mirror of https://github.com/pjreddie/darknet.git
1686 lines
47 KiB
C
1686 lines
47 KiB
C
#include "data.h"
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#include "utils.h"
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#include "image.h"
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#include "cuda.h"
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#include <stdio.h>
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#include <stdlib.h>
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#include <string.h>
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pthread_mutex_t mutex = PTHREAD_MUTEX_INITIALIZER;
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list *get_paths(char *filename)
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{
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char *path;
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FILE *file = fopen(filename, "r");
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if(!file) file_error(filename);
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list *lines = make_list();
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while((path=fgetl(file))){
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list_insert(lines, path);
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}
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fclose(file);
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return lines;
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}
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/*
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char **get_random_paths_indexes(char **paths, int n, int m, int *indexes)
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{
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char **random_paths = calloc(n, sizeof(char*));
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int i;
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pthread_mutex_lock(&mutex);
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for(i = 0; i < n; ++i){
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int index = rand()%m;
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indexes[i] = index;
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random_paths[i] = paths[index];
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if(i == 0) printf("%s\n", paths[index]);
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}
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pthread_mutex_unlock(&mutex);
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return random_paths;
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}
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*/
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char **get_random_paths(char **paths, int n, int m)
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{
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char **random_paths = calloc(n, sizeof(char*));
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int i;
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pthread_mutex_lock(&mutex);
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for(i = 0; i < n; ++i){
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int index = rand()%m;
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random_paths[i] = paths[index];
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//if(i == 0) printf("%s\n", paths[index]);
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}
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pthread_mutex_unlock(&mutex);
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return random_paths;
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}
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char **find_replace_paths(char **paths, int n, char *find, char *replace)
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{
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char **replace_paths = calloc(n, sizeof(char*));
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int i;
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for(i = 0; i < n; ++i){
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char replaced[4096];
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find_replace(paths[i], find, replace, replaced);
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replace_paths[i] = copy_string(replaced);
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}
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return replace_paths;
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}
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matrix load_image_paths_gray(char **paths, int n, int w, int h)
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{
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int i;
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matrix X;
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X.rows = n;
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X.vals = calloc(X.rows, sizeof(float*));
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X.cols = 0;
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for(i = 0; i < n; ++i){
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image im = load_image(paths[i], w, h, 3);
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image gray = grayscale_image(im);
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free_image(im);
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im = gray;
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X.vals[i] = im.data;
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X.cols = im.h*im.w*im.c;
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}
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return X;
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}
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matrix load_image_paths(char **paths, int n, int w, int h)
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{
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int i;
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matrix X;
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X.rows = n;
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X.vals = calloc(X.rows, sizeof(float*));
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X.cols = 0;
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for(i = 0; i < n; ++i){
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image im = load_image_color(paths[i], w, h);
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X.vals[i] = im.data;
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X.cols = im.h*im.w*im.c;
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}
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return X;
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}
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matrix load_image_augment_paths(char **paths, int n, int min, int max, int size, float angle, float aspect, float hue, float saturation, float exposure, int center)
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{
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int i;
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matrix X;
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X.rows = n;
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X.vals = calloc(X.rows, sizeof(float*));
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X.cols = 0;
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for(i = 0; i < n; ++i){
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image im = load_image_color(paths[i], 0, 0);
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image crop;
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if(center){
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crop = center_crop_image(im, size, size);
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} else {
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crop = random_augment_image(im, angle, aspect, min, max, size, size);
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}
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int flip = rand()%2;
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if (flip) flip_image(crop);
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random_distort_image(crop, hue, saturation, exposure);
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/*
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show_image(im, "orig");
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show_image(crop, "crop");
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cvWaitKey(0);
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*/
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//grayscale_image_3c(crop);
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free_image(im);
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X.vals[i] = crop.data;
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X.cols = crop.h*crop.w*crop.c;
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}
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return X;
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}
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box_label *read_boxes(char *filename, int *n)
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{
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FILE *file = fopen(filename, "r");
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if(!file) file_error(filename);
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float x, y, h, w;
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int id;
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int count = 0;
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int size = 64;
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box_label *boxes = calloc(size, sizeof(box_label));
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while(fscanf(file, "%d %f %f %f %f", &id, &x, &y, &w, &h) == 5){
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if(count == size) {
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size = size * 2;
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boxes = realloc(boxes, size*sizeof(box_label));
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}
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boxes[count].id = id;
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boxes[count].x = x;
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boxes[count].y = y;
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boxes[count].h = h;
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boxes[count].w = w;
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boxes[count].left = x - w/2;
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boxes[count].right = x + w/2;
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boxes[count].top = y - h/2;
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boxes[count].bottom = y + h/2;
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++count;
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}
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fclose(file);
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*n = count;
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return boxes;
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}
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void randomize_boxes(box_label *b, int n)
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{
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int i;
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for(i = 0; i < n; ++i){
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box_label swap = b[i];
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int index = rand()%n;
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b[i] = b[index];
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b[index] = swap;
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}
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}
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void correct_boxes(box_label *boxes, int n, float dx, float dy, float sx, float sy, int flip)
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{
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int i;
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for(i = 0; i < n; ++i){
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if(boxes[i].x == 0 && boxes[i].y == 0) {
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boxes[i].x = 999999;
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boxes[i].y = 999999;
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boxes[i].w = 999999;
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boxes[i].h = 999999;
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continue;
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}
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boxes[i].left = boxes[i].left * sx - dx;
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boxes[i].right = boxes[i].right * sx - dx;
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boxes[i].top = boxes[i].top * sy - dy;
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boxes[i].bottom = boxes[i].bottom* sy - dy;
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if(flip){
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float swap = boxes[i].left;
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boxes[i].left = 1. - boxes[i].right;
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boxes[i].right = 1. - swap;
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}
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boxes[i].left = constrain(0, 1, boxes[i].left);
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boxes[i].right = constrain(0, 1, boxes[i].right);
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boxes[i].top = constrain(0, 1, boxes[i].top);
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boxes[i].bottom = constrain(0, 1, boxes[i].bottom);
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boxes[i].x = (boxes[i].left+boxes[i].right)/2;
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boxes[i].y = (boxes[i].top+boxes[i].bottom)/2;
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boxes[i].w = (boxes[i].right - boxes[i].left);
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boxes[i].h = (boxes[i].bottom - boxes[i].top);
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boxes[i].w = constrain(0, 1, boxes[i].w);
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boxes[i].h = constrain(0, 1, boxes[i].h);
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}
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}
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void fill_truth_swag(char *path, float *truth, int classes, int flip, float dx, float dy, float sx, float sy)
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{
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char labelpath[4096];
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find_replace(path, "images", "labels", labelpath);
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find_replace(labelpath, "JPEGImages", "labels", labelpath);
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find_replace(labelpath, ".jpg", ".txt", labelpath);
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find_replace(labelpath, ".JPG", ".txt", labelpath);
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find_replace(labelpath, ".JPEG", ".txt", labelpath);
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int count = 0;
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box_label *boxes = read_boxes(labelpath, &count);
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randomize_boxes(boxes, count);
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correct_boxes(boxes, count, dx, dy, sx, sy, flip);
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float x,y,w,h;
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int id;
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int i;
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for (i = 0; i < count && i < 90; ++i) {
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x = boxes[i].x;
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y = boxes[i].y;
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w = boxes[i].w;
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h = boxes[i].h;
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id = boxes[i].id;
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if (w < .0 || h < .0) continue;
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int index = (4+classes) * i;
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truth[index++] = x;
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truth[index++] = y;
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truth[index++] = w;
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truth[index++] = h;
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if (id < classes) truth[index+id] = 1;
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}
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free(boxes);
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}
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void fill_truth_region(char *path, float *truth, int classes, int num_boxes, int flip, float dx, float dy, float sx, float sy)
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{
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char labelpath[4096];
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find_replace(path, "images", "labels", labelpath);
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find_replace(labelpath, "JPEGImages", "labels", labelpath);
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find_replace(labelpath, ".jpg", ".txt", labelpath);
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find_replace(labelpath, ".png", ".txt", labelpath);
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find_replace(labelpath, ".JPG", ".txt", labelpath);
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find_replace(labelpath, ".JPEG", ".txt", labelpath);
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int count = 0;
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box_label *boxes = read_boxes(labelpath, &count);
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randomize_boxes(boxes, count);
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correct_boxes(boxes, count, dx, dy, sx, sy, flip);
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float x,y,w,h;
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int id;
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int i;
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for (i = 0; i < count; ++i) {
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x = boxes[i].x;
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y = boxes[i].y;
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w = boxes[i].w;
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h = boxes[i].h;
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id = boxes[i].id;
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if (w < .005 || h < .005) continue;
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int col = (int)(x*num_boxes);
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int row = (int)(y*num_boxes);
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x = x*num_boxes - col;
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y = y*num_boxes - row;
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int index = (col+row*num_boxes)*(5+classes);
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if (truth[index]) continue;
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truth[index++] = 1;
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if (id < classes) truth[index+id] = 1;
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index += classes;
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truth[index++] = x;
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truth[index++] = y;
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truth[index++] = w;
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truth[index++] = h;
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}
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free(boxes);
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}
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void load_rle(image im, int *rle, int n)
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{
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int count = 0;
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int curr = 0;
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int i,j;
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for(i = 0; i < n; ++i){
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for(j = 0; j < rle[i]; ++j){
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im.data[count++] = curr;
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}
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curr = 1 - curr;
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}
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for(; count < im.h*im.w*im.c; ++count){
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im.data[count] = curr;
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}
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}
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void or_image(image src, image dest, int c)
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{
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int i;
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for(i = 0; i < src.w*src.h; ++i){
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if(src.data[i]) dest.data[dest.w*dest.h*c + i] = 1;
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}
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}
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void exclusive_image(image src)
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{
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int k, j, i;
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int s = src.w*src.h;
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for(k = 0; k < src.c-1; ++k){
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for(i = 0; i < s; ++i){
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if (src.data[k*s + i]){
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for(j = k+1; j < src.c; ++j){
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src.data[j*s + i] = 0;
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}
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}
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}
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}
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}
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box bound_image(image im)
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{
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int x,y;
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int minx = im.w;
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int miny = im.h;
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int maxx = 0;
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int maxy = 0;
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for(y = 0; y < im.h; ++y){
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for(x = 0; x < im.w; ++x){
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if(im.data[y*im.w + x]){
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minx = (x < minx) ? x : minx;
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miny = (y < miny) ? y : miny;
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maxx = (x > maxx) ? x : maxx;
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maxy = (y > maxy) ? y : maxy;
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}
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}
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}
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box b = {minx, miny, maxx-minx + 1, maxy-miny + 1};
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//printf("%f %f %f %f\n", b.x, b.y, b.w, b.h);
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return b;
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}
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void fill_truth_iseg(char *path, int num_boxes, float *truth, int classes, int w, int h, augment_args aug, int flip, int mw, int mh)
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{
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char labelpath[4096];
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find_replace(path, "images", "mask", labelpath);
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find_replace(labelpath, "JPEGImages", "mask", labelpath);
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find_replace(labelpath, ".jpg", ".txt", labelpath);
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find_replace(labelpath, ".JPG", ".txt", labelpath);
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find_replace(labelpath, ".JPEG", ".txt", labelpath);
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FILE *file = fopen(labelpath, "r");
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if(!file) file_error(labelpath);
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char buff[32788];
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int id;
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int i = 0;
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int j;
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image part = make_image(w, h, 1);
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while((fscanf(file, "%d %s", &id, buff) == 2) && i < num_boxes){
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int n = 0;
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int *rle = read_intlist(buff, &n, 0);
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load_rle(part, rle, n);
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image sized = rotate_crop_image(part, aug.rad, aug.scale, aug.w, aug.h, aug.dx, aug.dy, aug.aspect);
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if(flip) flip_image(sized);
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image mask = resize_image(sized, mw, mh);
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truth[i*(mw*mh+1)] = id;
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for(j = 0; j < mw*mh; ++j){
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truth[i*(mw*mh + 1) + 1 + j] = mask.data[j];
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}
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++i;
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free_image(mask);
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free_image(sized);
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free(rle);
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}
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if(i < num_boxes) truth[i*(mw*mh+1)] = -1;
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fclose(file);
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free_image(part);
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}
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void fill_truth_mask(char *path, int num_boxes, float *truth, int classes, int w, int h, augment_args aug, int flip, int mw, int mh)
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{
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char labelpath[4096];
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find_replace(path, "images", "mask", labelpath);
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find_replace(labelpath, "JPEGImages", "mask", labelpath);
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find_replace(labelpath, ".jpg", ".txt", labelpath);
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find_replace(labelpath, ".JPG", ".txt", labelpath);
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find_replace(labelpath, ".JPEG", ".txt", labelpath);
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FILE *file = fopen(labelpath, "r");
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if(!file) file_error(labelpath);
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char buff[32788];
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int id;
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int i = 0;
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image part = make_image(w, h, 1);
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while((fscanf(file, "%d %s", &id, buff) == 2) && i < num_boxes){
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int n = 0;
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int *rle = read_intlist(buff, &n, 0);
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load_rle(part, rle, n);
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image sized = rotate_crop_image(part, aug.rad, aug.scale, aug.w, aug.h, aug.dx, aug.dy, aug.aspect);
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if(flip) flip_image(sized);
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box b = bound_image(sized);
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if(b.w > 0){
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image crop = crop_image(sized, b.x, b.y, b.w, b.h);
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image mask = resize_image(crop, mw, mh);
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truth[i*(4 + mw*mh + 1) + 0] = (b.x + b.w/2.)/sized.w;
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truth[i*(4 + mw*mh + 1) + 1] = (b.y + b.h/2.)/sized.h;
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truth[i*(4 + mw*mh + 1) + 2] = b.w/sized.w;
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truth[i*(4 + mw*mh + 1) + 3] = b.h/sized.h;
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int j;
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for(j = 0; j < mw*mh; ++j){
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truth[i*(4 + mw*mh + 1) + 4 + j] = mask.data[j];
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}
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truth[i*(4 + mw*mh + 1) + 4 + mw*mh] = id;
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free_image(crop);
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free_image(mask);
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++i;
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}
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free_image(sized);
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free(rle);
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}
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fclose(file);
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free_image(part);
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}
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void fill_truth_detection(char *path, int num_boxes, float *truth, int classes, int flip, float dx, float dy, float sx, float sy)
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{
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char labelpath[4096];
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find_replace(path, "images", "labels", labelpath);
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find_replace(labelpath, "JPEGImages", "labels", labelpath);
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find_replace(labelpath, "raw", "labels", labelpath);
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find_replace(labelpath, ".jpg", ".txt", labelpath);
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find_replace(labelpath, ".png", ".txt", labelpath);
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find_replace(labelpath, ".JPG", ".txt", labelpath);
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find_replace(labelpath, ".JPEG", ".txt", labelpath);
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int count = 0;
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box_label *boxes = read_boxes(labelpath, &count);
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randomize_boxes(boxes, count);
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correct_boxes(boxes, count, dx, dy, sx, sy, flip);
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if(count > num_boxes) count = num_boxes;
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float x,y,w,h;
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int id;
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int i;
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int sub = 0;
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for (i = 0; i < count; ++i) {
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x = boxes[i].x;
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y = boxes[i].y;
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w = boxes[i].w;
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h = boxes[i].h;
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id = boxes[i].id;
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if ((w < .001 || h < .001)) {
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++sub;
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continue;
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}
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truth[(i-sub)*5+0] = x;
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truth[(i-sub)*5+1] = y;
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truth[(i-sub)*5+2] = w;
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truth[(i-sub)*5+3] = h;
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truth[(i-sub)*5+4] = id;
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}
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free(boxes);
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|
}
|
|
|
|
#define NUMCHARS 37
|
|
|
|
void print_letters(float *pred, int n)
|
|
{
|
|
int i;
|
|
for(i = 0; i < n; ++i){
|
|
int index = max_index(pred+i*NUMCHARS, NUMCHARS);
|
|
printf("%c", int_to_alphanum(index));
|
|
}
|
|
printf("\n");
|
|
}
|
|
|
|
void fill_truth_captcha(char *path, int n, float *truth)
|
|
{
|
|
char *begin = strrchr(path, '/');
|
|
++begin;
|
|
int i;
|
|
for(i = 0; i < strlen(begin) && i < n && begin[i] != '.'; ++i){
|
|
int index = alphanum_to_int(begin[i]);
|
|
if(index > 35) printf("Bad %c\n", begin[i]);
|
|
truth[i*NUMCHARS+index] = 1;
|
|
}
|
|
for(;i < n; ++i){
|
|
truth[i*NUMCHARS + NUMCHARS-1] = 1;
|
|
}
|
|
}
|
|
|
|
data load_data_captcha(char **paths, int n, int m, int k, int w, int h)
|
|
{
|
|
if(m) paths = get_random_paths(paths, n, m);
|
|
data d = {0};
|
|
d.shallow = 0;
|
|
d.X = load_image_paths(paths, n, w, h);
|
|
d.y = make_matrix(n, k*NUMCHARS);
|
|
int i;
|
|
for(i = 0; i < n; ++i){
|
|
fill_truth_captcha(paths[i], k, d.y.vals[i]);
|
|
}
|
|
if(m) free(paths);
|
|
return d;
|
|
}
|
|
|
|
data load_data_captcha_encode(char **paths, int n, int m, int w, int h)
|
|
{
|
|
if(m) paths = get_random_paths(paths, n, m);
|
|
data d = {0};
|
|
d.shallow = 0;
|
|
d.X = load_image_paths(paths, n, w, h);
|
|
d.X.cols = 17100;
|
|
d.y = d.X;
|
|
if(m) free(paths);
|
|
return d;
|
|
}
|
|
|
|
void fill_truth(char *path, char **labels, int k, float *truth)
|
|
{
|
|
int i;
|
|
memset(truth, 0, k*sizeof(float));
|
|
int count = 0;
|
|
for(i = 0; i < k; ++i){
|
|
if(strstr(path, labels[i])){
|
|
truth[i] = 1;
|
|
++count;
|
|
//printf("%s %s %d\n", path, labels[i], i);
|
|
}
|
|
}
|
|
if(count != 1 && (k != 1 || count != 0)) printf("Too many or too few labels: %d, %s\n", count, path);
|
|
}
|
|
|
|
void fill_hierarchy(float *truth, int k, tree *hierarchy)
|
|
{
|
|
int j;
|
|
for(j = 0; j < k; ++j){
|
|
if(truth[j]){
|
|
int parent = hierarchy->parent[j];
|
|
while(parent >= 0){
|
|
truth[parent] = 1;
|
|
parent = hierarchy->parent[parent];
|
|
}
|
|
}
|
|
}
|
|
int i;
|
|
int count = 0;
|
|
for(j = 0; j < hierarchy->groups; ++j){
|
|
//printf("%d\n", count);
|
|
int mask = 1;
|
|
for(i = 0; i < hierarchy->group_size[j]; ++i){
|
|
if(truth[count + i]){
|
|
mask = 0;
|
|
break;
|
|
}
|
|
}
|
|
if (mask) {
|
|
for(i = 0; i < hierarchy->group_size[j]; ++i){
|
|
truth[count + i] = SECRET_NUM;
|
|
}
|
|
}
|
|
count += hierarchy->group_size[j];
|
|
}
|
|
}
|
|
|
|
matrix load_regression_labels_paths(char **paths, int n, int k)
|
|
{
|
|
matrix y = make_matrix(n, k);
|
|
int i,j;
|
|
for(i = 0; i < n; ++i){
|
|
char labelpath[4096];
|
|
find_replace(paths[i], "images", "labels", labelpath);
|
|
find_replace(labelpath, "JPEGImages", "labels", labelpath);
|
|
find_replace(labelpath, ".BMP", ".txt", labelpath);
|
|
find_replace(labelpath, ".JPEG", ".txt", labelpath);
|
|
find_replace(labelpath, ".JPG", ".txt", labelpath);
|
|
find_replace(labelpath, ".JPeG", ".txt", labelpath);
|
|
find_replace(labelpath, ".Jpeg", ".txt", labelpath);
|
|
find_replace(labelpath, ".PNG", ".txt", labelpath);
|
|
find_replace(labelpath, ".TIF", ".txt", labelpath);
|
|
find_replace(labelpath, ".bmp", ".txt", labelpath);
|
|
find_replace(labelpath, ".jpeg", ".txt", labelpath);
|
|
find_replace(labelpath, ".jpg", ".txt", labelpath);
|
|
find_replace(labelpath, ".png", ".txt", labelpath);
|
|
find_replace(labelpath, ".tif", ".txt", labelpath);
|
|
|
|
FILE *file = fopen(labelpath, "r");
|
|
for(j = 0; j < k; ++j){
|
|
fscanf(file, "%f", &(y.vals[i][j]));
|
|
}
|
|
fclose(file);
|
|
}
|
|
return y;
|
|
}
|
|
|
|
matrix load_labels_paths(char **paths, int n, char **labels, int k, tree *hierarchy)
|
|
{
|
|
matrix y = make_matrix(n, k);
|
|
int i;
|
|
for(i = 0; i < n && labels; ++i){
|
|
fill_truth(paths[i], labels, k, y.vals[i]);
|
|
if(hierarchy){
|
|
fill_hierarchy(y.vals[i], k, hierarchy);
|
|
}
|
|
}
|
|
return y;
|
|
}
|
|
|
|
matrix load_tags_paths(char **paths, int n, int k)
|
|
{
|
|
matrix y = make_matrix(n, k);
|
|
int i;
|
|
//int count = 0;
|
|
for(i = 0; i < n; ++i){
|
|
char label[4096];
|
|
find_replace(paths[i], "images", "labels", label);
|
|
find_replace(label, ".jpg", ".txt", label);
|
|
FILE *file = fopen(label, "r");
|
|
if (!file) continue;
|
|
//++count;
|
|
int tag;
|
|
while(fscanf(file, "%d", &tag) == 1){
|
|
if(tag < k){
|
|
y.vals[i][tag] = 1;
|
|
}
|
|
}
|
|
fclose(file);
|
|
}
|
|
//printf("%d/%d\n", count, n);
|
|
return y;
|
|
}
|
|
|
|
char **get_labels(char *filename)
|
|
{
|
|
list *plist = get_paths(filename);
|
|
char **labels = (char **)list_to_array(plist);
|
|
free_list(plist);
|
|
return labels;
|
|
}
|
|
|
|
void free_data(data d)
|
|
{
|
|
if(!d.shallow){
|
|
free_matrix(d.X);
|
|
free_matrix(d.y);
|
|
}else{
|
|
free(d.X.vals);
|
|
free(d.y.vals);
|
|
}
|
|
}
|
|
|
|
image get_segmentation_image(char *path, int w, int h, int classes)
|
|
{
|
|
char labelpath[4096];
|
|
find_replace(path, "images", "mask", labelpath);
|
|
find_replace(labelpath, "JPEGImages", "mask", labelpath);
|
|
find_replace(labelpath, ".jpg", ".txt", labelpath);
|
|
find_replace(labelpath, ".JPG", ".txt", labelpath);
|
|
find_replace(labelpath, ".JPEG", ".txt", labelpath);
|
|
image mask = make_image(w, h, classes);
|
|
FILE *file = fopen(labelpath, "r");
|
|
if(!file) file_error(labelpath);
|
|
char buff[32788];
|
|
int id;
|
|
image part = make_image(w, h, 1);
|
|
while(fscanf(file, "%d %s", &id, buff) == 2){
|
|
int n = 0;
|
|
int *rle = read_intlist(buff, &n, 0);
|
|
load_rle(part, rle, n);
|
|
or_image(part, mask, id);
|
|
free(rle);
|
|
}
|
|
//exclusive_image(mask);
|
|
fclose(file);
|
|
free_image(part);
|
|
return mask;
|
|
}
|
|
|
|
image get_segmentation_image2(char *path, int w, int h, int classes)
|
|
{
|
|
char labelpath[4096];
|
|
find_replace(path, "images", "mask", labelpath);
|
|
find_replace(labelpath, "JPEGImages", "mask", labelpath);
|
|
find_replace(labelpath, ".jpg", ".txt", labelpath);
|
|
find_replace(labelpath, ".JPG", ".txt", labelpath);
|
|
find_replace(labelpath, ".JPEG", ".txt", labelpath);
|
|
image mask = make_image(w, h, classes+1);
|
|
int i;
|
|
for(i = 0; i < w*h; ++i){
|
|
mask.data[w*h*classes + i] = 1;
|
|
}
|
|
FILE *file = fopen(labelpath, "r");
|
|
if(!file) file_error(labelpath);
|
|
char buff[32788];
|
|
int id;
|
|
image part = make_image(w, h, 1);
|
|
while(fscanf(file, "%d %s", &id, buff) == 2){
|
|
int n = 0;
|
|
int *rle = read_intlist(buff, &n, 0);
|
|
load_rle(part, rle, n);
|
|
or_image(part, mask, id);
|
|
for(i = 0; i < w*h; ++i){
|
|
if(part.data[i]) mask.data[w*h*classes + i] = 0;
|
|
}
|
|
free(rle);
|
|
}
|
|
//exclusive_image(mask);
|
|
fclose(file);
|
|
free_image(part);
|
|
return mask;
|
|
}
|
|
|
|
data load_data_seg(int n, char **paths, int m, int w, int h, int classes, int min, int max, float angle, float aspect, float hue, float saturation, float exposure, int div)
|
|
{
|
|
char **random_paths = get_random_paths(paths, n, m);
|
|
int i;
|
|
data d = {0};
|
|
d.shallow = 0;
|
|
|
|
d.X.rows = n;
|
|
d.X.vals = calloc(d.X.rows, sizeof(float*));
|
|
d.X.cols = h*w*3;
|
|
|
|
|
|
d.y.rows = n;
|
|
d.y.cols = h*w*classes/div/div;
|
|
d.y.vals = calloc(d.X.rows, sizeof(float*));
|
|
|
|
for(i = 0; i < n; ++i){
|
|
image orig = load_image_color(random_paths[i], 0, 0);
|
|
augment_args a = random_augment_args(orig, angle, aspect, min, max, w, h);
|
|
image sized = rotate_crop_image(orig, a.rad, a.scale, a.w, a.h, a.dx, a.dy, a.aspect);
|
|
|
|
int flip = rand()%2;
|
|
if(flip) flip_image(sized);
|
|
random_distort_image(sized, hue, saturation, exposure);
|
|
d.X.vals[i] = sized.data;
|
|
|
|
image mask = get_segmentation_image(random_paths[i], orig.w, orig.h, classes);
|
|
//image mask = make_image(orig.w, orig.h, classes+1);
|
|
image sized_m = rotate_crop_image(mask, a.rad, a.scale/div, a.w/div, a.h/div, a.dx/div, a.dy/div, a.aspect);
|
|
|
|
if(flip) flip_image(sized_m);
|
|
d.y.vals[i] = sized_m.data;
|
|
|
|
free_image(orig);
|
|
free_image(mask);
|
|
|
|
/*
|
|
image rgb = mask_to_rgb(sized_m, classes);
|
|
show_image(rgb, "part");
|
|
show_image(sized, "orig");
|
|
cvWaitKey(0);
|
|
free_image(rgb);
|
|
*/
|
|
}
|
|
free(random_paths);
|
|
return d;
|
|
}
|
|
|
|
data load_data_iseg(int n, char **paths, int m, int w, int h, int classes, int boxes, int div, int min, int max, float angle, float aspect, float hue, float saturation, float exposure)
|
|
{
|
|
char **random_paths = get_random_paths(paths, n, m);
|
|
int i;
|
|
data d = {0};
|
|
d.shallow = 0;
|
|
|
|
d.X.rows = n;
|
|
d.X.vals = calloc(d.X.rows, sizeof(float*));
|
|
d.X.cols = h*w*3;
|
|
|
|
d.y = make_matrix(n, (((w/div)*(h/div))+1)*boxes);
|
|
|
|
for(i = 0; i < n; ++i){
|
|
image orig = load_image_color(random_paths[i], 0, 0);
|
|
augment_args a = random_augment_args(orig, angle, aspect, min, max, w, h);
|
|
image sized = rotate_crop_image(orig, a.rad, a.scale, a.w, a.h, a.dx, a.dy, a.aspect);
|
|
|
|
int flip = rand()%2;
|
|
if(flip) flip_image(sized);
|
|
random_distort_image(sized, hue, saturation, exposure);
|
|
d.X.vals[i] = sized.data;
|
|
//show_image(sized, "image");
|
|
|
|
fill_truth_iseg(random_paths[i], boxes, d.y.vals[i], classes, orig.w, orig.h, a, flip, w/div, h/div);
|
|
|
|
free_image(orig);
|
|
|
|
/*
|
|
image rgb = mask_to_rgb(sized_m, classes);
|
|
show_image(rgb, "part");
|
|
show_image(sized, "orig");
|
|
cvWaitKey(0);
|
|
free_image(rgb);
|
|
*/
|
|
}
|
|
free(random_paths);
|
|
return d;
|
|
}
|
|
|
|
data load_data_mask(int n, char **paths, int m, int w, int h, int classes, int boxes, int coords, int min, int max, float angle, float aspect, float hue, float saturation, float exposure)
|
|
{
|
|
char **random_paths = get_random_paths(paths, n, m);
|
|
int i;
|
|
data d = {0};
|
|
d.shallow = 0;
|
|
|
|
d.X.rows = n;
|
|
d.X.vals = calloc(d.X.rows, sizeof(float*));
|
|
d.X.cols = h*w*3;
|
|
|
|
d.y = make_matrix(n, (coords+1)*boxes);
|
|
|
|
for(i = 0; i < n; ++i){
|
|
image orig = load_image_color(random_paths[i], 0, 0);
|
|
augment_args a = random_augment_args(orig, angle, aspect, min, max, w, h);
|
|
image sized = rotate_crop_image(orig, a.rad, a.scale, a.w, a.h, a.dx, a.dy, a.aspect);
|
|
|
|
int flip = rand()%2;
|
|
if(flip) flip_image(sized);
|
|
random_distort_image(sized, hue, saturation, exposure);
|
|
d.X.vals[i] = sized.data;
|
|
//show_image(sized, "image");
|
|
|
|
fill_truth_mask(random_paths[i], boxes, d.y.vals[i], classes, orig.w, orig.h, a, flip, 14, 14);
|
|
|
|
free_image(orig);
|
|
|
|
/*
|
|
image rgb = mask_to_rgb(sized_m, classes);
|
|
show_image(rgb, "part");
|
|
show_image(sized, "orig");
|
|
cvWaitKey(0);
|
|
free_image(rgb);
|
|
*/
|
|
}
|
|
free(random_paths);
|
|
return d;
|
|
}
|
|
|
|
data load_data_region(int n, char **paths, int m, int w, int h, int size, int classes, float jitter, float hue, float saturation, float exposure)
|
|
{
|
|
char **random_paths = get_random_paths(paths, n, m);
|
|
int i;
|
|
data d = {0};
|
|
d.shallow = 0;
|
|
|
|
d.X.rows = n;
|
|
d.X.vals = calloc(d.X.rows, sizeof(float*));
|
|
d.X.cols = h*w*3;
|
|
|
|
|
|
int k = size*size*(5+classes);
|
|
d.y = make_matrix(n, k);
|
|
for(i = 0; i < n; ++i){
|
|
image orig = load_image_color(random_paths[i], 0, 0);
|
|
|
|
int oh = orig.h;
|
|
int ow = orig.w;
|
|
|
|
int dw = (ow*jitter);
|
|
int dh = (oh*jitter);
|
|
|
|
int pleft = rand_uniform(-dw, dw);
|
|
int pright = rand_uniform(-dw, dw);
|
|
int ptop = rand_uniform(-dh, dh);
|
|
int pbot = rand_uniform(-dh, dh);
|
|
|
|
int swidth = ow - pleft - pright;
|
|
int sheight = oh - ptop - pbot;
|
|
|
|
float sx = (float)swidth / ow;
|
|
float sy = (float)sheight / oh;
|
|
|
|
int flip = rand()%2;
|
|
image cropped = crop_image(orig, pleft, ptop, swidth, sheight);
|
|
|
|
float dx = ((float)pleft/ow)/sx;
|
|
float dy = ((float)ptop /oh)/sy;
|
|
|
|
image sized = resize_image(cropped, w, h);
|
|
if(flip) flip_image(sized);
|
|
random_distort_image(sized, hue, saturation, exposure);
|
|
d.X.vals[i] = sized.data;
|
|
|
|
fill_truth_region(random_paths[i], d.y.vals[i], classes, size, flip, dx, dy, 1./sx, 1./sy);
|
|
|
|
free_image(orig);
|
|
free_image(cropped);
|
|
}
|
|
free(random_paths);
|
|
return d;
|
|
}
|
|
|
|
data load_data_compare(int n, char **paths, int m, int classes, int w, int h)
|
|
{
|
|
if(m) paths = get_random_paths(paths, 2*n, m);
|
|
int i,j;
|
|
data d = {0};
|
|
d.shallow = 0;
|
|
|
|
d.X.rows = n;
|
|
d.X.vals = calloc(d.X.rows, sizeof(float*));
|
|
d.X.cols = h*w*6;
|
|
|
|
int k = 2*(classes);
|
|
d.y = make_matrix(n, k);
|
|
for(i = 0; i < n; ++i){
|
|
image im1 = load_image_color(paths[i*2], w, h);
|
|
image im2 = load_image_color(paths[i*2+1], w, h);
|
|
|
|
d.X.vals[i] = calloc(d.X.cols, sizeof(float));
|
|
memcpy(d.X.vals[i], im1.data, h*w*3*sizeof(float));
|
|
memcpy(d.X.vals[i] + h*w*3, im2.data, h*w*3*sizeof(float));
|
|
|
|
int id;
|
|
float iou;
|
|
|
|
char imlabel1[4096];
|
|
char imlabel2[4096];
|
|
find_replace(paths[i*2], "imgs", "labels", imlabel1);
|
|
find_replace(imlabel1, "jpg", "txt", imlabel1);
|
|
FILE *fp1 = fopen(imlabel1, "r");
|
|
|
|
while(fscanf(fp1, "%d %f", &id, &iou) == 2){
|
|
if (d.y.vals[i][2*id] < iou) d.y.vals[i][2*id] = iou;
|
|
}
|
|
|
|
find_replace(paths[i*2+1], "imgs", "labels", imlabel2);
|
|
find_replace(imlabel2, "jpg", "txt", imlabel2);
|
|
FILE *fp2 = fopen(imlabel2, "r");
|
|
|
|
while(fscanf(fp2, "%d %f", &id, &iou) == 2){
|
|
if (d.y.vals[i][2*id + 1] < iou) d.y.vals[i][2*id + 1] = iou;
|
|
}
|
|
|
|
for (j = 0; j < classes; ++j){
|
|
if (d.y.vals[i][2*j] > .5 && d.y.vals[i][2*j+1] < .5){
|
|
d.y.vals[i][2*j] = 1;
|
|
d.y.vals[i][2*j+1] = 0;
|
|
} else if (d.y.vals[i][2*j] < .5 && d.y.vals[i][2*j+1] > .5){
|
|
d.y.vals[i][2*j] = 0;
|
|
d.y.vals[i][2*j+1] = 1;
|
|
} else {
|
|
d.y.vals[i][2*j] = SECRET_NUM;
|
|
d.y.vals[i][2*j+1] = SECRET_NUM;
|
|
}
|
|
}
|
|
fclose(fp1);
|
|
fclose(fp2);
|
|
|
|
free_image(im1);
|
|
free_image(im2);
|
|
}
|
|
if(m) free(paths);
|
|
return d;
|
|
}
|
|
|
|
data load_data_swag(char **paths, int n, int classes, float jitter)
|
|
{
|
|
int index = rand()%n;
|
|
char *random_path = paths[index];
|
|
|
|
image orig = load_image_color(random_path, 0, 0);
|
|
int h = orig.h;
|
|
int w = orig.w;
|
|
|
|
data d = {0};
|
|
d.shallow = 0;
|
|
d.w = w;
|
|
d.h = h;
|
|
|
|
d.X.rows = 1;
|
|
d.X.vals = calloc(d.X.rows, sizeof(float*));
|
|
d.X.cols = h*w*3;
|
|
|
|
int k = (4+classes)*90;
|
|
d.y = make_matrix(1, k);
|
|
|
|
int dw = w*jitter;
|
|
int dh = h*jitter;
|
|
|
|
int pleft = rand_uniform(-dw, dw);
|
|
int pright = rand_uniform(-dw, dw);
|
|
int ptop = rand_uniform(-dh, dh);
|
|
int pbot = rand_uniform(-dh, dh);
|
|
|
|
int swidth = w - pleft - pright;
|
|
int sheight = h - ptop - pbot;
|
|
|
|
float sx = (float)swidth / w;
|
|
float sy = (float)sheight / h;
|
|
|
|
int flip = rand()%2;
|
|
image cropped = crop_image(orig, pleft, ptop, swidth, sheight);
|
|
|
|
float dx = ((float)pleft/w)/sx;
|
|
float dy = ((float)ptop /h)/sy;
|
|
|
|
image sized = resize_image(cropped, w, h);
|
|
if(flip) flip_image(sized);
|
|
d.X.vals[0] = sized.data;
|
|
|
|
fill_truth_swag(random_path, d.y.vals[0], classes, flip, dx, dy, 1./sx, 1./sy);
|
|
|
|
free_image(orig);
|
|
free_image(cropped);
|
|
|
|
return d;
|
|
}
|
|
|
|
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)
|
|
{
|
|
char **random_paths = get_random_paths(paths, n, m);
|
|
int i;
|
|
data d = {0};
|
|
d.shallow = 0;
|
|
|
|
d.X.rows = n;
|
|
d.X.vals = calloc(d.X.rows, sizeof(float*));
|
|
d.X.cols = h*w*3;
|
|
|
|
d.y = make_matrix(n, 5*boxes);
|
|
for(i = 0; i < n; ++i){
|
|
image orig = load_image_color(random_paths[i], 0, 0);
|
|
image sized = make_image(w, h, orig.c);
|
|
fill_image(sized, .5);
|
|
|
|
float dw = jitter * orig.w;
|
|
float dh = jitter * orig.h;
|
|
|
|
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;
|
|
|
|
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);
|
|
|
|
place_image(orig, nw, nh, dx, dy, sized);
|
|
|
|
random_distort_image(sized, hue, saturation, exposure);
|
|
|
|
int flip = rand()%2;
|
|
if(flip) flip_image(sized);
|
|
d.X.vals[i] = sized.data;
|
|
|
|
|
|
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;
|
|
}
|
|
|
|
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;
|
|
}
|
|
|
|
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;
|
|
}
|
|
|
|
void *load_threads(void *ptr)
|
|
{
|
|
int i;
|
|
load_args args = *(load_args *)ptr;
|
|
if (args.threads == 0) args.threads = 1;
|
|
data *out = args.d;
|
|
int total = args.n;
|
|
free(ptr);
|
|
data *buffers = calloc(args.threads, sizeof(data));
|
|
pthread_t *threads = calloc(args.threads, sizeof(pthread_t));
|
|
for(i = 0; i < args.threads; ++i){
|
|
args.d = buffers + i;
|
|
args.n = (i+1) * total/args.threads - i * total/args.threads;
|
|
threads[i] = load_data_in_thread(args);
|
|
}
|
|
for(i = 0; i < args.threads; ++i){
|
|
pthread_join(threads[i], 0);
|
|
}
|
|
*out = concat_datas(buffers, args.threads);
|
|
out->shallow = 0;
|
|
for(i = 0; i < args.threads; ++i){
|
|
buffers[i].shallow = 1;
|
|
free_data(buffers[i]);
|
|
}
|
|
free(buffers);
|
|
free(threads);
|
|
return 0;
|
|
}
|
|
|
|
void load_data_blocking(load_args args)
|
|
{
|
|
struct load_args *ptr = calloc(1, sizeof(struct load_args));
|
|
*ptr = args;
|
|
load_thread(ptr);
|
|
}
|
|
|
|
pthread_t load_data(load_args args)
|
|
{
|
|
pthread_t thread;
|
|
struct load_args *ptr = calloc(1, sizeof(struct load_args));
|
|
*ptr = args;
|
|
if(pthread_create(&thread, 0, load_threads, ptr)) error("Thread creation failed");
|
|
return thread;
|
|
}
|
|
|
|
data load_data_writing(char **paths, int n, int m, int w, int h, int out_w, int out_h)
|
|
{
|
|
if(m) paths = get_random_paths(paths, n, m);
|
|
char **replace_paths = find_replace_paths(paths, n, ".png", "-label.png");
|
|
data d = {0};
|
|
d.shallow = 0;
|
|
d.X = load_image_paths(paths, n, w, h);
|
|
d.y = load_image_paths_gray(replace_paths, n, out_w, out_h);
|
|
if(m) free(paths);
|
|
int i;
|
|
for(i = 0; i < n; ++i) free(replace_paths[i]);
|
|
free(replace_paths);
|
|
return d;
|
|
}
|
|
|
|
data load_data_old(char **paths, int n, int m, char **labels, int k, int w, int h)
|
|
{
|
|
if(m) paths = get_random_paths(paths, n, m);
|
|
data d = {0};
|
|
d.shallow = 0;
|
|
d.X = load_image_paths(paths, n, w, h);
|
|
d.y = load_labels_paths(paths, n, labels, k, 0);
|
|
if(m) free(paths);
|
|
return d;
|
|
}
|
|
|
|
/*
|
|
data load_data_study(char **paths, int n, int m, char **labels, int k, int min, int max, int size, float angle, float aspect, float hue, float saturation, float exposure)
|
|
{
|
|
data d = {0};
|
|
d.indexes = calloc(n, sizeof(int));
|
|
if(m) paths = get_random_paths_indexes(paths, n, m, d.indexes);
|
|
d.shallow = 0;
|
|
d.X = load_image_augment_paths(paths, n, min, max, size, angle, aspect, hue, saturation, exposure);
|
|
d.y = load_labels_paths(paths, n, labels, k);
|
|
if(m) free(paths);
|
|
return d;
|
|
}
|
|
*/
|
|
|
|
data load_data_super(char **paths, int n, int m, int w, int h, int scale)
|
|
{
|
|
if(m) paths = get_random_paths(paths, n, m);
|
|
data d = {0};
|
|
d.shallow = 0;
|
|
|
|
int i;
|
|
d.X.rows = n;
|
|
d.X.vals = calloc(n, sizeof(float*));
|
|
d.X.cols = w*h*3;
|
|
|
|
d.y.rows = n;
|
|
d.y.vals = calloc(n, sizeof(float*));
|
|
d.y.cols = w*scale * h*scale * 3;
|
|
|
|
for(i = 0; i < n; ++i){
|
|
image im = load_image_color(paths[i], 0, 0);
|
|
image crop = random_crop_image(im, w*scale, h*scale);
|
|
int flip = rand()%2;
|
|
if (flip) flip_image(crop);
|
|
image resize = resize_image(crop, w, h);
|
|
d.X.vals[i] = resize.data;
|
|
d.y.vals[i] = crop.data;
|
|
free_image(im);
|
|
}
|
|
|
|
if(m) free(paths);
|
|
return d;
|
|
}
|
|
|
|
data load_data_regression(char **paths, int n, int m, int k, int min, int max, int size, float angle, float aspect, float hue, float saturation, float exposure)
|
|
{
|
|
if(m) paths = get_random_paths(paths, n, m);
|
|
data d = {0};
|
|
d.shallow = 0;
|
|
d.X = load_image_augment_paths(paths, n, min, max, size, angle, aspect, hue, saturation, exposure, 0);
|
|
d.y = load_regression_labels_paths(paths, n, k);
|
|
if(m) free(paths);
|
|
return d;
|
|
}
|
|
|
|
data select_data(data *orig, int *inds)
|
|
{
|
|
data d = {0};
|
|
d.shallow = 1;
|
|
d.w = orig[0].w;
|
|
d.h = orig[0].h;
|
|
|
|
d.X.rows = orig[0].X.rows;
|
|
d.y.rows = orig[0].X.rows;
|
|
|
|
d.X.cols = orig[0].X.cols;
|
|
d.y.cols = orig[0].y.cols;
|
|
|
|
d.X.vals = calloc(orig[0].X.rows, sizeof(float *));
|
|
d.y.vals = calloc(orig[0].y.rows, sizeof(float *));
|
|
int i;
|
|
for(i = 0; i < d.X.rows; ++i){
|
|
d.X.vals[i] = orig[inds[i]].X.vals[i];
|
|
d.y.vals[i] = orig[inds[i]].y.vals[i];
|
|
}
|
|
return d;
|
|
}
|
|
|
|
data *tile_data(data orig, int divs, int size)
|
|
{
|
|
data *ds = calloc(divs*divs, sizeof(data));
|
|
int i, j;
|
|
#pragma omp parallel for
|
|
for(i = 0; i < divs*divs; ++i){
|
|
data d;
|
|
d.shallow = 0;
|
|
d.w = orig.w/divs * size;
|
|
d.h = orig.h/divs * size;
|
|
d.X.rows = orig.X.rows;
|
|
d.X.cols = d.w*d.h*3;
|
|
d.X.vals = calloc(d.X.rows, sizeof(float*));
|
|
|
|
d.y = copy_matrix(orig.y);
|
|
#pragma omp parallel for
|
|
for(j = 0; j < orig.X.rows; ++j){
|
|
int x = (i%divs) * orig.w / divs - (d.w - orig.w/divs)/2;
|
|
int y = (i/divs) * orig.h / divs - (d.h - orig.h/divs)/2;
|
|
image im = float_to_image(orig.w, orig.h, 3, orig.X.vals[j]);
|
|
d.X.vals[j] = crop_image(im, x, y, d.w, d.h).data;
|
|
}
|
|
ds[i] = d;
|
|
}
|
|
return ds;
|
|
}
|
|
|
|
data resize_data(data orig, int w, int h)
|
|
{
|
|
data d = {0};
|
|
d.shallow = 0;
|
|
d.w = w;
|
|
d.h = h;
|
|
int i;
|
|
d.X.rows = orig.X.rows;
|
|
d.X.cols = w*h*3;
|
|
d.X.vals = calloc(d.X.rows, sizeof(float*));
|
|
|
|
d.y = copy_matrix(orig.y);
|
|
#pragma omp parallel for
|
|
for(i = 0; i < orig.X.rows; ++i){
|
|
image im = float_to_image(orig.w, orig.h, 3, orig.X.vals[i]);
|
|
d.X.vals[i] = resize_image(im, w, h).data;
|
|
}
|
|
return d;
|
|
}
|
|
|
|
data load_data_augment(char **paths, int n, int m, char **labels, int k, tree *hierarchy, int min, int max, int size, float angle, float aspect, float hue, float saturation, float exposure, int center)
|
|
{
|
|
if(m) paths = get_random_paths(paths, n, m);
|
|
data d = {0};
|
|
d.shallow = 0;
|
|
d.w=size;
|
|
d.h=size;
|
|
d.X = load_image_augment_paths(paths, n, min, max, size, angle, aspect, hue, saturation, exposure, center);
|
|
d.y = load_labels_paths(paths, n, labels, k, hierarchy);
|
|
if(m) free(paths);
|
|
return d;
|
|
}
|
|
|
|
data load_data_tag(char **paths, int n, int m, int k, int min, int max, int size, float angle, float aspect, float hue, float saturation, float exposure)
|
|
{
|
|
if(m) paths = get_random_paths(paths, n, m);
|
|
data d = {0};
|
|
d.w = size;
|
|
d.h = size;
|
|
d.shallow = 0;
|
|
d.X = load_image_augment_paths(paths, n, min, max, size, angle, aspect, hue, saturation, exposure, 0);
|
|
d.y = load_tags_paths(paths, n, k);
|
|
if(m) free(paths);
|
|
return d;
|
|
}
|
|
|
|
matrix concat_matrix(matrix m1, matrix m2)
|
|
{
|
|
int i, count = 0;
|
|
matrix m;
|
|
m.cols = m1.cols;
|
|
m.rows = m1.rows+m2.rows;
|
|
m.vals = calloc(m1.rows + m2.rows, sizeof(float*));
|
|
for(i = 0; i < m1.rows; ++i){
|
|
m.vals[count++] = m1.vals[i];
|
|
}
|
|
for(i = 0; i < m2.rows; ++i){
|
|
m.vals[count++] = m2.vals[i];
|
|
}
|
|
return m;
|
|
}
|
|
|
|
data concat_data(data d1, data d2)
|
|
{
|
|
data d = {0};
|
|
d.shallow = 1;
|
|
d.X = concat_matrix(d1.X, d2.X);
|
|
d.y = concat_matrix(d1.y, d2.y);
|
|
d.w = d1.w;
|
|
d.h = d1.h;
|
|
return d;
|
|
}
|
|
|
|
data concat_datas(data *d, int n)
|
|
{
|
|
int i;
|
|
data out = {0};
|
|
for(i = 0; i < n; ++i){
|
|
data new = concat_data(d[i], out);
|
|
free_data(out);
|
|
out = new;
|
|
}
|
|
return out;
|
|
}
|
|
|
|
data load_categorical_data_csv(char *filename, int target, int k)
|
|
{
|
|
data d = {0};
|
|
d.shallow = 0;
|
|
matrix X = csv_to_matrix(filename);
|
|
float *truth_1d = pop_column(&X, target);
|
|
float **truth = one_hot_encode(truth_1d, X.rows, k);
|
|
matrix y;
|
|
y.rows = X.rows;
|
|
y.cols = k;
|
|
y.vals = truth;
|
|
d.X = X;
|
|
d.y = y;
|
|
free(truth_1d);
|
|
return d;
|
|
}
|
|
|
|
data load_cifar10_data(char *filename)
|
|
{
|
|
data d = {0};
|
|
d.shallow = 0;
|
|
long i,j;
|
|
matrix X = make_matrix(10000, 3072);
|
|
matrix y = make_matrix(10000, 10);
|
|
d.X = X;
|
|
d.y = y;
|
|
|
|
FILE *fp = fopen(filename, "rb");
|
|
if(!fp) file_error(filename);
|
|
for(i = 0; i < 10000; ++i){
|
|
unsigned char bytes[3073];
|
|
fread(bytes, 1, 3073, fp);
|
|
int class = bytes[0];
|
|
y.vals[i][class] = 1;
|
|
for(j = 0; j < X.cols; ++j){
|
|
X.vals[i][j] = (double)bytes[j+1];
|
|
}
|
|
}
|
|
scale_data_rows(d, 1./255);
|
|
//normalize_data_rows(d);
|
|
fclose(fp);
|
|
return d;
|
|
}
|
|
|
|
void get_random_batch(data d, int n, float *X, float *y)
|
|
{
|
|
int j;
|
|
for(j = 0; j < n; ++j){
|
|
int index = rand()%d.X.rows;
|
|
memcpy(X+j*d.X.cols, d.X.vals[index], d.X.cols*sizeof(float));
|
|
memcpy(y+j*d.y.cols, d.y.vals[index], d.y.cols*sizeof(float));
|
|
}
|
|
}
|
|
|
|
void get_next_batch(data d, int n, int offset, float *X, float *y)
|
|
{
|
|
int j;
|
|
for(j = 0; j < n; ++j){
|
|
int index = offset + j;
|
|
memcpy(X+j*d.X.cols, d.X.vals[index], d.X.cols*sizeof(float));
|
|
if(y) memcpy(y+j*d.y.cols, d.y.vals[index], d.y.cols*sizeof(float));
|
|
}
|
|
}
|
|
|
|
void smooth_data(data d)
|
|
{
|
|
int i, j;
|
|
float scale = 1. / d.y.cols;
|
|
float eps = .1;
|
|
for(i = 0; i < d.y.rows; ++i){
|
|
for(j = 0; j < d.y.cols; ++j){
|
|
d.y.vals[i][j] = eps * scale + (1-eps) * d.y.vals[i][j];
|
|
}
|
|
}
|
|
}
|
|
|
|
data load_all_cifar10()
|
|
{
|
|
data d = {0};
|
|
d.shallow = 0;
|
|
int i,j,b;
|
|
matrix X = make_matrix(50000, 3072);
|
|
matrix y = make_matrix(50000, 10);
|
|
d.X = X;
|
|
d.y = y;
|
|
|
|
|
|
for(b = 0; b < 5; ++b){
|
|
char buff[256];
|
|
sprintf(buff, "data/cifar/cifar-10-batches-bin/data_batch_%d.bin", b+1);
|
|
FILE *fp = fopen(buff, "rb");
|
|
if(!fp) file_error(buff);
|
|
for(i = 0; i < 10000; ++i){
|
|
unsigned char bytes[3073];
|
|
fread(bytes, 1, 3073, fp);
|
|
int class = bytes[0];
|
|
y.vals[i+b*10000][class] = 1;
|
|
for(j = 0; j < X.cols; ++j){
|
|
X.vals[i+b*10000][j] = (double)bytes[j+1];
|
|
}
|
|
}
|
|
fclose(fp);
|
|
}
|
|
//normalize_data_rows(d);
|
|
scale_data_rows(d, 1./255);
|
|
smooth_data(d);
|
|
return d;
|
|
}
|
|
|
|
data load_go(char *filename)
|
|
{
|
|
FILE *fp = fopen(filename, "rb");
|
|
matrix X = make_matrix(3363059, 361);
|
|
matrix y = make_matrix(3363059, 361);
|
|
int row, col;
|
|
|
|
if(!fp) file_error(filename);
|
|
char *label;
|
|
int count = 0;
|
|
while((label = fgetl(fp))){
|
|
int i;
|
|
if(count == X.rows){
|
|
X = resize_matrix(X, count*2);
|
|
y = resize_matrix(y, count*2);
|
|
}
|
|
sscanf(label, "%d %d", &row, &col);
|
|
char *board = fgetl(fp);
|
|
|
|
int index = row*19 + col;
|
|
y.vals[count][index] = 1;
|
|
|
|
for(i = 0; i < 19*19; ++i){
|
|
float val = 0;
|
|
if(board[i] == '1') val = 1;
|
|
else if(board[i] == '2') val = -1;
|
|
X.vals[count][i] = val;
|
|
}
|
|
++count;
|
|
free(label);
|
|
free(board);
|
|
}
|
|
X = resize_matrix(X, count);
|
|
y = resize_matrix(y, count);
|
|
|
|
data d = {0};
|
|
d.shallow = 0;
|
|
d.X = X;
|
|
d.y = y;
|
|
|
|
|
|
fclose(fp);
|
|
|
|
return d;
|
|
}
|
|
|
|
|
|
void randomize_data(data d)
|
|
{
|
|
int i;
|
|
for(i = d.X.rows-1; i > 0; --i){
|
|
int index = rand()%i;
|
|
float *swap = d.X.vals[index];
|
|
d.X.vals[index] = d.X.vals[i];
|
|
d.X.vals[i] = swap;
|
|
|
|
swap = d.y.vals[index];
|
|
d.y.vals[index] = d.y.vals[i];
|
|
d.y.vals[i] = swap;
|
|
}
|
|
}
|
|
|
|
void scale_data_rows(data d, float s)
|
|
{
|
|
int i;
|
|
for(i = 0; i < d.X.rows; ++i){
|
|
scale_array(d.X.vals[i], d.X.cols, s);
|
|
}
|
|
}
|
|
|
|
void translate_data_rows(data d, float s)
|
|
{
|
|
int i;
|
|
for(i = 0; i < d.X.rows; ++i){
|
|
translate_array(d.X.vals[i], d.X.cols, s);
|
|
}
|
|
}
|
|
|
|
data copy_data(data d)
|
|
{
|
|
data c = {0};
|
|
c.w = d.w;
|
|
c.h = d.h;
|
|
c.shallow = 0;
|
|
c.num_boxes = d.num_boxes;
|
|
c.boxes = d.boxes;
|
|
c.X = copy_matrix(d.X);
|
|
c.y = copy_matrix(d.y);
|
|
return c;
|
|
}
|
|
|
|
void normalize_data_rows(data d)
|
|
{
|
|
int i;
|
|
for(i = 0; i < d.X.rows; ++i){
|
|
normalize_array(d.X.vals[i], d.X.cols);
|
|
}
|
|
}
|
|
|
|
data get_data_part(data d, int part, int total)
|
|
{
|
|
data p = {0};
|
|
p.shallow = 1;
|
|
p.X.rows = d.X.rows * (part + 1) / total - d.X.rows * part / total;
|
|
p.y.rows = d.y.rows * (part + 1) / total - d.y.rows * part / total;
|
|
p.X.cols = d.X.cols;
|
|
p.y.cols = d.y.cols;
|
|
p.X.vals = d.X.vals + d.X.rows * part / total;
|
|
p.y.vals = d.y.vals + d.y.rows * part / total;
|
|
return p;
|
|
}
|
|
|
|
data get_random_data(data d, int num)
|
|
{
|
|
data r = {0};
|
|
r.shallow = 1;
|
|
|
|
r.X.rows = num;
|
|
r.y.rows = num;
|
|
|
|
r.X.cols = d.X.cols;
|
|
r.y.cols = d.y.cols;
|
|
|
|
r.X.vals = calloc(num, sizeof(float *));
|
|
r.y.vals = calloc(num, sizeof(float *));
|
|
|
|
int i;
|
|
for(i = 0; i < num; ++i){
|
|
int index = rand()%d.X.rows;
|
|
r.X.vals[i] = d.X.vals[index];
|
|
r.y.vals[i] = d.y.vals[index];
|
|
}
|
|
return r;
|
|
}
|
|
|
|
data *split_data(data d, int part, int total)
|
|
{
|
|
data *split = calloc(2, sizeof(data));
|
|
int i;
|
|
int start = part*d.X.rows/total;
|
|
int end = (part+1)*d.X.rows/total;
|
|
data train;
|
|
data test;
|
|
train.shallow = test.shallow = 1;
|
|
|
|
test.X.rows = test.y.rows = end-start;
|
|
train.X.rows = train.y.rows = d.X.rows - (end-start);
|
|
train.X.cols = test.X.cols = d.X.cols;
|
|
train.y.cols = test.y.cols = d.y.cols;
|
|
|
|
train.X.vals = calloc(train.X.rows, sizeof(float*));
|
|
test.X.vals = calloc(test.X.rows, sizeof(float*));
|
|
train.y.vals = calloc(train.y.rows, sizeof(float*));
|
|
test.y.vals = calloc(test.y.rows, sizeof(float*));
|
|
|
|
for(i = 0; i < start; ++i){
|
|
train.X.vals[i] = d.X.vals[i];
|
|
train.y.vals[i] = d.y.vals[i];
|
|
}
|
|
for(i = start; i < end; ++i){
|
|
test.X.vals[i-start] = d.X.vals[i];
|
|
test.y.vals[i-start] = d.y.vals[i];
|
|
}
|
|
for(i = end; i < d.X.rows; ++i){
|
|
train.X.vals[i-(end-start)] = d.X.vals[i];
|
|
train.y.vals[i-(end-start)] = d.y.vals[i];
|
|
}
|
|
split[0] = train;
|
|
split[1] = test;
|
|
return split;
|
|
}
|
|
|