diff --git a/Makefile b/Makefile
index 03be1436c3ea784417d0c3f1340555dd97b00014..28a0d17aca28226b41e9b2205ac91b8112a74490 100644
--- a/Makefile
+++ b/Makefile
@@ -3,7 +3,7 @@ CUDNN=0
OPENCV=0
DEBUG=0
-ARCH= --gpu-architecture=compute_20 --gpu-code=compute_20
+ARCH= --gpu-architecture=compute_52 --gpu-code=compute_52
VPATH=./src/
EXEC=darknet
diff --git a/cfg/darknet.cfg b/cfg/darknet.cfg
index ff0d33e4cbf46baf90e90839ed88cac0031c37cf..a96f4d0c5ddf6241da550723585b1c68962b5f30 100644
--- a/cfg/darknet.cfg
+++ b/cfg/darknet.cfg
@@ -14,7 +14,6 @@ power=4
max_batches=500000
[convolutional]
-batch_normalize=1
filters=16
size=3
stride=1
@@ -26,7 +25,6 @@ size=2
stride=2
[convolutional]
-batch_normalize=1
filters=32
size=3
stride=1
@@ -38,7 +36,6 @@ size=2
stride=2
[convolutional]
-batch_normalize=1
filters=64
size=3
stride=1
@@ -50,7 +47,6 @@ size=2
stride=2
[convolutional]
-batch_normalize=1
filters=128
size=3
stride=1
@@ -62,7 +58,6 @@ size=2
stride=2
[convolutional]
-batch_normalize=1
filters=256
size=3
stride=1
@@ -74,7 +69,6 @@ size=2
stride=2
[convolutional]
-batch_normalize=1
filters=512
size=3
stride=1
@@ -86,7 +80,6 @@ size=2
stride=2
[convolutional]
-batch_normalize=1
filters=1024
size=3
stride=1
diff --git a/cfg/imagenet1k.dataset b/cfg/imagenet1k.dataset
new file mode 100644
index 0000000000000000000000000000000000000000..92d711d7f6c5466f3d8c61560a5f204488609923
--- /dev/null
+++ b/cfg/imagenet1k.dataset
@@ -0,0 +1,9 @@
+classes=1000
+labels = data/inet.labels.list
+names = data/shortnames.txt
+train = /data/imagenet/imagenet1k.train.list
+valid = /data/imagenet/imagenet1k.valid.list
+top=5
+test = /Users/pjreddie/Documents/sites/selfie/paths.list
+backup = /home/pjreddie/backup/
+
diff --git a/src/classifier.c b/src/classifier.c
index 7060c5e5acdb890bdadcab01e1b3892c736608a7..5104608f7f522f2d522ce8a36ebafd14f82d135a 100644
--- a/src/classifier.c
+++ b/src/classifier.c
@@ -38,7 +38,7 @@ list *read_data_cfg(char *filename)
return options;
}
-void train_classifier(char *datacfg, char *cfgfile, char *weightfile)
+void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int clear)
{
data_seed = time(0);
srand(time(0));
@@ -49,6 +49,7 @@ void train_classifier(char *datacfg, char *cfgfile, char *weightfile)
if(weightfile){
load_weights(&net, weightfile);
}
+ if(clear) *net.seen = 0;
printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
int imgs = net.batch;
@@ -96,14 +97,14 @@ void train_classifier(char *datacfg, char *cfgfile, char *weightfile)
printf("Loaded: %lf seconds\n", sec(clock()-time));
time=clock();
-/*
- int u;
- for(u = 0; u < net.batch; ++u){
- image im = float_to_image(net.w, net.h, 3, train.X.vals[u]);
- show_image(im, "loaded");
- cvWaitKey(0);
- }
- */
+ /*
+ int u;
+ for(u = 0; u < net.batch; ++u){
+ image im = float_to_image(net.w, net.h, 3, train.X.vals[u]);
+ show_image(im, "loaded");
+ cvWaitKey(0);
+ }
+ */
float loss = train_network(net, train);
if(avg_loss == -1) avg_loss = loss;
@@ -116,7 +117,7 @@ void train_classifier(char *datacfg, char *cfgfile, char *weightfile)
sprintf(buff, "%s/%s_%d.weights",backup_directory,base, epoch);
save_weights(net, buff);
}
- if(*net.seen%100 == 0){
+ if(get_current_batch(net)%100 == 0){
char buff[256];
sprintf(buff, "%s/%s.backup",backup_directory,base);
save_weights(net, buff);
@@ -378,8 +379,8 @@ void validate_classifier_single(char *datacfg, char *filename, char *weightfile)
//cvWaitKey(0);
float *pred = network_predict(net, crop.data);
+ if(resized.data != im.data) free_image(resized);
free_image(im);
- free_image(resized);
free_image(crop);
top_k(pred, classes, topk, indexes);
@@ -441,7 +442,7 @@ void validate_classifier_multi(char *datacfg, char *filename, char *weightfile)
flip_image(r);
p = network_predict(net, r.data);
axpy_cpu(classes, 1, p, 1, pred, 1);
- free_image(r);
+ if(r.data != im.data) free_image(r);
}
free_image(im);
top_k(pred, classes, topk, indexes);
@@ -501,6 +502,46 @@ void predict_classifier(char *datacfg, char *cfgfile, char *weightfile, char *fi
}
}
+
+void label_classifier(char *datacfg, char *filename, char *weightfile)
+{
+ int i;
+ network net = parse_network_cfg(filename);
+ set_batch_network(&net, 1);
+ if(weightfile){
+ load_weights(&net, weightfile);
+ }
+ srand(time(0));
+
+ list *options = read_data_cfg(datacfg);
+
+ char *label_list = option_find_str(options, "names", "data/labels.list");
+ char *test_list = option_find_str(options, "test", "data/train.list");
+ int classes = option_find_int(options, "classes", 2);
+
+ char **labels = get_labels(label_list);
+ list *plist = get_paths(test_list);
+
+ char **paths = (char **)list_to_array(plist);
+ int m = plist->size;
+ free_list(plist);
+
+ for(i = 0; i < m; ++i){
+ image im = load_image_color(paths[i], 0, 0);
+ image resized = resize_min(im, net.w);
+ image crop = crop_image(resized, (resized.w - net.w)/2, (resized.h - net.h)/2, net.w, net.h);
+ float *pred = network_predict(net, crop.data);
+
+ if(resized.data != im.data) free_image(resized);
+ free_image(im);
+ free_image(crop);
+ int ind = max_index(pred, classes);
+
+ printf("%s\n", labels[ind]);
+ }
+}
+
+
void test_classifier(char *datacfg, char *cfgfile, char *weightfile, int target_layer)
{
int curr = 0;
@@ -649,6 +690,7 @@ void run_classifier(int argc, char **argv)
}
int cam_index = find_int_arg(argc, argv, "-c", 0);
+ int clear = find_arg(argc, argv, "-clear");
char *data = argv[3];
char *cfg = argv[4];
char *weights = (argc > 5) ? argv[5] : 0;
@@ -656,9 +698,10 @@ void run_classifier(int argc, char **argv)
char *layer_s = (argc > 7) ? argv[7]: 0;
int layer = layer_s ? atoi(layer_s) : -1;
if(0==strcmp(argv[2], "predict")) predict_classifier(data, cfg, weights, filename);
- else if(0==strcmp(argv[2], "train")) train_classifier(data, cfg, weights);
+ else if(0==strcmp(argv[2], "train")) train_classifier(data, cfg, weights, clear);
else if(0==strcmp(argv[2], "demo")) demo_classifier(data, cfg, weights, cam_index, filename);
else if(0==strcmp(argv[2], "test")) test_classifier(data, cfg, weights, layer);
+ else if(0==strcmp(argv[2], "label")) label_classifier(data, cfg, weights);
else if(0==strcmp(argv[2], "valid")) validate_classifier(data, cfg, weights);
else if(0==strcmp(argv[2], "valid10")) validate_classifier_10(data, cfg, weights);
else if(0==strcmp(argv[2], "validmulti")) validate_classifier_multi(data, cfg, weights);
diff --git a/src/convolutional_kernels.cu b/src/convolutional_kernels.cu
index 0cd51245fd53a55a4dd24eb26d4393ec07d467ea..cb50561979af2e93d3b8b444e15b8b579695498b 100644
--- a/src/convolutional_kernels.cu
+++ b/src/convolutional_kernels.cu
@@ -161,6 +161,7 @@ void backward_convolutional_layer_gpu(convolutional_layer l, network_state state
l.filter_updates_gpu);
if(state.delta){
+ if(l.binary || l.xnor) swap_binary(&l);
cudnnConvolutionBackwardData(cudnn_handle(),
&one,
l.filterDesc,
@@ -174,6 +175,7 @@ void backward_convolutional_layer_gpu(convolutional_layer l, network_state state
&one,
l.dsrcTensorDesc,
state.delta);
+ if(l.binary || l.xnor) swap_binary(&l);
}
#else
diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c
index 303f1efe414cb968d8fd5d0d215861bc0e2e78ad..5575aacf2e37cdd61f17f78ac4e45120aac7c7b8 100644
--- a/src/convolutional_layer.c
+++ b/src/convolutional_layer.c
@@ -88,8 +88,8 @@ image get_convolutional_delta(convolutional_layer l)
return float_to_image(w,h,c,l.delta);
}
-#ifdef CUDNN
size_t get_workspace_size(layer l){
+ #ifdef CUDNN
size_t most = 0;
size_t s = 0;
cudnnGetConvolutionForwardWorkspaceSize(cudnn_handle(),
@@ -117,8 +117,10 @@ size_t get_workspace_size(layer l){
&s);
if (s > most) most = s;
return most;
+ #else
+ return (size_t)l.out_h*l.out_w*l.size*l.size*l.c*sizeof(float);
+ #endif
}
-#endif
convolutional_layer make_convolutional_layer(int batch, int h, int w, int c, int n, int size, int stride, int pad, ACTIVATION activation, int batch_normalize, int binary, int xnor)
{
@@ -154,8 +156,6 @@ convolutional_layer make_convolutional_layer(int batch, int h, int w, int c, int
l.outputs = l.out_h * l.out_w * l.out_c;
l.inputs = l.w * l.h * l.c;
- l.col_image = calloc(out_h*out_w*size*size*c, sizeof(float));
- l.workspace_size = out_h*out_w*size*size*c*sizeof(float);
l.output = calloc(l.batch*out_h * out_w * n, sizeof(float));
l.delta = calloc(l.batch*out_h * out_w * n, sizeof(float));
@@ -255,10 +255,9 @@ convolutional_layer make_convolutional_layer(int batch, int h, int w, int c, int
CUDNN_CONVOLUTION_BWD_FILTER_PREFER_FASTEST,
0,
&l.bf_algo);
- l.workspace_size = get_workspace_size(l);
-
#endif
#endif
+ l.workspace_size = get_workspace_size(l);
l.activation = activation;
fprintf(stderr, "Convolutional Layer: %d x %d x %d image, %d filters -> %d x %d x %d image\n", h,w,c,n, out_h, out_w, n);
@@ -315,8 +314,6 @@ void resize_convolutional_layer(convolutional_layer *l, int w, int h)
l->outputs = l->out_h * l->out_w * l->out_c;
l->inputs = l->w * l->h * l->c;
- l->col_image = realloc(l->col_image,
- out_h*out_w*l->size*l->size*l->c*sizeof(float));
l->output = realloc(l->output,
l->batch*out_h * out_w * l->n*sizeof(float));
l->delta = realloc(l->delta,
@@ -328,7 +325,43 @@ void resize_convolutional_layer(convolutional_layer *l, int w, int h)
l->delta_gpu = cuda_make_array(l->delta, l->batch*out_h*out_w*l->n);
l->output_gpu = cuda_make_array(l->output, l->batch*out_h*out_w*l->n);
+ #ifdef CUDNN
+ cudnnSetTensor4dDescriptor(l->dsrcTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, l->batch, l->c, l->h, l->w);
+ cudnnSetTensor4dDescriptor(l->ddstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, l->batch, l->out_c, l->out_h, l->out_w);
+ cudnnSetFilter4dDescriptor(l->dfilterDesc, CUDNN_DATA_FLOAT, CUDNN_TENSOR_NCHW, l->n, l->c, l->size, l->size);
+
+ cudnnSetTensor4dDescriptor(l->srcTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, l->batch, l->c, l->h, l->w);
+ cudnnSetTensor4dDescriptor(l->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, l->batch, l->out_c, l->out_h, l->out_w);
+ cudnnSetFilter4dDescriptor(l->filterDesc, CUDNN_DATA_FLOAT, CUDNN_TENSOR_NCHW, l->n, l->c, l->size, l->size);
+ int padding = l->pad ? l->size/2 : 0;
+ cudnnSetConvolution2dDescriptor(l->convDesc, padding, padding, l->stride, l->stride, 1, 1, CUDNN_CROSS_CORRELATION);
+ cudnnGetConvolutionForwardAlgorithm(cudnn_handle(),
+ l->srcTensorDesc,
+ l->filterDesc,
+ l->convDesc,
+ l->dstTensorDesc,
+ CUDNN_CONVOLUTION_FWD_PREFER_FASTEST,
+ 0,
+ &l->fw_algo);
+ cudnnGetConvolutionBackwardDataAlgorithm(cudnn_handle(),
+ l->filterDesc,
+ l->ddstTensorDesc,
+ l->convDesc,
+ l->dsrcTensorDesc,
+ CUDNN_CONVOLUTION_BWD_DATA_PREFER_FASTEST,
+ 0,
+ &l->bd_algo);
+ cudnnGetConvolutionBackwardFilterAlgorithm(cudnn_handle(),
+ l->srcTensorDesc,
+ l->ddstTensorDesc,
+ l->convDesc,
+ l->dfilterDesc,
+ CUDNN_CONVOLUTION_BWD_FILTER_PREFER_FASTEST,
+ 0,
+ &l->bf_algo);
+ #endif
#endif
+ l->workspace_size = get_workspace_size(*l);
}
void add_bias(float *output, float *biases, int batch, int n, int size)
@@ -386,7 +419,7 @@ void forward_convolutional_layer(convolutional_layer l, network_state state)
int n = out_h*out_w;
char *a = l.cfilters;
- float *b = l.col_image;
+ float *b = state.workspace;
float *c = l.output;
for(i = 0; i < l.batch; ++i){
@@ -407,7 +440,7 @@ void forward_convolutional_layer(convolutional_layer l, network_state state)
int n = out_h*out_w;
float *a = l.filters;
- float *b = l.col_image;
+ float *b = state.workspace;
float *c = l.output;
for(i = 0; i < l.batch; ++i){
@@ -439,7 +472,7 @@ void backward_convolutional_layer(convolutional_layer l, network_state state)
for(i = 0; i < l.batch; ++i){
float *a = l.delta + i*m*k;
- float *b = l.col_image;
+ float *b = state.workspace;
float *c = l.filter_updates;
float *im = state.input+i*l.c*l.h*l.w;
@@ -451,11 +484,11 @@ void backward_convolutional_layer(convolutional_layer l, network_state state)
if(state.delta){
a = l.filters;
b = l.delta + i*m*k;
- c = l.col_image;
+ c = state.workspace;
gemm(1,0,n,k,m,1,a,n,b,k,0,c,k);
- col2im_cpu(l.col_image, l.c, l.h, l.w, l.size, l.stride, l.pad, state.delta+i*l.c*l.h*l.w);
+ col2im_cpu(state.workspace, l.c, l.h, l.w, l.size, l.stride, l.pad, state.delta+i*l.c*l.h*l.w);
}
}
}
diff --git a/src/darknet.c b/src/darknet.c
index bf662d9ebf62ffd0617a94e53b447bc03c4fb84e..a9b2433294c017b5fcb41e17e13eb4ad48c9087c 100644
--- a/src/darknet.c
+++ b/src/darknet.c
@@ -270,6 +270,8 @@ int main(int argc, char **argv)
run_dice(argc, argv);
} else if (0 == strcmp(argv[1], "writing")){
run_writing(argc, argv);
+ } else if (0 == strcmp(argv[1], "3d")){
+ composite_3d(argv[2], argv[3], argv[4]);
} else if (0 == strcmp(argv[1], "test")){
test_resize(argv[2]);
} else if (0 == strcmp(argv[1], "captcha")){
diff --git a/src/data.c b/src/data.c
index fdc4a1db7b6f8cf76cf4835e4c17c4c83478377c..fcbdfc9b230513f3e920764dbdc078dd9ed8f583 100644
--- a/src/data.c
+++ b/src/data.c
@@ -271,7 +271,7 @@ void fill_truth_region(char *path, float *truth, int classes, int num_boxes, int
free(boxes);
}
-void fill_truth_detection(char *path, 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)
{
char *labelpath = find_replace(path, "images", "labels");
labelpath = find_replace(labelpath, "JPEGImages", "labels");
@@ -283,7 +283,7 @@ void fill_truth_detection(char *path, float *truth, int classes, int flip, float
box_label *boxes = read_boxes(labelpath, &count);
randomize_boxes(boxes, count);
correct_boxes(boxes, count, dx, dy, sx, sy, flip);
- if(count > 17) count = 17;
+ if(count > num_boxes) count = num_boxes;
float x,y,w,h;
int id;
int i;
@@ -297,11 +297,11 @@ void fill_truth_detection(char *path, float *truth, int classes, int flip, float
if (w < .01 || h < .01) continue;
- truth[i*5] = id;
- truth[i*5+2] = x;
- truth[i*5+3] = y;
- truth[i*5+4] = w;
- truth[i*5+5] = h;
+ truth[i*5+0] = id;
+ truth[i*5+1] = x;
+ truth[i*5+2] = y;
+ truth[i*5+3] = w;
+ truth[i*5+4] = h;
}
free(boxes);
}
@@ -601,7 +601,7 @@ data load_data_swag(char **paths, int n, int classes, float jitter)
return d;
}
-data load_data_detection(int n, int boxes, char **paths, int m, int w, int h, int classes, float jitter)
+data load_data_detection(int n, char **paths, int m, int w, int h, int boxes, int classes, float jitter)
{
char **random_paths = get_random_paths(paths, n, m);
int i;
@@ -643,7 +643,7 @@ data load_data_detection(int n, int boxes, char **paths, int m, int w, int h, in
if(flip) flip_image(sized);
d.X.vals[i] = sized.data;
- fill_truth_detection(random_paths[i], d.y.vals[i], classes, flip, dx, dy, 1./sx, 1./sy);
+ fill_truth_detection(random_paths[i], boxes, d.y.vals[i], classes, flip, dx, dy, 1./sx, 1./sy);
free_image(orig);
free_image(cropped);
@@ -669,12 +669,12 @@ void *load_thread(void *ptr)
*a.d = load_data_augment(a.paths, a.n, a.m, a.labels, a.classes, a.min, a.max, a.size);
} else if (a.type == STUDY_DATA){
*a.d = load_data_study(a.paths, a.n, a.m, a.labels, a.classes, a.min, a.max, a.size);
- } else if (a.type == DETECTION_DATA){
- *a.d = load_data_detection(a.n, a.num_boxes, a.paths, a.m, a.classes, a.w, a.h, a.background);
} 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 == REGION_DATA){
*a.d = load_data_region(a.n, a.paths, a.m, a.w, a.h, a.num_boxes, a.classes, a.jitter);
+ } 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);
} 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){
diff --git a/src/data.h b/src/data.h
index a7347a82ed95091caed9ed9456b2d9ffc14b186c..11363f19ffe6e98395dcd91e4fe13616718e9eb8 100644
--- a/src/data.h
+++ b/src/data.h
@@ -70,7 +70,7 @@ void print_letters(float *pred, int n);
data load_data_captcha(char **paths, int n, int m, int k, int w, int h);
data load_data_captcha_encode(char **paths, int n, int m, int w, int h);
data load_data(char **paths, int n, int m, char **labels, int k, int w, int h);
-data load_data_detection(int n, int boxes, char **paths, int m, int w, int h, int classes, float jitter);
+data load_data_detection(int n, char **paths, int m, int w, int h, int boxes, int classes, float jitter);
data load_data_tag(char **paths, int n, int m, int k, int min, int max, int size);
data load_data_augment(char **paths, int n, int m, char **labels, int k, int min, int max, int size);
data load_data_study(char **paths, int n, int m, char **labels, int k, int min, int max, int size);
diff --git a/src/image.c b/src/image.c
index aff5f642f30ebf4ec1aedb71c8822f2d52ea7dea..92833dfb77521ca170104e54ad400996afa162d0 100644
--- a/src/image.c
+++ b/src/image.c
@@ -491,6 +491,8 @@ void show_image_cv(image p, const char *name)
int r = j + dy;
int c = i + dx;
float val = 0;
+ r = constrain_int(r, 0, im.h-1);
+ c = constrain_int(c, 0, im.w-1);
if (r >= 0 && r < im.h && c >= 0 && c < im.w) {
val = get_pixel(im, c, r, k);
}
@@ -501,586 +503,653 @@ void show_image_cv(image p, const char *name)
return cropped;
}
- image resize_min(image im, int min)
- {
- int w = im.w;
- int h = im.h;
- if(w < h){
- h = (h * min) / w;
- w = min;
- } else {
- w = (w * min) / h;
- h = min;
+int best_3d_shift_r(image a, image b, int min, int max)
+{
+ if(min == max) return min;
+ int mid = floor((min + max) / 2.);
+ image c1 = crop_image(b, 0, mid, b.w, b.h);
+ image c2 = crop_image(b, 0, mid+1, b.w, b.h);
+ float d1 = dist_array(c1.data, a.data, a.w*a.h*a.c, 10);
+ float d2 = dist_array(c2.data, a.data, a.w*a.h*a.c, 10);
+ free_image(c1);
+ free_image(c2);
+ if(d1 < d2) return best_3d_shift_r(a, b, min, mid);
+ else return best_3d_shift_r(a, b, mid+1, max);
+}
+
+int best_3d_shift(image a, image b, int min, int max)
+{
+ int i;
+ int best = 0;
+ float best_distance = FLT_MAX;
+ for(i = min; i <= max; i += 2){
+ image c = crop_image(b, 0, i, b.w, b.h);
+ float d = dist_array(c.data, a.data, a.w*a.h*a.c, 100);
+ if(d < best_distance){
+ best_distance = d;
+ best = i;
}
- if(w == im.w && h == im.h) return im;
- image resized = resize_image(im, w, h);
- return resized;
+ printf("%d %f\n", i, d);
+ free_image(c);
}
+ return best;
+}
- image random_crop_image(image im, int low, int high, int size)
- {
- int r = rand_int(low, high);
- image resized = resize_min(im, r);
- int dx = rand_int(0, resized.w - size);
- int dy = rand_int(0, resized.h - size);
- image crop = crop_image(resized, dx, dy, size, size);
-
- if(resized.data != im.data) free_image(resized);
- return crop;
- }
+void composite_3d(char *f1, char *f2, char *out)
+{
+ if(!out) out = "out";
+ image a = load_image(f1, 0,0,0);
+ image b = load_image(f2, 0,0,0);
+ int shift = best_3d_shift_r(a, b, -a.h/100, a.h/100);
- float three_way_max(float a, float b, float c)
- {
- return (a > b) ? ( (a > c) ? a : c) : ( (b > c) ? b : c) ;
- }
+ image c1 = crop_image(b, 10, shift, b.w, b.h);
+ float d1 = dist_array(c1.data, a.data, a.w*a.h*a.c, 100);
+ image c2 = crop_image(b, -10, shift, b.w, b.h);
+ float d2 = dist_array(c2.data, a.data, a.w*a.h*a.c, 100);
- float three_way_min(float a, float b, float c)
- {
- return (a < b) ? ( (a < c) ? a : c) : ( (b < c) ? b : c) ;
+ if(d2 < d1){
+ image swap = a;
+ a = b;
+ b = swap;
+ shift = -shift;
+ printf("swapped, %d\n", shift);
+ }
+ else{
+ printf("%d\n", shift);
}
- // http://www.cs.rit.edu/~ncs/color/t_convert.html
- void rgb_to_hsv(image im)
- {
- assert(im.c == 3);
- int i, j;
- float r, g, b;
- float h, s, v;
- for(j = 0; j < im.h; ++j){
- for(i = 0; i < im.w; ++i){
- r = get_pixel(im, i , j, 0);
- g = get_pixel(im, i , j, 1);
- b = get_pixel(im, i , j, 2);
- float max = three_way_max(r,g,b);
- float min = three_way_min(r,g,b);
- float delta = max - min;
- v = max;
- if(max == 0){
- s = 0;
- h = -1;
- }else{
- s = delta/max;
- if(r == max){
- h = (g - b) / delta;
- } else if (g == max) {
- h = 2 + (b - r) / delta;
- } else {
- h = 4 + (r - g) / delta;
- }
- if (h < 0) h += 6;
- }
- set_pixel(im, i, j, 0, h);
- set_pixel(im, i, j, 1, s);
- set_pixel(im, i, j, 2, v);
- }
- }
+ image c = crop_image(b, 0, shift, a.w, a.h);
+ int i;
+ for(i = 0; i < c.w*c.h; ++i){
+ c.data[i] = a.data[i];
}
+#ifdef OPENCV
+ save_image_jpg(c, out);
+#else
+ save_image(c, out);
+#endif
+}
- void hsv_to_rgb(image im)
- {
- assert(im.c == 3);
- int i, j;
- float r, g, b;
- float h, s, v;
- float f, p, q, t;
- for(j = 0; j < im.h; ++j){
- for(i = 0; i < im.w; ++i){
- h = get_pixel(im, i , j, 0);
- s = get_pixel(im, i , j, 1);
- v = get_pixel(im, i , j, 2);
- if (s == 0) {
- r = g = b = v;
+image resize_min(image im, int min)
+{
+ int w = im.w;
+ int h = im.h;
+ if(w < h){
+ h = (h * min) / w;
+ w = min;
+ } else {
+ w = (w * min) / h;
+ h = min;
+ }
+ if(w == im.w && h == im.h) return im;
+ image resized = resize_image(im, w, h);
+ return resized;
+}
+
+image random_crop_image(image im, int low, int high, int size)
+{
+ int r = rand_int(low, high);
+ image resized = resize_min(im, r);
+ int dx = rand_int(0, resized.w - size);
+ int dy = rand_int(0, resized.h - size);
+ image crop = crop_image(resized, dx, dy, size, size);
+
+ if(resized.data != im.data) free_image(resized);
+ return crop;
+}
+
+float three_way_max(float a, float b, float c)
+{
+ return (a > b) ? ( (a > c) ? a : c) : ( (b > c) ? b : c) ;
+}
+
+float three_way_min(float a, float b, float c)
+{
+ return (a < b) ? ( (a < c) ? a : c) : ( (b < c) ? b : c) ;
+}
+
+// http://www.cs.rit.edu/~ncs/color/t_convert.html
+void rgb_to_hsv(image im)
+{
+ assert(im.c == 3);
+ int i, j;
+ float r, g, b;
+ float h, s, v;
+ for(j = 0; j < im.h; ++j){
+ for(i = 0; i < im.w; ++i){
+ r = get_pixel(im, i , j, 0);
+ g = get_pixel(im, i , j, 1);
+ b = get_pixel(im, i , j, 2);
+ float max = three_way_max(r,g,b);
+ float min = three_way_min(r,g,b);
+ float delta = max - min;
+ v = max;
+ if(max == 0){
+ s = 0;
+ h = -1;
+ }else{
+ s = delta/max;
+ if(r == max){
+ h = (g - b) / delta;
+ } else if (g == max) {
+ h = 2 + (b - r) / delta;
} else {
- int index = floor(h);
- f = h - index;
- p = v*(1-s);
- q = v*(1-s*f);
- t = v*(1-s*(1-f));
- if(index == 0){
- r = v; g = t; b = p;
- } else if(index == 1){
- r = q; g = v; b = p;
- } else if(index == 2){
- r = p; g = v; b = t;
- } else if(index == 3){
- r = p; g = q; b = v;
- } else if(index == 4){
- r = t; g = p; b = v;
- } else {
- r = v; g = p; b = q;
- }
+ h = 4 + (r - g) / delta;
}
- set_pixel(im, i, j, 0, r);
- set_pixel(im, i, j, 1, g);
- set_pixel(im, i, j, 2, b);
+ if (h < 0) h += 6;
}
+ set_pixel(im, i, j, 0, h);
+ set_pixel(im, i, j, 1, s);
+ set_pixel(im, i, j, 2, v);
}
}
+}
- image grayscale_image(image im)
- {
- assert(im.c == 3);
- int i, j, k;
- image gray = make_image(im.w, im.h, 1);
- float scale[] = {0.587, 0.299, 0.114};
- for(k = 0; k < im.c; ++k){
- for(j = 0; j < im.h; ++j){
- for(i = 0; i < im.w; ++i){
- gray.data[i+im.w*j] += scale[k]*get_pixel(im, i, j, k);
+void hsv_to_rgb(image im)
+{
+ assert(im.c == 3);
+ int i, j;
+ float r, g, b;
+ float h, s, v;
+ float f, p, q, t;
+ for(j = 0; j < im.h; ++j){
+ for(i = 0; i < im.w; ++i){
+ h = get_pixel(im, i , j, 0);
+ s = get_pixel(im, i , j, 1);
+ v = get_pixel(im, i , j, 2);
+ if (s == 0) {
+ r = g = b = v;
+ } else {
+ int index = floor(h);
+ f = h - index;
+ p = v*(1-s);
+ q = v*(1-s*f);
+ t = v*(1-s*(1-f));
+ if(index == 0){
+ r = v; g = t; b = p;
+ } else if(index == 1){
+ r = q; g = v; b = p;
+ } else if(index == 2){
+ r = p; g = v; b = t;
+ } else if(index == 3){
+ r = p; g = q; b = v;
+ } else if(index == 4){
+ r = t; g = p; b = v;
+ } else {
+ r = v; g = p; b = q;
}
}
+ set_pixel(im, i, j, 0, r);
+ set_pixel(im, i, j, 1, g);
+ set_pixel(im, i, j, 2, b);
}
- return gray;
}
+}
- image threshold_image(image im, float thresh)
- {
- int i;
- image t = make_image(im.w, im.h, im.c);
- for(i = 0; i < im.w*im.h*im.c; ++i){
- t.data[i] = im.data[i]>thresh ? 1 : 0;
+image grayscale_image(image im)
+{
+ assert(im.c == 3);
+ int i, j, k;
+ image gray = make_image(im.w, im.h, 1);
+ float scale[] = {0.587, 0.299, 0.114};
+ for(k = 0; k < im.c; ++k){
+ for(j = 0; j < im.h; ++j){
+ for(i = 0; i < im.w; ++i){
+ gray.data[i+im.w*j] += scale[k]*get_pixel(im, i, j, k);
+ }
}
- return t;
}
+ return gray;
+}
- image blend_image(image fore, image back, float alpha)
- {
- assert(fore.w == back.w && fore.h == back.h && fore.c == back.c);
- image blend = make_image(fore.w, fore.h, fore.c);
- int i, j, k;
- for(k = 0; k < fore.c; ++k){
- for(j = 0; j < fore.h; ++j){
- for(i = 0; i < fore.w; ++i){
- float val = alpha * get_pixel(fore, i, j, k) +
- (1 - alpha)* get_pixel(back, i, j, k);
- set_pixel(blend, i, j, k, val);
- }
- }
- }
- return blend;
+image threshold_image(image im, float thresh)
+{
+ int i;
+ image t = make_image(im.w, im.h, im.c);
+ for(i = 0; i < im.w*im.h*im.c; ++i){
+ t.data[i] = im.data[i]>thresh ? 1 : 0;
}
+ return t;
+}
- void scale_image_channel(image im, int c, float v)
- {
- int i, j;
- for(j = 0; j < im.h; ++j){
- for(i = 0; i < im.w; ++i){
- float pix = get_pixel(im, i, j, c);
- pix = pix*v;
- set_pixel(im, i, j, c, pix);
+image blend_image(image fore, image back, float alpha)
+{
+ assert(fore.w == back.w && fore.h == back.h && fore.c == back.c);
+ image blend = make_image(fore.w, fore.h, fore.c);
+ int i, j, k;
+ for(k = 0; k < fore.c; ++k){
+ for(j = 0; j < fore.h; ++j){
+ for(i = 0; i < fore.w; ++i){
+ float val = alpha * get_pixel(fore, i, j, k) +
+ (1 - alpha)* get_pixel(back, i, j, k);
+ set_pixel(blend, i, j, k, val);
}
}
}
+ return blend;
+}
- image binarize_image(image im)
- {
- image c = copy_image(im);
- int i;
- for(i = 0; i < im.w * im.h * im.c; ++i){
- if(c.data[i] > .5) c.data[i] = 1;
- else c.data[i] = 0;
+void scale_image_channel(image im, int c, float v)
+{
+ int i, j;
+ for(j = 0; j < im.h; ++j){
+ for(i = 0; i < im.w; ++i){
+ float pix = get_pixel(im, i, j, c);
+ pix = pix*v;
+ set_pixel(im, i, j, c, pix);
}
- return c;
- }
-
- void saturate_image(image im, float sat)
- {
- rgb_to_hsv(im);
- scale_image_channel(im, 1, sat);
- hsv_to_rgb(im);
- constrain_image(im);
}
+}
- void exposure_image(image im, float sat)
- {
- rgb_to_hsv(im);
- scale_image_channel(im, 2, sat);
- hsv_to_rgb(im);
- constrain_image(im);
+image binarize_image(image im)
+{
+ image c = copy_image(im);
+ int i;
+ for(i = 0; i < im.w * im.h * im.c; ++i){
+ if(c.data[i] > .5) c.data[i] = 1;
+ else c.data[i] = 0;
}
+ return c;
+}
- void saturate_exposure_image(image im, float sat, float exposure)
- {
- rgb_to_hsv(im);
- scale_image_channel(im, 1, sat);
- scale_image_channel(im, 2, exposure);
- hsv_to_rgb(im);
- constrain_image(im);
- }
+void saturate_image(image im, float sat)
+{
+ rgb_to_hsv(im);
+ scale_image_channel(im, 1, sat);
+ hsv_to_rgb(im);
+ constrain_image(im);
+}
- /*
- image saturate_image(image im, float sat)
- {
- image gray = grayscale_image(im);
- image blend = blend_image(im, gray, sat);
- free_image(gray);
- constrain_image(blend);
- return blend;
- }
+void exposure_image(image im, float sat)
+{
+ rgb_to_hsv(im);
+ scale_image_channel(im, 2, sat);
+ hsv_to_rgb(im);
+ constrain_image(im);
+}
- image brightness_image(image im, float b)
- {
- image bright = make_image(im.w, im.h, im.c);
- return bright;
- }
- */
+void saturate_exposure_image(image im, float sat, float exposure)
+{
+ rgb_to_hsv(im);
+ scale_image_channel(im, 1, sat);
+ scale_image_channel(im, 2, exposure);
+ hsv_to_rgb(im);
+ constrain_image(im);
+}
- float bilinear_interpolate(image im, float x, float y, int c)
- {
- int ix = (int) floorf(x);
- int iy = (int) floorf(y);
+/*
+ image saturate_image(image im, float sat)
+ {
+ image gray = grayscale_image(im);
+ image blend = blend_image(im, gray, sat);
+ free_image(gray);
+ constrain_image(blend);
+ return blend;
+ }
+
+ image brightness_image(image im, float b)
+ {
+ image bright = make_image(im.w, im.h, im.c);
+ return bright;
+ }
+ */
+
+float bilinear_interpolate(image im, float x, float y, int c)
+{
+ int ix = (int) floorf(x);
+ int iy = (int) floorf(y);
- float dx = x - ix;
- float dy = y - iy;
+ float dx = x - ix;
+ float dy = y - iy;
- float val = (1-dy) * (1-dx) * get_pixel_extend(im, ix, iy, c) +
- dy * (1-dx) * get_pixel_extend(im, ix, iy+1, c) +
- (1-dy) * dx * get_pixel_extend(im, ix+1, iy, c) +
- dy * dx * get_pixel_extend(im, ix+1, iy+1, c);
- return val;
- }
+ float val = (1-dy) * (1-dx) * get_pixel_extend(im, ix, iy, c) +
+ dy * (1-dx) * get_pixel_extend(im, ix, iy+1, c) +
+ (1-dy) * dx * get_pixel_extend(im, ix+1, iy, c) +
+ dy * dx * get_pixel_extend(im, ix+1, iy+1, c);
+ return val;
+}
- image resize_image(image im, int w, int h)
- {
- image resized = make_image(w, h, im.c);
- image part = make_image(w, im.h, im.c);
- int r, c, k;
- float w_scale = (float)(im.w - 1) / (w - 1);
- float h_scale = (float)(im.h - 1) / (h - 1);
- for(k = 0; k < im.c; ++k){
- for(r = 0; r < im.h; ++r){
- for(c = 0; c < w; ++c){
- float val = 0;
- if(c == w-1 || im.w == 1){
- val = get_pixel(im, im.w-1, r, k);
- } else {
- float sx = c*w_scale;
- int ix = (int) sx;
- float dx = sx - ix;
- val = (1 - dx) * get_pixel(im, ix, r, k) + dx * get_pixel(im, ix+1, r, k);
- }
- set_pixel(part, c, r, k, val);
+image resize_image(image im, int w, int h)
+{
+ image resized = make_image(w, h, im.c);
+ image part = make_image(w, im.h, im.c);
+ int r, c, k;
+ float w_scale = (float)(im.w - 1) / (w - 1);
+ float h_scale = (float)(im.h - 1) / (h - 1);
+ for(k = 0; k < im.c; ++k){
+ for(r = 0; r < im.h; ++r){
+ for(c = 0; c < w; ++c){
+ float val = 0;
+ if(c == w-1 || im.w == 1){
+ val = get_pixel(im, im.w-1, r, k);
+ } else {
+ float sx = c*w_scale;
+ int ix = (int) sx;
+ float dx = sx - ix;
+ val = (1 - dx) * get_pixel(im, ix, r, k) + dx * get_pixel(im, ix+1, r, k);
}
+ set_pixel(part, c, r, k, val);
}
}
- for(k = 0; k < im.c; ++k){
- for(r = 0; r < h; ++r){
- float sy = r*h_scale;
- int iy = (int) sy;
- float dy = sy - iy;
- for(c = 0; c < w; ++c){
- float val = (1-dy) * get_pixel(part, c, iy, k);
- set_pixel(resized, c, r, k, val);
- }
- if(r == h-1 || im.h == 1) continue;
- for(c = 0; c < w; ++c){
- float val = dy * get_pixel(part, c, iy+1, k);
- add_pixel(resized, c, r, k, val);
- }
+ }
+ for(k = 0; k < im.c; ++k){
+ for(r = 0; r < h; ++r){
+ float sy = r*h_scale;
+ int iy = (int) sy;
+ float dy = sy - iy;
+ for(c = 0; c < w; ++c){
+ float val = (1-dy) * get_pixel(part, c, iy, k);
+ set_pixel(resized, c, r, k, val);
+ }
+ if(r == h-1 || im.h == 1) continue;
+ for(c = 0; c < w; ++c){
+ float val = dy * get_pixel(part, c, iy+1, k);
+ add_pixel(resized, c, r, k, val);
}
}
-
- free_image(part);
- return resized;
}
+ free_image(part);
+ return resized;
+}
+
#include "cuda.h"
- void test_resize(char *filename)
- {
- image im = load_image(filename, 0,0, 3);
- float mag = mag_array(im.data, im.w*im.h*im.c);
- printf("L2 Norm: %f\n", mag);
- image gray = grayscale_image(im);
+void test_resize(char *filename)
+{
+ image im = load_image(filename, 0,0, 3);
+ float mag = mag_array(im.data, im.w*im.h*im.c);
+ printf("L2 Norm: %f\n", mag);
+ image gray = grayscale_image(im);
- image sat2 = copy_image(im);
- saturate_image(sat2, 2);
+ image sat2 = copy_image(im);
+ saturate_image(sat2, 2);
- image sat5 = copy_image(im);
- saturate_image(sat5, .5);
+ image sat5 = copy_image(im);
+ saturate_image(sat5, .5);
- image exp2 = copy_image(im);
- exposure_image(exp2, 2);
+ image exp2 = copy_image(im);
+ exposure_image(exp2, 2);
- image exp5 = copy_image(im);
- exposure_image(exp5, .5);
+ image exp5 = copy_image(im);
+ exposure_image(exp5, .5);
- image bin = binarize_image(im);
+ image bin = binarize_image(im);
#ifdef GPU
- image r = resize_image(im, im.w, im.h);
- image black = make_image(im.w*2 + 3, im.h*2 + 3, 9);
- image black2 = make_image(im.w, im.h, 3);
-
- float *r_gpu = cuda_make_array(r.data, r.w*r.h*r.c);
- float *black_gpu = cuda_make_array(black.data, black.w*black.h*black.c);
- float *black2_gpu = cuda_make_array(black2.data, black2.w*black2.h*black2.c);
- shortcut_gpu(3, r.w, r.h, 1, r_gpu, black.w, black.h, 3, black_gpu);
- //flip_image(r);
- //shortcut_gpu(3, r.w, r.h, 1, r.data, black.w, black.h, 3, black.data);
-
- shortcut_gpu(3, black.w, black.h, 3, black_gpu, black2.w, black2.h, 1, black2_gpu);
- cuda_pull_array(black_gpu, black.data, black.w*black.h*black.c);
- cuda_pull_array(black2_gpu, black2.data, black2.w*black2.h*black2.c);
- show_image_layers(black, "Black");
- show_image(black2, "Recreate");
+ image r = resize_image(im, im.w, im.h);
+ image black = make_image(im.w*2 + 3, im.h*2 + 3, 9);
+ image black2 = make_image(im.w, im.h, 3);
+
+ float *r_gpu = cuda_make_array(r.data, r.w*r.h*r.c);
+ float *black_gpu = cuda_make_array(black.data, black.w*black.h*black.c);
+ float *black2_gpu = cuda_make_array(black2.data, black2.w*black2.h*black2.c);
+ shortcut_gpu(3, r.w, r.h, 1, r_gpu, black.w, black.h, 3, black_gpu);
+ //flip_image(r);
+ //shortcut_gpu(3, r.w, r.h, 1, r.data, black.w, black.h, 3, black.data);
+
+ shortcut_gpu(3, black.w, black.h, 3, black_gpu, black2.w, black2.h, 1, black2_gpu);
+ cuda_pull_array(black_gpu, black.data, black.w*black.h*black.c);
+ cuda_pull_array(black2_gpu, black2.data, black2.w*black2.h*black2.c);
+ show_image_layers(black, "Black");
+ show_image(black2, "Recreate");
#endif
- show_image(im, "Original");
- show_image(bin, "Binary");
- show_image(gray, "Gray");
- show_image(sat2, "Saturation-2");
- show_image(sat5, "Saturation-.5");
- show_image(exp2, "Exposure-2");
- show_image(exp5, "Exposure-.5");
+ show_image(im, "Original");
+ show_image(bin, "Binary");
+ show_image(gray, "Gray");
+ show_image(sat2, "Saturation-2");
+ show_image(sat5, "Saturation-.5");
+ show_image(exp2, "Exposure-2");
+ show_image(exp5, "Exposure-.5");
#ifdef OPENCV
- cvWaitKey(0);
+ cvWaitKey(0);
#endif
- }
+}
#ifdef OPENCV
- image ipl_to_image(IplImage* src)
- {
- unsigned char *data = (unsigned char *)src->imageData;
- int h = src->height;
- int w = src->width;
- int c = src->nChannels;
- int step = src->widthStep;
- image out = make_image(w, h, c);
- int i, j, k, count=0;;
-
- for(k= 0; k < c; ++k){
- for(i = 0; i < h; ++i){
- for(j = 0; j < w; ++j){
- out.data[count++] = data[i*step + j*c + k]/255.;
- }
+image ipl_to_image(IplImage* src)
+{
+ unsigned char *data = (unsigned char *)src->imageData;
+ int h = src->height;
+ int w = src->width;
+ int c = src->nChannels;
+ int step = src->widthStep;
+ image out = make_image(w, h, c);
+ int i, j, k, count=0;;
+
+ for(k= 0; k < c; ++k){
+ for(i = 0; i < h; ++i){
+ for(j = 0; j < w; ++j){
+ out.data[count++] = data[i*step + j*c + k]/255.;
}
}
- return out;
}
+ return out;
+}
- image load_image_cv(char *filename, int channels)
- {
- IplImage* src = 0;
- int flag = -1;
- if (channels == 0) flag = -1;
- else if (channels == 1) flag = 0;
- else if (channels == 3) flag = 1;
- else {
- fprintf(stderr, "OpenCV can't force load with %d channels\n", channels);
- }
-
- 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);
- return make_image(10,10,3);
- //exit(0);
- }
- image out = ipl_to_image(src);
- cvReleaseImage(&src);
- rgbgr_image(out);
- return out;
+image load_image_cv(char *filename, int channels)
+{
+ IplImage* src = 0;
+ int flag = -1;
+ if (channels == 0) flag = -1;
+ else if (channels == 1) flag = 0;
+ else if (channels == 3) flag = 1;
+ else {
+ fprintf(stderr, "OpenCV can't force load with %d channels\n", channels);
}
+ 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);
+ return make_image(10,10,3);
+ //exit(0);
+ }
+ image out = ipl_to_image(src);
+ cvReleaseImage(&src);
+ rgbgr_image(out);
+ return out;
+}
+
#endif
- image load_image_stb(char *filename, int channels)
- {
- int w, h, c;
- unsigned char *data = stbi_load(filename, &w, &h, &c, channels);
- if (!data) {
- fprintf(stderr, "Cannot load image \"%s\"\nSTB Reason: %s\n", filename, stbi_failure_reason());
- exit(0);
- }
- if(channels) c = channels;
- int i,j,k;
- image im = make_image(w, h, c);
- for(k = 0; k < c; ++k){
- for(j = 0; j < h; ++j){
- for(i = 0; i < w; ++i){
- int dst_index = i + w*j + w*h*k;
- int src_index = k + c*i + c*w*j;
- im.data[dst_index] = (float)data[src_index]/255.;
- }
+image load_image_stb(char *filename, int channels)
+{
+ int w, h, c;
+ unsigned char *data = stbi_load(filename, &w, &h, &c, channels);
+ if (!data) {
+ fprintf(stderr, "Cannot load image \"%s\"\nSTB Reason: %s\n", filename, stbi_failure_reason());
+ exit(0);
+ }
+ if(channels) c = channels;
+ int i,j,k;
+ image im = make_image(w, h, c);
+ for(k = 0; k < c; ++k){
+ for(j = 0; j < h; ++j){
+ for(i = 0; i < w; ++i){
+ int dst_index = i + w*j + w*h*k;
+ int src_index = k + c*i + c*w*j;
+ im.data[dst_index] = (float)data[src_index]/255.;
}
}
- free(data);
- return im;
}
+ free(data);
+ return im;
+}
- image load_image(char *filename, int w, int h, int c)
- {
+image load_image(char *filename, int w, int h, int c)
+{
#ifdef OPENCV
- image out = load_image_cv(filename, c);
+ image out = load_image_cv(filename, c);
#else
- image out = load_image_stb(filename, c);
+ image out = load_image_stb(filename, c);
#endif
- if((h && w) && (h != out.h || w != out.w)){
- image resized = resize_image(out, w, h);
- free_image(out);
- out = resized;
- }
- return out;
+ if((h && w) && (h != out.h || w != out.w)){
+ image resized = resize_image(out, w, h);
+ free_image(out);
+ out = resized;
}
+ return out;
+}
- image load_image_color(char *filename, int w, int h)
- {
- return load_image(filename, w, h, 3);
- }
+image load_image_color(char *filename, int w, int h)
+{
+ return load_image(filename, w, h, 3);
+}
- image get_image_layer(image m, int l)
- {
- image out = make_image(m.w, m.h, 1);
- int i;
- for(i = 0; i < m.h*m.w; ++i){
- out.data[i] = m.data[i+l*m.h*m.w];
- }
- return out;
+image get_image_layer(image m, int l)
+{
+ image out = make_image(m.w, m.h, 1);
+ int i;
+ for(i = 0; i < m.h*m.w; ++i){
+ out.data[i] = m.data[i+l*m.h*m.w];
}
+ return out;
+}
- float get_pixel(image m, int x, int y, int c)
- {
- assert(x < m.w && y < m.h && c < m.c);
- return m.data[c*m.h*m.w + y*m.w + x];
- }
- float get_pixel_extend(image m, int x, int y, int c)
- {
- if(x < 0 || x >= m.w || y < 0 || y >= m.h || c < 0 || c >= m.c) return 0;
- return get_pixel(m, x, y, c);
- }
- void set_pixel(image m, int x, int y, int c, float val)
- {
- assert(x < m.w && y < m.h && c < m.c);
- m.data[c*m.h*m.w + y*m.w + x] = val;
- }
- void add_pixel(image m, int x, int y, int c, float val)
- {
- assert(x < m.w && y < m.h && c < m.c);
- m.data[c*m.h*m.w + y*m.w + x] += val;
- }
+float get_pixel(image m, int x, int y, int c)
+{
+ assert(x < m.w && y < m.h && c < m.c);
+ return m.data[c*m.h*m.w + y*m.w + x];
+}
+float get_pixel_extend(image m, int x, int y, int c)
+{
+ if(x < 0 || x >= m.w || y < 0 || y >= m.h || c < 0 || c >= m.c) return 0;
+ return get_pixel(m, x, y, c);
+}
+void set_pixel(image m, int x, int y, int c, float val)
+{
+ assert(x < m.w && y < m.h && c < m.c);
+ m.data[c*m.h*m.w + y*m.w + x] = val;
+}
+void add_pixel(image m, int x, int y, int c, float val)
+{
+ assert(x < m.w && y < m.h && c < m.c);
+ m.data[c*m.h*m.w + y*m.w + x] += val;
+}
- void print_image(image m)
- {
- int i, j, k;
- for(i =0 ; i < m.c; ++i){
- for(j =0 ; j < m.h; ++j){
- for(k = 0; k < m.w; ++k){
- printf("%.2lf, ", m.data[i*m.h*m.w + j*m.w + k]);
- if(k > 30) break;
- }
- printf("\n");
- if(j > 30) break;
+void print_image(image m)
+{
+ int i, j, k;
+ for(i =0 ; i < m.c; ++i){
+ for(j =0 ; j < m.h; ++j){
+ for(k = 0; k < m.w; ++k){
+ printf("%.2lf, ", m.data[i*m.h*m.w + j*m.w + k]);
+ if(k > 30) break;
}
printf("\n");
+ if(j > 30) break;
}
printf("\n");
}
+ printf("\n");
+}
- image collapse_images_vert(image *ims, int n)
- {
- int color = 1;
- int border = 1;
- int h,w,c;
- w = ims[0].w;
- h = (ims[0].h + border) * n - border;
- c = ims[0].c;
- if(c != 3 || !color){
- w = (w+border)*c - border;
- c = 1;
+image collapse_images_vert(image *ims, int n)
+{
+ int color = 1;
+ int border = 1;
+ int h,w,c;
+ w = ims[0].w;
+ h = (ims[0].h + border) * n - border;
+ c = ims[0].c;
+ if(c != 3 || !color){
+ w = (w+border)*c - border;
+ c = 1;
+ }
+
+ image filters = make_image(w, h, c);
+ int i,j;
+ for(i = 0; i < n; ++i){
+ int h_offset = i*(ims[0].h+border);
+ image copy = copy_image(ims[i]);
+ //normalize_image(copy);
+ if(c == 3 && color){
+ embed_image(copy, filters, 0, h_offset);
}
-
- image filters = make_image(w, h, c);
- int i,j;
- for(i = 0; i < n; ++i){
- int h_offset = i*(ims[0].h+border);
- image copy = copy_image(ims[i]);
- //normalize_image(copy);
- if(c == 3 && color){
- embed_image(copy, filters, 0, h_offset);
- }
- else{
- for(j = 0; j < copy.c; ++j){
- int w_offset = j*(ims[0].w+border);
- image layer = get_image_layer(copy, j);
- embed_image(layer, filters, w_offset, h_offset);
- free_image(layer);
- }
+ else{
+ for(j = 0; j < copy.c; ++j){
+ int w_offset = j*(ims[0].w+border);
+ image layer = get_image_layer(copy, j);
+ embed_image(layer, filters, w_offset, h_offset);
+ free_image(layer);
}
- free_image(copy);
}
- return filters;
- }
+ free_image(copy);
+ }
+ return filters;
+}
- image collapse_images_horz(image *ims, int n)
- {
- int color = 1;
- int border = 1;
- int h,w,c;
- int size = ims[0].h;
- h = size;
- w = (ims[0].w + border) * n - border;
- c = ims[0].c;
- if(c != 3 || !color){
- h = (h+border)*c - border;
- c = 1;
+image collapse_images_horz(image *ims, int n)
+{
+ int color = 1;
+ int border = 1;
+ int h,w,c;
+ int size = ims[0].h;
+ h = size;
+ w = (ims[0].w + border) * n - border;
+ c = ims[0].c;
+ if(c != 3 || !color){
+ h = (h+border)*c - border;
+ c = 1;
+ }
+
+ image filters = make_image(w, h, c);
+ int i,j;
+ for(i = 0; i < n; ++i){
+ int w_offset = i*(size+border);
+ image copy = copy_image(ims[i]);
+ //normalize_image(copy);
+ if(c == 3 && color){
+ embed_image(copy, filters, w_offset, 0);
}
-
- image filters = make_image(w, h, c);
- int i,j;
- for(i = 0; i < n; ++i){
- int w_offset = i*(size+border);
- image copy = copy_image(ims[i]);
- //normalize_image(copy);
- if(c == 3 && color){
- embed_image(copy, filters, w_offset, 0);
- }
- else{
- for(j = 0; j < copy.c; ++j){
- int h_offset = j*(size+border);
- image layer = get_image_layer(copy, j);
- embed_image(layer, filters, w_offset, h_offset);
- free_image(layer);
- }
+ else{
+ for(j = 0; j < copy.c; ++j){
+ int h_offset = j*(size+border);
+ image layer = get_image_layer(copy, j);
+ embed_image(layer, filters, w_offset, h_offset);
+ free_image(layer);
}
- free_image(copy);
}
- return filters;
- }
-
- void show_image_normalized(image im, const char *name)
- {
- image c = copy_image(im);
- normalize_image(c);
- show_image(c, name);
- free_image(c);
+ free_image(copy);
}
+ return filters;
+}
- void show_images(image *ims, int n, char *window)
- {
- image m = collapse_images_vert(ims, n);
- /*
- int w = 448;
- int h = ((float)m.h/m.w) * 448;
- if(h > 896){
- h = 896;
- w = ((float)m.w/m.h) * 896;
- }
- image sized = resize_image(m, w, h);
- */
- normalize_image(m);
- image sized = resize_image(m, m.w, m.h);
- save_image(sized, window);
- show_image(sized, window);
- free_image(sized);
- free_image(m);
- }
+void show_image_normalized(image im, const char *name)
+{
+ image c = copy_image(im);
+ normalize_image(c);
+ show_image(c, name);
+ free_image(c);
+}
- void free_image(image m)
- {
- free(m.data);
- }
+void show_images(image *ims, int n, char *window)
+{
+ image m = collapse_images_vert(ims, n);
+ /*
+ int w = 448;
+ int h = ((float)m.h/m.w) * 448;
+ if(h > 896){
+ h = 896;
+ w = ((float)m.w/m.h) * 896;
+ }
+ image sized = resize_image(m, w, h);
+ */
+ normalize_image(m);
+ image sized = resize_image(m, m.w, m.h);
+ save_image(sized, window);
+ show_image(sized, window);
+ free_image(sized);
+ free_image(m);
+}
+
+void free_image(image m)
+{
+ free(m.data);
+}
diff --git a/src/image.h b/src/image.h
index bf6ef9992770edd3c8bdd989ce96f293c26db539..ece7cb6a99590e51b333693a8b07dd91cf026c38 100644
--- a/src/image.h
+++ b/src/image.h
@@ -44,6 +44,7 @@ void saturate_exposure_image(image im, float sat, float exposure);
void hsv_to_rgb(image im);
void rgbgr_image(image im);
void constrain_image(image im);
+void composite_3d(char *f1, char *f2, char *out);
image grayscale_image(image im);
image threshold_image(image im, float thresh);
diff --git a/src/layer.h b/src/layer.h
index c3697ce2de0c8d5a728f093fe3748b68e94c05c8..d53fe387cd406b4d0fa6b82fecbb5e405bbb4212 100644
--- a/src/layer.h
+++ b/src/layer.h
@@ -50,6 +50,7 @@ struct layer{
int h,w,c;
int out_h, out_w, out_c;
int n;
+ int max_boxes;
int groups;
int size;
int side;
diff --git a/src/network.c b/src/network.c
index 8f39f7b4b445e7a5d4f2655e75eee7682e90681c..88b70857cdf314fbf17fdd14063f0cab71348d37 100644
--- a/src/network.c
+++ b/src/network.c
@@ -137,6 +137,7 @@ network make_network(int n)
void forward_network(network net, network_state state)
{
+ state.workspace = net.workspace;
int i;
for(i = 0; i < net.n; ++i){
state.index = i;
@@ -400,6 +401,7 @@ int resize_network(network *net, int w, int h)
net->w = w;
net->h = h;
int inputs = 0;
+ size_t workspace_size = 0;
//fprintf(stderr, "Resizing to %d x %d...", w, h);
//fflush(stderr);
for (i = 0; i < net->n; ++i){
@@ -419,12 +421,20 @@ int resize_network(network *net, int w, int h)
}else{
error("Cannot resize this type of layer");
}
+ if(l.workspace_size > workspace_size) workspace_size = l.workspace_size;
inputs = l.outputs;
net->layers[i] = l;
w = l.out_w;
h = l.out_h;
if(l.type == AVGPOOL) break;
}
+#ifdef GPU
+ cuda_free(net->workspace);
+ net->workspace = cuda_make_array(0, (workspace_size-1)/sizeof(float)+1);
+#else
+ free(net->workspace);
+ net->workspace = calloc(1, (workspace_size-1)/sizeof(float)+1);
+#endif
//fprintf(stderr, " Done!\n");
return 0;
}
diff --git a/src/parser.c b/src/parser.c
index d5288aa92bc8d87232a50bfe708c0ebbead9ba58..d12b5c18312fa4c3125bad61f88ff81606bced0c 100644
--- a/src/parser.c
+++ b/src/parser.c
@@ -257,6 +257,7 @@ detection_layer parse_detection(list *options, size_params params)
layer.softmax = option_find_int(options, "softmax", 0);
layer.sqrt = option_find_int(options, "sqrt", 0);
+ layer.max_boxes = option_find_int_quiet(options, "max",30);
layer.coord_scale = option_find_float(options, "coord_scale", 1);
layer.forced = option_find_int(options, "forced", 0);
layer.object_scale = option_find_float(options, "object_scale", 1);
@@ -600,8 +601,11 @@ network parse_network_cfg(char *filename)
net.outputs = get_network_output_size(net);
net.output = get_network_output(net);
if(workspace_size){
+ //printf("%ld\n", workspace_size);
#ifdef GPU
net.workspace = cuda_make_array(0, (workspace_size-1)/sizeof(float)+1);
+#else
+ net.workspace = calloc(1, workspace_size);
#endif
}
return net;
diff --git a/src/rnn.c b/src/rnn.c
index c7c70a433c3407f18b89aca6a01629bf214adbf0..5e229baab6ce7f9d5833b0655797409ff98d7e78 100644
--- a/src/rnn.c
+++ b/src/rnn.c
@@ -280,6 +280,104 @@ void test_char_rnn(char *cfgfile, char *weightfile, int num, char *seed, float t
printf("\n");
}
+void test_tactic_rnn(char *cfgfile, char *weightfile, int num, char *seed, float temp, int rseed, char *token_file)
+{
+ char **tokens = 0;
+ if(token_file){
+ size_t n;
+ tokens = read_tokens(token_file, &n);
+ }
+
+ srand(rseed);
+ char *base = basecfg(cfgfile);
+ fprintf(stderr, "%s\n", base);
+
+ network net = parse_network_cfg(cfgfile);
+ if(weightfile){
+ load_weights(&net, weightfile);
+ }
+ int inputs = get_network_input_size(net);
+
+ int i, j;
+ for(i = 0; i < net.n; ++i) net.layers[i].temperature = temp;
+ int c = 0;
+ int len = strlen(seed);
+ float *input = calloc(inputs, sizeof(float));
+ float *out;
+
+ while((c = getc(stdin)) != EOF){
+ input[c] = 1;
+ out = network_predict(net, input);
+ input[c] = 0;
+ }
+ for(i = 0; i < num; ++i){
+ for(j = 0; j < inputs; ++j){
+ if (out[j] < .0001) out[j] = 0;
+ }
+ int next = sample_array(out, inputs);
+ if(c == '.' && next == '\n') break;
+ c = next;
+ print_symbol(c, tokens);
+
+ input[c] = 1;
+ out = network_predict(net, input);
+ input[c] = 0;
+ }
+ printf("\n");
+}
+
+void valid_tactic_rnn(char *cfgfile, char *weightfile, char *seed)
+{
+ char *base = basecfg(cfgfile);
+ fprintf(stderr, "%s\n", base);
+
+ network net = parse_network_cfg(cfgfile);
+ if(weightfile){
+ load_weights(&net, weightfile);
+ }
+ int inputs = get_network_input_size(net);
+
+ int count = 0;
+ int words = 1;
+ int c;
+ int len = strlen(seed);
+ float *input = calloc(inputs, sizeof(float));
+ int i;
+ for(i = 0; i < len; ++i){
+ c = seed[i];
+ input[(int)c] = 1;
+ network_predict(net, input);
+ input[(int)c] = 0;
+ }
+ float sum = 0;
+ c = getc(stdin);
+ float log2 = log(2);
+ int in = 0;
+ while(c != EOF){
+ int next = getc(stdin);
+ if(next == EOF) break;
+ if(next < 0 || next >= 255) error("Out of range character");
+
+ input[c] = 1;
+ float *out = network_predict(net, input);
+ input[c] = 0;
+
+ if(c == '.' && next == '\n') in = 0;
+ if(!in) {
+ if(c == '>' && next == '>'){
+ in = 1;
+ ++words;
+ }
+ c = next;
+ continue;
+ }
+ ++count;
+ sum += log(out[next])/log2;
+ c = next;
+ printf("%d %d Perplexity: %4.4f Word Perplexity: %4.4f\n", count, words, pow(2, -sum/count), pow(2, -sum/words));
+ }
+}
+
void valid_char_rnn(char *cfgfile, char *weightfile, char *seed)
{
char *base = basecfg(cfgfile);
@@ -389,6 +487,8 @@ void run_char_rnn(int argc, char **argv)
char *weights = (argc > 4) ? argv[4] : 0;
if(0==strcmp(argv[2], "train")) train_char_rnn(cfg, weights, filename, clear, tokenized);
else if(0==strcmp(argv[2], "valid")) valid_char_rnn(cfg, weights, seed);
+ else if(0==strcmp(argv[2], "validtactic")) valid_tactic_rnn(cfg, weights, seed);
else if(0==strcmp(argv[2], "vec")) vec_char_rnn(cfg, weights, seed);
else if(0==strcmp(argv[2], "generate")) test_char_rnn(cfg, weights, len, seed, temp, rseed, tokens);
+ else if(0==strcmp(argv[2], "generatetactic")) test_tactic_rnn(cfg, weights, len, seed, temp, rseed, tokens);
}
diff --git a/src/utils.c b/src/utils.c
index 1541e0520c72ed75cac9b35e948dd6b7add658f5..90af5cf76addad1dd6cac5c91286cd3ec4960f3f 100644
--- a/src/utils.c
+++ b/src/utils.c
@@ -424,6 +424,13 @@ float variance_array(float *a, int n)
return variance;
}
+int constrain_int(int a, int min, int max)
+{
+ if (a < min) return min;
+ if (a > max) return max;
+ return a;
+}
+
float constrain(float min, float max, float a)
{
if (a < min) return min;
@@ -431,6 +438,14 @@ float constrain(float min, float max, float a)
return a;
}
+float dist_array(float *a, float *b, int n, int sub)
+{
+ int i;
+ float sum = 0;
+ for(i = 0; i < n; i += sub) sum += pow(a[i]-b[i], 2);
+ return sqrt(sum);
+}
+
float mse_array(float *a, int n)
{
int i;
diff --git a/src/utils.h b/src/utils.h
index 7e49818c7c788dce84cfa6a25e99104bbf46d8e7..cba7f6f265fb47d5d4c52f4d025b45d12ed51272 100644
--- a/src/utils.h
+++ b/src/utils.h
@@ -36,6 +36,7 @@ void scale_array(float *a, int n, float s);
void translate_array(float *a, int n, float s);
int max_index(float *a, int n);
float constrain(float min, float max, float a);
+int constrain_int(int a, int min, int max);
float mse_array(float *a, int n);
float rand_normal();
size_t rand_size_t();
@@ -46,6 +47,7 @@ float mean_array(float *a, int n);
void mean_arrays(float **a, int n, int els, float *avg);
float variance_array(float *a, int n);
float mag_array(float *a, int n);
+float dist_array(float *a, float *b, int n, int sub);
float **one_hot_encode(float *a, int n, int k);
float sec(clock_t clocks);
int find_int_arg(int argc, char **argv, char *arg, int def);