我用PaddleHub给人像戴上了口罩

import paddlehub as hub
import cv2
import os
face_landmark = hub.Module(name="face_landmark_localization")
result = face_landmark.keypoint_detection(images=[cv2.imread('1.jpg')], output_dir='face_landmark_output',
                                          visualization=False)
# 左上：2
# 右上：16
# 最下中：9
# print(result)
data = result[0]['data'][0]
print(data)
print(len(data))
print("左上：2 ", data[1])
print("右上：16 ", data[15])
print("最下中：9 ", data[8])
width = int(data[15][0] - data[1][0])+50
height = int(data[8][1] - data[29][1])
# dx = int(data[1][0] + width / 2)
# dy = int(data[8][1] - height / 2)
dx = int(data[1][0])-10
dy = int(data[1][1])
print(width, height, dx, dy)

# 第二步：合成
def merge(face_pic, w, h, dx, dy):
    mask_path = 'mask.png'
    face = cv2.imread(face_pic, cv2.IMREAD_COLOR)  # 捕获图像1
    print('face pic shape: ', face.shape)
    # 口罩
    # IMREAD_UNCHANGED  If set, return the loaded image as is (with alpha channel, otherwise it gets cropped).
    # 因此Png必须是4通道的IMREAD_UNCHANGED
    mask = cv2.imread(mask_path, cv2.IMREAD_UNCHANGED)
    # mask = cv2.resize(mask, [w, h])
    mask = cv2.resize(mask, (w, h))
    rows, cols, channels = mask.shape
    print('mask shape: ', rows, cols, channels)
    roi = face[dy:dy + rows, dx:dx + cols]
    print('dx + rows,  dy + cols', dy + rows, dx + cols)
    for i in range(rows):
        for j in range(cols):
            if not (mask[i, j][3] == 0):  # 透明的意思
                roi[i, j][0] = mask[i, j][0]
                roi[i, j][1] = mask[i, j][1]
                roi[i, j][2] = mask[i, j][2]
    face[dy:dy + rows, dx:dx + cols] = roi
    merge_img_path = 're_' + face_pic
    cv2.imwrite(merge_img_path, face)
    return 're_' + face_pic
merge('1.jpg', width, height, dx, dy)