diff --git a/.gitignore b/.gitignore index 06e7e35..ef9ed18 100644 --- a/.gitignore +++ b/.gitignore @@ -7,9 +7,11 @@ *.pyc *.vscode *.pdf +*.pt package.json package-lock.json venv/ +runs/ __MACOSX/ setting.py node_modules/ \ No newline at end of file diff --git a/机器学习/README.md b/ayf_ocr/README.md similarity index 100% rename from 机器学习/README.md rename to ayf_ocr/README.md diff --git a/ayf_ocr/yolo_databases/README.md b/ayf_ocr/yolo_databases/README.md new file mode 100644 index 0000000..447b57c --- /dev/null +++ b/ayf_ocr/yolo_databases/README.md @@ -0,0 +1,215 @@ +# 知识点：数据标注，yolov5目标检测框架 + +## voc数据集 + + VOC2012 + ├── Annotations 所有的图像标注信息(XML文件) + ├── ImageSets + │ ├── Action 人的行为动作图像信息 + │ ├── Layout 人的各个部位图像信息 + │ │ + │ ├── Main 目标检测分类图像信息 + │ │ ├── train.txt 训练集(5717) + │ │ ├── val.txt 验证集(5823) + │ │ └── trainval.txt 训练集+验证集(11540) + │ │ + │ └── Segmentation 目标分割图像信息 + │ ├── train.txt 训练集(1464) + │ ├── val.txt 验证集(1449) + │ └── trainval.txt 训练集+验证集(2913) + │ + ├── JPEGImages 所有图像文件 + ├── SegmentationClass 语义分割png图（基于类别） + └── SegmentationObject 实例分割png图（基于目标） + +### 如何数据标注 + +精灵标注助手 + +http://www.jinglingbiaozhu.com/ + +创建标注项目 + +![属性文本](./img/1.png) + +手动标注 + +![属性文本](./img/2.png) + +导出voc数据，导出数据到Annotations文件夹 + +![属性文本](./img/3.png) + +查看导出的xml数据信息 + + + + JPEGImages + 16329967796329784.jpg + E:\code\js_reverse\ayf_ocr\yolo_databases\VOC2012\JPEGImages\16329967796329784.jpg + + Unknown + + + 150 + 150 + 3 + + + 0 + + 缺口 + Unspecified + 0 + 0 + + 56 + 86 + 100 + 135 + + + + +## 目标检测框架 yolov5 + +github下载 + +https://github.com/ultralytics/yolov5 + +Python>=3.7.0和PyTorch>=1.7 + + git clone https://github.com/ultralytics/yolov5 # clone + cd yolov5 + pip install -r requirements.txt # install + +这里的 yolov5 数据集不支持中文路径 + +创建数据集目录`VOC2012/1_makedir.py` + + import os + + # 第一步。对目录环境进行检查，是否满足处理要求 + def file_exists(filename, message): + if not os.path.exists(filename): + raise FileExistsError(message) + def make_dirs(dirs): + try: + os.makedirs(dirs) + except FileExistsError: + print(dirs + ' 目录存在，自动跳过') + + # 第二步。创建关键目录 + file_exists('Annotations', 'VOC标注，Annotations目录不存在') + file_exists('JPEGImages', 'VOC标注，JPEGImages 图片目录不存在') + make_dirs('images/train') + make_dirs('images/val') + make_dirs('labels/train') + make_dirs('labels/val') + +生成需要训练的数据`VOC2012/2_movefiles.py`，执行时修改类别 + + # ------------------------------------------------------------ + # ↓↓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| + + classes = ['缺口'] + + # ↑↑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| + # ------------------------------------------------------------ + +训练集/验证集图片数据 +![属性文本](./img/5.png) + +训练集/验证集标签数据（坐标数据归一化） +![属性文本](./img/6.png) + +## 生成yolov5所需要的训练数据 + +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， + +运行`\slide\pic_product.py`,两种模式`train/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 + ) + +生成的数据如下 +![属性文本](./img/7.png) + +## 开始训练yolov5 + +**一、设置训练参数 yolov5\train.py** + +1.训练模型`yolov5s.pt` + + parser.add_argument('--weights', type=str, default=ROOT / 'yolov5s.pt', help='initial weights path') + +github可以下载weights + +![属性文本](./img/4.png) + +2.训练数据`data` + + parser.add_argument('--data', type=str, default=ROOT / 'data/my.yaml', help='dataset.yaml path') + +![属性文本](./img/8.png) + +my.yaml文件 + + train: E:\code\js_reverse\ayf_ocr\yolo_databases\slide\Annotations\train.txt + val: E:\code\js_reverse\ayf_ocr\yolo_databases\slide\Annotations\val.txt + + + # Classes + names: + 0: 0 + +3.训练数据迭代次数`epochs` + + parser.add_argument('--epochs', type=int, default=30, help='total training epochs') + +4.训练图片大小`imgsz` + + parser.add_argument('--imgsz', '--img', '--img-size', type=int, default=150, help='train, val image size (pixels)') + +**二、将训练的分类修改成1 yolov5\models\yolov5s.yaml** + + nc: 1 # number of classes + +**三、启动训练 yolov5\train.py** + +![属性文本](./img/9.png) + +会生成一个run文件 + +![属性文本](./img/10.png) + +**四、启动推理 yolov5\detect.py** + +1.训练模型`best.pt` + + parser.add_argument('--weights', nargs='+', type=str, default=ROOT / 'runs/train/exp/weights/best.pt', help='model path or triton URL') + +2.资源内容`source` + + parser.add_argument('--source', type=str, default=ROOT / '../slide/Annotations/images/val', help='file/dir/URL/glob/screen/0(webcam)') + +3.图片大小`imgsz` + + parser.add_argument('--imgsz', '--img', '--img-size', nargs='+', type=int, default=[150], help='inference size h,w') + +生成的文件夹 + +![属性文本](./img/11.png) + +推理效果 + +![属性文本](./img/12.png) \ No newline at end of file diff --git a/ayf_ocr/yolo_databases/VOC2012/1_makedir.py b/ayf_ocr/yolo_databases/VOC2012/1_makedir.py new file mode 100644 index 0000000..3b16fad --- /dev/null +++ b/ayf_ocr/yolo_databases/VOC2012/1_makedir.py @@ -0,0 +1,20 @@ +import os + +# 第一步。对目录环境进行检查，是否满足处理要求 +def file_exists(filename, message): + if not os.path.exists(filename): + raise FileExistsError(message) +def make_dirs(dirs): + try: + os.makedirs(dirs) + except FileExistsError: + print(dirs + ' 目录存在，自动跳过') + +# 第二步。创建关键目录 +file_exists('Annotations', 'VOC标注，Annotations目录不存在') +file_exists('JPEGImages', 'VOC标注，JPEGImages 图片目录不存在') +make_dirs('images/train') +make_dirs('images/val') +make_dirs('labels/train') +make_dirs('labels/val') + diff --git a/ayf_ocr/yolo_databases/VOC2012/2_movefiles.py b/ayf_ocr/yolo_databases/VOC2012/2_movefiles.py new file mode 100644 index 0000000..fb37b64 --- /dev/null +++ b/ayf_ocr/yolo_databases/VOC2012/2_movefiles.py @@ -0,0 +1,104 @@ +import os +import xml.etree.ElementTree as ET +import logging +import shutil + +# 提取图像并将其分类为训练集与训练测试集 ：train validation +Images = os.listdir('JPEGImages') +Images = [i for i in Images if i.split('.')[-1] == 'jpg'] +print('提取到有效jpg图片共{}张'.format(len(Images))) +# 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 +distribution_rate = 0.9 + +# ------------------------------------------------------------ +# ↓↓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| + +classes = ['缺口'] + +# ↑↑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| +# ------------------------------------------------------------ + + +# 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 +# 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 +train = Images[0: int(distribution_rate*len(Images))] +validation = Images[int(distribution_rate*len(Images)):] +if train == 0 or validation == 0: + raise FileExistsError('没有找到训练集的图片或测试集图片，请检查目录') + +# 获取绝对路径。为了好看 把 \ 处理成 / +ab_path = os.path.dirname(os.path.abspath(__file__)).replace('\\', '/') +print(ab_path) +with open('train.txt', 'w', encoding='utf-8') as f: + for i in train: + f.write(ab_path + '/images/train/' + i + '\n') + shutil.copy('JPEGImages/' + i, 'images/train') + +with open('val.txt', 'w', encoding='utf-8') as f: + for i in validation: + f.write(ab_path + '/images/val/' + i + '\n') + shutil.copy('JPEGImages/' + i, 'images/val') + +print('图片移动/复制完成，训练索引 train.txt and val.txt 生成完毕') + + +# 预检测 xml与图片的对应关系，这里要求严格一一对应 + + +xml_file = os.listdir('Annotations') +xml_file = [i for i in xml_file if i.split('.')[-1] == 'xml'] +xml_file_check = [i.split('.')[0]+'.xml' for i in Images if i.split('.')[-1] == 'jpg'] +if xml_file_check != xml_file: + raise FileExistsError('Annotations 中xml文件与JPEGImages图片不对应，请仔细检测！') +# 下面将 xml文件标注提取并生成label +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 write_labels(xml_file_path, write_to_file_path): + with open(xml_file_path, 'r', encoding='utf-8') as f: + tree = ET.parse(f) + root = tree.getroot() + size = root.find('size') + w = int(size.find('width').text) + h = int(size.find('height').text) + with open(write_to_file_path, 'w', encoding='utf-8') as f2: + for obj in root.iter('object'): + xml_name = obj.find('name').text + if xml_name not in classes: + logging.warning('正在检索该对象不存在设定classes，应该引起重视') + continue + cls_id = classes.index(xml_name) + xmlbox = obj.find('bndbox') + b = (float(xmlbox.find('xmin').text), float(xmlbox.find('xmax').text), float(xmlbox.find('ymin').text), float(xmlbox.find('ymax').text)) + b1, b2, b3, b4 = b + # 标注越界修正 + if b2 > w: + b2 = w + if b4 > h: + b4 = h + b = (b1, b2, b3, b4) + bb = convert((w, h), b) + write_message = str(cls_id) + " " + " ".join([str(a) for a in bb]) + '\n' + f2.write(write_message) + if not write_message: + 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') + +for i in train: + write_labels('Annotations/' + i.split('.')[0] + '.xml', 'labels/train/{}'.format(i.split('.')[0] + '.txt')) + +for i in validation: + write_labels('Annotations/' + i.split('.')[0] + '.xml', 'labels/val/{}'.format(i.split('.')[0] + '.txt')) + +# 最后一步 在当前目录下生成索引 + +print('finish work') diff --git a/ayf_ocr/yolo_databases/VOC2012/Annotations/16329967796329784.xml b/ayf_ocr/yolo_databases/VOC2012/Annotations/16329967796329784.xml new file mode 100644 index 0000000..f506bad --- /dev/null +++ b/ayf_ocr/yolo_databases/VOC2012/Annotations/16329967796329784.xml @@ -0,0 +1,28 @@ + + +JPEGImages +16329967796329784.jpg +E:\code\js_reverse\ayf_ocr\yolo_databases\VOC2012\JPEGImages\16329967796329784.jpg + + Unknown + + + 150 + 150 + 3 + + +0 + + 缺口 + Unspecified + 0 + 0 + + 56 + 86 + 100 + 135 + + + diff --git a/ayf_ocr/yolo_databases/VOC2012/Annotations/16329967796409855.xml b/ayf_ocr/yolo_databases/VOC2012/Annotations/16329967796409855.xml new file mode 100644 index 0000000..d11be4b --- /dev/null +++ b/ayf_ocr/yolo_databases/VOC2012/Annotations/16329967796409855.xml @@ -0,0 +1,28 @@ + + +JPEGImages +16329967796409855.jpg +E:\code\js_reverse\ayf_ocr\yolo_databases\VOC2012\JPEGImages\16329967796409855.jpg + + Unknown + + + 150 + 150 + 3 + + +0 + + 缺口 + Unspecified + 0 + 0 + + 51 + 42 + 97 + 93 + + + diff --git a/ayf_ocr/yolo_databases/VOC2012/Annotations/16329967796494925.xml b/ayf_ocr/yolo_databases/VOC2012/Annotations/16329967796494925.xml new file mode 100644 index 0000000..eb472ac --- /dev/null +++ b/ayf_ocr/yolo_databases/VOC2012/Annotations/16329967796494925.xml @@ -0,0 +1,28 @@ + + +JPEGImages +16329967796494925.jpg +E:\code\js_reverse\ayf_ocr\yolo_databases\VOC2012\JPEGImages\16329967796494925.jpg + + Unknown + + + 150 + 150 + 3 + + +0 + + 缺口 + Unspecified + 0 + 0 + + 73 + 30 + 119 + 85 + + + diff --git a/ayf_ocr/yolo_databases/VOC2012/Annotations/16329967796625037.xml b/ayf_ocr/yolo_databases/VOC2012/Annotations/16329967796625037.xml new file mode 100644 index 0000000..da8122e --- /dev/null +++ b/ayf_ocr/yolo_databases/VOC2012/Annotations/16329967796625037.xml @@ -0,0 +1,28 @@ + + +JPEGImages +16329967796625037.jpg +E:\code\js_reverse\ayf_ocr\yolo_databases\VOC2012\JPEGImages\16329967796625037.jpg + + Unknown + + + 150 + 150 + 3 + + +0 + + 缺口 + Unspecified + 0 + 0 + + 55 + 82 + 100 + 130 + + + diff --git a/ayf_ocr/yolo_databases/VOC2012/Annotations/16329967796715117.xml b/ayf_ocr/yolo_databases/VOC2012/Annotations/16329967796715117.xml new file mode 100644 index 0000000..3a794f1 --- /dev/null +++ b/ayf_ocr/yolo_databases/VOC2012/Annotations/16329967796715117.xml @@ -0,0 +1,28 @@ + + +JPEGImages +16329967796715117.jpg +E:\code\js_reverse\ayf_ocr\yolo_databases\VOC2012\JPEGImages\16329967796715117.jpg + + Unknown + + + 150 + 150 + 3 + + +0 + + 缺口 + Unspecified + 0 + 0 + + 27 + 17 + 75 + 69 + + + diff --git a/ayf_ocr/yolo_databases/VOC2012/Annotations/16329967796840222.xml b/ayf_ocr/yolo_databases/VOC2012/Annotations/16329967796840222.xml new file mode 100644 index 0000000..8b0b0c4 --- /dev/null +++ b/ayf_ocr/yolo_databases/VOC2012/Annotations/16329967796840222.xml @@ -0,0 +1,28 @@ + + +JPEGImages +16329967796840222.jpg +E:\code\js_reverse\ayf_ocr\yolo_databases\VOC2012\JPEGImages\16329967796840222.jpg + + Unknown + + + 150 + 150 + 3 + + +0 + + 缺口 + Unspecified + 0 + 0 + + 99 + 57 + 147 + 109 + + + diff --git a/ayf_ocr/yolo_databases/VOC2012/ImageSets/Main/test.txt b/ayf_ocr/yolo_databases/VOC2012/ImageSets/Main/test.txt new file mode 100644 index 0000000..e69de29 diff --git a/ayf_ocr/yolo_databases/VOC2012/ImageSets/Main/train.txt b/ayf_ocr/yolo_databases/VOC2012/ImageSets/Main/train.txt new file mode 100644 index 0000000..e69de29 diff --git a/ayf_ocr/yolo_databases/VOC2012/ImageSets/Main/trainval.txt b/ayf_ocr/yolo_databases/VOC2012/ImageSets/Main/trainval.txt new file mode 100644 index 0000000..e69de29 diff --git a/ayf_ocr/yolo_databases/VOC2012/ImageSets/Main/val.txt b/ayf_ocr/yolo_databases/VOC2012/ImageSets/Main/val.txt new file mode 100644 index 0000000..e69de29 diff --git a/ayf_ocr/yolo_databases/VOC2012/JPEGImages/16329967796329784.jpg 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b/ayf_ocr/yolo_databases/slide/drag_test/10.jpg new file mode 100644 index 0000000..3017bc7 Binary files /dev/null and b/ayf_ocr/yolo_databases/slide/drag_test/10.jpg differ diff --git a/ayf_ocr/yolo_databases/slide/pic_product.py b/ayf_ocr/yolo_databases/slide/pic_product.py new file mode 100644 index 0000000..f89c923 --- /dev/null +++ b/ayf_ocr/yolo_databases/slide/pic_product.py @@ -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 + ) diff --git a/ayf_ocr/yolo_databases/slide/test.png b/ayf_ocr/yolo_databases/slide/test.png new file mode 100644 index 0000000..0f4ecd1 Binary files /dev/null and b/ayf_ocr/yolo_databases/slide/test.png differ diff --git a/ayf_ocr/yolo_databases/slide/test.py b/ayf_ocr/yolo_databases/slide/test.py new file mode 100644 index 0000000..1eed981 --- /dev/null +++ b/ayf_ocr/yolo_databases/slide/test.py @@ -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") \ No newline at end of file