毕业季–DIY毕业照

本项目针对疫情期间毕业生无法拍摄毕业照的遗憾，提供DIY毕业照解决方案。通过AI换lian将个人人脸合成到样本图，再经毕业服装抠图与合成、人体抠图与学校背景合成，完成毕业照制作。使用paddlehub等工具实现，但存在服装学科颜色、帽子垂穗处理等需完善的瑕疵，最终祝福毕业生前程似锦。

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项目简介

由于疫情的影响，许多毕业生他们可能都没办法拥有一张属于自己的毕业照，这将成为许多人说遗憾。于是便做了这样一个DIY毕业照的项目，最后祝福各位毕业生前程似锦，万事如意。

效果展示

人脸照片：

合成毕业照：

学士服：

硕士服：

博士服：

（PS:示例图片均来源于互联网，如有侵权，请联系删除）

一、安装必要的包

In [1]

!pip install --upgrade paddlehub -i https://mirror.baidu.com/pypi/simple!hub install deeplabv3p_xception65_humanseg==1.1.2

二、AI换lian

把自己的脸合成到样本图上

只需修改im1，im2

im1：自己脸的图片

im2：样本图

In [ ]

import cv2import numpy as npimport paddlehub as hub

   In [ ]

def get_image_size(image):    """    获取图片大小（高度,宽度）    :param image: image    :return: （高度,宽度）    """    image_size = (image.shape[0], image.shape[1])    return image_sizedef get_face_landmarks(image):    """    获取人脸标志，68个特征点    :param image: image    :param face_detector: dlib.get_frontal_face_detector    :param shape_predictor: dlib.shape_predictor    :return: np.array([[],[]]), 68个特征点    """    dets = face_landmark.keypoint_detection([image])    num_faces = len(dets[0]['data'][0])    if num_faces == 0:        print("Sorry, there were no faces found.")        return None    # shape = shape_predictor(image, dets[0])    face_landmarks = np.array([[p[0], p[1]] for p in dets[0]['data'][0]])    return face_landmarksdef get_face_mask(image_size, face_landmarks):    """    获取人脸掩模    :param image_size: 图片大小    :param face_landmarks: 68个特征点    :return: image_mask, 掩模图片    """    mask = np.zeros(image_size, dtype=np.int32)    points = np.concatenate([face_landmarks[0:16], face_landmarks[26:17:-1]])    points = np.array(points, dtype=np.int32)    cv2.fillPoly(img=mask, pts=[points], color=255)    # mask = np.zeros(image_size, dtype=np.uint8)    # points = cv2.convexHull(face_landmarks)  # 凸包    # cv2.fillConvexPoly(mask, points, color=255)    return mask.astype(np.uint8)def get_affine_image(image1, image2, face_landmarks1, face_landmarks2):    """    获取图片1仿射变换后的图片    :param image1: 图片1, 要进行仿射变换的图片    :param image2: 图片2, 只要用来获取图片大小，生成与之大小相同的仿射变换图片    :param face_landmarks1: 图片1的人脸特征点    :param face_landmarks2: 图片2的人脸特征点    :return: 仿射变换后的图片    """    three_points_index = [18, 8, 25]    M = cv2.getAffineTransform(face_landmarks1[three_points_index].astype(np.float32),                               face_landmarks2[three_points_index].astype(np.float32))    dsize = (image2.shape[1], image2.shape[0])    affine_image = cv2.warpAffine(image1, M, dsize)    return affine_image.astype(np.uint8)def get_mask_center_point(image_mask):    """    获取掩模的中心点坐标    :param image_mask: 掩模图片    :return: 掩模中心    """    image_mask_index = np.argwhere(image_mask > 0)    miny, minx = np.min(image_mask_index, axis=0)    maxy, maxx = np.max(image_mask_index, axis=0)    center_point = ((maxx + minx) // 2, (maxy + miny) // 2)    return center_pointdef get_mask_union(mask1, mask2):    """    获取两个掩模掩盖部分的并集    :param mask1: mask_image, 掩模1    :param mask2: mask_image, 掩模2    :return: 两个掩模掩盖部分的并集    """    mask = np.min([mask1, mask2], axis=0)  # 掩盖部分并集    mask = ((cv2.blur(mask, (5, 5)) == 255) * 255).astype(np.uint8)  # 缩小掩模大小    mask = cv2.blur(mask, (3, 3)).astype(np.uint8)  # 模糊掩模    return maskdef skin_color_adjustment(im1, im2, mask=None):    """    肤色调整    :param im1: 图片1    :param im2: 图片2    :param mask: 人脸 mask. 如果存在，使用人脸部分均值来求肤色变换系数；否则，使用高斯模糊来求肤色变换系数    :return: 根据图片2的颜色调整的图片1    """    if mask is None:        im1_ksize = 55        im2_ksize = 55        im1_factor = cv2.GaussianBlur(im1, (im1_ksize, im1_ksize), 0).astype(np.float)        im2_factor = cv2.GaussianBlur(im2, (im2_ksize, im2_ksize), 0).astype(np.float)    else:        im1_face_image = cv2.bitwise_and(im1, im1, mask=mask)        im2_face_image = cv2.bitwise_and(im2, im2, mask=mask)        im1_factor = np.mean(im1_face_image, axis=(0, 1))        im2_factor = np.mean(im2_face_image, axis=(0, 1))    im1 = np.clip((im1.astype(np.float) * im2_factor / np.clip(im1_factor, 1e-6, None)), 0, 255).astype(np.uint8)    return im1def main():    im1 = cv2.imread("face.png")  # face_image    im1 = cv2.resize(im1, (600, im1.shape[0] * 600 // im1.shape[1]))    landmarks1 = get_face_landmarks(im1)  # 68_face_landmarks    if landmarks1 is None:        print('{}:检测不到人脸'.format(image_face_path))        exit(1)    im1_size = get_image_size(im1)  # 脸图大小    im1_mask = get_face_mask(im1_size, landmarks1)  # 脸图人脸掩模    # ret_val, im2 = cam.read()  # camera_image    im2 = cv2.imread("di_zhao.png")    landmarks2 = get_face_landmarks(im2)  # 68_face_landmarks    if landmarks2 is not None:        im2_size = get_image_size(im2)  # 摄像头图片大小        im2_mask = get_face_mask(im2_size, landmarks2)  # 摄像头图片人脸掩模        affine_im1 = get_affine_image(im1, im2, landmarks1, landmarks2)  # im1（脸图）仿射变换后的图片        affine_im1_mask = get_affine_image(im1_mask, im2, landmarks1, landmarks2)  # im1（脸图）仿射变换后的图片的人脸掩模        union_mask = get_mask_union(im2_mask, affine_im1_mask)  # 掩模合并        # affine_im1_face_image = cv2.bitwise_and(affine_im1, affine_im1, mask=union_mask)  # im1（脸图）的脸        # im2_face_image = cv2.bitwise_and(im2, im2, mask=union_mask)  # im2（摄像头图片）的脸        # cv2.imshow('affine_im1_face_image', affine_im1_face_image)        # cv2.imshow('im2_face_image', im2_face_image)        affine_im1 = skin_color_adjustment(affine_im1, im2, mask=union_mask)  # 肤色调整        point = get_mask_center_point(affine_im1_mask)  # im1（脸图）仿射变换后的图片的人脸掩模的中心点        seamless_im = cv2.seamlessClone(affine_im1, im2, mask=union_mask, p=point, flags=cv2.NORMAL_CLONE)  # 进行泊松融合        # cv2.imshow('affine_im1', affine_im1)        # cv2.imshow('im2', im2)        # cv2.imshow('seamless_im', seamless_im)        cv2.imwrite('hecheng.jpg', seamless_im)        # plt.imshow(seamless_im)        # plt.show()    else:        cv2.imshow('seamless_im', im2)        # plt.imshow(im2)        # plt.show()if __name__ == '__main__':    face_landmark = hub.Module(name="face_landmark_localization")    main()

[2022-06-07 11:22:24,086] [ WARNING] - The _initialize method in HubModule will soon be deprecated, you can use the __init__() to handle the initialization of the object[2022-06-07 11:22:24,177] [ WARNING] - The _initialize method in HubModule will soon be deprecated, you can use the __init__() to handle the initialization of the object

三、毕业服装抠图与合成

In [ ]

#调用一些相关的包import matplotlibimport matplotlib.pyplot as plt import matplotlib.image as mpimg import cv2from PIL import Imageimport numpy as npimport paddlehub as hub

   In [ ]

# S1  衣服图片抠图 ---------------------------------------------------------------------module = hub.Module(name="deeplabv3p_xception65_humanseg")res = module.segmentation(paths = ["bo.png"], visualization=True, output_dir='pic_output')res_img_path = './pic_output/bo.png'img = mpimg.imread(res_img_path)plt.figure(figsize=(10, 10))plt.imshow(img)plt.axis('off')plt.show()

[2022-06-07 16:11:24,181] [ WARNING] - The _initialize method in HubModule will soon be deprecated, you can use the __init__() to handle the initialization of the object

               In [ ]

# S2  显示原始图片 ---------------------------------------------------------------------# 原始图片test_img_path = ["hecheng.jpg"]#import numpy as np #wpb addimg = mpimg.imread(test_img_path[0]) # 展示 原始图片plt.figure(figsize=(10,10))plt.imshow(img) #wpb comment#plt.imshow(img.astype(np.uint8))#wpb addplt.axis('off') plt.show()

               In [ ]

# S3  获取关键点图像 ---------------------------------------------------------------------module = hub.Module(name="human_pose_estimation_resnet50_mpii")res = module.keypoint_detection(paths = ["hecheng.jpg"], visualization=True, output_dir='pic_output')res_img_path = './pic_output/hecheng.jpg'img = mpimg.imread(res_img_path)plt.figure(figsize=(10, 10))plt.imshow(img)plt.axis('off')plt.show()print(res)

[2022-06-07 16:12:03,050] [ WARNING] - The _initialize method in HubModule will soon be deprecated, you can use the __init__() to handle the initialization of the object

image saved in pic_output/hechengtime=1654589524.jpg

[{'path': 'hecheng.jpg', 'data': OrderedDict([('left_ankle', [205, 698]), ('left_knee', [200, 698]), ('left_hip', [211, 490]), ('right_hip', [269, 482]), ('right_knee', [274, 705]), ('right_ankle', [264, 297]), ('pelvis', [227, 490]), ('thorax', [242, 319]), ('upper_neck', [242, 267]), ('head_top', [242, 133]), ('right_wrist', [190, 467]), ('right_elbow', [110, 423]), ('right_shoulder', [153, 319]), ('left_shoulder', [332, 319]), ('left_elbow', [369, 430]), ('left_wrist', [279, 467])])}]

       In [ ]

# S4  换衣服 ---------------------------------------------------------------------#获取衣服位置left_posx=res[0]["data"]["right_shoulder"][0]left_posy=res[0]["data"]["right_shoulder"][1]right_posx=res[0]["data"]["left_ankle"][0]right_posy=res[0]["data"]["left_ankle"][1]print(left_posx, left_posy)print(right_posx, right_posy)#读取图片Image1 = Image.open('hecheng.jpg') Image1copy = Image1.copy() Image2 = Image.open('pic_output/bo.png') Image2copy = Image2.copy() #resize clothes       可以对抠出的服装图片进行放大缩小width,height=Image1copy.sizenewsize=(int(width*1.0),int(height*0.9))Image2copy = Image2.resize(newsize)#制定要粘贴左上角坐标       可以抠出的服装图片进行移动position=(int(left_posx*-0.07),int(left_posy*0.55) ) # ,right_posx, right_posyprint(position)# 换衣服 ， 应该还有更好的方法进行照片合成Image1copy.paste(Image2copy,position,Image2copy) # 将翻转后图像region  粘贴到  原图im 中的 box位置  # 存为新文件  #Image1copy.save('./pic_output/newclothes.png') Image1copy.save('./pic_output/newclothes_bo.jpg') # 显示穿着新衣的照片img = mpimg.imread('./pic_output/newclothes_bo.jpg') plt.figure(figsize=(10,10))plt.imshow(img) plt.axis('off') plt.show()

153 319205 698(-10, 175)

四、合成学校背景图片

In [2]

import paddlehub as hubimport matplotlib.pyplot as plt import matplotlib.image as mpimg import cv2from PIL import Imageimport numpy as npimport math

   In [7]

import paddlehub as hubimport numpy as npimport matplotlib.pyplot as plt import matplotlib.image as mpimg #加载预训练模型"deeplabv3p_xception65_humanseghumanseg = hub.Module(name="deeplabv3p_xception65_humanseg")#可以添加多张图片img_path = ["hecheng.jpg"]results = humanseg.segmentation(data={"image":img_path},visualization=True, output_dir='humanseg_output')#遍历图片抠图结果for i in range(len(img_path)):    #呈现原图    img1 = mpimg.imread(img_path[i])    plt.figure(figsize=(10,10))     plt.imshow(img1)          plt.axis('off')         plt.show()    result=results[i]    print(result)    #打印 抠图结果的数字列表    # print(result["data"].shape)        #以图形方式呈现结果    prediction = result["data"]        plt.imshow(prediction)        plt.show()    #运用线性代数实现：使用抠图数据剪切原图    newimg = np.zeros(img1.shape)     newimg[:,:,0] = img1[:,:,0] * (prediction>0)       newimg[:,:,1] = img1[:,:,1] * (prediction>0)      newimg[:,:,2] = img1[:,:,2] * (prediction>0)        newimg = newimg.astype(np.uint8)         # 抠图结果展示        plt.figure(figsize=(10,10))          plt.imshow(newimg)         plt.axis('off')         plt.show()

[2022-06-07 16:48:59,533] [ WARNING] - The _initialize method in HubModule will soon be deprecated, you can use the __init__() to handle the initialization of the object

{'save_path': 'humanseg_output/hecheng.png', 'data': array([[0., 0., 0., ..., 0., 0., 0.],       [0., 0., 0., ..., 0., 0., 0.],       [0., 0., 0., ..., 0., 0., 0.],       ...,       [0., 0., 0., ..., 0., 0., 0.],       [0., 0., 0., ..., 0., 0., 0.],       [0., 0., 0., ..., 0., 0., 0.]], dtype=float32)}

               In [8]

base_image = Image.open(f'xuexiao.jpeg').convert('RGB')fore_image = Image.open(f'humanseg_output/hecheng.png').resize(base_image.size)# 图片加权合成scope_map = np.array(fore_image)[:,:,-1] / 255scope_map = scope_map[:,:,np.newaxis]scope_map = np.repeat(scope_map, repeats=3, axis=2)res_image = np.multiply(scope_map, np.array(fore_image)[:,:,:3]) + np.multiply((1-scope_map), np.array(base_image))#保存图片res_image = Image.fromarray(np.uint8(res_image))res_image.save(f"humanseg_output/hecheng_xue.jpg")print('照片合成完毕')plt.figure(figsize=(10,10))plt.imshow(res_image) plt.axis('off') plt.show()

照片合成完毕

总结

本次项目主要使用了脸部抠图+合成、衣服抠图+合成、人体抠图+背景合成，这三大块的功能来完成。但是仍然存在瑕疵。例如服装上学科代表的颜色和帽子垂穗颜色不能更换、以及最后合成后的帽子垂穗会消失，这都是后面需要完善的地方。

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