fingers_recog.py 文件源码

def _extract_arm(self, img):
        # find center region of image frame (assume center region is 21 x 21 px)
        center_half = 10 # (=(21-1)/2)  
        center = img[self.height/2 - center_half : self.height/2 + center_half, self.width/2 - center_half : self.width/2 + center_half]

        # determine median depth value
        median_val = np.median(center)

        '''mask the image such that all pixels whose depth values
        lie within a particular range are gray and the rest are black
        '''

        img = np.where(abs(img-median_val) <= self.abs_depth_dev, 128, 0).astype(np.uint8)

        # Apply morphology operation to fill small holes in the image
        kernel = np.ones((5,5), np.uint8)
        img = cv2.morphologyEx(img, cv2.MORPH_CLOSE, kernel)

        # Find connected regions (to hand) to remove background objects
        # Use floodfill with a small image area (7 x 7 px) that is set gray color value
        kernel2 = 3
        img[self.height/2-kernel2:self.height/2+kernel2, self.width/2-kernel2:self.width/2+kernel2] = 128

        # a black mask to mask the 'non-connected' components black
        mask = np.zeros((self.height + 2, self.width + 2), np.uint8)
        floodImg = img.copy()

        # Use floodFill function to paint the connected regions white 
        cv2.floodFill(floodImg, mask, (self.width/2, self.height/2), 255, flags=(4 | 255 << 8))

        # apply a binary threshold to show only connected hand region
        ret, floodedImg = cv2.threshold(floodImg, 129, 255, cv2.THRESH_BINARY)

        return floodedImg