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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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：
变换后：
原图+头饰：
或者你会喜欢这一款
原图+切换背景+头饰:
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
Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple
Collecting paddlehub==1.6.2
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Installing collected packages: paddlehub
Found existing installation: paddlehub 2.0.4
Uninstalling paddlehub-2.0.4:
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Successfully installed paddlehub-1.6.2
/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: ""
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from collections import Sequence, defaultdict
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from collections import MutableMapping # since Python 2.4?
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from collections import Iterable, Mapping
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from collections import Sized
Downloading deeplabv3p_xception65_humanseg
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Uncompress /home/aistudio/.paddlehub/tmp/tmpxrc3h3w7/deeplabv3p_xception65_humanseg
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Successfully installed deeplabv3p_xception65_humanseg-1.0.0
In [3]
import os os.environ["CUDA_VISIBLE_DEVICES"]="0"
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
/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
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()
[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
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()
照片合成完毕
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
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()
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)
[2021-10-27 17:01:41,620] [ INFO] - Installing ultra_light_fast_generic_face_detector_1mb_640 module
Downloading ultra_light_fast_generic_face_detector_1mb_640 [==================================================] 100.00% Uncompress /home/aistudio/.paddlehub/tmp/tmpxx90yrr9/ultra_light_fast_generic_face_detector_1mb_640 [==================================================] 100.00%
[2021-10-27 17:01:42,188] [ INFO] - Successfully installed ultra_light_fast_generic_face_detector_1mb_640-1.1.2
{'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'}
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")
{'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'}
