训练自己的yolo3模型用于识别机动车及部分道路信息_yolo 车辆识别教程

训练自己的yolo3模型用于识别机动车及部分道路信息

本文主要记录自己训练yolo3模型的整个过程，大部分参考Keras/Tensorflow+python+yolo3训练自己的数据集，感谢原作者。
我的环境如下

python                    3.7.3
tensorflow                1.14.0
opencv                    3.4.1
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1.数据采集与处理

1. 数据采集
   使用python的opencv库将训练的视频的每一帧截取保存为一张图片，最后共计1782张图片作为训练数据，如图所示。
   
2. 数据处理
   使用lableimg软件对每一张图片中的机动车、红绿灯、横道线、道路行驶箭头、车牌进行标注，如图所示
   

lableimg具体操作参照labelimg使用教程
注意：标注的同类别需要相同的命名，比如机动车命名为car，道路停止线命名为line。
最后形成1782个xml文件，xml文件内容如图所示，记录了类别名与坐标信息。

至此数据准备完毕。

2.模型训练

1. 下载代码，做好准备工作
   大佬的yolo3代码
   下载yolo3代码，在里面新建VOCdevkit文件夹，然后在VOCdevkit文件夹中陆续新建如图的文件夹（我的方法很蠢QAQ）
   
   将自己用于训练的1782张图片放入JPEGImages里面，如图所示。
   
   将labelimg生成的xml文件放入Annotations里面，如图所示。
   
2. 在VOC2007文件夹中新建test.py脚本并运行，在Main中生成trainval.test.train.val四个txt文件，代码如下。

import os
import random

trainval_percent = 0.1
train_percent = 0.9
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()
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3. 找到voc_annotation.py脚本，将代码中classes列表的元素改为用labelimg标注每个类别的命名，再运行代码。
   
   在keras-yolo3-master中生成2007_train.txt、2007_val.txt、2007_test.txt三个文件夹，重命名三个文件夹分别为train.txt、val.txt、test.txt。
4. 在keras-yolo3-master中找到yolo3.cfg文件并打开，ctrl+f搜 yolo, 总共会搜出3个含有yolo的地方，将classes改为自己类别的个数（也就是使用labelimg标记类别的个数），filters改为如图的样子。一共要改三处。
   
5. 找到model_data文件夹下的coco_classes.txt和voc_classes.txt，将内容更改为自己标记的类别名称，如图所示，两个txt文件夹内容一样。
   
6. 找到train.py将内容替换为如下代码。

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 = '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()
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找到yolo_video.py将内容替换为如下代码。

import sys
import argparse
from yolo import YOLO, detect_video
from PIL import Image
import os
# def detect_img(yolo):
#     while True:
#         img = input('Input image filename:')
#         try:
#             image = Image.open(img)
#         except:
#             print('Open Error! Try again!')
#             continue
#         else:
#             r_image = yolo.detect_image(image)
#             r_image.show()
#     yolo.close_session()

import glob
def detect_img(yolo):
    path = "VOCdevkit/VOC2007/JPEGImages/*.jpg"
    outdir = "VOCdevkit/VOC2007/SegmentationClass"
    for jpgfile in glob.glob(path):
        img = Image.open(jpgfile)
        img = yolo.detect_image(img)
        img.save(os.path.join(outdir, os.path.basename(jpgfile)))
    yolo.close_session()

FLAGS = None

if __name__ == '__main__':
    # class YOLO defines the default value, so suppress any default here
    parser = argparse.ArgumentParser(argument_default=argparse.SUPPRESS)
    '''
    Command line options
    '''
    parser.add_argument(
        '--model', type=str,
        help='path to model weight file, default ' + YOLO.get_defaults("model_path")
    )

    parser.add_argument(
        '--anchors', type=str,
        help='path to anchor definitions, default ' + YOLO.get_defaults("anchors_path")
    )

    parser.add_argument(
        '--classes', type=str,
        help='path to class definitions, default ' + YOLO.get_defaults("classes_path")
    )

    parser.add_argument(
        '--gpu_num', type=int,
        help='Number of GPU to use, default ' + str(YOLO.get_defaults("gpu_num"))
    )

    parser.add_argument(
        '--image', default=False, action="store_true",
        help='Image detection mode, will ignore all positional arguments'
    )
    '''
    Command line positional arguments -- for video detection mode
    '''
    parser.add_argument(
        "--input", nargs='?', type=str,required=False,default='./path2your_video',
        help = "Video input path"
    )

    parser.add_argument(
        "--output", nargs='?', type=str, default="",
        help = "[Optional] Video output path"
    )

    FLAGS = parser.parse_args()

    if FLAGS.image:
        """
        Image detection mode, disregard any remaining command line arguments
        """
        print("Image detection mode")
        if "input" in FLAGS:
            print(" Ignoring remaining command line arguments: " + FLAGS.input + "," + FLAGS.output)
        detect_img(YOLO(**vars(FLAGS)))
    elif "input" in FLAGS:
        detect_video(YOLO(**vars(FLAGS)), FLAGS.input, FLAGS.output)
    else:
        print("Must specify at least video_input_path.  See usage with --help.")

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7. 运行train.py训练模型，loss达到10几的时候效果就可以了。最后在log的000文件夹中生成trained_weights.h5权重文件。

3.测试模型

进入终端cd到keras-yolo3-master文件夹下输入命名：python yolo_video.py --image进行图片的识别，输入命令：python yolo_video.py --input 视频路径\video_1.avi --output 视频保存路径\res_1.avi进行视频的识别。