dm_preprocess.py 文件源码

def segment_breast(cls, img, low_int_threshold=.05, crop=True):
        '''Perform breast segmentation
        Args:
            low_int_threshold([float or int]): Low intensity threshold to 
                    filter out background. It can be a fraction of the max 
                    intensity value or an integer intensity value.
            crop ([bool]): Whether or not to crop the image.
        Returns:
            An image of the segmented breast.
        NOTES: the low_int_threshold is applied to an image of dtype 'uint8',
            which has a max value of 255.
        '''
        # Create img for thresholding and contours.
        img_8u = (img.astype('float32')/img.max()*255).astype('uint8')
        if low_int_threshold < 1.:
            low_th = int(img_8u.max()*low_int_threshold)
        else:
            low_th = int(low_int_threshold)
        _, img_bin = cv2.threshold(
            img_8u, low_th, maxval=255, type=cv2.THRESH_BINARY)
        ver = (cv2.__version__).split('.')
        if int(ver[0]) < 3:
            contours,_ = cv2.findContours(
                img_bin.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
        else:
            _,contours,_ = cv2.findContours(
                img_bin.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
        cont_areas = [ cv2.contourArea(cont) for cont in contours ]
        idx = np.argmax(cont_areas)  # find the largest contour, i.e. breast.
        breast_mask = cv2.drawContours(
            np.zeros_like(img_bin), contours, idx, 255, -1)  # fill the contour.
        # segment the breast.
        img_breast_only = cv2.bitwise_and(img, img, mask=breast_mask)
        x,y,w,h = cv2.boundingRect(contours[idx])
        if crop:
            img_breast_only = img_breast_only[y:y+h, x:x+w]
        return img_breast_only, (x,y,w,h)