有趣的地方-基于Colab训练的yolov4-tiny自定义数据集（可用于OpenCV For Unity）

1.打开文档，点击【文件】【在云端硬盘中保存一份副本】，即将文档复制到自己云端硬盘。

2.打开该文件，按文中提示进行。

【代码执行程序】【更改运行时类型】修改运行时为GPU（免费的GPU不好用，收费的好用，某宝上几十元就可用一个月）

步骤1）

!git clone https://github.com/AlexeyAB/darknet

步骤2）在云盘上新建文件夹yolov4-tiny，在yolov4-tiny中再新增training文件夹

步骤3a）将数据集上传到yolov4-tiny文件夹下

这里说明一下数据集的处理注意事件

（1）这里用的图像标注工具为labelImg（注：labelImg经常闪退，可以使用其他标记工具，可以见这篇博客《几种Yolo图像标注工具》）

（2）切换到YOLO模式

（3）在View中开启自动保存模式

（4）当图片文件夹改变时，txt保存文件夹也要改变（【File】【Change Save Dir】）

（5）图片格式只能为jpg(小写)，大写的JPG，或jepg、png等都不行。

（6）训练集中的图片尺寸可以一样，比如500*550，300*400都可以

步骤3b）下载 darknet/cfg下的yolov4-tiny-custom.cfg

修改yolov4-tiny-custom.cfg（与文档中的说明有差异）

有两处yolo需要改

修改完后上传到yolov4-tiny文件夹

步骤3c）在yolov4-tiny文件夹，新建文件obj.data、obj.names

classes = 2
train = data/train.txt
valid = data/test.txt
names = data/obj.names
backup = /mydrive/yolov4-tiny/training

步骤3d）在github下载process.py,然后上传到yolov4-tiny

import glob, os

# Current directory
current_dir = os.path.dirname(os.path.abspath(__file__))

print(current_dir)

current_dir = 'data/obj'

# Percentage of images to be used for the test set
percentage_test = 10;

# Create and/or truncate train.txt and test.txt
file_train = open('data/train.txt', 'w')
file_test = open('data/test.txt', 'w')

# Populate train.txt and test.txt
counter = 1
index_test = round(100 / percentage_test)
for pathAndFilename in glob.iglob(os.path.join(current_dir, "*.jpg")):
    title, ext = os.path.splitext(os.path.basename(pathAndFilename))

    if counter == index_test:
        counter = 1
        file_test.write("data/obj" + "/" + title + '.jpg' + "\n")
    else:
        file_train.write("data/obj" + "/" + title + '.jpg' + "\n")
        counter = counter + 1

4)步加载云盘

#mount drive
%cd ..
from google.colab import drive
drive.mount('/content/gdrive')

# this creates a symbolic link so that now the path /content/gdrive/My\ Drive/ is equal to /mydrive
!ln -s /content/gdrive/My\ Drive/ /mydrive

# list contents in yolov4-tiny folder in your drive
!ls /mydrive/yolov4-tiny

5)步开启OpenCV和GPU

# change makefile to have GPU and OPENCV enabled
# also set CUDNN, CUDNN_HALF and LIBSO to 1

%cd /content/darknet/
!sed -i 's/OPENCV=0/OPENCV=1/' Makefile
!sed -i 's/GPU=0/GPU=1/' Makefile
!sed -i 's/CUDNN=0/CUDNN=1/' Makefile
!sed -i 's/CUDNN_HALF=0/CUDNN_HALF=1/' Makefile
!sed -i 's/LIBSO=0/LIBSO=1/' Makefile

6)步构建darknet

# build darknet
!make

7)复制文件到darknet文件夹下

# Clean the data and cfg folders first except the labels folder in data which is required

%cd data/
!find -maxdepth 1 -type f -exec rm -rf {} \;
%cd ..

%rm -rf cfg/
%mkdir cfg

#copy the datasets zip file to the root darknet folder
!cp /mydrive/yolov4-tiny/obj.zip ../

# unzip the datasets and their contents so that they are now in /darknet/data/ folder
!unzip ../obj.zip -d data/

#copy the custom cfg file from the drive to the darknet/cfg folder
!cp /mydrive/yolov4-tiny/yolov4-tiny-custom.cfg ./cfg

# copy the obj.names and obj.data files so that they are now in /darknet/data/ folder
!cp /mydrive/yolov4-tiny/obj.names ./data
!cp /mydrive/yolov4-tiny/obj.data  ./data

#copy the process.py file from the drive to the darknet directory
!cp /mydrive/yolov4-tiny/process.py ./

8)运行process.py,创建train.txt和test.txt在data目录下

# run process.py ( this creates the train.txt and test.txt files in our darknet/data folder )
!python process.py

# list the contents of data folder to check if the train.txt and test.txt files have been created
!ls data/

9)下载预训练文件

# Download the yolov4-tiny pre-trained weights file
!wget https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4-tiny.conv.29

10)开始训练

# train your custom detector! (uncomment %%capture below if you run into memory issues or your Colab is crashing)
# %%capture

!./darknet detector train data/obj.data cfg/yolov4-tiny-custom.cfg yolov4-tiny.conv.29 -dont_show -map

如果提示错误：./darknet: error while loading shared libraries: libcuda.so.1: cannot open shared object file: No such file or directory

运行时类型改为GPU，参照上文。

训练成功是这样的

如果中途退出了，接着上次的断点继续训练

#to restart training your custom detector where you left off(using the weights that were saved last)

!./darknet detector train data/obj.data cfg/yolov4-tiny-custom.cfg /mydrive/yolov4-tiny/training/yolov4-tiny-custom_last.weights -dont_show -map

11）查看训练结果

# define helper function imShow
def imShow(path):
  import cv2
  import matplotlib.pyplot as plt
  %matplotlib inline

  image = cv2.imread(path)
  height, width = image.shape[:2]
  resized_image = cv2.resize(image,(3*width, 3*height), interpolation = cv2.INTER_CUBIC)

  fig = plt.gcf()
  fig.set_size_inches(18, 10)
  plt.axis("off")
  plt.imshow(cv2.cvtColor(resized_image, cv2.COLOR_BGR2RGB))
  #plt.show('')

#only works if the training does not get interrupted
imShow('chart.png')

在Unity For Unity2.5.9（Unity2021.3.35f1）中使用

在菜单【Tools】【OpenCV for Unity】【Open Setup Tools】,将“OpenCVForUnity/StreamingAssets”移动到“Assest下/StreamingAssets"

将训练用的coco.names、yolov4-tiny-custom.cfg、yolov4-tiny-custom_best.weights放到Assets\StreamingAssets\OpenCVForUnity\dnn下

打开ObjectDetectionYOLOv4Example

配置识别文件