("BLUR_KERNEL_WIDTH")), int(config.get("BLUR_KERNEL_HEIGHT")))
processed = cv2.GaussianBlur(processed, blur_kernel, 0)
return processed
def finalise(self, name):
print 'finalising recording'
input_paths = sorted(glob(path.join(self.output_dir, '*')))
output_path = path.join(tempfile.gettempdir(), name + '.gif')
args = ['convert',
'-delay', str(int(100.0 / int(config.get('OUTPUT_FPS')))),
'-loop', '0']
args += input_paths
args += [output_path]
exit_status = subprocess.call(args)
if exit_status == 0:
print 'wrote %s' % output_path
shutil.rmtree(self.output_dir)
return output_path
else:
print 'error writing %s' % output_path
def exceeds_max_duration(self, frame):
return self.first_write_time and \
frame.time - self.first_write_time > int(config.get('MAX_RECORDING_SECONDS'))
def exceeds_max_fps(self, frame):
return self.last_write_time and \
frame.time - self.last_write_time < 1.0 / int(config.get('OUTPUT_FPS'))
def downscale(image):
orig_h, orig_w = image.shape[:2]
width = int(config.get("FRAME_WIDTH"))
ratio = width / float(orig_w)
height = int(orig_h * ratio)
return cv2.resize(image, (width, height), interpolation=cv2.INTER_AREA)
