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https://github.com/pjreddie/darknet.git
synced 2023-08-10 21:13:14 +03:00
probably stuff changed
This commit is contained in:
parent
390a0cf923
commit
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5
Makefile
5
Makefile
@ -31,16 +31,17 @@ OBJ+=convolutional_kernels.o deconvolutional_kernels.o activation_kernels.o im2c
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endif
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OBJS = $(addprefix $(OBJDIR), $(OBJ))
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DEPS = $(wildcard src/*.h) Makefile
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all: $(EXEC)
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$(EXEC): $(OBJS)
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$(CC) $(COMMON) $(CFLAGS) $(LDFLAGS) $^ -o $@
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$(OBJDIR)%.o: %.c
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$(OBJDIR)%.o: %.c $(DEPS)
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$(CC) $(COMMON) $(CFLAGS) -c $< -o $@
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$(OBJDIR)%.o: %.cu
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$(OBJDIR)%.o: %.cu $(DEPS)
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$(NVCC) $(ARCH) $(COMMON) --compiler-options "$(CFLAGS)" -c $< -o $@
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.PHONY: clean
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@ -33,7 +33,7 @@ connected_layer *make_connected_layer(int batch, int inputs, int outputs, ACTIVA
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float scale = 1./sqrt(inputs);
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for(i = 0; i < inputs*outputs; ++i){
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//layer->weights[i] = scale*rand_normal();
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layer->weights[i] = 2*scale*rand_uniform() - scale;
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}
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for(i = 0; i < outputs; ++i){
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@ -61,7 +61,7 @@ convolutional_layer *make_convolutional_layer(int batch, int h, int w, int c, in
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layer->biases = calloc(n, sizeof(float));
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layer->bias_updates = calloc(n, sizeof(float));
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float scale = 1./sqrt(size*size*c);
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for(i = 0; i < c*n*size*size; ++i) layer->filters[i] = scale*rand_normal();
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for(i = 0; i < c*n*size*size; ++i) layer->filters[i] = 2*scale*rand_uniform() - scale;
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for(i = 0; i < n; ++i){
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layer->biases[i] = scale;
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}
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@ -10,7 +10,7 @@ image get_crop_image(crop_layer layer)
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return float_to_image(w,h,c,layer.output);
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}
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crop_layer *make_crop_layer(int batch, int h, int w, int c, int crop_height, int crop_width, int flip)
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crop_layer *make_crop_layer(int batch, int h, int w, int c, int crop_height, int crop_width, int flip, float angle)
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{
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fprintf(stderr, "Crop Layer: %d x %d -> %d x %d x %d image\n", h,w,crop_height,crop_width,c);
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crop_layer *layer = calloc(1, sizeof(crop_layer));
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@ -19,6 +19,7 @@ crop_layer *make_crop_layer(int batch, int h, int w, int c, int crop_height, int
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layer->w = w;
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layer->c = c;
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layer->flip = flip;
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layer->angle = angle;
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layer->crop_width = crop_width;
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layer->crop_height = crop_height;
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layer->output = calloc(crop_width*crop_height * c*batch, sizeof(float));
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@ -10,6 +10,7 @@ typedef struct {
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int crop_width;
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int crop_height;
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int flip;
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float angle;
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float *output;
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#ifdef GPU
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float *output_gpu;
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@ -17,7 +18,7 @@ typedef struct {
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} crop_layer;
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image get_crop_image(crop_layer layer);
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crop_layer *make_crop_layer(int batch, int h, int w, int c, int crop_height, int crop_width, int flip);
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crop_layer *make_crop_layer(int batch, int h, int w, int c, int crop_height, int crop_width, int flip, float angle);
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void forward_crop_layer(const crop_layer layer, network_state state);
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#ifdef GPU
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@ -61,7 +61,8 @@ extern "C" void forward_crop_layer_gpu(crop_layer layer, network_state state)
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int flip = (layer.flip && rand()%2);
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int dh = rand()%(layer.h - layer.crop_height + 1);
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int dw = rand()%(layer.w - layer.crop_width + 1);
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float angle = rand_uniform() - .5;
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float radians = layer.angle*3.14159/180.;
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float angle = 2*radians*rand_uniform() - radians;
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if(!state.train){
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angle = 0;
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flip = 0;
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@ -76,5 +77,12 @@ extern "C" void forward_crop_layer_gpu(crop_layer layer, network_state state)
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forward_crop_layer_kernel<<<cuda_gridsize(size), BLOCK>>>(state.input, size, layer.c, layer.h, layer.w,
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layer.crop_height, layer.crop_width, dh, dw, flip, angle, layer.output_gpu);
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check_error(cudaPeekAtLastError());
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/*
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cuda_pull_array(layer.output_gpu, layer.output, size);
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image im = float_to_image(layer.crop_width, layer.crop_height, layer.c, layer.output + 14*(size/layer.batch));
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show_image(im, "cropped");
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cvWaitKey(0);
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*/
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}
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@ -93,7 +93,6 @@ void visualize(char *cfgfile, char *weightfile)
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int main(int argc, char **argv)
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{
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//test_resize(argv[1]);
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//test_convolutional_layer();
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if(argc < 2){
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fprintf(stderr, "usage: %s <function>\n", argv[0]);
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@ -114,6 +113,8 @@ int main(int argc, char **argv)
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run_imagenet(argc, argv);
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} else if (0 == strcmp(argv[1], "detection")){
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run_detection(argc, argv);
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} else if (0 == strcmp(argv[1], "test")){
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test_resize(argv[2]);
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} else if (0 == strcmp(argv[1], "captcha")){
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run_captcha(argc, argv);
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} else if (0 == strcmp(argv[1], "change")){
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15
src/data.c
15
src/data.c
@ -294,6 +294,8 @@ data load_data_detection_jitter_random(int n, char **paths, int m, int classes,
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d.y = make_matrix(n, k);
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for(i = 0; i < n; ++i){
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image orig = load_image_color(random_paths[i], 0, 0);
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translate_image(orig, -128);
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scale_image(orig, 1./128);
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int oh = orig.h;
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int ow = orig.w;
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@ -310,6 +312,13 @@ data load_data_detection_jitter_random(int n, char **paths, int m, int classes,
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float sx = (float)swidth / ow;
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float sy = (float)sheight / oh;
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/*
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float angle = rand_uniform()*.1 - .05;
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image rot = rotate_image(orig, angle);
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free_image(orig);
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orig = rot;
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*/
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int flip = rand()%2;
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image cropped = crop_image(orig, pleft, ptop, swidth, sheight);
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@ -333,8 +342,6 @@ void *load_detection_thread(void *ptr)
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printf("Loading data: %d\n", rand());
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struct load_args a = *(struct load_args*)ptr;
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*a.d = load_data_detection_jitter_random(a.n, a.paths, a.m, a.classes, a.w, a.h, a.num_boxes, a.background);
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translate_data_rows(*a.d, -128);
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scale_data_rows(*a.d, 1./128);
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free(ptr);
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return 0;
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}
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@ -435,7 +442,7 @@ data load_cifar10_data(char *filename)
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X.vals[i][j] = (double)bytes[j+1];
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}
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}
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translate_data_rows(d, -144);
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translate_data_rows(d, -128);
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scale_data_rows(d, 1./128);
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//normalize_data_rows(d);
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fclose(fp);
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@ -491,7 +498,7 @@ data load_all_cifar10()
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fclose(fp);
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}
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//normalize_data_rows(d);
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translate_data_rows(d, -144);
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translate_data_rows(d, -128);
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scale_data_rows(d, 1./128);
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return d;
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}
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@ -93,9 +93,9 @@ void train_detection(char *cfgfile, char *weightfile)
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load_thread = load_data_detection_thread(imgs, paths, plist->size, classes, net.w, net.h, side, side, background, &buffer);
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/*
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image im = float_to_image(im_dim, im_dim, 3, train.X.vals[114]);
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image im = float_to_image(net.w, net.h, 3, train.X.vals[114]);
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draw_detection(im, train.y.vals[114], 7);
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*/
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*/
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printf("Loaded: %lf seconds\n", sec(clock()-time));
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time=clock();
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79
src/image.c
79
src/image.c
@ -150,7 +150,6 @@ image copy_image(image p)
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return copy;
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}
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void show_image(image p, char *name)
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{
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int x,y,k;
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@ -317,7 +316,7 @@ image crop_image(image im, int dx, int dy, int w, int h)
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for(i = 0; i < w; ++i){
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int r = j + dy;
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int c = i + dx;
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float val = 128;
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float val = 0;
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if (r >= 0 && r < im.h && c >= 0 && c < im.w) {
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val = get_pixel(im, c, r, k);
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}
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@ -328,6 +327,54 @@ image crop_image(image im, int dx, int dy, int w, int h)
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return cropped;
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}
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image grayscale_image(image im)
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{
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assert(im.c == 3);
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int i, j, k;
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image gray = make_image(im.w, im.h, im.c);
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float scale[] = {0.114, 0.587, 0.299};
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for(k = 0; k < im.c; ++k){
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for(j = 0; j < im.h; ++j){
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for(i = 0; i < im.w; ++i){
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gray.data[i+im.w*j] += scale[k]*get_pixel(im, i, j, k);
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}
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}
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}
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memcpy(gray.data + im.w*im.h*1, gray.data, sizeof(float)*im.w*im.h);
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memcpy(gray.data + im.w*im.h*2, gray.data, sizeof(float)*im.w*im.h);
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return gray;
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}
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image blend_image(image fore, image back, float alpha)
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{
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assert(fore.w == back.w && fore.h == back.h && fore.c == back.c);
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image blend = make_image(fore.w, fore.h, fore.c);
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int i, j, k;
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for(k = 0; k < fore.c; ++k){
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for(j = 0; j < fore.h; ++j){
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for(i = 0; i < fore.w; ++i){
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float val = alpha * get_pixel(fore, i, j, k) +
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(1 - alpha)* get_pixel(back, i, j, k);
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set_pixel(blend, i, j, k, val);
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}
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}
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}
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return blend;
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}
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image saturate_image(image im, float sat)
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{
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image gray = grayscale_image(im);
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image blend = blend_image(im, gray, sat);
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free_image(gray);
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return blend;
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}
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image brightness_image(image im, float b)
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{
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image bright = make_image(im.w, im.h, im.c);
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}
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float billinear_interpolate(image im, float x, float y, int c)
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{
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int ix = (int) floorf(x);
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@ -337,9 +384,9 @@ float billinear_interpolate(image im, float x, float y, int c)
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float dy = y - iy;
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float val = (1-dy) * (1-dx) * get_pixel_extend(im, ix, iy, c) +
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dy * (1-dx) * get_pixel_extend(im, ix, iy+1, c) +
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(1-dy) * dx * get_pixel_extend(im, ix+1, iy, c) +
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dy * dx * get_pixel_extend(im, ix+1, iy+1, c);
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dy * (1-dx) * get_pixel_extend(im, ix, iy+1, c) +
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(1-dy) * dx * get_pixel_extend(im, ix+1, iy, c) +
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dy * dx * get_pixel_extend(im, ix+1, iy+1, c);
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return val;
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}
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@ -374,14 +421,22 @@ void test_resize(char *filename)
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image rot = rotate_image(big, .02);
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image rot2 = rotate_image(big, 3.14159265/2.);
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image test = rotate_image(im, .6);
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image gray = grayscale_image(im);
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image sat = saturate_image(im, 2);
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image sat2 = saturate_image(im, .5);
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show_image(im, "original");
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show_image(small, "smaller");
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show_image(big, "bigger");
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show_image(crop, "crop");
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show_image(crop2, "crop2");
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show_image(rot, "rot");
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show_image(rot2, "rot2");
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show_image(test, "test");
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show_image(gray, "gray");
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show_image(sat, "sat");
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show_image(sat2, "sat2");
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/*
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show_image(small, "smaller");
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show_image(big, "bigger");
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show_image(crop, "crop");
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show_image(crop2, "crop2");
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show_image(rot, "rot");
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show_image(rot2, "rot2");
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show_image(test, "test");
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*/
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cvWaitKey(0);
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}
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@ -186,6 +186,7 @@ crop_layer *parse_crop(list *options, size_params params)
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int crop_height = option_find_int(options, "crop_height",1);
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int crop_width = option_find_int(options, "crop_width",1);
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int flip = option_find_int(options, "flip",0);
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float angle = option_find_float(options, "angle",0);
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int batch,h,w,c;
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h = params.h;
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@ -194,7 +195,7 @@ crop_layer *parse_crop(list *options, size_params params)
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batch=params.batch;
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if(!(h && w && c)) error("Layer before crop layer must output image.");
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crop_layer *layer = make_crop_layer(batch,h,w,c,crop_height,crop_width,flip);
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crop_layer *layer = make_crop_layer(batch,h,w,c,crop_height,crop_width,flip, angle);
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option_unused(options);
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return layer;
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}
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