操作系统：Windows 10
IDE：Pycharm
Python： 3.6.2 且已安装好 tensorflow , keras，pyqt5,lxml包
二、快速使用yolo3预测图片
keras-yolo3源代码, 下载到本地后用 Pycharm 打开。
初始权重文件，在QQ群文件中，下载好后放在 上述文件keras-yolo3 一级目录下。
命令行中执行如下命令将 darknet 下的 yolov3 配置文件转换成 keras 适用的 .h5 文件。
命令：python convert.py yolov3.cfg yolov3.weights model_data/yolo.h5
运行预测图像程序
命令：python yolo_video.py --image
一切正常的话，会让你输入待识别的图片路径，图片目录以keras-yolo3为一级目录。若待测图片放在该一级目录下，则直接输入图片名即可。
命令：Input image filename:test.jpg
三、训练自己的数据集进行目标检测
在该项目中新建文件夹如下所示：
安装数据标记工具 labelImg
用 powershell 进入到该项目根目录下，执行
命令：pyrcc5 -o resources.py resources.qrc
命令：python labelImg.py
弹出用户界面，使用如下：
在 keras-yolo3 一级目录下新建 test.py ,如上上图。复制如下代码：
/**华丽的代码分割线**/
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()
/**华丽的代码分割线**/
运行之后，在keras-yolo3-master\VOCdevkit\VOC2007\ImageSets\Main目录下就是制作好的数据集。
修改voc_annotion.py中的classes变量为自己需要的各式标签
/**华丽的代码分割线**/
classes = ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9"] #这里是10个数字标签
# classes = ["aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", "chair", "cow", "diningtable", "dog", "horse", "motorbike", "person", "pottedplant", "sheep", "sofa", "train", "tvmonitor"]
/**华丽的代码分割线**/
然后运行该文件，会在keras-yolo3-master一级目录下生成三个2007_***.txt的文件。
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。
/**华丽的代码分割线**/
[convolutional]
size=1
stride=1
pad=1
filters=45 # 3*(5+len(classes)). original value = 255
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=10 #train labels. original value = 80
num=9
jitter=.3
ignore_thresh = .5
truth_thresh = 1
random=0 #if you memory is small, choice 0. origninal value = 1
/**华丽的代码分割线**/
修改model_data下的voc_classes.txt为自己训练的类别
/**华丽的代码分割线**/
label0
label2
...
...
label9
/**华丽的代码分割线**/
修改train.py代码如下，做训练。
/**华丽的代码分割线**/
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()
/**华丽的代码分割线**/
记得在keras-yolo3-master中新建文件logs\000，这个文件是用来存放自己的数据集训练得到的模型。
修改yolo.py文件
/**华丽的代码分割线**/
_defaults = {
"model_path": 'logs/000/trained_weights.h5', #此处修改成自己的路径
"anchors_path": 'model_data/yolo_anchors.txt', #此处修改成自己的路径
"classes_path": 'model_data/voc_classes.txt', #此处修改成自己的路径
"score" : 0.3,
"iou" : 0.45,
"model_image_size" : (416, 416),
"gpu_num" : 1,
}
/**华丽的代码分割线**/
运行预测图像程序
/**华丽的代码分割线**/
python yolo_video.py --image
/**华丽的代码分割线**/