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formatting cleanup

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This commit is contained in:
Blake Blackshear
2021-02-17 07:23:32 -06:00
parent b8f72a5bcb
commit 39ff49e054
23 changed files with 2621 additions and 1736 deletions
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52
frigate/motion.py
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@@ -4,26 +4,37 @@ import numpy as np
from frigate.config import MotionConfig
class MotionDetector():
class MotionDetector:
def __init__(self, frame_shape, config: MotionConfig):
self.config = config
self.frame_shape = frame_shape
self.resize_factor = frame_shape[0]/config.frame_height
self.motion_frame_size = (config.frame_height, config.frame_height*frame_shape[1]//frame_shape[0])
self.resize_factor = frame_shape[0] / config.frame_height
self.motion_frame_size = (
config.frame_height,
config.frame_height * frame_shape[1] // frame_shape[0],
)
self.avg_frame = np.zeros(self.motion_frame_size, np.float)
self.avg_delta = np.zeros(self.motion_frame_size, np.float)
self.motion_frame_count = 0
self.frame_counter = 0
resized_mask = cv2.resize(config.mask, dsize=(self.motion_frame_size[1], self.motion_frame_size[0]), interpolation=cv2.INTER_LINEAR)
self.mask = np.where(resized_mask==[0])
resized_mask = cv2.resize(
config.mask,
dsize=(self.motion_frame_size[1], self.motion_frame_size[0]),
interpolation=cv2.INTER_LINEAR,
)
self.mask = np.where(resized_mask == [0])
def detect(self, frame):
motion_boxes = []
gray = frame[0:self.frame_shape[0], 0:self.frame_shape[1]]
gray = frame[0 : self.frame_shape[0], 0 : self.frame_shape[1]]
# resize frame
resized_frame = cv2.resize(gray, dsize=(self.motion_frame_size[1], self.motion_frame_size[0]), interpolation=cv2.INTER_LINEAR)
resized_frame = cv2.resize(
gray,
dsize=(self.motion_frame_size[1], self.motion_frame_size[0]),
interpolation=cv2.INTER_LINEAR,
)
# TODO: can I improve the contrast of the grayscale image here?
@@ -48,7 +59,9 @@ class MotionDetector():
# compute the threshold image for the current frame
# TODO: threshold
current_thresh = cv2.threshold(frameDelta, self.config.threshold, 255, cv2.THRESH_BINARY)[1]
current_thresh = cv2.threshold(
frameDelta, self.config.threshold, 255, cv2.THRESH_BINARY
)[1]
# black out everything in the avg_delta where there isnt motion in the current frame
avg_delta_image = cv2.convertScaleAbs(self.avg_delta)
@@ -56,7 +69,9 @@ class MotionDetector():
# then look for deltas above the threshold, but only in areas where there is a delta
# in the current frame. this prevents deltas from previous frames from being included
thresh = cv2.threshold(avg_delta_image, self.config.threshold, 255, cv2.THRESH_BINARY)[1]
thresh = cv2.threshold(
avg_delta_image, self.config.threshold, 255, cv2.THRESH_BINARY
)[1]
# dilate the thresholded image to fill in holes, then find contours
# on thresholded image
@@ -70,16 +85,27 @@ class MotionDetector():
contour_area = cv2.contourArea(c)
if contour_area > self.config.contour_area:
x, y, w, h = cv2.boundingRect(c)
motion_boxes.append((int(x*self.resize_factor), int(y*self.resize_factor), int((x+w)*self.resize_factor), int((y+h)*self.resize_factor)))
motion_boxes.append(
(
int(x * self.resize_factor),
int(y * self.resize_factor),
int((x + w) * self.resize_factor),
int((y + h) * self.resize_factor),
)
)
if len(motion_boxes) > 0:
self.motion_frame_count += 1
if self.motion_frame_count >= 10:
# only average in the current frame if the difference persists for a bit
cv2.accumulateWeighted(resized_frame, self.avg_frame, self.config.frame_alpha)
cv2.accumulateWeighted(
resized_frame, self.avg_frame, self.config.frame_alpha
)
else:
# when no motion, just keep averaging the frames together
cv2.accumulateWeighted(resized_frame, self.avg_frame, self.config.frame_alpha)
cv2.accumulateWeighted(
resized_frame, self.avg_frame, self.config.frame_alpha
)
self.motion_frame_count = 0
return motion_boxes