用python给微信头像加圣诞帽的疑问

一杯茶_ZR 2017-12-28 10:19:33

这是作者在github的源代码（github源码：https://github.com/LiuXiaolong19920720/Add-Christmas-Hat）
shape_predictor_5_face_landmarks.dat：https://github.com/LiuXiaolong19920720/Add-Christmas-Hat/blob/master/shape_predictor_5_face_landmarks.dat
我的运行环境：OSX10.12.3+python3.5+pycharm
运行代码后提示错误：

Connected to pydev debugger (build 172.4343.24)
Traceback (most recent call last):
File "/Applications/PyCharm.app/Contents/helpers/pydev/pydevd.py", line 1599, in <module>
globals = debugger.run(setup['file'], None, None, is_module)
File "/Applications/PyCharm.app/Contents/helpers/pydev/pydevd.py", line 1026, in run
pydev_imports.execfile(file, globals, locals) # execute the script
File "/Applications/PyCharm.app/Contents/helpers/pydev/_pydev_imps/_pydev_execfile.py", line 18, in execfile
exec(compile(contents+"\n", file, 'exec'), glob, loc)
File "/Users/zhuangrui/Documents/Python/Python_practice/WeChat_Christmas_Cap.py", line 123, in <module>
output = add_hat(img, hat_img)
File "/Users/zhuangrui/Documents/Python/Python_practice/WeChat_Christmas_Cap.py", line 34, in add_hat
shape = predictor(img, d)
TypeError: 'NoneType' object is not callable

错误是：'NoneType' object is not callable，nonetype不可调用，提示line 34 ： shape = predictor(img, d)，其中 predictor() 为predictor = dlib.shape_predictor("shape_predictor_68_face_landmarks.dat")，print的结果为none

这个问题如何解决呢？谢谢！

import dlib

import numpy

from skimage import io

import cv2





# 给img中的人头像加上圣诞帽，人脸最好为正脸

def add_hat(img, hat_img):

    # 分离rgba通道，合成rgb三通道帽子图，a通道后面做mask用

    r, g, b, a = cv2.split(hat_img)

    rgb_hat = cv2.merge((r, g, b))



    cv2.imwrite("hat_alpha.jpg", a)

    # ------------------------- 用dlib的人脸检测代替OpenCV的人脸检测-----------------------



    # dlib人脸关键点检测器

    #predictor_path = "shape_predictor_68_face_landmarks.dat"

    #predictor = dlib.shape_predictor(predictor_path)

    predictor = dlib.shape_predictor("shape_predictor_68_face_landmarks.dat")

    # dlib正脸检测器

    detector = dlib.get_frontal_face_detector()



    # 正脸检测

    dets = detector(img, 1)



    # 如果检测到人脸

    if len(dets) > 0:

        for d in dets:

            x, y, w, h = d.left(), d.top(), d.right() - d.left(), d.bottom() - d.top()

            # x,y,w,h = faceRect

            # cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),2,8,0)



            # 关键点检测，5个关键点

            shape = predictor(img, d)

            # for point in shape.parts():

            #     cv2.circle(img,(point.x,point.y),3,color=(0,255,0))



            # cv2.imshow("image",img)

            # cv2.waitKey()



            # 选取左右眼眼角的点

            point1 = shape.part(0)

            point2 = shape.part(2)



            # 求两点中心

            eyes_center = ((point1.x + point2.x) // 2, (point1.y + point2.y) // 2)



            # cv2.circle(img,eyes_center,3,color=(0,255,0))

            # cv2.imshow("image",img)

            # cv2.waitKey()



            #  根据人脸大小调整帽子大小

            factor = 1.5

            resized_hat_h = int(round(rgb_hat.shape[0] * w / rgb_hat.shape[1] * factor))

            resized_hat_w = int(round(rgb_hat.shape[1] * w / rgb_hat.shape[1] * factor))



            if resized_hat_h > y:

                resized_hat_h = y - 1



            # 根据人脸大小调整帽子大小

            resized_hat = cv2.resize(rgb_hat, (resized_hat_w, resized_hat_h))



            # 用alpha通道作为mask

            mask = cv2.resize(a, (resized_hat_w, resized_hat_h))

            mask_inv = cv2.bitwise_not(mask)



            # 帽子相对与人脸框上线的偏移量

            dh = 0

            dw = 0

            # 原图ROI

            # bg_roi = img[y+dh-resized_hat_h:y+dh, x+dw:x+dw+resized_hat_w]

            bg_roi = img[y + dh - resized_hat_h:y + dh,

                     (eyes_center[0] - resized_hat_w // 3):(eyes_center[0] + resized_hat_w // 3 * 2)]



            # 原图ROI中提取放帽子的区域

            bg_roi = bg_roi.astype(float)

            mask_inv = cv2.merge((mask_inv, mask_inv, mask_inv))

            alpha = mask_inv.astype(float) / 255



            # 相乘之前保证两者大小一致（可能会由于四舍五入原因不一致）

            alpha = cv2.resize(alpha, (bg_roi.shape[1], bg_roi.shape[0]))

            # print("alpha size: ",alpha.shape)

            # print("bg_roi size: ",bg_roi.shape)

            bg = cv2.multiply(alpha, bg_roi)

            bg = bg.astype('uint8')



            cv2.imwrite("bg.jpg", bg)

            # cv2.imshow("image",img)

            # cv2.waitKey()



            # 提取帽子区域

            hat = cv2.bitwise_and(resized_hat, resized_hat, mask=mask)

            cv2.imwrite("hat.jpg", hat)



            # cv2.imshow("hat",hat)

            # cv2.imshow("bg",bg)



            # print("bg size: ",bg.shape)

            # print("hat size: ",hat.shape)



            # 相加之前保证两者大小一致（可能会由于四舍五入原因不一致）

            hat = cv2.resize(hat, (bg_roi.shape[1], bg_roi.shape[0]))

            # 两个ROI区域相加

            add_hat = cv2.add(bg, hat)

            # cv2.imshow("add_hat",add_hat)



            # 把添加好帽子的区域放回原图

            img[y + dh - resized_hat_h:y + dh,

            (eyes_center[0] - resized_hat_w // 3):(eyes_center[0] + resized_hat_w // 3 * 2)] = add_hat



            # 展示效果

            # cv2.imshow("img",img )

            # cv2.waitKey(0)



            return img





# 读取帽子图，第二个参数-1表示读取为rgba通道，否则为rgb通道

hat_img = cv2.imread("hat2.png", -1)



# 读取头像图

img = cv2.imread("test.jpg")

output = add_hat(img, hat_img)



# 展示效果

cv2.imshow("output", output)

cv2.waitKey(0)

cv2.imwrite("output.jpg", output)

# import glob as gb



# img_path = gb.glob("./images/*.jpg")



# for path in img_path:

#     img = cv2.imread(path)



#     # 添加帽子

#     output = add_hat(img,hat_img)



#     # 展示效果

#     cv2.imshow("output",output )

#     cv2.waitKey(0)



cv2.destroyAllWindows()