| | |
| | | int i; |
| | | pthread_mutex_lock(&mutex); |
| | | for(i = 0; i < n; ++i){ |
| | | int index = rand()%m; |
| | | int index = random_gen()%m; |
| | | indexes[i] = index; |
| | | random_paths[i] = paths[index]; |
| | | if(i == 0) printf("%s\n", paths[index]); |
| | |
| | | char **random_paths = calloc(n, sizeof(char*)); |
| | | int i; |
| | | pthread_mutex_lock(&mutex); |
| | | for(i = 0; i < n; ++i){ |
| | | int index = rand()%m; |
| | | //printf("n = %d \n", n); |
| | | for(i = 0; i < n; ++i){ |
| | | int index = random_gen() % m; |
| | | random_paths[i] = paths[index]; |
| | | //if(i == 0) printf("%s\n", paths[index]); |
| | | //printf("grp: %s\n", paths[index]); |
| | | } |
| | | pthread_mutex_unlock(&mutex); |
| | | return random_paths; |
| | |
| | | for(i = 0; i < n; ++i){ |
| | | image im = load_image_color(paths[i], 0, 0); |
| | | image crop = random_augment_image(im, angle, aspect, min, max, size); |
| | | int flip = rand()%2; |
| | | int flip = random_gen()%2; |
| | | if (flip) flip_image(crop); |
| | | random_distort_image(crop, hue, saturation, exposure); |
| | | |
| | |
| | | int i; |
| | | for(i = 0; i < n; ++i){ |
| | | box_label swap = b[i]; |
| | | int index = rand()%n; |
| | | int index = random_gen()%n; |
| | | b[i] = b[index]; |
| | | b[index] = swap; |
| | | } |
| | |
| | | { |
| | | int i; |
| | | for(i = 0; i < n; ++i){ |
| | | if(boxes[i].x == 0 && boxes[i].y == 0) { |
| | | boxes[i].x = 999999; |
| | | boxes[i].y = 999999; |
| | | boxes[i].w = 999999; |
| | | boxes[i].h = 999999; |
| | | continue; |
| | | } |
| | | boxes[i].left = boxes[i].left * sx - dx; |
| | | boxes[i].right = boxes[i].right * sx - dx; |
| | | boxes[i].top = boxes[i].top * sy - dy; |
| | |
| | | h = boxes[i].h; |
| | | id = boxes[i].id; |
| | | |
| | | if (w < .01 || h < .01) continue; |
| | | if (w < .001 || h < .001) continue; |
| | | |
| | | int col = (int)(x*num_boxes); |
| | | int row = (int)(y*num_boxes); |
| | |
| | | free(boxes); |
| | | } |
| | | |
| | | void fill_truth_detection(char *path, int num_boxes, float *truth, int classes, int flip, float dx, float dy, float sx, float sy) |
| | | void fill_truth_detection(char *path, int num_boxes, float *truth, int classes, int flip, float dx, float dy, float sx, float sy, |
| | | int small_object, int net_w, int net_h) |
| | | { |
| | | 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; |
| | | int i; |
| | | box_label *boxes = read_boxes(labelpath, &count); |
| | | if (small_object == 1) { |
| | | float lowest_w = 1.F / net_w; |
| | | float lowest_h = 1.F / net_h; |
| | | printf(" lowest_w = %f, lowest_h = %f \n", lowest_w, lowest_h); |
| | | for (i = 0; i < count; ++i) { |
| | | if (boxes[i].w < lowest_w) boxes[i].w = lowest_w; |
| | | if (boxes[i].h < lowest_h) boxes[i].h = lowest_h; |
| | | } |
| | | } |
| | | randomize_boxes(boxes, count); |
| | | correct_boxes(boxes, count, dx, dy, sx, sy, flip); |
| | | if(count > num_boxes) count = num_boxes; |
| | | float x,y,w,h; |
| | | int id; |
| | | int i; |
| | | |
| | | for (i = 0; i < count; ++i) { |
| | | x = boxes[i].x; |
| | |
| | | h = boxes[i].h; |
| | | id = boxes[i].id; |
| | | |
| | | if (w < .01 || h < .01) continue; |
| | | // not detect small objects |
| | | if ((w < 0.001F || h < 0.001F)) continue; |
| | | |
| | | truth[i*5+0] = x; |
| | | truth[i*5+1] = y; |
| | |
| | | float sx = (float)swidth / ow; |
| | | float sy = (float)sheight / oh; |
| | | |
| | | int flip = rand()%2; |
| | | int flip = random_gen()%2; |
| | | image cropped = crop_image(orig, pleft, ptop, swidth, sheight); |
| | | |
| | | float dx = ((float)pleft/ow)/sx; |
| | |
| | | |
| | | data load_data_swag(char **paths, int n, int classes, float jitter) |
| | | { |
| | | int index = rand()%n; |
| | | int index = random_gen()%n; |
| | | char *random_path = paths[index]; |
| | | |
| | | image orig = load_image_color(random_path, 0, 0); |
| | |
| | | float sx = (float)swidth / w; |
| | | float sy = (float)sheight / h; |
| | | |
| | | int flip = rand()%2; |
| | | int flip = random_gen()%2; |
| | | image cropped = crop_image(orig, pleft, ptop, swidth, sheight); |
| | | |
| | | float dx = ((float)pleft/w)/sx; |
| | |
| | | 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) |
| | | #ifdef OPENCV |
| | | #include "opencv2/highgui/highgui_c.h" |
| | | #include "opencv2/imgproc/imgproc_c.h" |
| | | #include "opencv2/core/version.hpp" |
| | | #ifndef CV_VERSION_EPOCH |
| | | #include "opencv2/videoio/videoio_c.h" |
| | | #include "opencv2/imgcodecs/imgcodecs_c.h" |
| | | #endif |
| | | |
| | | #include "http_stream.h" |
| | | |
| | | 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, int small_object) |
| | | { |
| | | char **random_paths = get_random_paths(paths, n, m); |
| | | int i; |
| | |
| | | |
| | | d.y = make_matrix(n, 5*boxes); |
| | | for(i = 0; i < n; ++i){ |
| | | image orig = load_image_color(random_paths[i], 0, 0); |
| | | const char *filename = random_paths[i]; |
| | | |
| | | int oh = orig.h; |
| | | int ow = orig.w; |
| | | int flag = 1; |
| | | IplImage *src; |
| | | if ((src = cvLoadImage(filename, flag)) == 0) |
| | | { |
| | | fprintf(stderr, "Cannot load image \"%s\"\n", filename); |
| | | char buff[256]; |
| | | sprintf(buff, "echo %s >> bad.list", filename); |
| | | system(buff); |
| | | continue; |
| | | //exit(0); |
| | | } |
| | | |
| | | int oh = src->height; |
| | | int ow = src->width; |
| | | |
| | | 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 pleft = rand_uniform_strong(-dw, dw); |
| | | int pright = rand_uniform_strong(-dw, dw); |
| | | int ptop = rand_uniform_strong(-dh, dh); |
| | | int pbot = rand_uniform_strong(-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); |
| | | int flip = random_gen()%2; |
| | | |
| | | 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; |
| | | float dhue = rand_uniform_strong(-hue, hue); |
| | | float dsat = rand_scale(saturation); |
| | | float dexp = rand_scale(exposure); |
| | | |
| | | fill_truth_detection(random_paths[i], boxes, d.y.vals[i], classes, flip, dx, dy, 1./sx, 1./sy); |
| | | image ai = image_data_augmentation(src, w, h, pleft, ptop, swidth, sheight, flip, jitter, dhue, dsat, dexp); |
| | | d.X.vals[i] = ai.data; |
| | | |
| | | //show_image(ai, "aug"); |
| | | //cvWaitKey(0); |
| | | |
| | | free_image(orig); |
| | | free_image(cropped); |
| | | fill_truth_detection(filename, boxes, d.y.vals[i], classes, flip, dx, dy, 1./sx, 1./sy, small_object, w, h); |
| | | |
| | | cvReleaseImage(&src); |
| | | } |
| | | free(random_paths); |
| | | return d; |
| | | } |
| | | #else // OPENCV |
| | | 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, int small_object) |
| | | { |
| | | 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); |
| | | |
| | | int oh = orig.h; |
| | | int ow = orig.w; |
| | | |
| | | int dw = (ow*jitter); |
| | | int dh = (oh*jitter); |
| | | |
| | | int pleft = rand_uniform_strong(-dw, dw); |
| | | int pright = rand_uniform_strong(-dw, dw); |
| | | int ptop = rand_uniform_strong(-dh, dh); |
| | | int pbot = rand_uniform_strong(-dh, dh); |
| | | |
| | | int swidth = ow - pleft - pright; |
| | | int sheight = oh - ptop - pbot; |
| | | |
| | | float sx = (float)swidth / ow; |
| | | float sy = (float)sheight / oh; |
| | | |
| | | int flip = random_gen() % 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_detection(random_paths[i], boxes, d.y.vals[i], classes, flip, dx, dy, 1. / sx, 1. / sy, small_object, w, h); |
| | | |
| | | free_image(orig); |
| | | free_image(cropped); |
| | | } |
| | | free(random_paths); |
| | | return d; |
| | | } |
| | | #endif // OPENCV |
| | | |
| | | void *load_thread(void *ptr) |
| | | { |
| | | //printf("Loading data: %d\n", rand()); |
| | | //srand(time(0)); |
| | | //printf("Loading data: %d\n", random_gen()); |
| | | load_args a = *(struct load_args*)ptr; |
| | | if(a.exposure == 0) a.exposure = 1; |
| | | if(a.saturation == 0) a.saturation = 1; |
| | |
| | | } 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); |
| | | *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, a.small_object); |
| | | } 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){ |
| | |
| | | } 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); |
| | | } |
| | |
| | | |
| | | void *load_threads(void *ptr) |
| | | { |
| | | //srand(time(0)); |
| | | int i; |
| | | load_args args = *(load_args *)ptr; |
| | | if (args.threads == 0) args.threads = 1; |
| | |
| | | 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; |
| | | int flip = random_gen()%2; |
| | | if (flip) flip_image(crop); |
| | | image resize = resize_image(crop, w, h); |
| | | d.X.vals[i] = resize.data; |
| | |
| | | for(i = 0; i < 10000; ++i){ |
| | | unsigned char bytes[3073]; |
| | | fread(bytes, 1, 3073, fp); |
| | | int class = bytes[0]; |
| | | y.vals[i][class] = 1; |
| | | int class_id = bytes[0]; |
| | | y.vals[i][class_id] = 1; |
| | | for(j = 0; j < X.cols; ++j){ |
| | | X.vals[i][j] = (double)bytes[j+1]; |
| | | } |
| | |
| | | { |
| | | int j; |
| | | for(j = 0; j < n; ++j){ |
| | | int index = rand()%d.X.rows; |
| | | int index = random_gen()%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)); |
| | | } |
| | |
| | | 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; |
| | | int class_id = bytes[0]; |
| | | y.vals[i+b*10000][class_id] = 1; |
| | | for(j = 0; j < X.cols; ++j){ |
| | | X.vals[i+b*10000][j] = (double)bytes[j+1]; |
| | | } |
| | |
| | | { |
| | | int i; |
| | | for(i = d.X.rows-1; i > 0; --i){ |
| | | int index = rand()%i; |
| | | int index = random_gen()%i; |
| | | float *swap = d.X.vals[index]; |
| | | d.X.vals[index] = d.X.vals[i]; |
| | | d.X.vals[i] = swap; |
| | |
| | | |
| | | int i; |
| | | for(i = 0; i < num; ++i){ |
| | | int index = rand()%d.X.rows; |
| | | int index = random_gen()%d.X.rows; |
| | | r.X.vals[i] = d.X.vals[index]; |
| | | r.y.vals[i] = d.y.vals[index]; |
| | | } |