yolov3系列(二)-keras-yolo3训练自己的数据

训练自己的数据进行目标检测

0.建立相关的目录

在项目根目录下新建VOCdevkit\VOC2007\Annotations、VOCdevkit\VOC2007\ImageSets\Main、VOCdevkit\VOC2007\JPEGImages、logs\000四个目录

1. 使用标注工具labelimg标注数据

链接：https://pan.baidu.com/s/1SO4NqNSfXyKMNCGQA-4LVQ 提取码：ydqi

• 标注数据
  open dir 选择需要标注的数据目录,change save dir 选择要保存的目录VOCdevkit\VOC2007\Annotations,use default label 勾选,填写一个标签名称,create Rectbox 标注数据,保存即可,如下图
  
  数据标注完成以后,会在VOCdevkit\VOC2007\Annotations目录下生成相关的.xml文件

2. 生成训练集测试集验证集

• 在VOC2007目录下新建一个dataShape.py文件,目的是用来分割数据,运行此文件会在VOCdevkit\VOC2007\ImageSets\Main目录下生成test.txt train.txt trainval.txt val.txt四个文件，dataShape.py文件代码如下:

import os
import random

trainval_percent = 0.2
train_percent = 0.8
xmlfilepath = 'Annotations'
txtsavepath = 'ImageSets\Main'
total_xml = os.listdir(xmlfilepath)

num = len(total_xml)
list = range(num)
tv = int(num * trainval_percent)
tr = int(tv * train_percent)
trainval = random.sample(list, tv)
train = random.sample(trainval, tr)

ftrainval = open('ImageSets/Main/trainval.txt', 'w')
ftest = open('ImageSets/Main/test.txt', 'w')
ftrain = open('ImageSets/Main/train.txt', 'w')
fval = open('ImageSets/Main/val.txt', 'w')

for i in list:
   name = total_xml[i][:-4] + '\n'
   if i in trainval:
       ftrainval.write(name)
       if i in train:
           ftest.write(name)
       else:
           fval.write(name)
   else:
       ftrain.write(name)

ftrainval.close()
ftrain.close()
fval.close()
ftest.close()

3.生成yolo3所需的train.txt,val.txt,test.txt

生成的数据集不能供yolov3直接使用。需要运行voc_annotation.py ，classes以检测一个类为例(眼睛)，在voc_annotation.py需改你的数据集为：

classes = ["eye"]

运行python voc_annotation.py会生成 2007_train.txt``2007_test.txt``2007_val.txt，把这三个txt文件分别改名为 train.txt``test.txt``val.txt

利用voc制作自己的数据集

4.修改参数文件yolo3.cfg

打开yolo3.cfg文件。搜索yolo(共出现三次)，每次按下图都要修改

[convolutional]
size=1
stride=1
pad=1
# filters：3*(5+len(classes));<===> 3*(5+1)
filters=18
activation=linear


[yolo]
mask = 6,7,8
anchors = 10,13,  16,30,  33,23,  30,61,  62,45,  59,119,  116,90,  156,198,  373,326
#  classes: len(classes) = 1，这里是"eye"一类
classes=1
num=9
jitter=.3
ignore_thresh = .5
truth_thresh = 1
# random：改为0
random=0

5.修改model_data下的voc_classes.txt为自己训练的类别

eye

6.生成yolo_anchors.txt文件

运行 python kmeans.py，会在根目录下生成yolo_anchors.txt文件,剪切到 model_data目录下

7.修改train.py代码（用下面代码直接替换原来的代码）

因为 train.py会报错。本人电脑 win10家庭版.坑死人了

"""
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 = '2007_train.txt'
    log_dir = 'logs/000/'
    classes_path = 'model_data/voc_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 = 0.1
    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=500,
            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)-7
            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()

8.生成模型

运行python train.py，会在 logs\000下生成日志文件和trained_weights_stage_1.h5模型文件

9.测试训练效果

把生成的trained_weights_stage_1.h5模型文件,改为yolo.h5,放在 model_data目录下,运行 python yolo_video.py --image,输入图片路径,查看测试效果

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2020.01.02 20:54