使用yolov4训练自己的数据集——科大讯飞X光安检图像识别baseline

迷南。 2023-03-06 03:50 7阅读 0赞

### 文章目录 ###

*  一、cfg文件修改
 *  二、data/下新建配置文件
 *  三、创建标注文件
 *  四、划分训练集、验证集与对应标签
 *   *  4.1 split\_data.py
     *  4.2 xml2txt.py
     *  4.3 creattxt.py
 *  五、训练
 *  六、推理

整体代码结构：  
![在这里插入图片描述][20200805122752732.png]

# 一、cfg文件修改 #

创建yolov4-mydatasets.cfg，因为我用了yolov4.cfg训练太吃显存，2080TI的机子batchsize设置为2也会爆显存。其主要原因是mish函数太占显存了。

修改的地方：  
将mish函数替代为relu函数，大大降低显存使用！  
另外filters设置为：**（class数目 + 4 +1） × 3 = 45**（我这里10个类别）分别对应三个detect header  
classes=10 修改为类别数目，我这里是10（所以这里和filters一共修改6处）  
其他的超参数可以适当修改。

[convolutional]
    size=1
    stride=1
    pad=1
    filters=45
    activation=linear
    
    
    [yolo]
    mask = 0,1,2
    anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401
    classes=10
    num=9
    jitter=.3
    ignore_thresh = .7
    truth_thresh = 1
    scale_x_y = 1.2
    iou_thresh=0.213
    cls_normalizer=1.0
    iou_normalizer=0.07
    iou_loss=ciou
    nms_kind=greedynms
    beta_nms=0.6
    
    
    [route]
    layers = -4
    
    [convolutional]
    batch_normalize=1
    size=3
    stride=2
    pad=1
    filters=256
    activation=leaky
    
    [route]
    layers = -1, -16
    
    [convolutional]
    batch_normalize=1
    filters=256
    size=1
    stride=1
    pad=1
    activation=leaky
    
    [convolutional]
    batch_normalize=1
    size=3
    stride=1
    pad=1
    filters=512
    activation=leaky
    
    [convolutional]
    batch_normalize=1
    filters=256
    size=1
    stride=1
    pad=1
    activation=leaky
    
    [convolutional]
    batch_normalize=1
    size=3
    stride=1
    pad=1
    filters=512
    activation=leaky
    
    [convolutional]
    batch_normalize=1
    filters=256
    size=1
    stride=1
    pad=1
    activation=leaky
    
    [convolutional]
    batch_normalize=1
    size=3
    stride=1
    pad=1
    filters=512
    activation=leaky
    
    [convolutional]
    size=1
    stride=1
    pad=1
    filters=45
    activation=linear
    
    
    [yolo]
    mask = 3,4,5
    anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401
    classes=10
    num=9
    jitter=.3
    ignore_thresh = .7
    truth_thresh = 1
    scale_x_y = 1.1
    iou_thresh=0.213
    cls_normalizer=1.0
    iou_normalizer=0.07
    iou_loss=ciou
    nms_kind=greedynms
    beta_nms=0.6
    
    
    [route]
    layers = -4
    
    [convolutional]
    batch_normalize=1
    size=3
    stride=2
    pad=1
    filters=512
    activation=leaky
    
    [route]
    layers = -1, -37
    
    [convolutional]
    batch_normalize=1
    filters=512
    size=1
    stride=1
    pad=1
    activation=leaky
    
    [convolutional]
    batch_normalize=1
    size=3
    stride=1
    pad=1
    filters=1024
    activation=leaky
    
    [convolutional]
    batch_normalize=1
    filters=512
    size=1
    stride=1
    pad=1
    activation=leaky
    
    [convolutional]
    batch_normalize=1
    size=3
    stride=1
    pad=1
    filters=1024
    activation=leaky
    
    [convolutional]
    batch_normalize=1
    filters=512
    size=1
    stride=1
    pad=1
    activation=leaky
    
    [convolutional]
    batch_normalize=1
    size=3
    stride=1
    pad=1
    filters=1024
    activation=leaky
    
    [convolutional]
    size=1
    stride=1
    pad=1
    filters=45
    activation=linear
    
    
    [yolo]
    mask = 6,7,8
    anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401
    classes=10
    num=9
    jitter=.3
    ignore_thresh = .7
    truth_thresh = 1
    random=1
    scale_x_y = 1.05
    iou_thresh=0.213
    cls_normalizer=1.0
    iou_normalizer=0.07
    iou_loss=ciou
    nms_kind=greedynms
    beta_nms=0.6

# 二、data/下新建配置文件 #

在data下新建mydatasets.names文件，把自己的类别写进去即可

knife
    scissors
    lighter
    zippooil
    pressure
    slingshot
    handcuffs
    nailpolish
    powerbank
    firecrackers

在data/下新建mydatasets.data文件，填入以下内容即可：

classes= 10
    train=data/train.txt
    valid=data/valid.txt
    names=data/mydatasets.names

# 三、创建标注文件 #

可以使用LabelImg,Labme,Labelbox, CVAT来标注数据，对于目标检测而言需要标注bounding box即可。然后需要将标注转换为和darknet format相同的标注形式，每一个图像生成一个\*.txt的标注文件（如果该图像没有标注目标则不用创建\*.txt文件）。创建的\*.txt文件遵循如下规则：

*  每一行存放一个标注类别
 *  每一行的内容包括class x\_center y\_center width height
 *  Bounding box 的坐标信息是归一化之后的（0-1）
 *  class label转化为index时计数是从0开始的

# 四、划分训练集、验证集与对应标签 #

在data/下，新建文件夹命名为“mydatasets”。即：data/mydatasets/，再划分train文件夹（训练数据集），labels文件夹（标签集），test文件夹。数据集、测试集与标签分别放到对应位置。如下图所示：  
![在这里插入图片描述][2020080511352869.png]  
再先划分训练数据集train，将train数据集划分为train/valid数据集：

## 4.1 split\_data.py ##

import os
    import glob
    import json
    import shutil
    import numpy as np
    import xml.etree.ElementTree as ET
    
    img_train = './images/train/'
    img_val = './images/valid/'
    
    label_train = './labels/train/'
    label_val = './labels/valid/'
    
    allimgs = glob.glob('mydatasets/train/' + "/*.jpg")
    allimgs = np.sort(allimgs)
    np.random.seed(100)
    np.random.shuffle(allimgs)
    
    train_ratio = 0.9
    train_num = int(len(allimgs) * train_ratio)
    
    # 得到训练和验证数据集列表
    img_list_train = allimgs[:train_num]
    img_list_val = allimgs[train_num:]
    
    # 创建文件夹
    if os.path.exists(img_train):
        shutil.rmtree(img_train)
        os.mkdir(img_train)
    else:
        os.mkdir(img_train)
    
    if os.path.exists(img_val):
        shutil.rmtree(img_val)
        os.mkdir(img_val)
    else:
        os.mkdir(img_val)
    
    if os.path.exists(label_train):
        shutil.rmtree(label_train)
        os.mkdir(label_train)
    else:
        os.mkdir(label_train)
    
    if os.path.exists(label_val):
        shutil.rmtree(label_val)
        os.mkdir(label_val)
    else:
        os.mkdir(label_val)
    
    # 移动val数据到指定位置
    for i in img_list_val:
        print(i.split('.')[0].split('/')[1][6:])
        img_id = i.split('.')[0].split('/')[1][6:]
        print(img_id)
        # jpg
        shutil.copy(i, img_val + img_id + '.jpg')
        # xml
        shutil.copy('mydatasets/label/' + 'new_' + img_id + '.xml', label_val + img_id + '.xml')
    
    # 移动train数据到指定位置
    for i in img_list_train:
        img_id = i.split('.')[0].split('/')[1][6:]
        print(img_id)
        # jpg
        shutil.copy(i, img_train + img_id + '.jpg')
        # xml
        shutil.copy('mydatasets/label/' + 'new_' + img_id + '.xml', label_train + img_id + '.xml')

如下图：  
![在这里插入图片描述][20200805114115705.png]

## 4.2 xml2txt.py ##

yolo的标签格式是txt格式，所以我们还需将xml标签转为txt格式。先新建txt\_train、txt\_valid文件夹，用于存放转换后的txt标签文件。  
**注意：**

*  第一次运行xml2txt.py，得到训练集的txt文件，再修改/labels/train/为/labels/valid/，得到验证集的txt文件。
 *  转换完成后将原来的train，valid文件夹删除，并将txt\_train，txt\_valid文件夹重命名为train，valid。

import xml.etree.ElementTree as ET
    import pickle
    import os
    from os import listdir, getcwd
    from os.path import join
    import bs4
    from PIL import Image
    
    
    classes = ["knife","scissors","lighter","zippooil",
    "pressure","slingshot","handcuffs","nailpolish",
    "powerbank","firecrackers"]  #为了获得cls id
    
    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 convert_annotation(image_id):
        global none_counts
    
        # 输入文件xml
        in_file = open('./labels/train/%s.xml' % (image_id))
        # 输出label txt
        out_file = open('./labels/train/%s.txt' % (image_id), 'w')
        tree = ET.parse(in_file)
        root = tree.getroot()
        size = root.find('size')
        print(image_id)
    
        w = int(size.find('width').text)
        h = int(size.find('height').text)
        for obj in root.iter('object'):
            cls = obj.find('name').text
            if cls not in classes:
                continue
            cls_id = classes.index(cls)
            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))
            bb = convert((w, h), b)
            out_file.write(str(cls_id) + " " + " ".join([str(a) for a in bb]) + '\n')
    
    
    
    if __name__=='__main__':
        xml_count = 0
        none_counts = 0
        list_file = os.listdir('./labels/train/')
        for file in list_file:
            print(file)
            # image_id = file.replace('.xml', '')
            image_id = file.split('.')[0]
            convert_annotation(image_id)
            xml_count = xml_count + 1
        print('没有size字段的xml文件数目：{}'.format(none_counts))
        print('总xml个数是 {}'.format(xml_count))

## 4.3 creattxt.py ##

这一步需要生成train.txt和valid.txt

# 根据训练数据集和验证数据集persontrain.txt and personvalid.txt
    import os, random, shutil
    
    trainDir = 'images/train/'
    validDir = 'images/valid/'
    
    train_pathDir = os.listdir(trainDir)  # 取图片的原始路径
    print('训练集图片数目: {}'.format(len(train_pathDir)))
    
    valid_pathDir = os.listdir(validDir)  # 取图片的原始路径
    print('验证集图片数目: {}'.format(len(valid_pathDir)))
    
    # 删除persontrain.txt and personvalid.txt
    if(os.path.exists('train.txt')):
         os.remove('train.txt')
         print('删除train.txt成功')
    
    if(os.path.exists('valid.txt')):
         os.remove('valid.txt')
         print('删除valid.txt成功')
    
    def text_save(root, filename, data):  # filename为写入CSV文件的路径，data为要写入数据列表.
        file = open(filename, 'a')
        for i in range(len(data)):
            s = str(data[i]).replace('[', '').replace(']', '')  # 去除[],这两行按数据不同，可以选择
            s = 'data/' + root + s.replace("'", '').replace(',', '') + '\n'  # 去除单引号，逗号，每行末尾追加换行符
            file.write(s)
        file.close()
        print("保存文件成功")
    
    if __name__ == '__main__':
        text_save(trainDir, './train.txt', train_pathDir)
        text_save(validDir, './valid.txt', valid_pathDir)
        print('train.txt 有 {} 行'.format(len([i for i in open('./train.txt', 'r')])))
        print('valid.txt 有 {} 行'.format(len([i for i in open('./valid.txt', 'r')])))

得到的txt文件格式部分内容如下：

data/images/train/1.jpg
    data/images/train/10.jpg
    data/images/train/1000.jpg
    data/images/train/1001.jpg
    data/images/train/1002.jpg
    data/images/train/1005.jpg
    data/images/train/1006.jpg
    data/images/train/1007.jpg
    data/images/train/1008.jpg
    data/images/train/1009.jpg
    data/images/train/101.jpg
    data/images/train/1010.jpg
    data/images/train/1011.jpg
    data/images/train/1012.jpg
    data/images/train/1015.jpg
    data/images/train/1018.jpg
    data/images/train/1019.jpg

# 五、训练 #

完成上述步骤后即可训练，在train.py下运行

if __name__ == '__main__':
        parser = argparse.ArgumentParser()
        parser.add_argument('--epochs', type=int, default=300)  # 500200 batches at bs 16, 117263 COCO images = 273 epochs
        parser.add_argument('--batch-size', type=int, default=16)  # effective bs = batch_size * accumulate = 16 * 4 = 64
        parser.add_argument('--cfg', type=str, default='cfg/yolov4-mydatasets.cfg', help='*.cfg path')
        parser.add_argument('--data', type=str, default='data/mydatasets.data', help='*.data path')
        parser.add_argument('--multi-scale', action='store_true', help='adjust (67%% - 150%%) img_size every 10 batches')
        parser.add_argument('--img-size', nargs='+', type=int, default=[320, 640], help='[min_train, max-train, test]')
        parser.add_argument('--rect', action='store_true', help='rectangular training')
        parser.add_argument('--resume', action='store_true', help='resume training from last.pt')
        parser.add_argument('--nosave', action='store_true', help='only save final checkpoint')
        parser.add_argument('--notest', action='store_true', help='only test final epoch')
        parser.add_argument('--evolve', action='store_true', help='evolve hyperparameters')
        parser.add_argument('--bucket', type=str, default='', help='gsutil bucket')
        parser.add_argument('--cache-images', action='store_true', help='cache images for faster training')
        parser.add_argument('--weights', type=str, default='weights/yolov4.weights', help='initial weights path')
        parser.add_argument('--name', default='', help='renames results.txt to results_name.txt if supplied')
        parser.add_argument('--device', default='', help='device id (i.e. 0 or 0,1 or cpu)')
        parser.add_argument('--adam', action='store_true', help='use adam optimizer')
        parser.add_argument('--single-cls', action='store_true', help='train as single-class dataset')
        opt = parser.parse_args()
        opt.weights = last if opt.resume else opt.weights
        check_git_status()
        print(opt)

# 六、推理 #

运行detect.py

import argparse
    
    from models import *  # set ONNX_EXPORT in models.py
    from utils.datasets import *
    from utils.utils import *
    
    
    def detect(save_img=False):
        img_size = (320, 192) if ONNX_EXPORT else opt.img_size  # (320, 192) or (416, 256) or (608, 352) for (height, width)
        out, source, weights, half, view_img, save_txt = opt.output, opt.source, opt.weights, opt.half, opt.view_img, opt.save_txt
        webcam = source == '0' or source.startswith('rtsp') or source.startswith('http') or source.endswith('.txt')
    
        # Initialize
        device = torch_utils.select_device(device='cpu' if ONNX_EXPORT else opt.device)
        if os.path.exists(out):
            shutil.rmtree(out)  # delete output folder
        os.makedirs(out)  # make new output folder
    
        # Initialize model
        model = Darknet(opt.cfg, img_size)
    
        # Load weights
        attempt_download(weights)
        if weights.endswith('.pt'):  # pytorch format
            model.load_state_dict(torch.load(weights, map_location=device)['model'])
        else:  # darknet format
            load_darknet_weights(model, weights)
    
        # Second-stage classifier
        classify = False
        if classify:
            modelc = torch_utils.load_classifier(name='resnet101', n=2)  # initialize
            modelc.load_state_dict(torch.load('weights/resnet101.pt', map_location=device)['model'])  # load weights
            modelc.to(device).eval()
    
        # Eval mode
        model.to(device).eval()
    
        # Fuse Conv2d + BatchNorm2d layers
        # model.fuse()
    
        # Export mode
        if ONNX_EXPORT:
            model.fuse()
            img = torch.zeros((1, 3) + img_size)  # (1, 3, 320, 192)
            f = opt.weights.replace(opt.weights.split('.')[-1], 'onnx')  # *.onnx filename
            torch.onnx.export(model, img, f, verbose=False, opset_version=11,
                              input_names=['images'], output_names=['classes', 'boxes'])
    
            # Validate exported model
            import onnx
            model = onnx.load(f)  # Load the ONNX model
            onnx.checker.check_model(model)  # Check that the IR is well formed
            print(onnx.helper.printable_graph(model.graph))  # Print a human readable representation of the graph
            return
    
        # Half precision
        half = half and device.type != 'cpu'  # half precision only supported on CUDA
        if half:
            model.half()
    
        # Set Dataloader
        vid_path, vid_writer = None, None
        if webcam:
            view_img = True
            torch.backends.cudnn.benchmark = True  # set True to speed up constant image size inference
            dataset = LoadStreams(source, img_size=img_size)
        else:
            save_img = True
            dataset = LoadImages(source, img_size=img_size)
    
        # Get names and colors
        names = load_classes(opt.names)
        colors = [[random.randint(0, 255) for _ in range(3)] for _ in range(len(names))]
    
        # Run inference
        t0 = time.time()
        img = torch.zeros((1, 3, img_size, img_size), device=device)  # init img
        _ = model(img.half() if half else img.float()) if device.type != 'cpu' else None  # run once
        for path, img, im0s, vid_cap in dataset:
            img = torch.from_numpy(img).to(device)
            img = img.half() if half else img.float()  # uint8 to fp16/32
            img /= 255.0  # 0 - 255 to 0.0 - 1.0
            if img.ndimension() == 3:
                img = img.unsqueeze(0)
    
            # Inference
            t1 = torch_utils.time_synchronized()
            pred = model(img, augment=opt.augment)[0]
            t2 = torch_utils.time_synchronized()
    
            # to float
            if half:
                pred = pred.float()
    
            # Apply NMS
            pred = non_max_suppression(pred, opt.conf_thres, opt.iou_thres,
                                       multi_label=False, classes=opt.classes, agnostic=opt.agnostic_nms)
    
            # Apply Classifier
            if classify:
                pred = apply_classifier(pred, modelc, img, im0s)
    
            # Process detections
            for i, det in enumerate(pred):  # detections per image
                if webcam:  # batch_size >= 1
                    p, s, im0 = path[i], '%g: ' % i, im0s[i]
                else:
                    p, s, im0 = path, '', im0s
    
                save_path = str(Path(out) / Path(p).name)
                s += '%gx%g ' % img.shape[2:]  # print string
                if det is not None and len(det):
                    # Rescale boxes from img_size to im0 size
                    det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round()
    
                    # Print results
                    for c in det[:, -1].unique():
                        n = (det[:, -1] == c).sum()  # detections per class
                        s += '%g %ss, ' % (n, names[int(c)])  # add to string
    
                    # Write results
                    for *xyxy, conf, cls in det:
                        if save_txt:  # Write to file
                            with open(save_path + '.txt', 'a') as file:
                                file.write(('%g ' * 6 + '\n') % (*xyxy, cls, conf))
    
                        if save_img or view_img:  # Add bbox to image
                            label = '%s %.2f' % (names[int(cls)], conf)
                            plot_one_box(xyxy, im0, label=label, color=colors[int(cls)])
    
                # Print time (inference + NMS)
                print('%sDone. (%.3fs)' % (s, t2 - t1))
    
                # Stream results
                if view_img:
                    cv2.imshow(p, im0)
                    if cv2.waitKey(1) == ord('q'):  # q to quit
                        raise StopIteration
    
                # Save results (image with detections)
                if save_img:
                    if dataset.mode == 'images':
                        cv2.imwrite(save_path, im0)
                    else:
                        if vid_path != save_path:  # new video
                            vid_path = save_path
                            if isinstance(vid_writer, cv2.VideoWriter):
                                vid_writer.release()  # release previous video writer
    
                            fps = vid_cap.get(cv2.CAP_PROP_FPS)
                            w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
                            h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
                            vid_writer = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*opt.fourcc), fps, (w, h))
                        vid_writer.write(im0)
    
        if save_txt or save_img:
            print('Results saved to %s' % os.getcwd() + os.sep + out)
            if platform == 'darwin':  # MacOS
                os.system('open ' + save_path)
    
        print('Done. (%.3fs)' % (time.time() - t0))
    
    
    if __name__ == '__main__':
        parser = argparse.ArgumentParser()
        parser.add_argument('--cfg', type=str, default='cfg/yolov4-mydatasets.cfg', help='*.cfg path')
        parser.add_argument('--names', type=str, default='data/mydatasets.names', help='*.names path')
        parser.add_argument('--weights', type=str, default='weights/best.pt', help='weights path')
        parser.add_argument('--source', type=str, default='img', help='source')  # input file/folder, 0 for webcam
        parser.add_argument('--output', type=str, default='output', help='output folder')  # output folder
        parser.add_argument('--img-size', type=int, default=512, help='inference size (pixels)')
        parser.add_argument('--conf-thres', type=float, default=0.3, help='object confidence threshold')
        parser.add_argument('--iou-thres', type=float, default=0.6, help='IOU threshold for NMS')
        parser.add_argument('--fourcc', type=str, default='mp4v', help='output video codec (verify ffmpeg support)')
        parser.add_argument('--half', action='store_true', help='half precision FP16 inference')
        parser.add_argument('--device', default='', help='device id (i.e. 0 or 0,1) or cpu')
        parser.add_argument('--view-img', action='store_true', help='display results')
        parser.add_argument('--save-txt', action='store_true', help='save results to *.txt')
        parser.add_argument('--classes', nargs='+', type=int, help='filter by class')
        parser.add_argument('--agnostic-nms', action='store_true', help='class-agnostic NMS')
        parser.add_argument('--augment', action='store_true', help='augmented inference')
        opt = parser.parse_args()
        print(opt)
    
        with torch.no_grad():
            detect()

效果：  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dhMXR6eQ_size_16_color_FFFFFF_t_70]  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dhMXR6eQ_size_16_color_FFFFFF_t_70 1]

[20200805122752732.png]: /images/20230208/344504ef051e4aafb30e0727d1974609.png
[2020080511352869.png]: /images/20230208/1ebbd0c7cf224bc6a78a63692e574f5c.png
[20200805114115705.png]: /images/20230208/8650199876e9481bab45d7fac3f474cf.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dhMXR6eQ_size_16_color_FFFFFF_t_70]: /images/20230208/5391447c2a0f4317991aad6d60160a11.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dhMXR6eQ_size_16_color_FFFFFF_t_70 1]: /images/20230208/a754ecac601e4276b8225c908041475a.png