predict.py 文件源码

def find_bbox(mask_binary, margin_factor=None):
    ret, thresh = cv2.threshold(mask_binary, 127, 255, 0)
    _, contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)

    # Find the index of the largest contour
    areas = [cv2.contourArea(c) for c in contours]
    if len(areas) == 0:
        return (0, 0, mask_binary.shape[0], mask_binary.shape[1], False)
    else:
        max_index = np.argmax(areas)
        cnt = contours[max_index]

        x, y, w, h = cv2.boundingRect(cnt)

        if margin_factor != None and margin_factor > 0:
            wm = w * margin_factor
            hm = h * margin_factor
            x -= wm
            y -= hm
            w += 2 * wm
            h += 2 * hm
            x = max(0, x)
            y = max(0, y)
            X = min(x + w, mask_binary.shape[1])
            Y = min(y + h, mask_binary.shape[0])
            w = X - x
            h = Y - y
        return (int(x), int(y), int(w), int(h), True)