万圣节人物写真生成器（PaddleHub实现）-人工智能-PHP中文网

本文介绍零成本DIY网红万圣节写真的方法：用PaddleHub的deeplabv3p_xception65_humanseg模型进行人物抠图并更换背景（需注意背景尺寸匹配），再通过ultra_light_fast_generic_face_detector_1mb_640模型检测脸部，添加经美图秀秀处理成透明的头饰，含具体操作步骤与效果展示。

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万圣节人物写真生成器（paddlehub实现） - php中文网

在万圣节来临之际，手把手教大家如何零成本DIY网红万圣节写真，绚丽多彩的丰富背景以及炫酷的万圣节贴图随你选择，还支持自定义上传万圣节素材，我们先来抢先看看效果~

原图：

万圣节人物写真生成器（PaddleHub实现） - php中文网

变换后：

原图+头饰：

万圣节人物写真生成器（PaddleHub实现） - php中文网

或者你会喜欢这一款

原图+切换背景+头饰:

万圣节人物写真生成器（PaddleHub实现） - php中文网

踩坑贴士：

1.如果需要更换背景的话，最好找到接近原图尺寸的背景图，不然原图里面的人物会因为尺寸不同导致拉伸变丑！！

2.DIY头饰，首先我们要把头饰弄成透明的，不然不透明会把人物的脸给挡住

Giiso写作机器人

Giiso写作机器人，让写作更简单

查看详情

具体操作：在网上找喜欢的头饰照片，然后打开电脑端的美图秀秀，然后进行抠图，再把背景设成透明即可，然后再上传到这儿。

这里是教程：头饰变透明教程

倒包、设置必要环境

In [1]

!pip install paddlehub==1.6.2 -i https://pypi.tuna.tsinghua.edu.cn/simple
!hub install deeplabv3p_xception65_humanseg==1.0.0

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Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple
Collecting paddlehub==1.6.2
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     |████████████████████████████████| 215kB 4.2MB/s eta 0:00:01
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Downloading deeplabv3p_xception65_humanseg
[==================================================] 100.00%
Uncompress /home/aistudio/.paddlehub/tmp/tmpxrc3h3w7/deeplabv3p_xception65_humanseg
[==================================================] 100.00%
Successfully installed deeplabv3p_xception65_humanseg-1.0.0

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In [3]

import os
os.environ["CUDA_VISIBLE_DEVICES"]="0"

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友情提示：首先大家喜欢哪种风格照片，就执行对应的步骤就可以了。

如果只需要添加头饰就可以跳转到 “步骤二、添加头饰” 即可；如果需要更换背景图片以及加头饰则从这里开始执行。

即

路线1：原图+头饰

路线2：原图+切换背景+头饰

刚开始是想着先加头饰再切换背景，但是执行切换背景抠图的时候会把原来加的头饰给去掉了（因为只扣人体的轮廓），因此路线2要先更换背景再加头饰

一、切换背景

1、导入相关的库

In [5]

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

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/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/nltk/decorators.py:68: DeprecationWarning: `formatargspec` is deprecated since Python 3.5. Use `signature` and the `Signature` object directly
  regargs, varargs, varkwargs, defaults, formatvalue=lambda value: ""
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/nltk/lm/counter.py:15: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working
  from collections import Sequence, defaultdict
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/__init__.py:107: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working
  from collections import MutableMapping
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/rcsetup.py:20: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working
  from collections import Iterable, Mapping
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/colors.py:53: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working
  from collections import Sized

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2、人物抠图

In [6]

# 显示处理之后的图片import paddle
paddle.enable_static()
humanseg = hub.Module(name="deeplabv3p_xception65_humanseg")
path = ["picture/people.jpg"]
results = humanseg.segmentation(data={"image":path})# 预测结果展示test_img_path = results[0]["processed"]
img = mpimg.imread(test_img_path)# 展示预测结果图片plt.figure(figsize=(10,10))
plt.imshow(img) 
plt.axis('off') 
plt.show()

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[2021-10-27 17:01:08,990] [    INFO] - Installing deeplabv3p_xception65_humanseg module
[2021-10-27 17:01:09,108] [    INFO] - Module deeplabv3p_xception65_humanseg already installed in /home/aistudio/.paddlehub/modules/deeplabv3p_xception65_humanseg
[2021-10-27 17:01:09,917] [    INFO] - 0 pretrained paramaters loaded by PaddleHub
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/cbook/__init__.py:2349: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working
  if isinstance(obj, collections.Iterator):
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/cbook/__init__.py:2366: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working
  return list(data) if isinstance(data, collections.MappingView) else data

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<Figure size 720x720 with 1 Axes>

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3、合成背景

In [7]

base_image = Image.open(f'work/wsj.jpg').convert('RGB')
fore_image = Image.open(f'humanseg_output/people.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/1.png")print('照片合成完毕')
plt.figure(figsize=(10,10))
plt.imshow(res_image) 
plt.axis('off') 
plt.show()

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照片合成完毕

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<Figure size 720x720 with 1 Axes>

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二、添加头饰

1、导入相关的包

In [8]

import numpy as npimport matplotlib.image as mpimgimport matplotlib.pyplot as pltimport osfrom PIL import Imageimport paddlehub as hubimport cv2import shutil

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2、展示需要人物图片

In [9]

test_img_path = ["humanseg_output/1.png"]#需要戴头饰的人物照片import matplotlib.pyplot as plt 
import matplotlib.image as mpimg 

img = mpimg.imread(test_img_path[0]) 

# 展示待预测图片plt.figure(figsize=(10,10))
plt.imshow(img) 
plt.axis('off') 
plt.show()

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<Figure size 720x720 with 1 Axes>

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3、脸部检测

In [10]

import paddlehub as hub

module = hub.Module(name="ultra_light_fast_generic_face_detector_1mb_640")

input_dict = {"image": test_img_path}# execute predict and print the resultresults = module.face_detection(data=input_dict, visualization=True)for result in results:    print(result)

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[2021-10-27 17:01:41,620] [    INFO] - Installing ultra_light_fast_generic_face_detector_1mb_640 module

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Downloading ultra_light_fast_generic_face_detector_1mb_640
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Uncompress /home/aistudio/.paddlehub/tmp/tmpxx90yrr9/ultra_light_fast_generic_face_detector_1mb_640
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[2021-10-27 17:01:42,188] [    INFO] - Successfully installed ultra_light_fast_generic_face_detector_1mb_640-1.1.2

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{'data': [{'left': 588.3775024414062, 'right': 673.67138671875, 'top': 520.2343139648438, 'bottom': 640.1370239257812, 'confidence': 0.9997701048851013}], 'path': 'humanseg_output/1.png', 'save_path': 'face_detector_640_predict_output/1.png'}

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4、合成带头饰图片

In [11]

if os.path.exists('./HeCheng'):    pass      else:
    os.mkdir('./HeCheng')#创建文件夹test_path='humanseg_output/1.png'#需要合成人物的图片toushi='work/wsj_maozi.png'#头饰照片savepath='HeCheng'#保存合成后图片文件夹x = results[0]if x.get('data'): 
    for result in results:        print(result)    # box 为头像在图片中的位置
    # box(x1, y1, x2, y2)
    # x1,y1 为头像左上角的位置
    # x2,y2 为头像右下角的位置
    x1 = results[0]['data'][0]['left']
    y1 = results[0]['data'][0]['top']
    x2 = results[0]['data'][0]['right']
    y2 = results[0]['data'][0]['bottom']    # 头饰图片尺寸
    fruit_size = (538, 310)    #偏移量
    addx = -230 #这个可以根据实际情况调节
    addy = -120 #这个可以根据实际情况调节
    box = (int(x1)+addx,int(y1)+addy,int(x1)+fruit_size[0]+addx,int(y1)+ fruit_size[1]+addy)    #print(x1)

    pil_im1 = Image.open(test_path)
    pil_im2 = Image.open(toushi).convert('RGBA')
    region = pil_im1.crop(box)#cut from the picture
    region = region.transpose(Image.ROTATE_270)#rotate the image
    pil_im1.paste(pil_im2, box, pil_im2)
    plt.imshow(pil_im1)
    pil_im1.save(savepath+'/'+'1.jpg')#合成后的图片else:    print("没有检测到")

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{'data': [{'left': 588.3775024414062, 'right': 673.67138671875, 'top': 520.2343139648438, 'bottom': 640.1370239257812, 'confidence': 0.9997701048851013}], 'path': 'humanseg_output/1.png', 'save_path': 'face_detector_640_predict_output/1.png'}

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