1.Keras环境搭建
先安装tensorflow、再安装keras（注意tensorflow与keras版本要匹配）
降低keras版本，直接安装新版本，会自动卸载旧版本

pip install keras==2.2.5 -i https://pypi.tuna.tsinghua.edu.cn/simple/

2.分类-》检测-》分割

3.初步使用:keras+yolov3+pycharm
使用pycharm时导入anaconda环境
1）下载yolo3代码
2）下载权重
https://pjreddie.com/media/files/yolov3.weights并将权重放在keras-yolo3的文件夹下
3）执行命令将darknet下的yolo3配置文件转换为keras适用的h5文件

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

4）使用命令行执行 检测图片

Python yolo_video.py --image

使用检测视频

python yolo_video.py --input = XXX.mp4 

5）输入图片路径进行目标探测
说明：yolo自带识别的分类有：
“飞机”，“自行车”，“鸟”，“船”，“瓶”，“公共汽车”，“汽车”，“猫”，“椅子”，“牛”，“餐桌”，“狗”，“ 马”，“摩托车”，“人”，“盆栽植物”，“绵羊”，“沙发”，“火车”，“电视监控器”等总共80类，其中涉及到不良信息的分类有刀具。

4.制作自己的数据集进行训练
（1）创建文件结构目录
（1）在keras-yolo3-master文件夹中创建如下结构的文件夹：
JPEGImages用于存放你的图片；Annotations用于存放标注文件(xml格式，Darknet框架下需要转换为txt格式)；ImageSets/Main用于存放自动生成的训练集、验证集和测试集的图片路径的txt文件；

（2）标注数据：
LabelImg软件下载：https://github.com/tzutalin/labelImg
（解压得到labelImg-master文件夹）
在Anaconda环境中切换到该文件夹目录，并且安装好依赖项：pyqt5和lxml；安装完成后执行如下命令即可打开软件使用：

pip install pyqt5
pip install lxml
pyrcc5 -o resources.py resources.qrc (该句执行完后没有输出)
python labelImg.py

备注：运行最后一个命令行时报错如下：

解决方法：
将LabelImg-master中的resources.py复制到lib文件中

（3）在VOC2007文件夹下新建 test.py

import os
import randomtrainval_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()

运行test.py文件，ImageSets/Main目录下生成如下四个文件

(4)修改类别
1.打开keras-yolo3-master目录下voc_annotation.py文件
2.修改你数据集的类别名称

运行该文件之后会在主目录keras-yolo3-master下生成3个.txt文件，手动将文件名修改为train.txt \ val.txt \ test.txt

（5）参数设定
修改yolo3.cfg文件参数
1.Pycharm打开yolo3.cfg
2.快捷键Ctrl + F 查找yolo（一共3个yolo），每一处的filter 、classes、random都需做相应更改
filter = 3*(len(classes)+5) #我这里只有一类，所以是18
classes = 1 #有几类写几类
random = 0 #显存小设为0，否则为1

（6）修改classes.txt
打开model_data/coco_classes.txt 和 voc_classes.txt 文件，修改classes

(7)训练
在主目录下新建文件夹 logs/000
修改train.py

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, EarlyStoppingfrom yolo3.model import preprocess_true_boxes, yolo_body, tiny_yolo_body, yolo_loss
from yolo3.utils import get_random_datadef _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, hwmodel = 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 = 10val_split = 0.1with open(annotation_path) as f:lines = f.readlines()np.random.shuffle(lines)num_val = int(len(lines)*val_split)num_train = len(lines) - num_valprint('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_namesdef 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 sessionimage_input = Input(shape=(None, None, 3))h, w = input_shapenum_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)-7for i in range(num): model_body.layers[i].trainable = Falseprint('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 = 0while True:image_data = []box_data = []for b in range(batch_size):i %= nimage, box = get_random_data(annotation_lines[i], input_shape, random=True)image_data.append(image)box_data.append(box)i += 1image_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 Nonereturn data_generator(annotation_lines, batch_size, input_shape, anchors, num_classes)if __name__ == '__main__':_main()

运行train.py 观察loss损失值，降到十几左右就可以停止训练了,logs/000目录下生成trained-weights.h5
损失值是用来评估模型的预测值和真实值的不一致程度，损失函数越小，代表模型的鲁棒性越好。
注意：keras-yolov3中因为模型构建是使用了l2正则化的措施，所以最终模型的loss不会降到特别低，一般keras-yolov3loss降到10左右就效果便可以了

备注：运行期间出现以下错误

解决方法：
1）根本原因：tensorflow与keras版本不匹配 降低keras版本
2）在D:\Tools\anaconda\Lib\site-packages\keras\backend
找到__init__.py文件增加以下语句导入包
from .load_backend import control_flow_ops
from .load_backend import set_image_dim_ordering

（8）重新设置参数文件路径
打开 yolo.py 文件夹，修改
1.权重文件路径
2.分类路径

（9）测试
检测图片：python yolo_video.py --image
监测视频：python yolo_video.py --input=run.mp4