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82 lines
3.9 KiB
Python
82 lines
3.9 KiB
Python
import cv2
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import imutils
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import numpy as np
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class MotionDetector():
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def __init__(self, frame_shape, mask, resize_factor=4):
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self.frame_shape = frame_shape
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self.resize_factor = resize_factor
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self.motion_frame_size = (int(frame_shape[0]/resize_factor), int(frame_shape[1]/resize_factor))
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self.avg_frame = np.zeros(self.motion_frame_size, np.float)
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self.avg_delta = np.zeros(self.motion_frame_size, np.float)
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self.motion_frame_count = 0
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self.frame_counter = 0
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resized_mask = cv2.resize(mask, dsize=(self.motion_frame_size[1], self.motion_frame_size[0]), interpolation=cv2.INTER_LINEAR)
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self.mask = np.where(resized_mask==[0])
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def detect(self, frame):
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motion_boxes = []
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gray = frame[0:self.frame_shape[0], 0:self.frame_shape[1]]
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# resize frame
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resized_frame = cv2.resize(gray, dsize=(self.motion_frame_size[1], self.motion_frame_size[0]), interpolation=cv2.INTER_LINEAR)
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# convert to grayscale
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# resized_frame = cv2.cvtColor(resized_frame, cv2.COLOR_BGR2GRAY)
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# mask frame
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resized_frame[self.mask] = [255]
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# it takes ~30 frames to establish a baseline
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# dont bother looking for motion
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if self.frame_counter < 30:
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self.frame_counter += 1
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else:
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# compare to average
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frameDelta = cv2.absdiff(resized_frame, cv2.convertScaleAbs(self.avg_frame))
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# compute the average delta over the past few frames
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# the alpha value can be modified to configure how sensitive the motion detection is.
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# higher values mean the current frame impacts the delta a lot, and a single raindrop may
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# register as motion, too low and a fast moving person wont be detected as motion
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# this also assumes that a person is in the same location across more than a single frame
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cv2.accumulateWeighted(frameDelta, self.avg_delta, 0.2)
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# compute the threshold image for the current frame
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current_thresh = cv2.threshold(frameDelta, 25, 255, cv2.THRESH_BINARY)[1]
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# black out everything in the avg_delta where there isnt motion in the current frame
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avg_delta_image = cv2.convertScaleAbs(self.avg_delta)
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avg_delta_image[np.where(current_thresh==[0])] = [0]
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# then look for deltas above the threshold, but only in areas where there is a delta
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# in the current frame. this prevents deltas from previous frames from being included
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thresh = cv2.threshold(avg_delta_image, 25, 255, cv2.THRESH_BINARY)[1]
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# dilate the thresholded image to fill in holes, then find contours
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# on thresholded image
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thresh = cv2.dilate(thresh, None, iterations=2)
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cnts = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
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cnts = imutils.grab_contours(cnts)
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# loop over the contours
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for c in cnts:
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# if the contour is big enough, count it as motion
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contour_area = cv2.contourArea(c)
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if contour_area > 100:
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x, y, w, h = cv2.boundingRect(c)
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motion_boxes.append((x*self.resize_factor, y*self.resize_factor, (x+w)*self.resize_factor, (y+h)*self.resize_factor))
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if len(motion_boxes) > 0:
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self.motion_frame_count += 1
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# TODO: this really depends on FPS
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if self.motion_frame_count >= 10:
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# only average in the current frame if the difference persists for at least 3 frames
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cv2.accumulateWeighted(resized_frame, self.avg_frame, 0.2)
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else:
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# when no motion, just keep averaging the frames together
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cv2.accumulateWeighted(resized_frame, self.avg_frame, 0.2)
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self.motion_frame_count = 0
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return motion_boxes |