毕业季--DIY毕业照

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

import cv2import numpy as npimport paddlehub as hub

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()

#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

# 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()

# 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()

# 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)

# 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.size
newsize=(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()

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

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()

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()