PaddleHub创意---美颜证件照

加载待美颜图片，检测关键点

import cv2
import paddlehub as hub
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import numpy as np
import math
from PIL import Image
src_img = cv2.imread('1.jpg')module = hub.Module(name="face_landmark_localization")
result = module.keypoint_detection(images=[src_img])tmp_img = src_img.copy()
for index, point in enumerate(result[0]['data'][0]):# cv2.putText(img, str(index), (int(point[0]), int(point[1])), cv2.FONT_HERSHEY_COMPLEX, 3, (0,0,255), -1)cv2.circle(tmp_img, (int(point[0]), int(point[1])), 2, (0, 0, 255), -1)res_img_path = 'face_landmark.jpg'
cv2.imwrite(res_img_path, tmp_img)img = mpimg.imread(res_img_path)
# 展示预测68个关键点结果
plt.figure(figsize=(10,10))
plt.imshow(img)
plt.axis('off')
plt.show()

1. 瘦脸

首先介绍如何利用识别到的68个关键点完成瘦脸功能。利用其中3号点到5号点距离作为瘦左脸距离，13号点到15号点距离作为瘦右脸距离。同时利用局部平移算法完成瘦脸

def thin_face(image, face_landmark):"""实现自动人像瘦脸image： 人像图片face_landmark: 人脸关键点"""end_point = face_landmark[30]# 瘦左脸，3号点到5号点的距离作为瘦脸距离dist_left = np.linalg.norm(face_landmark[3] - face_landmark[5])image = local_traslation_warp(image, face_landmark[3], end_point, dist_left)# 瘦右脸，13号点到15号点的距离作为瘦脸距离dist_right = np.linalg.norm(face_landmark[13] - face_landmark[15])image = local_traslation_warp(image, face_landmark[13], end_point, dist_right)return image

def local_traslation_warp(image, start_point, end_point, radius):"""局部平移算法"""radius_square = math.pow(radius, 2)image_cp = image.copy()dist_se = math.pow(np.linalg.norm(end_point - start_point), 2)height, width, channel = image.shapefor i in range(width):for j in range(height):# 计算该点是否在形变圆的范围之内# 优化，第一步，直接判断是会在（start_point[0], start_point[1])的矩阵框中if math.fabs(i - start_point[0]) > radius and math.fabs(j - start_point[1]) > radius:continuedistance = (i - start_point[0]) * (i - start_point[0]) + (j - start_point[1]) * (j - start_point[1])if (distance < radius_square):# 计算出（i,j）坐标的原坐标# 计算公式中右边平方号里的部分ratio = (radius_square - distance) / (radius_square - distance + dist_se)ratio = ratio * ratio# 映射原位置new_x = i - ratio * (end_point[0] - start_point[0])new_y = j - ratio * (end_point[1] - start_point[1])new_x = new_x if new_x >=0 else 0new_x = new_x if new_x < height-1 else height-2new_y = new_y if new_y >= 0 else 0new_y = new_y if new_y < width-1 else width-2# 根据双线性插值法得到new_x, new_y的值image_cp[j, i] = bilinear_insert(image, new_x, new_y)return image_cpdef bilinear_insert(image, new_x, new_y):"""双线性插值法"""w, h, c = image.shapeif c == 3:x1 = int(new_x)x2 = x1 + 1y1 = int(new_y)y2 = y1 + 1part1 = image[y1, x1].astype(np.float) * (float(x2) - new_x) * (float(y2) - new_y)part2 = image[y1, x2].astype(np.float) * (new_x - float(x1)) * (float(y2) - new_y)part3 = image[y2, x1].astype(np.float) * (float(x2) - new_x) * (new_y - float(y1))part4 = image[y2, x2].astype(np.float) * (new_x - float(x1)) * (new_y - float(y1))insertValue = part1 + part2 + part3 + part4return insertValue.astype(np.int8)

2. 大眼

对人像中的眼睛进行放大。在识别到的左右眼中的一个位置，对其进行缩放(图像局部缩放)，实现大眼。

def enlarge_eyes(image, face_landmark, radius=15, strength=10):"""放大眼睛image： 人像图片face_landmark: 人脸关键点radius: 眼睛放大范围半径strength：眼睛放大程度"""# 以左眼最低点和最高点之间的中点为圆心left_eye_top = face_landmark[37]left_eye_bottom = face_landmark[41]left_eye_center = (left_eye_top + left_eye_bottom)/2# 以右眼最低点和最高点之间的中点为圆心right_eye_top = face_landmark[43]right_eye_bottom = face_landmark[47]right_eye_center = (right_eye_top + right_eye_bottom)/2# 放大双眼local_zoom_warp(image, left_eye_center, radius=radius, strength=strength)local_zoom_warp(image, right_eye_center, radius=radius, strength=strength)def local_zoom_warp(image, point, radius, strength):"""图像局部缩放算法"""height = image.shape[0]width = image.shape[1]      left =int(point[0] - radius) if point[0] - radius >= 0 else 0top = int(point[1] - radius) if point[1] - radius >= 0 else 0right = int(point[0] + radius) if point[0] + radius < width else width-1bottom = int(point[1] + radius) if point[1] + radius < height  else height-1radius_square = math.pow(radius, 2)for y in range(top, bottom):offset_y = y - point[1]for x in range(left, right):offset_x = x - point[0]dist_xy = offset_x * offset_x + offset_y * offset_yif dist_xy <= radius_square:scale = 1 - dist_xy / radius_squarescale = 1 - strength / 100 * scalenew_x = offset_x * scale + point[0]new_y = offset_y * scale + point[1]new_x = new_x if new_x >=0 else 0new_x = new_x if new_x < height-1 else height-2new_y = new_y if new_y >= 0 else 0new_y = new_y if new_y < width-1 else width-2image[y, x] = bilinear_insert(image, new_x, new_y)

3. 红唇

目前已经叠加了瘦脸、大眼的美颜功能，我们还可以给人像增添气色，给人像画上红唇。我们只需将识别到的唇部位置给涂上红色即可达到相应的目的。

def rouge(image, face_landmark, ruby=True):"""自动涂口红image： 人像图片face_landmark: 人脸关键点ruby：是否需要深色口红"""image_cp = image.copy()if ruby:rouge_color = (0,0,255)else:rouge_color = (0,0,200)points=face_landmark[48:68]hull = cv2.convexHull(points)cv2.drawContours(image, [hull], -1, rouge_color, -1)cv2.addWeighted(image, 0.2, image_cp, 0.9, 0, image_cp)return image_cp

4. 美白

最后我们还可以加上美肤功能。由于标记出来的68个关键点没有涵盖额头的位置，我们需要预估额头位置。为了简单估计二头所在区域，本教程以0号、16号点所在线段为直径的半圆为额头位置。

def whitening(img, face_landmark):"""美白"""# 简单估计额头所在区域# 根据0号、16号点画出额头(以0号、16号点所在线段为直径的半圆)radius=(np.linalg.norm(face_landmark[0] - face_landmark[16]) / 2).astype('int32')center_abs=tuple(((face_landmark[0] + face_landmark[16]) / 2).astype('int32'))angle=np.degrees(np.arctan((lambda l:l[1]/l[0])(face_landmark[16]-face_landmark[0]))).astype('int32')face = np.zeros_like(img)cv2.ellipse(face,center_abs,(radius,radius),angle,180,360,(255,255,255),2)points=face_landmark[0:17]hull = cv2.convexHull(points)cv2.polylines(face, [hull], True, (255,255,255), 2)index = face >0face[index] = img[index]dst = np.zeros_like(face)# v1:磨皮程度v1 = 3# v2: 细节程度v2 = 2tmp1 = cv2.bilateralFilter(face, v1 * 5, v1 * 12.5, v1 * 12.5)tmp1 = cv2.subtract(tmp1,face)tmp1 = cv2.add(tmp1,(10,10,10,128))tmp1 = cv2.GaussianBlur(tmp1,(2*v2-1, 2*v2-1),0)tmp1 = cv2.add(img,tmp1)dst = cv2.addWeighted(img, 0.1, tmp1, 0.9, 0.0)dst = cv2.add(dst,(10, 10, 10,255))index = dst>0img[index] = dst[index]return img

face_landmark = np.array(result[0]['data'][0], dtype='int')
#瘦脸
src_img = thin_face(src_img, face_landmark)res_img_path = 'res.jpg'
cv2.imwrite(res_img_path, src_img)
# 在瘦脸的基础上，继续放大双眼
enlarge_eyes(src_img, face_landmark, radius=13, strength=13)
# 继续叠加红唇
src_img = rouge(src_img, face_landmark)
# 美白
src_img = whitening(src_img, face_landmark)

5. 制作不同寸证件照

利用PaddleHub制作不同寸登记照片，并生成多种颜色的背景（白色，红色，蓝色）

face_landmark = hub.Module(name="face_landmark_localization")
human_seg = hub.Module(name="deeplabv3p_xception65_humanseg")
# 裁剪成为1寸大小的图片
def crop_face(pic_path,rate=1.3):# 人脸识别result = face_landmark.keypoint_detection(paths=[pic_path])face=np.array(result[0]['data'][0],dtype=np.int64)# 剪裁left = face[:,0].min()right= face[:,0].max()w = right-leftcw=int((right+left)/2)upper = face[:,1].min()lower = face[:,1].max()h= lower-upperch=int((lower+upper)/2)h = int(413*w/295)box=(cw-rate*w, ch-rate*h, cw+rate*w, ch+rate*h)img=Image.open(pic_path)img=img.crop(box)img=img.resize((295, 413),Image.ANTIALIAS)# display(img)return img# 进行人物抠图
def seg_face(img):result = human_seg.segmentation(images=[cv2.cvtColor(np.array(img),cv2.COLOR_RGB2BGR)],use_gpu=False,visualization=True,output_dir='humanseg_output')print("抠图输出文件：",result[0]["save_path"])# display(Image.open(result[0]["save_path"]))return result[0]["save_path"]# 改变颜色
def change_color(pic_path,thresh=100):def cut_person(img,num=[255,255,255]):img=np.array(img).transpose((2,0,1))person=[]for i in range(3):a=img[i]mask=np.array((img[3]<thresh),dtype=np.uint8)# mask=cv2.erode(mask,None,iterations=1)mask=cv2.dilate(mask,None,iterations=2)mask=np.array(mask,dtype=np.bool)a[mask]=num[i]person.append(a)img=np.array(person).transpose((1,2,0))im=Image.fromarray(img)display(im)return imcut=cut_person(Image.open(pic_path),num=[255,255,255])cut=cut_person(Image.open(pic_path),num=[255,0,0])cut=cut_person(Image.open(pic_path),num=[0,0,255])# 生成三种底色的登记照片
def id_photo(pic_path,rate=1.3,thresh=2):img=crop_face(pic_path,rate)pic_path=seg_face(img)change_color(pic_path,thresh)

[32m[2020-08-24 19:40:21,555] [    INFO] - Installing face_landmark_localization module[0m
[32m[2020-08-24 19:40:21,558] [    INFO] - Module face_landmark_localization already installed in /home/aistudio/.paddlehub/modules/face_landmark_localization[0m
[32m[2020-08-24 19:40:21,582] [    INFO] - Installing ultra_light_fast_generic_face_detector_1mb_640 module[0m
[32m[2020-08-24 19:40:21,585] [    INFO] - Module ultra_light_fast_generic_face_detector_1mb_640 already installed in /home/aistudio/.paddlehub/modules/ultra_light_fast_generic_face_detector_1mb_640[0m
[32m[2020-08-24 19:40:21,799] [    INFO] - Installing deeplabv3p_xception65_humanseg module[0m
[32m[2020-08-24 19:40:21,802] [    INFO] - Module deeplabv3p_xception65_humanseg already installed in /home/aistudio/.paddlehub/modules/deeplabv3p_xception65_humanseg[0m

for pic in ["1.jpg","res.jpg"]:display(Image.open(pic).resize((295, 413),Image.ANTIALIAS))[32m[2020-08-24 19:40:21,802] [    INFO] - Module deeplabv3p_xception65_humanseg already installed in /home/aistudio/.paddlehub/modules/deeplabv3p_xception65_humanseg[0m```python
for pic in ["1.jpg","res.jpg"]:display(Image.open(pic).resize((295, 413),Image.ANTIALIAS))id_photo(pic,rate=1.5,thresh=50)

6. 原图

7. 美颜后

参考:https://aistudio.baidu.com/aistudio/projectdetail/753469
https://aistudio.baidu.com/aistudio/projectdetail/389512

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