一、背景
kaggle上有这样一个题目，关于盐份预测的语义分割题目。TGS Salt Identification Challenge | Kaggle https://www.kaggle.com/c/tgs-salt-identification-challenge
二、过程
1、下载数据，https://www.kaggle.com/c/tgs-salt-identification-challenge/data
数据说明：
train.csv
id rle_mask
4000项，即有4000张图片 depths.csv
id z
z（地下深度，英尺）
22000项（为train和test图片张数总和）
[50, 959] test
18000张图片 sample_submission.csv
5f3b26ac68,1 2626 2628 100
数据从1开始，行数和列数都要调整为从1开始,
对于python来说，不需要转置，对于opencv来说要转置
数据处理：
（1）对于每张拍摄好的原始图片，有对应的深度信息，为了方便fcn训练，我们把深度信息也存入到图片中。可以用opencv将已标注好的原图，和带预测的原图的b通道保存原来的灰度信息，将g通道保存depth/256(整数倍)，将r通道保存depth%256(取余数)。
#include "opencv2/opencv.hpp"
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include <iostream>
#include <fstream>
#include <vector>
#include <map>
#include <hash_map> int split(std::string str, std::string pattern, std::vector<std::string> &words)
{
std::string::size_type pos;
std::string word;
int num = 0;
str += pattern;
std::string::size_type size = str.size();
for (auto i = 0; i < size; i++) {
pos = str.find(pattern, i);
if (pos == i) {
continue;//if first string is pattern
}
if (pos < size) {
word = str.substr(i, pos - i);
words.push_back(word);
i = pos + pattern.size() - 1;
num++;
}
}
return num;
} int main(int argc, char** argv)
{
std::string input_path = "E:\\kaggle\\competition\\tgs\\test_images\\";
std::string out_path = "E:\\kaggle\\competition\\tgs\\test_images_out\\";
//read image name
std::ifstream fin(input_path + "filelist.txt");
std::string line;
std::vector<std::string> image_names;
while (!fin.eof()) {
std::getline(fin, line);
image_names.push_back(line);
//std::cout << line << std::endl;
//getchar();
} std::ifstream depth_file("E:\\kaggle\\competition\\tgs\\depths.csv");
std::getline(depth_file, line);
//std::vector<int> depths;
std::map<std::string, int> depths;
while (!depth_file.eof()) {
std::getline(depth_file, line);
std::vector<std::string> words;
int ret = split(line, ",", words);
if (ret == 2) {
//depths.push_back(atoi(words[1].c_str()));
//std::cout << "depth " << depths[depths.size() - 1] << std::endl;
//getchar();
depths[words[0]] = atoi(words[1].c_str());
//std::cout << words[0] << ": " << depths[words[0]] << std::endl;
//getchar();
}
}
std::cout << "depths size = " << depths.size() << std::endl; //read image
for (int i = 0; i < image_names.size(); ++i) {
std::cout << "i = " << i << std::endl;
cv::Mat src = cv::imread(input_path + image_names[i] + ".png", cv::IMREAD_GRAYSCALE);
if (src.empty()) {
return -1;
}
cv::Mat dst = cv::Mat::zeros(src.size(), CV_8UC3);
for (int r = 0; r < dst.rows; ++r) {
uchar *sptr = src.ptr<uchar>(r);
cv::Vec3b *ptr = dst.ptr<cv::Vec3b>(r);
for (int c = 0; c < dst.cols; ++c) {
ptr[c][0] = sptr[c];
ptr[c][1] = depths[image_names[i]] / 256;
ptr[c][2] = depths[image_names[i]] % 256;
}
}
cv::imwrite(out_path + image_names[i] + ".png", dst);
} std::cout << "finish!" << std::endl;
getchar();
return 0;
}
更改输入输出路径，对于已标注好的原图片用同样的方式处理。
（2）因为我们的题目是二分类的语义分割，所以分割的结果的label只能是0和1，所以必须将masks中的图片为255更改为1。
int main(int argc, char** argv)
{
//read image name
std::ifstream fin("filelist.txt");
std::string line;
std::vector<std::string> image_names;
while (!fin.eof()) {
std::getline(fin, line);
image_names.push_back(line);
} //modify value
for (int num = 0; num < image_names.size(); ++num) {
cv::Mat src;
src = cv::imread("masks/" + image_names[num], -1);
if (src.empty()) {
std::cout << "fail: " << num << image_names[num] << std::endl;
getchar();
return -1;
} cv::Mat dst;
src.convertTo(dst, CV_8UC1);
for (int j = 0; j < dst.rows; ++j) {
uchar *ptr = dst.ptr<uchar>(j);
for (int i = 0; i < dst.cols; ++i) {
if (ptr[i] >= 128) {
ptr[i] = 1;
}
}
}
cv::imwrite("out/" + image_names[num], dst);
}
}
也可直接下载处理好的图片：链接：https://pan.baidu.com/s/1CAPIvQ6PayZ97eqeTpBcow 密码：h3t9
2、下载语义分割的开源代码
shelhamer/fcn.berkeleyvision.org: Fully Convolutional Networks for Semantic Segmentation by Jonathan Long*, Evan Shelhamer*, and Trevor Darrell. CVPR 2015 and PAMI 2016. https://github.com/shelhamer/fcn.berkeleyvision.org
3、下载修改好的代码
https://github.com/litingpan/fcn
4、将tgs-fcn32s、tgs-fcn16s、tgs-fcn8s复制到fcn.berkeleyvision.org文件夹中，将data/tgs复制到fcn.berkeleyvision.org/data文件夹中，将1中处理好的数据拷贝至tgs对应文件夹中。
5、训练
（1）fcn32s训练
fcn.berkeleyvision.org\tgs-fcn32s>python solve.py
（2）训练fcn16s
fcn.berkeleyvision.org\tgs-fcn16s>python solve.py
(3)训练fcn8s
fcn.berkeleyvision.org\tgs-fcn8s>python solve.py
可以看到经过32倍、16倍、8倍上采样最终达到overall accuracy（总体精度）为0.928，mean accuracy（平均精度）为0.887，mean IU（平均交并比）为0.827，fwavacc（带权重交并比）为0.866。
6、预测
我们用fcn8s训练好的模型进行预测。
fcn.berkeleyvision.org\tgs-fcn8s>python infers.py
输出的结果在fcn.berkeleyvision.org\data\tgs\predict\masksout文件夹中，因为值是不是0就是1，所以感觉图片都是黑色的，如果想要可视化可以用opencv将1改为255，重新保存图片。
7、将预测结果存到csv文件中。
int main(int argc, char** argv)
{
//read image name
std::ifstream fin("masksout/filelist.txt");
std::string line;
std::vector<std::string> image_names;
while (!fin.eof()) {
std::getline(fin, line);
image_names.push_back(line);
} std::ofstream fout("submission.csv");
fout << "id,rle_mask" << std::endl;
for (int k = 0; k < image_names.size(); ++k) {
std::cout << "k = " << k << std::endl;
cv::Mat src = cv::imread("masksout/" + image_names[k]); if (src.empty()) {
return -1;
} fout << image_names[k].substr(0, image_names[k].size()-4) << ",";
cv::Mat gray;
cv::cvtColor(src, gray, CV_BGR2GRAY); cv::Mat trans;
cv::transpose(gray, trans); //fill hole
cv::Mat tmp;
cv::Mat hole;
trans.convertTo(tmp, CV_8UC1, 255);
dip::fillHole(tmp, hole); bool flag = false;
int sum = 0;
std::vector<int> list;
int start_id = 0;
for (int j = 0; j < src.rows; ++j) {
uchar *ptr = hole.ptr<uchar>(j);
for (int i = 0; i < src.cols; ++i) { if (ptr[i] && !flag) {
flag = true;
start_id = j*gray.rows + i+1;
sum = 0;
sum++;
}
else if (ptr[i] && flag) {
sum++;
}
else if (!ptr[i] && flag){
flag = false;
list.push_back(start_id);
list.push_back(sum);
//std::cout << "start_id = " << start_id << ", " << "sum = " << sum << std::endl;
//getchar();
}
}
}//for j
for (int n = 0; n < list.size(); ++n) {
if (n == 0) {
fout << list[0];
}
else {
fout << " " << list[n];
}
}
fout << std::endl;
if (list.size() % 2 != 0) {
std::cout << "error " << image_names[k] << std::endl;
} }
std::cout << "finish!" << std::endl;
getchar();
return 0;
}
其中fillHole函数为
namespace dip {
void fillHole(const cv::Mat &src, cv::Mat &dst)
{
cv::Size sz = src.size();
cv::Mat tmp = cv::Mat::zeros(sz.height + 2, sz.width + 2, src.type());
src.copyTo(tmp(cv::Range(1, sz.height + 1), cv::Range(1, sz.width + 1)));
cv::floodFill(tmp, cv::Point(0, 0), cv::Scalar(255));
cv::Mat cut;
tmp(cv::Range(1, sz.height + 1), cv::Range(1, sz.width + 1)).copyTo(cut);
dst = src | (~cut);
}
}8、提交结果
看来这个结果离比赛要求的答案还差很远。
end