yolo v3系列之训练自己的数据集（上篇）

所有代码已经上传到github <https://github.com/gittigxuy/yolo-v3_face_detection/>上了，求star:

本篇文章是基于https://github.com/SpikeKing/keras-yolo3-detection
<https://github.com/SpikeKing/keras-yolo3-detection>，这个人脸检测repo进行训练的

环境是ubuntu 14.04,cuda 8.0,cudnn 6.0.21

requirements:请参考
https://github.com/SpikeKing/keras-yolo3-detection/blob/master/requirements-gpu.txt

<https://github.com/SpikeKing/keras-yolo3-detection/blob/master/requirements-gpu.txt>

 

一.准备阶段：

具体参考：https://www.jianshu.com/p/8214d947e031
<https://www.jianshu.com/p/8214d947e031>

先运行convert.py将cfg模型+weights文件转化为yolo.h5文件作为预训练模型

下载好wider数据集之后，对于数据进行处理，运行wider_annotation.py文件，变成yolo v3可以读入的数据格式

二.训练阶段

修改这四个变量的路径，使其找到相应的文件
annotation_path = '/home/xuy/code/keras-yolo3/2007_train.txt' log_dir =
'logs/' classes_path = 'model_data/voc_classes.txt' anchors_path =
'model_data/yolo_anchors.txt'
并且，如果电脑配置低的话，需要调小batch_size以及epoch的次数，否则会在unfreeze阶段产生out of memory的错误

具体参数设置请参考：https://github.com/SpikeKing/keras-yolo3-detection/issues/4
<https://github.com/SpikeKing/keras-yolo3-detection/issues/4>

最终的结果：训练到60epoch左右被early stop了，loss值大概在26左右

三.利用模型进行测试：

1.图片测试：【对于小物体的效果并不是很好，对于单个人效果很好】

贴一个效果比较差的图



2.对于视频进行检测：

yolo3_predict_pic.py:对于图片进行测试
#!/usr/bin/env python # -- coding: utf-8 -- """ Copyright (c) 2018. All rights
reserved. Created by C. L. Wang on 2018/7/4 """ """ Run a YOLO_v3 style
detection model on test images. """ import colorsys import os from timeit
import default_timer as timer import numpy as np from PIL import Image,
ImageFont, ImageDraw from keras import backend as K from keras.layers import
Input from yolo3.model import yolo_eval, yolo_body from yolo3.utils import
letterbox_image #用来存储预测结果的txt文件 predict_result =
'/home/xuy/code/mAP/predicted/' #wider数据集的val-set的图片 img_root_path =
'/home/xuy/code/keras-yolo3-detection/wider_dataset/WIDER_val/images'
#img_path是单个图片的测试 # img_path =
'/home/xuy/code/keras-yolo3-detection/wider_dataset/WIDER_train/images/0--Parade/0_Parade_marchingband_1_5.jpg'
# 先拿单张图片测试一下 #将预测结果的图片输出的路径 result_path =
'/home/xuy/code/keras-yolo3-detection/result/' def iterbrowse(path): for home,
dirs, files in os.walk(path): for filename in files: yield os.path.join(home,
filename) class YOLO(object): def __init__(self): self.anchors_path =
'configs/yolo_anchors.txt' # Anchors # self.model_path =
'model_data/yolo_weights.h5' # 模型文件 self.model_path =
'/home/xuy/code/keras-yolo3-detection/logs/trained_weights_final_train.h5' #
模型文件 # self.classes_path = 'configs/coco_classes.txt' # 类别文件 self.classes_path
= '/home/xuy/code/keras-yolo3-detection/configs/wider_classes.txt' # 类别文件
self.score = 0.1 # self.iou = 0.45 self.iou = 0.20 self.class_names =
self._get_class() # 获取类别 self.anchors = self._get_anchors() # 获取anchor
self.sess = K.get_session() self.model_image_size = (416, 416) # fixed size or
(None, None), hw self.boxes, self.scores, self.classes = self.generate() def
_get_class(self): classes_path = os.path.expanduser(self.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(self): anchors_path =
os.path.expanduser(self.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 generate(self): model_path =
os.path.expanduser(self.model_path) # 转换~ assert model_path.endswith('.h5'),
'Keras model or weights must be a .h5 file.' num_anchors = len(self.anchors) #
anchors的数量 num_classes = len(self.class_names) # 类别数 # 加载模型参数 self.yolo_model =
yolo_body(Input(shape=(None, None, 3)), 3, num_classes)
self.yolo_model.load_weights(model_path) print('{} model, {} anchors, and {}
classes loaded.'.format(model_path, num_anchors, num_classes)) # 不同的框，不同的颜色
hsv_tuples = [(float(x) / len(self.class_names), 1., 1.) for x in
range(len(self.class_names))] # 不同颜色 self.colors = list(map(lambda x:
colorsys.hsv_to_rgb(*x), hsv_tuples)) self.colors = list(map(lambda x:
(int(x[0] * 255), int(x[1] * 255), int(x[2] * 255)), self.colors)) # RGB
np.random.seed(10101) np.random.shuffle(self.colors) np.random.seed(None) #
根据检测参数，过滤框 self.input_image_shape = K.placeholder(shape=(2,)) boxes, scores,
classes = yolo_eval(self.yolo_model.output, self.anchors,
len(self.class_names), self.input_image_shape, score_threshold=self.score,
iou_threshold=self.iou) return boxes, scores, classes def detect_image(self,
image,img_path):#检测每一张图片的人脸位置 start = timer() # 起始时间
pic_filename=os.path.basename(img_path) #
txt_filename=pic_filename.replace("jpg","txt")
portion=os.path.splitext(pic_filename) if portion[1]=='.jpg':
txt_result=predict_result+portion[0]+'.txt' print('txt_result的路径是：'+txt_result)
if self.model_image_size != (None, None): # 416x416,
416=32*13，必须为32的倍数，最小尺度是除以32 assert self.model_image_size[0] % 32 == 0,
'Multiples of 32 required' assert self.model_image_size[1] % 32 == 0,
'Multiples of 32 required' boxed_image = letterbox_image(image,
tuple(reversed(self.model_image_size))) # 填充图像 else: new_image_size =
(image.width - (image.width % 32), image.height - (image.height % 32))
boxed_image = letterbox_image(image, new_image_size) image_data =
np.array(boxed_image, dtype='float32') print('detector size
{}'.format(image_data.shape)) image_data /= 255. # 转换0~1 image_data =
np.expand_dims(image_data, 0) # 添加批次维度，将图片增加1维 # 参数盒子、得分、类别；输入图像0~1，4维；原始图像的尺寸
out_boxes, out_scores, out_classes = self.sess.run( [self.boxes, self.scores,
self.classes], feed_dict={ self.yolo_model.input: image_data,
self.input_image_shape: [image.size[1], image.size[0]], K.learning_phase(): 0
}) print('Found {} boxes for {}'.format(len(out_boxes), 'img')) # 检测出的框 font =
ImageFont.truetype(font='font/FiraMono-Medium.otf', size=np.floor(3e-2 *
image.size[1] + 0.5).astype('int32')) # 字体 thickness = (image.size[0] +
image.size[1]) // 512 # 厚度 with open(txt_result,'a')as new_f: for i, c in
reversed(list(enumerate(out_classes))): predicted_class = self.class_names[c] #
类别 box = out_boxes[i] # 框 score = out_scores[i] # 执行度 label = '{}
{:.2f}'.format(predicted_class, score) # 标签 draw = ImageDraw.Draw(image) # 画图
label_size = draw.textsize(label, font) # 标签文字 top, left, bottom, right = box
top = max(0, np.floor(top + 0.5).astype('int32')) left = max(0, np.floor(left +
0.5).astype('int32')) bottom = min(image.size[1], np.floor(bottom +
0.5).astype('int32')) right = min(image.size[0], np.floor(right +
0.5).astype('int32')) print(label, (left, top), (right, bottom)) #
边框,这个就是【置信值，xmin,ymin,xmax,ymax】,可以做一下mAP值的分析了 new_f.write(str(label)+" "+
str(left) + " " + str(top) + " " + str(right) + " " + str(bottom) + '\n') if
top - label_size[1] >= 0: # 标签文字 text_origin = np.array([left, top -
label_size[1]]) else: text_origin = np.array([left, top + 1]) # My kingdom for
a good redistributable image drawing library. for i in range(thickness): # 画框
draw.rectangle( [left + i, top + i, right - i, bottom - i],
outline=self.colors[c]) #draw.rectangle( # 文字背景是红色 #[tuple(text_origin),
tuple(text_origin + label_size)], # fill=self.colors[c])
#draw.text(text_origin, label, fill=(0, 0, 0), font=font) # 文字内容，face+是人脸的概率值
del draw end = timer() print(end - start) # 检测执行时间 return image def
close_session(self): self.sess.close() def detect_img_for_test(yolo): for
img_path in iterbrowse(img_root_path): print('img_path的路径是：'+img_path) image =
Image.open(img_path) filename=os.path.basename(img_path)
print('filename'+filename) r_image = yolo.detect_image(image,img_path) #
r_image.show() # 先显示，然后再保存 r_image.save(result_path+filename) # for
parent,dirnames,filenames in os.walk(img_root_path): #三个参数：分别返回1.父目录
2.所有文件夹名字（不含路径） 3.所有文件名字 # for dirname in dirnames: # for filename in
filenames: # img_path=img_root_path+'/'+dirname+'/'+filename # print(img_path)
# image = Image.open(img_path) # r_image = yolo.detect_image(image) # #
r_image.show() # 先显示，然后再保存 # r_image.save(result_path+filename) # image =
Image.open(img_path) # r_image = yolo.detect_image(image) # #
r_image.show()#先显示，然后再保存 # r_image.save('/home/xuy/code/keras-yolo3-detection/'
+ 'result2.jpg') yolo.close_session() if __name__ == '__main__':
detect_img_for_test(YOLO())
然后使用yolo3_predict_video.py调用yolo class
# -*- coding:utf-8 -*- __author__ = 'xuy' ''' 基于视频的人脸检测 usage: python
yolo_video.py [video_path] [output_path(optional)] ''' import colorsys import
os import sys from timeit import default_timer as timer import numpy as np from
keras import backend as K from keras.models import load_model from keras.layers
import Input from PIL import Image, ImageFont, ImageDraw from yolo3.model
import yolo_eval, yolo_body, tiny_yolo_body from yolo3.utils import
letterbox_image if len(sys.argv) < 2: print("Usage: $ python {0} [video_path]
[output_path(optional)]", sys.argv[0]) exit() from yolo3_predict_pic import
YOLO def detect_video(yolo, video_path, output_path=""): import cv2 vid =
cv2.VideoCapture(video_path) if not vid.isOpened(): raise IOError("Couldn't
open webcam or video") video_FourCC = int(vid.get(cv2.CAP_PROP_FOURCC))
video_fps = vid.get(cv2.CAP_PROP_FPS) video_size =
(int(vid.get(cv2.CAP_PROP_FRAME_WIDTH)),
int(vid.get(cv2.CAP_PROP_FRAME_HEIGHT))) isOutput = True if output_path != ""
else False if isOutput: print("!!! TYPE:", type(output_path),
type(video_FourCC), type(video_fps), type(video_size)) out =
cv2.VideoWriter(output_path, video_FourCC, video_fps, video_size) accum_time =
0 curr_fps = 0 fps = "FPS: ??" prev_time = timer() while True: return_value,
frame = vid.read() image = Image.fromarray(frame) image =
yolo.detect_image(image) result = np.asarray(image) curr_time = timer()
exec_time = curr_time - prev_time prev_time = curr_time accum_time = accum_time
+ exec_time curr_fps = curr_fps + 1 if accum_time > 1: accum_time = accum_time
- 1 fps = "FPS: " + str(curr_fps) curr_fps = 0 cv2.putText(result, text=fps,
org=(3, 15), fontFace=cv2.FONT_HERSHEY_SIMPLEX, fontScale=0.50, color=(255, 0,
0), thickness=2) cv2.namedWindow("result", cv2.WINDOW_NORMAL)
cv2.imshow("result", result) if isOutput: out.write(result) if cv2.waitKey(1) &
0xFF == ord('q'): break yolo.close_session() if __name__ == '__main__':
video_path = sys.argv[1] if len(sys.argv) > 2: output_path = sys.argv[2]
detect_video(YOLO(), video_path, output_path)#在这里调用YOLO函数，从而使用了已经训练好的 else:
detect_video(YOLO(), video_path)
 

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