gezhuangzhuang

一、下载和测试模型

1. 下载YOLO-v3

git clone https://github.com/qqwweee/keras-yolo3.git

这是在Ubuntu里的命令,windows直接去 https://github.com/qqwweee/keras-yolo3下载、解压。得到一个 keras-yolo3-master 文件夹

2. 下载权重

wget https://pjreddie.com/media/files/yolov3.weights

去 https://pjreddie.com/media/files/yolov3.weights 下载权重。将 yolov3.weights 放入 keras-yolo3-master 文件夹

3. 生成 h5 文件

python convert.py yolov3.cfg yolov3.weights model_data/yolo.h5

执行convert.py文件,这是将darknet的yolo转换为用于keras的h5文件,生成的h5被保存在model_data下。命令中的 convert.py 和 yolo.cfg 已经在keras-yolo3-master 文件夹下,不需要单独下载。

4. 用已经被训练好的yolo.h5进行图片识别测试

python yolo_video.py --image

执行后会让你输入一张图片的路径,由于我准备的图片放在与yolo_video.py同级目录,所以直接输入图片名称,不需要加路径

这就表明测试成功了。

 

二、制作自己的VOC数据集

参考我原来写的博客:

我是在Ubuntu内标注然后移到Windows的,如果在Windows里安装了LabelImg,可以直接在Windows下标注。

最后文件布局为:

 

三、修改配置文件、执行训练

1. 复制 voc_annotation.py 到voc文件夹下,修改 voc_annotation.py 分类。如下图:

    执行 voc_annotation.py 获得这四个文件

import xml.etree.ElementTree as ET
from os import getcwd

sets=[(\'2018\', \'train\'), (\'2018\', \'val\'), (\'2018\', \'test\'), (\'2018\', \'trainval\')]

classes = []


def convert_annotation(year, image_id, list_file):
    in_file = open(\'VOCdevkit\VOC%s\Annotations\%s.xml\'%(year, image_id), encoding = \'utf-8\')
    tree=ET.parse(in_file)
    root = tree.getroot()

    for obj in root.iter(\'object\'):
        difficult = obj.find(\'difficult\').text
        cls = obj.find(\'name\').text
        if cls not in classes or int(difficult)==1:
            continue
        cls_id = classes.index(cls)
        xmlbox = obj.find(\'bndbox\')
        b = (int(xmlbox.find(\'xmin\').text), int(xmlbox.find(\'ymin\').text), int(xmlbox.find(\'xmax\').text), int(xmlbox.find(\'ymax\').text))
        list_file.write(" " + ",".join([str(a) for a in b]) + \',\' + str(cls_id))

wd = getcwd()

for year, image_set in sets:
    image_ids = open(\'VOCdevkit\VOC%s\ImageSets\Main\%s.txt\'%(year, image_set)).read().strip().split()
    list_file = open(\'%s_%s.txt\'%(year, image_set), \'w\')
    for image_id in image_ids:
        list_file.write(\'%s\VOCdevkit\VOC%s\JPEGImages\%s.jpg\'%(wd, year, image_id))
        convert_annotation(year, image_id, list_file)
        list_file.write(\'\n\')
        
    list_file.close()

网上都是 train、val、test、三个文件。但我觉得还应该加一个 trainval。还有将所有的 / 改为 \ (Windows下路径表示和linux下不同)。高亮部分是为了防止Windows读取错误(博主就恰好碰到了)

2. 在model_data文件夹下新建一个 my_classes.txt(可以根据你的数据来,比如你检测是花的种类,可以叫 flower.txt。起名最好有意义),将你的类别写入,一行一个。

3. 修改yolov3.cfg 文件

使用迁移学习思想,用已经预训练好的权重接着训练。需要下面的修改步骤:

IDE里直接打开cfg文件,ctrl+f搜 yolo, 总共会搜出3个含有yolo的地方。

每个地方都要修改3处,

          filter :3*(5+len(classes))

          classes:len(classes)  我的类别是17

          random:原来是1,显存小改为0

      

重新生成h5文件

python convert.py -w yolov3.cfg yolov3.weights model_data/yolo_weights.h5

 

4. 训练

执行下面的train.py

python train.py
"""
Retrain the YOLO model for your own dataset.
"""
import numpy as np
import keras.backend as K
from keras.layers import Input, Lambda
from keras.models import Model
from keras.callbacks import TensorBoard, ModelCheckpoint, EarlyStopping
 
from yolo3.model import preprocess_true_boxes, yolo_body, tiny_yolo_body, yolo_loss
from yolo3.utils import get_random_data
 
 
def _main():
    annotation_path = \'voc/2018_trainval.txt\'
    log_dir = \'model_data/logs/\'
    classes_path = \'model_data/my_classes.txt\'
    anchors_path = \'model_data/yolo_anchors.txt\'
    class_names = get_classes(classes_path)
    anchors = get_anchors(anchors_path)
    input_shape = (416,416) # multiple of 32, hw
    model = create_model(input_shape, anchors, len(class_names) )
    train(model, annotation_path, input_shape, anchors, len(class_names), log_dir=log_dir)
 
def train(model, annotation_path, input_shape, anchors, num_classes, log_dir=\'logs/\'):
    model.compile(optimizer=\'adam\', loss={
        \'yolo_loss\': lambda y_true, y_pred: y_pred})
    logging = TensorBoard(log_dir=log_dir)
    checkpoint = ModelCheckpoint(log_dir + "ep{epoch:03d}-loss{loss:.3f}-val_loss{val_loss:.3f}.h5",
        monitor=\'val_loss\', save_weights_only=True, save_best_only=True, period=1)
    batch_size = 10
    val_split = 0.2
    with open(annotation_path) as f:
        lines = f.readlines()
    np.random.shuffle(lines)
    num_val = int(len(lines)*val_split)
    num_train = len(lines) - num_val
    print(\'Train on {} samples, val on {} samples, with batch size {}.\'.format(num_train, num_val, batch_size))
 
    model.fit_generator(data_generator_wrap(lines[:num_train], batch_size, input_shape, anchors, num_classes),
            steps_per_epoch=max(1, num_train//batch_size),
            validation_data=data_generator_wrap(lines[num_train:], batch_size, input_shape, anchors, num_classes),
            validation_steps=max(1, num_val//batch_size),
            epochs=20,
            initial_epoch=0)
    model.save_weights(log_dir + \'trained_weights.h5\')
 
def get_classes(classes_path):
    with open(classes_path) as f:
        class_names = f.readlines()
    class_names = [c.strip() for c in class_names]
    return class_names
 
def get_anchors(anchors_path):
    with open(anchors_path) as f:
        anchors = f.readline()
    anchors = [float(x) for x in anchors.split(\',\')]
    return np.array(anchors).reshape(-1, 2)
 
def create_model(input_shape, anchors, num_classes, load_pretrained=False, freeze_body=False,
            weights_path=\'model_data/yolo_weights.h5\'):
    K.clear_session() # get a new session
    image_input = Input(shape=(None, None, 3))
    h, w = input_shape
    num_anchors = len(anchors)
    y_true = [Input(shape=(h//{0:32, 1:16, 2:8}[l], w//{0:32, 1:16, 2:8}[l], \
        num_anchors//3, num_classes+5)) for l in range(3)]
 
    model_body = yolo_body(image_input, num_anchors//3, num_classes)
    print(\'Create YOLOv3 model with {} anchors and {} classes.\'.format(num_anchors, num_classes))
 
    if load_pretrained:
        model_body.load_weights(weights_path, by_name=True, skip_mismatch=True)
        print(\'Load weights {}.\'.format(weights_path))
        if freeze_body:
            # Do not freeze 3 output layers.
            num = len(model_body.layers)-3
            for i in range(num): model_body.layers[i].trainable = False
            print(\'Freeze the first {} layers of total {} layers.\'.format(num, len(model_body.layers)))
 
    model_loss = Lambda(yolo_loss, output_shape=(1,), name=\'yolo_loss\',
        arguments={\'anchors\': anchors, \'num_classes\': num_classes, \'ignore_thresh\': 0.5})(
        [*model_body.output, *y_true])
    model = Model([model_body.input, *y_true], model_loss)
    return model
def data_generator(annotation_lines, batch_size, input_shape, anchors, num_classes):
    n = len(annotation_lines)
    np.random.shuffle(annotation_lines)
    i = 0
    while True:
        image_data = []
        box_data = []
        for b in range(batch_size):
            i %= n
            image, box = get_random_data(annotation_lines[i], input_shape, random=True)
            image_data.append(image)
            box_data.append(box)
            i += 1
        image_data = np.array(image_data)
        box_data = np.array(box_data)
        y_true = preprocess_true_boxes(box_data, input_shape, anchors, num_classes)
        yield [image_data, *y_true], np.zeros(batch_size)
 
def data_generator_wrap(annotation_lines, batch_size, input_shape, anchors, num_classes):
    n = len(annotation_lines)
    if n==0 or batch_size<=0: return None
    return data_generator(annotation_lines, batch_size, input_shape, anchors, num_classes)
 
if __name__ == \'__main__\':
    _main()

代码标红的地方,需要根据自己实际情况进行修改。

其他可以设置的参数

batch_size = 32:默认值比较大,对电脑性能有要求。可以调小。我设置的是10

val_split = 0.1 : 这个表示,验证集占训练集的比例。建议划分大点。不然验证集的图片会很少。不利于验证集loss的计算

epochs = 100,可以调小一点。我设置的是20

参考地址:

https://blog.csdn.net/m0_37857151/article/details/81330699

https://blog.csdn.net/mingqi1996/article/details/83343289

分类:

技术点:

相关文章: