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利用PaddleHub戴绿帽[人间的青草地需要浇水~]
被抖音洗脑的人间的青草地需要浇水~
驱使了我想要做本项目,项目已放在AIstudio
话不多说，开干！
# 安装Paddlehub
!pip install paddlehub==2.1.0
! hub install ultra_light_fast_generic_face_detector_1mb_640==1.1.2
通过paddlehub获取位置信息
import paddlehub as hub
module = hub.Module(name="ultra_light_fast_generic_face_detector_1mb_640", version="1.1.2")
res = module.face_detection(
paths = ["data/data96296/1.jpg"],
visualization=True,
output_dir='face_detection_output')
/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
[2021-06-22 16:33:08,556] [ WARNING] - The _initialize method in HubModule will soon be deprecated, you can use the __init__() to handle the initialization of the object
环境准备
import cv2
from PIL import Image
import numpy as np
from matplotlib import pyplot as plt
%matplotlib inline
filename = 'face_detection_output/1.jpg'
## [Load an image from a file]
img = cv2.imread(filename)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
plt.imshow(img)
/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
<matplotlib.image.AxesImage at 0x7fe05c0632d0>
res
# 返回
# res (list[dict]): 识别结果的列表，列表中每一个元素为 dict，关键字有'path', 'data'，相应的取值为：
# path (str): 原输入图片的路径；
# data (numpy.ndarray): 图像分割得到的结果，shape 为H * W，元素的取值为0-19，表示每个像素的分类结果，映射顺序与下面的调色板相同。
[{'data': [{'left': 221.29409790039062, 'right': 488.7735290527344, 'top': 78.7082290649414, 'bottom': 471.13763427734375, 'confidence': 0.9999988079071045}],
'path': 'data/data96296/1.jpg',
'save_path': 'face_detection_output/1.jpg'}]
# 处理为整数，小数不符合后续位置参数的要求
import math
data = res[0]['data']
left = data[0]['left']
left = math.ceil(left)
right = data[0]['right']
right = math.ceil(right)
top = data[0]['top']
top = math.ceil(top)
bottom = data[0]['bottom']
bottom = math.ceil(bottom)
print("left:",left)
print("right:",right)
print("top:",top)
print("bottom:",bottom)
left: 222
right: 489
top: 79
bottom: 472
有上面的数据可以知道:
框的左上角顶点位置为[left,top]
框的右上角顶点位置为[right,top]
得到这些信息后，再带上提前做好的帽子，就思路清晰了
将帽子尺寸改为[right-left+25,2.2*top] 25和2.2可以自己进行调整，合适为止。
# 储存帽子数据
hat_w , hat_h= right-left+40, 2.2*top
hat_w = math.ceil(hat_w)
hat_h = math.ceil(hat_h)
print(hat_w)
print(hat_h)
307
174
图片合成
from PIL import Image
mother_img = "data/data96296/1.jpg"
son_img = "data/data96296/lmz.png"
save_img = "result/result.png"
# 获取图片,方便后面的代码调用
M_Img = Image.open(mother_img)
S_Img = Image.open(son_img)
# 子图缩小的倍数1代表不变，2就代表原来的一半（没啥用，本来想通过这个控制图片）
factor = 1
# 给图片指定色彩显示格式
S_Img = S_Img.convert("RGBA")
# 获取图片的尺寸
M_Img_w, M_Img_h = M_Img.size # 获取被放图片的大小（母图）
print("母图尺寸：",M_Img.size)
S_Img_w, S_Img_h = S_Img.size # 获取小图的大小（子图）
print("子图尺寸：",S_Img.size)
size_w = int(S_Img_w / factor)
size_h = int(S_Img_h / factor)
# 防止子图尺寸大于母图
if S_Img_w > size_w:
S_Img_w = size_w
if S_Img_h > size_h:
S_Img_h = size_h
# 重新设置子图的尺寸
icon = S_Img.resize((hat_w, hat_h), Image.ANTIALIAS)
# 子图的左上角坐标位置
w = left-20
h = 0
# 粘贴子图到母图的指定坐标（当前居中）
M_Img.paste(icon, (w, h), mask=None)
# 保存图片
M_Img.save(save_img)
母图尺寸： (693, 539)
子图尺寸： (224, 236)
查看效果
filename = 'result/result.png'
## [Load an image from a file]
img = cv2.imread(filename)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
plt.imshow(img)
<matplotlib.image.AxesImage at 0x7fe050429050>
总结
- 利用PaddleHub获取人体的头部位置
- 合理计算出帽子所需要放置的位置
- 图片合成
- 很明显（我这个版本很失败，还没有找到好的办法去把图片的黑色去掉，郁闷（明明帽子图片是PNG且代码也写了RGBA））
我在AI Studio上获得黄金等级，点亮8个徽章，来互关呀~
