load_data_in_thread 方法去加载数据到 args.d 指针所指缓冲区中
#data.c
pthread_t load_data_in_thread(load_args args)
{
pthread_t thread;
struct load_args *ptr = calloc(1, sizeof(struct load_args));
*ptr = args;
if(pthread_create(&thread, 0, load_thread, ptr)) error("Thread creation failed");
return thread;
}
执行load_thread方法,开启线程加载数据
//data.c
void *load_thread(void *ptr)
{
//printf("Loading data: %d\n", rand());
load_args a = *(struct load_args*)ptr;
if(a.exposure == 0) a.exposure = 1;
if(a.saturation == 0) a.saturation = 1;
if(a.aspect == 0) a.aspect = 1;
if (a.type == OLD_CLASSIFICATION_DATA){
*a.d = load_data_old(a.paths, a.n, a.m, a.labels, a.classes, a.w, a.h);
} else if (a.type == REGRESSION_DATA){
*a.d = load_data_regression(a.paths, a.n, a.m, a.classes, a.min, a.max, a.size, a.angle, a.aspect, a.hue, a.saturation, a.exposure);
} else if (a.type == CLASSIFICATION_DATA){
*a.d = load_data_augment(a.paths, a.n, a.m, a.labels, a.classes, a.hierarchy, a.min, a.max, a.size, a.angle, a.aspect, a.hue, a.saturation, a.exposure, a.center);
} else if (a.type == SUPER_DATA){
*a.d = load_data_super(a.paths, a.n, a.m, a.w, a.h, a.scale);
} else if (a.type == WRITING_DATA){
*a.d = load_data_writing(a.paths, a.n, a.m, a.w, a.h, a.out_w, a.out_h);
} else if (a.type == ISEG_DATA){
*a.d = load_data_iseg(a.n, a.paths, a.m, a.w, a.h, a.classes, a.num_boxes, a.scale, a.min, a.max, a.angle, a.aspect, a.hue, a.saturation, a.exposure);
} else if (a.type == INSTANCE_DATA){
*a.d = load_data_mask(a.n, a.paths, a.m, a.w, a.h, a.classes, a.num_boxes, a.coords, a.min, a.max, a.angle, a.aspect, a.hue, a.saturation, a.exposure);
} else if (a.type == SEGMENTATION_DATA){
*a.d = load_data_seg(a.n, a.paths, a.m, a.w, a.h, a.classes, a.min, a.max, a.angle, a.aspect, a.hue, a.saturation, a.exposure, a.scale);
} else if (a.type == REGION_DATA){
*a.d = load_data_region(a.n, a.paths, a.m, a.w, a.h, a.num_boxes, a.classes, a.jitter, a.hue, a.saturation, a.exposure);
} else if (a.type == DETECTION_DATA){
*a.d = load_data_detection(a.n, a.paths, a.m, a.w, a.h, a.num_boxes, a.classes, a.jitter, a.hue, a.saturation, a.exposure);
} else if (a.type == SWAG_DATA){
*a.d = load_data_swag(a.paths, a.n, a.classes, a.jitter);
} else if (a.type == COMPARE_DATA){
*a.d = load_data_compare(a.n, a.paths, a.m, a.classes, a.w, a.h);
} else if (a.type == IMAGE_DATA){
*(a.im) = load_image_color(a.path, 0, 0);
*(a.resized) = resize_image(*(a.im), a.w, a.h);
} else if (a.type == LETTERBOX_DATA){
*(a.im) = load_image_color(a.path, 0, 0);
*(a.resized) = letterbox_image(*(a.im), a.w, a.h);
} else if (a.type == TAG_DATA){
*a.d = load_data_tag(a.paths, a.n, a.m, a.classes, a.min, a.max, a.size, a.angle, a.aspect, a.hue, a.saturation, a.exposure);
}
free(ptr);
return 0;
}
以上代码中,根据args的type 属性决定了 调用哪个方法去执行load_data;
通过加断点,我们发现 运行yolo train 的时候,调用的是 load_data_detection
//data.c
data load_data_detection(int n, char **paths, int m, int w, int h, int boxes, int classes, float jitter, float hue, float saturation, float exposure)
{
/*
* n batch_size
* paths 图片路径
* m 一次取多少张图片
* w,h 输入图片的宽高
* boxes 根据输入网络的w,h 来决定 y 向量中数目
*/
//随机获取到图片path
// printf("n: %d m: %d w: %d h:%d boxes:%d classes:%d jiter:");
char **random_paths = get_random_paths(paths, n, m);
int i;
data d = {0};
d.shallow = 0;
//一次加载N 张图片
d.X.rows = n;
d.X.vals = calloc(d.X.rows, sizeof(float*));
//每张图片 w * h * 3
d.X.cols = h*w*3;
//真实y 的数据是 n 维 ,5 * boxes 数量
d.y = make_matrix(n, 5*boxes);
for(i = 0; i < n; ++i){
//加载图片
image orig = load_image_color(random_paths[i], 0, 0);
//得到resize 后的 空 图
image sized = make_image(w, h, orig.c);
//填充一半图片
fill_image(sized, .5);
/*
* 这里是为数据添加抖动干扰，提高网络的泛化能力(其实就是crop，数据增广的一种).
配置文件的jitter=0.2，则宽高最多裁剪掉或者增加原始宽高的1/5.
*/
float dw = jitter * orig.w;
float dh = jitter * orig.h;
//这里进行产生随机值
float new_ar = (orig.w + rand_uniform(-dw, dw)) / (orig.h + rand_uniform(-dh, dh));
//float scale = rand_uniform(.25, 2);
float scale = 1;
float nw, nh;
//宽小于高
if(new_ar < 1){
nh = scale * h;
nw = nh * new_ar;
} else {
nw = scale * w;
nh = nw / new_ar;
}
float dx = rand_uniform(0, w - nw);
float dy = rand_uniform(0, h - nh);
//对图片进行裁剪,resized 后的图像保存在sized
place_image(orig, nw, nh, dx, dy, sized);
//对图片进色调、曝光度等的调整
random_distort_image(sized, hue, saturation, exposure);
int flip = rand()%2;
if(flip) flip_image(sized);
d.X.vals[i] = sized.data;
//图像进行变换抖动处理,需要对标签进行还原
fill_truth_detection(random_paths[i], boxes, d.y.vals[i], classes, flip, -dx/w, -dy/h, nw/w, nh/h);
free_image(orig);
}
free(random_paths);
return d;
}
//读取box信息,根据之前的图像变换 改变 x,y,w,h
void fill_truth_detection(char *path, int num_boxes, float *truth, int classes, int flip, float dx, float dy, float sx, float sy)
{
char labelpath[4096];
find_replace(path, "images", "labels", labelpath);
find_replace(labelpath, "JPEGImages", "labels", labelpath);
find_replace(labelpath, "raw", "labels", labelpath);
find_replace(labelpath, ".jpg", ".txt", labelpath);
find_replace(labelpath, ".png", ".txt", labelpath);
find_replace(labelpath, ".JPG", ".txt", labelpath);
find_replace(labelpath, ".JPEG", ".txt", labelpath);
int count = 0;
//获取到box 信息
box_label *boxes = read_boxes(labelpath, &count);
//对label 信息进行处理
randomize_boxes(boxes, count);
//将label 还原到 变形后的图像中去
correct_boxes(boxes, count, dx, dy, sx, sy, flip);
//一个图最多90个框
if(count > num_boxes) count = num_boxes;
float x,y,w,h;
int id;
int i;
int sub = 0;
/*
*原始object:2 0.36666666666666664 0.42824074074074076 0.17083333333333334 0.25277777777777777
* 变换后 2 0.3666666675 0.767904878 0.170833349 0.09741116
*
*/
for (i = 0; i < count; ++i) {
x = boxes[i].x;
y = boxes[i].y;
w = boxes[i].w;
h = boxes[i].h;
id = boxes[i].id;
//如果宽高 小于 原图的千分之一,
if ((w < .001 || h < .001)) {
++sub;
continue;
}
truth[(i-sub)*5+0] = x;
truth[(i-sub)*5+1] = y;
truth[(i-sub)*5+2] = w;
truth[(i-sub)*5+3] = h;
truth[(i-sub)*5+4] = id;
}
free(boxes);
}
到此,我们发现 yolo 的 load_data :
读取图片,resize 到网络宽高,然后抖动,移动整体画面曝光度色调等方式增广数据,并且 将 lable box 还原成 抖动过的图像数据中.并返回;
附录:
加载彩色图片:
image load_image_color(char *filename, int w, int h)
{
return load_image(filename, w, h, 3);
}
image load_image(char *filename, int w, int h, int c)
{
/*
* c 颜色通道
* w 宽度
* h 高度
*/
#ifdef OPENCV
image out = load_image_cv(filename, c);
#else
image out = load_image_stb(filename, c);
#endif
if((h && w) && (h != out.h || w != out.w)){
//需要调整宽高
image resized = resize_image(out, w, h);
free_image(out);
out = resized;
}
return out;
}
darknet 所用数据 结构体:
//matrix.h
//这里rows是一次加载到内存中的样本的个数（batch*net.subdivisions），cols就是样本的维度，**vals指向的是样本的值
typedef struct matrix{
int rows, cols;
float **vals;
} matrix;
typedef struct {
int w;
int h;
int c;
float *data;
} image;
typedef struct{
//图像 宽高
int w, h;
//X 为图像内容,y 为label
matrix X;
matrix y;
int shallow;
int *num_boxes;
box **boxes;
} data;
结构体:
```c
typedef struct load_args{
int threads;
char **paths;
char *path;
int n;
int m;
char **labels;
int h;
int w;
int out_w;
int out_h;
int nh;
int nw;
int num_boxes;
int min, max, size;
int classes;
int background;
int scale;
int center;
int coords;
float jitter;
float angle;
float aspect;
float saturation;
float exposure;
float hue;
data *d;
image *im;
image *resized;
data_type type;
tree *hierarchy;
} load_args;