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yolo_databases
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.gitignore
vendored
2
.gitignore
vendored
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*.pyc
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*.vscode
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*.pdf
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*.pt
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package.json
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package-lock.json
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venv/
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runs/
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__MACOSX/
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setting.py
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node_modules/
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215
ayf_ocr/yolo_databases/README.md
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ayf_ocr/yolo_databases/README.md
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# 知识点：数据标注，yolov5目标检测框架
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## voc数据集
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VOC2012
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├── Annotations 所有的图像标注信息(XML文件)
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├── ImageSets
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│ ├── Action 人的行为动作图像信息
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│ ├── Layout 人的各个部位图像信息
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│ │
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│ ├── Main 目标检测分类图像信息
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│ │ ├── train.txt 训练集(5717)
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│ │ ├── val.txt 验证集(5823)
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│ │ └── trainval.txt 训练集+验证集(11540)
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│ │
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│ └── Segmentation 目标分割图像信息
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│ ├── train.txt 训练集(1464)
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│ ├── val.txt 验证集(1449)
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│ └── trainval.txt 训练集+验证集(2913)
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│
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├── JPEGImages 所有图像文件
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├── SegmentationClass 语义分割png图（基于类别）
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└── SegmentationObject 实例分割png图（基于目标）
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### 如何数据标注
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精灵标注助手
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http://www.jinglingbiaozhu.com/
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创建标注项目
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手动标注
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导出voc数据，导出数据到Annotations文件夹
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查看导出的xml数据信息
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<?xml version="1.0" ?>
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<annotation>
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<folder>JPEGImages</folder>
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<filename>16329967796329784.jpg</filename>
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<path>E:\code\js_reverse\ayf_ocr\yolo_databases\VOC2012\JPEGImages\16329967796329784.jpg</path>
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<source>
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<database>Unknown</database>
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</source>
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<size>
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<width>150</width>
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<height>150</height>
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<depth>3</depth>
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</size>
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<segmented>0</segmented>
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<object>
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<name>缺口</name>
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<pose>Unspecified</pose>
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<truncated>0</truncated>
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<difficult>0</difficult>
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<bndbox>
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<xmin>56</xmin>
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<ymin>86</ymin>
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<xmax>100</xmax>
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<ymax>135</ymax>
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</bndbox>
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</object>
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</annotation>
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## 目标检测框架 yolov5
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github下载
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https://github.com/ultralytics/yolov5
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Python>=3.7.0和PyTorch>=1.7
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git clone https://github.com/ultralytics/yolov5 # clone
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cd yolov5
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pip install -r requirements.txt # install
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这里的 yolov5 数据集不支持中文路径
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创建数据集目录`VOC2012/1_makedir.py`
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import os
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# 第一步。对目录环境进行检查，是否满足处理要求
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def file_exists(filename, message):
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if not os.path.exists(filename):
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raise FileExistsError(message)
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def make_dirs(dirs):
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try:
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os.makedirs(dirs)
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except FileExistsError:
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print(dirs + ' 目录存在，自动跳过')
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# 第二步。创建关键目录
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file_exists('Annotations', 'VOC标注，Annotations目录不存在')
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file_exists('JPEGImages', 'VOC标注，JPEGImages 图片目录不存在')
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make_dirs('images/train')
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make_dirs('images/val')
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make_dirs('labels/train')
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make_dirs('labels/val')
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生成需要训练的数据`VOC2012/2_movefiles.py`，执行时修改类别
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# ------------------------------------------------------------
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# ↓↓标注的类别，很重要很重要，这里必须要改，按照自己的类别去改|
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classes = ['缺口']
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# ↑↑标注的类别，很重要很重要，这里必须要改，按照自己的类别去改|
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# ------------------------------------------------------------
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训练集/验证集图片数据
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训练集/验证集标签数据（坐标数据归一化）
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## 生成yolov5所需要的训练数据
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为了快速上手yolov5，直接生成数据进行测试，这里直接生成500张数据，运行yolov5需要数据集，
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运行`\slide\pic_product.py`,两种模式`train/val`
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number = input('输入要生成的数量')
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style = input('输入模式：train/val')
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for i in range(int(number)):
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print('正在生成{}，第{}/{}张'.format(style, i + 1, number))
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img = Image.open("drag/{}".format(random.choice(os.listdir('drag'))))
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path = addText(img,
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path='./Annotations/images/{}/{}.jpg'.format(style, str(time.time()).replace('.', '')),
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pic_size=(img.size[0], img.size[1]),
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size=24,
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style=style
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)
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生成的数据如下
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## 开始训练yolov5
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**一、设置训练参数 yolov5\train.py**
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1.训练模型`yolov5s.pt`
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parser.add_argument('--weights', type=str, default=ROOT / 'yolov5s.pt', help='initial weights path')
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github可以下载weights
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2.训练数据`data`
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parser.add_argument('--data', type=str, default=ROOT / 'data/my.yaml', help='dataset.yaml path')
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my.yaml文件
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train: E:\code\js_reverse\ayf_ocr\yolo_databases\slide\Annotations\train.txt
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val: E:\code\js_reverse\ayf_ocr\yolo_databases\slide\Annotations\val.txt
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# Classes
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names:
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0: 0
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3.训练数据迭代次数`epochs`
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parser.add_argument('--epochs', type=int, default=30, help='total training epochs')
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4.训练图片大小`imgsz`
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parser.add_argument('--imgsz', '--img', '--img-size', type=int, default=150, help='train, val image size (pixels)')
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**二、将训练的分类修改成1 yolov5\models\yolov5s.yaml**
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nc: 1 # number of classes
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**三、启动训练 yolov5\train.py**
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会生成一个run文件
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**四、启动推理 yolov5\detect.py**
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1.训练模型`best.pt`
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parser.add_argument('--weights', nargs='+', type=str, default=ROOT / 'runs/train/exp/weights/best.pt', help='model path or triton URL')
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2.资源内容`source`
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parser.add_argument('--source', type=str, default=ROOT / '../slide/Annotations/images/val', help='file/dir/URL/glob/screen/0(webcam)')
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3.图片大小`imgsz`
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parser.add_argument('--imgsz', '--img', '--img-size', nargs='+', type=int, default=[150], help='inference size h,w')
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生成的文件夹
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推理效果
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20
ayf_ocr/yolo_databases/VOC2012/1_makedir.py
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20
ayf_ocr/yolo_databases/VOC2012/1_makedir.py
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import os
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# 第一步。对目录环境进行检查，是否满足处理要求
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def file_exists(filename, message):
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if not os.path.exists(filename):
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raise FileExistsError(message)
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def make_dirs(dirs):
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try:
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os.makedirs(dirs)
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except FileExistsError:
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print(dirs + ' 目录存在，自动跳过')
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# 第二步。创建关键目录
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file_exists('Annotations', 'VOC标注，Annotations目录不存在')
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file_exists('JPEGImages', 'VOC标注，JPEGImages 图片目录不存在')
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make_dirs('images/train')
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make_dirs('images/val')
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make_dirs('labels/train')
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make_dirs('labels/val')
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104
ayf_ocr/yolo_databases/VOC2012/2_movefiles.py
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104
ayf_ocr/yolo_databases/VOC2012/2_movefiles.py
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import os
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import xml.etree.ElementTree as ET
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import logging
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import shutil
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# 提取图像并将其分类为训练集与训练测试集 ：train validation
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Images = os.listdir('JPEGImages')
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Images = [i for i in Images if i.split('.')[-1] == 'jpg']
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print('提取到有效jpg图片共{}张'.format(len(Images)))
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# 按照分配率将图片分类 分类率：train/validation 可以自己修改，可以不改，看心情
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distribution_rate = 0.9
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# ------------------------------------------------------------
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# ↓↓标注的类别，很重要很重要，这里必须要改，按照自己的类别去改|
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classes = ['缺口']
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# ↑↑标注的类别，很重要很重要，这里必须要改，按照自己的类别去改|
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# ------------------------------------------------------------
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# 正式移动图片到指定目录：.images 下, 并且生成训练索引 train.txt and val.txt 这一步会清空这两个文本的内容
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# 正式移动图片到指定目录：.images 下, 并且生成训练索引 train.txt and val.txt 这一步会清空这两个文本的内容
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train = Images[0: int(distribution_rate*len(Images))]
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validation = Images[int(distribution_rate*len(Images)):]
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if train == 0 or validation == 0:
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raise FileExistsError('没有找到训练集的图片或测试集图片，请检查目录')
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# 获取绝对路径。为了好看 把 \ 处理成 /
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ab_path = os.path.dirname(os.path.abspath(__file__)).replace('\\', '/')
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print(ab_path)
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with open('train.txt', 'w', encoding='utf-8') as f:
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for i in train:
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f.write(ab_path + '/images/train/' + i + '\n')
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shutil.copy('JPEGImages/' + i, 'images/train')
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with open('val.txt', 'w', encoding='utf-8') as f:
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for i in validation:
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f.write(ab_path + '/images/val/' + i + '\n')
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shutil.copy('JPEGImages/' + i, 'images/val')
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print('图片移动/复制完成，训练索引 train.txt and val.txt 生成完毕')
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# 预检测 xml与图片的对应关系，这里要求严格一一对应
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xml_file = os.listdir('Annotations')
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xml_file = [i for i in xml_file if i.split('.')[-1] == 'xml']
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xml_file_check = [i.split('.')[0]+'.xml' for i in Images if i.split('.')[-1] == 'jpg']
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if xml_file_check != xml_file:
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raise FileExistsError('Annotations 中xml文件与JPEGImages图片不对应，请仔细检测！')
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# 下面将 xml文件标注提取并生成label
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def convert(size, box):
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dw = 1. / (size[0])
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dh = 1. / (size[1])
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x = (box[0] + box[1]) / 2.0 - 1
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y = (box[2] + box[3]) / 2.0 - 1
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w = box[1] - box[0]
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h = box[3] - box[2]
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x = x * dw
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w = w * dw
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y = y * dh
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h = h * dh
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return x, y, w, h
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def write_labels(xml_file_path, write_to_file_path):
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with open(xml_file_path, 'r', encoding='utf-8') as f:
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tree = ET.parse(f)
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root = tree.getroot()
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size = root.find('size')
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w = int(size.find('width').text)
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h = int(size.find('height').text)
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with open(write_to_file_path, 'w', encoding='utf-8') as f2:
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for obj in root.iter('object'):
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xml_name = obj.find('name').text
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if xml_name not in classes:
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logging.warning('正在检索该对象不存在设定classes，应该引起重视')
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continue
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cls_id = classes.index(xml_name)
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xmlbox = obj.find('bndbox')
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b = (float(xmlbox.find('xmin').text), float(xmlbox.find('xmax').text), float(xmlbox.find('ymin').text), float(xmlbox.find('ymax').text))
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b1, b2, b3, b4 = b
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# 标注越界修正
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if b2 > w:
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b2 = w
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if b4 > h:
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b4 = h
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b = (b1, b2, b3, b4)
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bb = convert((w, h), b)
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write_message = str(cls_id) + " " + " ".join([str(a) for a in bb]) + '\n'
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f2.write(write_message)
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if not write_message:
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logging.warning('未在标注图片的xml文件中取得分类内容，此警告应引起重视，可能意味着分类参数不匹配。classes错误')
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for i in train:
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write_labels('Annotations/' + i.split('.')[0] + '.xml', 'labels/train/{}'.format(i.split('.')[0] + '.txt'))
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for i in validation:
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write_labels('Annotations/' + i.split('.')[0] + '.xml', 'labels/val/{}'.format(i.split('.')[0] + '.txt'))
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# 最后一步 在当前目录下生成索引
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print('finish work')
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<?xml version="1.0" ?>
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<annotation>
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<folder>JPEGImages</folder>
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<filename>16329967796329784.jpg</filename>
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<path>E:\code\js_reverse\ayf_ocr\yolo_databases\VOC2012\JPEGImages\16329967796329784.jpg</path>
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<source>
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<database>Unknown</database>
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</source>
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<size>
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<width>150</width>
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<height>150</height>
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<depth>3</depth>
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</size>
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<segmented>0</segmented>
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<object>
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<name>缺口</name>
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<pose>Unspecified</pose>
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<truncated>0</truncated>
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<difficult>0</difficult>
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<bndbox>
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<xmin>56</xmin>
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<ymin>86</ymin>
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<xmax>100</xmax>
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<ymax>135</ymax>
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</bndbox>
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</object>
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</annotation>
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<?xml version="1.0" ?>
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<annotation>
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<folder>JPEGImages</folder>
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<filename>16329967796409855.jpg</filename>
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<path>E:\code\js_reverse\ayf_ocr\yolo_databases\VOC2012\JPEGImages\16329967796409855.jpg</path>
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<source>
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<database>Unknown</database>
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</source>
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<size>
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<width>150</width>
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<height>150</height>
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<depth>3</depth>
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</size>
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<segmented>0</segmented>
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<object>
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<name>缺口</name>
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<pose>Unspecified</pose>
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<truncated>0</truncated>
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<difficult>0</difficult>
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<bndbox>
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<xmin>51</xmin>
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<ymin>42</ymin>
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<xmax>97</xmax>
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<ymax>93</ymax>
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</bndbox>
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</object>
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</annotation>
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@ -0,0 +1,28 @@
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<?xml version="1.0" ?>
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<annotation>
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||||
<folder>JPEGImages</folder>
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<filename>16329967796494925.jpg</filename>
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<path>E:\code\js_reverse\ayf_ocr\yolo_databases\VOC2012\JPEGImages\16329967796494925.jpg</path>
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<source>
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<database>Unknown</database>
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</source>
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<size>
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<width>150</width>
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||||
<height>150</height>
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<depth>3</depth>
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</size>
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<segmented>0</segmented>
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<object>
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<name>缺口</name>
|
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ayf_ocr/yolo_databases/slide/drag/1.jpg
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ayf_ocr/yolo_databases/slide/pic_product.py
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ayf_ocr/yolo_databases/slide/pic_product.py
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@ -0,0 +1,92 @@
|
||||
# -*- coding:utf-8 -*-
|
||||
from PIL import Image, ImageDraw, ImageFont
|
||||
import random
|
||||
import os
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
from matplotlib.font_manager import FontProperties
|
||||
import matplotlib.image as imgplt
|
||||
import time
|
||||
import cv2
|
||||
|
||||
|
||||
def convert(size, box):
|
||||
dw = 1. / (size[0])
|
||||
dh = 1. / (size[1])
|
||||
x = (box[0] + box[1]) / 2.0 - 1
|
||||
y = (box[2] + box[3]) / 2.0 - 1
|
||||
w = box[1] - box[0]
|
||||
h = box[3] - box[2]
|
||||
x = x * dw
|
||||
w = w * dw
|
||||
y = y * dh
|
||||
h = h * dh
|
||||
return x, y, w, h
|
||||
|
||||
|
||||
def addText(img, path, pic_size, size=50, style='train'):
|
||||
if style not in ['train', 'val']:
|
||||
raise EnvironmentError('style 模式不识别，只支持 train 和 val')
|
||||
|
||||
with open('./Annotations/labels/{}/{}'.format(style, path.split('/')[-1].replace('jpg', 'txt')), 'w') as label_text:
|
||||
print(pic_size[1] / 5)
|
||||
point = (random.randint(15, pic_size[0] - 50),
|
||||
random.randint(15, pic_size[0] - 50))
|
||||
# 计算 xbox坐标并且归一化
|
||||
xmin = point[0]
|
||||
ymin = point[1]
|
||||
xmax = point[0] + 50
|
||||
ymax = point[1] + 50
|
||||
b = (float(xmin), float(xmax), float(ymin), float(ymax))
|
||||
b1, b2, b3, b4 = b
|
||||
w = pic_size[1]
|
||||
h = pic_size[0]
|
||||
if b2 > w:
|
||||
b2 = w
|
||||
if b4 > h:
|
||||
b4 = h
|
||||
b = (b1, b2, b3, b4)
|
||||
|
||||
bb = convert((w, h), b)
|
||||
|
||||
write_message = '0' + " " + " ".join([str(a) for a in bb]) + '\n'
|
||||
label_text.write(write_message)
|
||||
# 创建映射文件 train.txt / val.txt
|
||||
with open('./Annotations/{}.txt'.format(style), 'a') as mapping:
|
||||
# 获取绝对路径。为了好看 把 \ 处理成 /
|
||||
ab_path = os.path.dirname(os.path.abspath(__file__)).replace('\\', '/')
|
||||
mapping.write(ab_path + '/{}'.format(path[2:]) + '\n')
|
||||
|
||||
im = Image.open("2.png")
|
||||
im = im.resize((50, 50), Image.ANTIALIAS)
|
||||
r, g, b, a = im.split()
|
||||
img.paste(im, point, mask=a)
|
||||
img = img.convert('RGB')
|
||||
img.save(path)
|
||||
return path
|
||||
|
||||
|
||||
# 第一步。对目录环境进行检查，是否满足处理要求
|
||||
def make_dirs(dirs):
|
||||
try:
|
||||
os.makedirs(dirs)
|
||||
except FileExistsError:
|
||||
print(dirs + ' 目录存在，自动跳过')
|
||||
|
||||
|
||||
make_dirs('Annotations/images/train')
|
||||
make_dirs('Annotations/images/val')
|
||||
make_dirs('Annotations/labels/train')
|
||||
make_dirs('Annotations/labels/val')
|
||||
|
||||
number = input('输入要生成的数量')
|
||||
style = input('输入模式：train/val')
|
||||
for i in range(int(number)):
|
||||
print('正在生成{}，第{}/{}张'.format(style, i + 1, number))
|
||||
img = Image.open("drag/{}".format(random.choice(os.listdir('drag'))))
|
||||
path = addText(img,
|
||||
path='./Annotations/images/{}/{}.jpg'.format(style, str(time.time()).replace('.', '')),
|
||||
pic_size=(img.size[0], img.size[1]),
|
||||
size=24,
|
||||
style=style
|
||||
)
|
||||
BIN
ayf_ocr/yolo_databases/slide/test.png
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BIN
ayf_ocr/yolo_databases/slide/test.png
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|
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18
ayf_ocr/yolo_databases/slide/test.py
Normal file
18
ayf_ocr/yolo_databases/slide/test.py
Normal file
@ -0,0 +1,18 @@
|
||||
from PIL import Image,ImageDraw,ImageFont
|
||||
|
||||
# img1 = Image.open('1.jpg')
|
||||
# img2 = Image.open('2.jpg')
|
||||
#
|
||||
# img = Image.new("RGB",img1.size)
|
||||
# img.paste(img1,(0,0))
|
||||
# img.paste(img2,(0,345))
|
||||
# img.save('3.png', quality=70)
|
||||
|
||||
|
||||
img = Image.open("1.jpg").convert('RGBA')
|
||||
im = Image.open("2.png")
|
||||
img = img.resize((35, 35), Image.ANTIALIAS)
|
||||
r,g,b,a = im.split()
|
||||
img.paste(im,(100, 100),mask = a)
|
||||
img = img.convert('RGB')
|
||||
img.save("test.png")
|
||||
Loading…
x
Reference in New Issue
Block a user