void inter_dcgan(char *cfgfile, char *weightfile)
{
network *net = load_network(cfgfile, weightfile, 0);
set_batch_network(net, 1);
srand(2222222);
clock_t time;
char buff[256];
char *input = buff;
int i, imlayer = 0;
for (i = 0; i < net->n; ++i) {
if (net->layers[i].out_c == 3) {
imlayer = i;
printf("%d\n", i);
break;
}
}
image start = random_unit_vector_image(net->w, net->h, net->c);
image end = random_unit_vector_image(net->w, net->h, net->c);
image im = make_image(net->w, net->h, net->c);
image orig = copy_image(start);
int c = 0;
int count = 0;
int max_count = 15;
while(1){
++c;
if(count == max_count){
count = 0;
free_image(start);
start = end;
end = random_unit_vector_image(net->w, net->h, net->c);
if(c > 300){
end = orig;
}
if(c>300 + max_count) return;
}
++count;
slerp(start.data, end.data, (float)count / max_count, im.w*im.h*im.c, im.data);
float *X = im.data;
time=clock();
network_predict(net, X);
image out = get_network_image_layer(net, imlayer);
//yuv_to_rgb(out);
normalize_image(out);
printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time));
//char buff[256];
sprintf(buff, "out%05d", c);
save_image(out, "out");
save_image(out, buff);
show_image(out, "out", 0);
}
}
void show_images(image *ims, int n, char *window)
{
image m = collapse_images_vert(ims, n);
save_image(m, window);
show_image(m, window);
free_image(m);
}
int main(int args, char ** argv) {
misc::process_args(args, argv);
load_image(file_name[0], left_layer.rgb, left_layer.H, left_layer.W);
load_image(file_name[1], right_layer.rgb, right_layer.H, right_layer.W);
timer.reset();
left.init(left_layer); right.init(right_layer);
//misc::median_filter_rgb(left.rgb, left.H, left.W, 1);
//misc::median_filter_rgb(right.rgb, right.H, right.W, 1);
if (use_lab) {
left_layer.computeLab();
// misc::median_filter_rgb(left.lab, left.H, left.W, 1);
}
left.buildTree(use_lab);
left_layer.computeGradient();
right_layer.computeGradient();
// next part : compute disparity
left.initDisparity();
updateTable(255 * 0.1);
// timer.check("build forest");
left.stereoMatch(right, 1, max_disparity, use_lab);
misc::median_filter(left.disparity, left.H, left.W); //TODO: why radius = 2, not 3 as dp
timer.check("all");
//save
save_image(file_name[2], left.disparity, left.H, left.W, scale);
//save_image(file_name[3], right.disparity, right.H, right.W, scale);
return 0;
}
void
image_get()
{
int i, t, index;
char *filename;
for (t = 0; t < IMAGE_QUERY_TURN; t++) {
printf("%d .. ", IMAGE_QUERY_TURN - t);
fflush(stdout);
for (i = 0; i < video.buffer.req.count; i++) {
buffer_dequeue(i);
buffer_enqueue(i);
}
}
printf("%d ..\n", IMAGE_QUERY_TURN - t);
fflush(stdout);
index = 0;
buffer_dequeue(index);
update_image_pixels(index);
filename = get_file_name("swc", "jpeg");
save_image(filename);
printf("%s saved.\n", filename);
free(filename);
buffer_enqueue(index);
}
int main(int args, char ** argv) {
misc::process_args(args, argv);
load_image(file_name[0], left_pyramid[0].rgb, left_pyramid[0].H, left_pyramid[0].W);
load_image(file_name[1], right_pyramid[0].rgb, right_pyramid[0].H, right_pyramid[0].W);
if (left_pyramid[0].H != right_pyramid[0].H || left_pyramid[0].W != right_pyramid[0].W) {
printf("The size of two pictures differ!! quit....");
return 0;
}
if (left_pyramid[0].H >= MAX_HEIGHT || right_pyramid[0].W >= MAX_WIDTH) {
printf("Input : %d x %d, program : %d %d\n", left_pyramid[0].H, left[0].W, MAX_HEIGHT, MAX_WIDTH);
puts("Image too big. change settings.hpp and re-compile.");
return 0;
}
timer.reset();
if (use_lab) {
left_pyramid[0].computeLab();
right_pyramid[0].computeLab();
}
for (int i = 0; i + 1 < levels; ++i) {
left_pyramid[i+1].shrinkPicture(left_pyramid[i+1].rgb, left_pyramid[i].rgb, left_pyramid[i].H, left_pyramid[i].W);
right_pyramid[i+1].shrinkPicture(right_pyramid[i+1].rgb, right_pyramid[i].rgb, right_pyramid[i].H, right_pyramid[i].W);
if (use_lab) {
left_pyramid[i+1].shrinkPicture(left_pyramid[i+1].lab, left_pyramid[i].lab, left_pyramid[i].H, left_pyramid[i].W);
right_pyramid[i+1].shrinkPicture(right_pyramid[i+1].lab, right_pyramid[i].lab, right_pyramid[i].H, right_pyramid[i].W);
}
}
for (int lvl = levels - 1; lvl >= 0; -- lvl) {
int idx = lvl % OBJ_NUM;
left[idx].init(left_pyramid[lvl]);
right[idx].init(right_pyramid[lvl]);
if (lvl == levels - 1) {
left[idx].noPrediction(max_disparity / (1 << lvl));
} else {
DP::getSupportProb(left[idx].rgb, right[idx].rgb,
left[idx].H, left[idx].W, max_disparity / (1 << lvl));
DP::getProbMatrix(lvl, max_disparity / (1 << (lvl + 1)), max_disparity / (1 << lvl), dataset);
DP::getInterval(pixel_intv_threshold * (1 << lvl)/*, tot_threshold*/);
left[idx].readPrediction(left[(lvl + 1)%OBJ_NUM].disparity);
// left[idx].intersectInterval(left[(lvl + 1) % OBJ_NUM]);
}
// Now use the INTERVAL to find the new disparities.
left_pyramid[lvl].computeGradient();
right_pyramid[lvl].computeGradient();
left[idx].buildForest(tree_intv_threshold, use_lab);
left[idx].noPrediction(max_disparity / (1 << lvl));
left[idx].initDisparity();
updateTable(255 * 0.1);
left[idx].stereoMatch(right[idx], 1, use_lab);
misc::median_filter(left[idx].disparity, left[idx].H, left[idx].W, 3);
// save_image(layername[lvl][0], left[idx].disparity, left[idx].H, left[idx].W, scale * (1 << lvl));
} // end of layer iteration.
timer.check("all");
save_image(file_name[2], left[0].disparity, left[0].H, left[0].W, scale);
return 0;
}
void Darknet::yoloImage(const QString &filename, float thresh)
{
const Mat ori = OpenCV::loadImage(getAbs(filename), -1);
Mat img;
cv::resize(ori, img, cv::Size(priv->net.w, priv->net.h));
image im = toDarkImage(img);
//image im = load_image_color((char *)qPrintable(getAbs(filename)), 0, 0);
//image sized = resize_image(im, priv->net.w, priv->net.h);
//float *X = sized.data;
float *X = im.data;
float *predictions = network_predict(priv->net, X);
float nms=.5;
const detection_layer l = priv->l;
convert_yolo_detections(predictions, l.classes, l.n, l.sqrt, l.side, 1, 1, thresh, priv->probs, priv->boxes, 0);
if (nms) do_nms_sort(priv->boxes, priv->probs, l.side*l.side*l.n, l.classes, nms);
draw_detections(im, l.side*l.side*l.n, thresh, priv->boxes, priv->probs, voc_names, 0, 20);
show_image(im, "predictions");
save_image(im, "predictions");
//show_image(sized, "resized");
free_image(im);
//free_image(sized);
}
static void
make_preview (void)
{
destroy_preview ();
if (jsvals.preview)
{
gchar *tn = gimp_temp_name ("jpeg");
if (! undo_touched)
{
/* we freeze undo saving so that we can avoid sucking up
* tile cache with our unneeded preview steps. */
gimp_image_undo_freeze (preview_image_ID);
undo_touched = TRUE;
}
save_image (tn,
preview_image_ID,
drawable_ID_global,
orig_image_ID_global,
TRUE, NULL);
if (display_ID == -1)
display_ID = gimp_display_new (preview_image_ID);
}
else
{
gtk_label_set_text (GTK_LABEL (preview_size), _("File size: unknown"));
gimp_displays_flush ();
}
}
void draw_loop(const char *dir, int start_image_num, int num_images)
{
int key, i;
IplImage *img;
CvSize size = { IMAGE_WIDTH, IMAGE_HEIGHT };
img = cvCreateImage(size, IPL_DEPTH_8U, 1); //3);
if (!img)
return;
for (i = start_image_num; i < start_image_num + num_images; i++) {
draw_shapes(img);
cvShowImage("shapegen", img);
save_image(dir, img, i);
if (dir)
key = 0xff & cvWaitKey(500);
else
key = 0xff & cvWaitKey(0);
if (key == ESCAPE_KEY)
break;
}
cvReleaseImage(&img);
}
static surface
create_image_base(const std::string& filename)
{
surface result = SDL_CreateRGBSurface(
SDL_SWSURFACE
, 1024
, 1024
, 32
, 0xFF0000
, 0xFF00
, 0xFF
, 0);
surface_lock lock(result);
Uint32 *pixels = lock.pixels();
for(Uint32 r = 0; r < 256; r += 4) {
for(Uint32 g = 0; g < 256; g += 2) {
for(Uint32 b = 0; b < 256; b += 2, ++pixels) {
*pixels = (0xFF << 24) | (r << 16) | (g << 8) | b;
}
}
}
save_image(result, filename);
return result;
}
void mkimg(char *cfgfile, char *weightfile, int h, int w, int num, char *prefix)
{
network *net = load_network(cfgfile, weightfile, 0);
image *ims = get_weights(net->layers[0]);
int n = net->layers[0].n;
int z;
for(z = 0; z < num; ++z){
image im = make_image(h, w, 3);
fill_image(im, .5);
int i;
for(i = 0; i < 100; ++i){
image r = copy_image(ims[rand()%n]);
rotate_image_cw(r, rand()%4);
random_distort_image(r, 1, 1.5, 1.5);
int dx = rand()%(w-r.w);
int dy = rand()%(h-r.h);
ghost_image(r, im, dx, dy);
free_image(r);
}
char buff[256];
sprintf(buff, "%s/gen_%d", prefix, z);
save_image(im, buff);
free_image(im);
}
free_network(net);
}
void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filename, float thresh, float hier_thresh)
{
int show_flag = 1;
list *options = read_data_cfg(datacfg);
char *name_list = option_find_str(options, "names", "data/names.list");
char **names = get_labels(name_list);
image **alphabet = load_alphabet();
network net = parse_network_cfg(cfgfile);
if(weightfile){
load_weights(&net, weightfile);
}
set_batch_network(&net, 1);
srand(2222222);
clock_t time;
char buff[256];
char *input = buff;
int j;
float nms=.4;
while(1){
if(filename){
strncpy(input, filename, 256);
} else {
printf("Enter Image Path: ");
fflush(stdout);
input = fgets(input, 256, stdin);
if(!input) return;
strtok(input, "\n");
}
image im = load_image_color(input,0,0);
image sized = resize_image(im, net.w, net.h);
layer l = net.layers[net.n-1];
box *boxes = calloc(l.w*l.h*l.n, sizeof(box));
float **probs = calloc(l.w*l.h*l.n, sizeof(float *));
for(j = 0; j < l.w*l.h*l.n; ++j) probs[j] = calloc(l.classes + 1, sizeof(float *));
float *X = sized.data;
time=clock();
network_predict(net, X);
printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time));
get_region_boxes(l, 1, 1, thresh, probs, boxes, 0, 0, hier_thresh);
if (l.softmax_tree && nms) do_nms_obj(boxes, probs, l.w*l.h*l.n, l.classes, nms);
else if (nms) do_nms_sort(boxes, probs, l.w*l.h*l.n, l.classes, nms);
draw_detections(im, l.w*l.h*l.n, thresh, boxes, probs, names, alphabet, l.classes, show_flag);
save_image(im, "predictions");
show_image(im, "predictions");
free_image(im);
free_image(sized);
free(boxes);
free_ptrs((void **)probs, l.w*l.h*l.n);
#ifdef OPENCV
cvWaitKey(0);
cvDestroyAllWindows();
#endif
if (filename) break;
}
}
void save_image(Image::ConstRefArg image,
std::string file_path, std::string format_name,
Logger* logger, FileFormat::ProgressTracker* tracker,
FileFormat::Registry::ConstRefArg r)
{
save_image(image, *make_file_output_stream(file_path),
file_path, format_name, logger, tracker, r);
}
/***********************************************************************//**
* @brief Save FITS image
*
* Saves the image into the FITS file.
***************************************************************************/
void GFitsImage::data_save(void)
{
// Save image
save_image(type(), pixels());
// Return
return;
}
int main(void) {
Image image;
image.width = DIM;
image.height = DIM;
mandel(-1.25, -2, 1.25, 0.5, &image);
save_image("mandelbrot.pnm", &image);
return EXIT_SUCCESS;
}
int test_layer_reverse()
{
// Blog: http://blog.csdn.net/fengbingchun/article/details/77160872
#ifdef __linux__
std::string image_name{ "test_data/images/lena.png" };
#else
std::string image_name{ "E:/GitCode/CUDA_Test/test_data/images/lena.png" };
#endif
cv::Mat matSrc = cv::imread(image_name);
CHECK(matSrc.data);
cv::cvtColor(matSrc, matSrc, CV_BGR2GRAY);
const int width{ 1511 }, height{ 1473 };
const auto length = width * height;
cv::resize(matSrc, matSrc, cv::Size(width, height));
cv::Mat matTmp1;
matSrc.convertTo(matTmp1, CV_32FC1);
float elapsed_time1{ 0.f }, elapsed_time2{ 0.f }; // milliseconds
const std::vector<int> vec{ 5, 7};
std::unique_ptr<float[]> dst1(new float[length]), dst2(new float[length]);
std::for_each(dst1.get(), dst1.get() + length, [](float& n) {n = 0.f; });
std::for_each(dst2.get(), dst2.get() + length, [](float& n) {n = 0.f; });
int ret = layer_reverse_cpu((float*)matTmp1.data, dst1.get(), length, vec, &elapsed_time1);
if (ret != 0) PRINT_ERROR_INFO(image_reverse_cpu);
ret = layer_reverse_gpu((float*)matTmp1.data, dst2.get(), length, vec, &elapsed_time2);
if (ret != 0) PRINT_ERROR_INFO(image_reverse_gpu);
compare_result(dst1.get(), dst2.get(), length);
cv::Mat matTmp2(height, width, CV_32FC1, dst2.get()), matDst;
matTmp2.convertTo(matDst, CV_8UC1);
#ifdef __linux__
save_image(matSrc, matDst, 400, 200, "test_data/images/image_reverse.png");
#else
save_image(matSrc, matDst, 400, 200, "E:/GitCode/CUDA_Test/test_data/images/image_reverse.png");
#endif
fprintf(stderr, "test layer reverse: cpu run time: %f ms, gpu run time: %f ms\n", elapsed_time1, elapsed_time2);
return 0;
}
void show_image(image p, char *name)
{
#ifdef OPENCV
show_image_cv(p, name);
#else
fprintf(stderr, "Not compiled with OpenCV, saving to %s.png instead\n", name);
save_image(p, name);
#endif
}
void factor_vm::primitive_save_image()
{
/* do a full GC to push everything into tenured space */
gc();
gc_root<byte_array> path(dpop(),this);
path.untag_check(this);
save_image((vm_char *)(path.untagged() + 1));
}
void test_lsd(char *cfgfile, char *weightfile, char *filename)
{
network net = parse_network_cfg(cfgfile);
if(weightfile){
load_weights(&net, weightfile);
}
set_batch_network(&net, 1);
srand(2222222);
clock_t time;
char buff[256];
char *input = buff;
int i, imlayer = 0;
for (i = 0; i < net.n; ++i) {
if (net.layers[i].out_c == 3) {
imlayer = i;
printf("%d\n", i);
break;
}
}
while(1){
if(filename){
strncpy(input, filename, 256);
}else{
printf("Enter Image Path: ");
fflush(stdout);
input = fgets(input, 256, stdin);
if(!input) return;
strtok(input, "\n");
}
image im = load_image_color(input, 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);
//grayscale_image_3c(crop);
float *X = crop.data;
time=clock();
network_predict(net, X);
image out = get_network_image_layer(net, imlayer);
//yuv_to_rgb(out);
constrain_image(out);
printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time));
show_image(out, "out");
show_image(crop, "crop");
save_image(out, "out");
#ifdef OPENCV
cvWaitKey(0);
#endif
free_image(im);
free_image(resized);
free_image(crop);
if (filename) break;
}
}
int
main(int argc, char* argv[])
{
try {
const toptions& options = toptions::parse(argc, argv);
surface surf(make_neutral_surface(
IMG_Load(options.input_filename.c_str())));
if(!surf) {
std::cerr << "Error: Failed to load input file »"
<< options.input_filename
<< "«.\n";
return EXIT_FAILURE;
}
std::vector<surface> surfaces;
if(options.count != 1) {
for(int i = 1; i < options.count; ++i) {
// make_neutral_surface make a deep-copy of the image.
surfaces.push_back(make_neutral_surface(surf));
}
}
surfaces.push_back(surf);
const clock_t begin = options.time ? get_begin_time() : 0;
for(int i = 0; i < options.count; ++i) {
BOOST_FOREACH(const std::string& filter, options.filters) {
filter_apply(surfaces[i], filter);
}
}
if(options.time) {
const clock_t end = std::clock();
std::cout << "Applying the filters took "
<< end - begin
<< " ticks, "
<< static_cast<double>(end - begin) / CLOCKS_PER_SEC
<< " seconds.\n";
}
if(!options.output_filename.empty()) {
save_image(surfaces[0], options.output_filename);
}
} catch(const texit& exit) {
return exit.status;
} catch(exploder_failure& err) {
std::cerr << "Error: Failed with error »" << err.message << "«.\n";
return EXIT_FAILURE;
}
return EXIT_SUCCESS;
}
int test_image_process_laplacian()
{
// Blog: http://blog.csdn.net/fengbingchun/article/details/79321200
#ifdef __linux__
cv::Mat src = cv::imread("test_data/images/lena.png", 0);
#else
cv::Mat src = cv::imread("E:/GitCode/CUDA_Test/test_data/images/lena.png", 0);
#endif
if (!src.data || src.channels() != 1) {
fprintf(stderr, "read image fail\n");
return -1;
}
int width{ 400 }, height{ 400 };
cv::resize(src, src, cv::Size(width, height));
std::unique_ptr<unsigned char[]> data1(new unsigned char[width * height]), data2(new unsigned char[width * height]);
float elapsed_time1{ 0.f }, elapsed_time2{ 0.f }; // milliseconds
int ksize{ 1 };
CHECK(laplacian_cpu(src.data, width, height, ksize, data1.get(), &elapsed_time1) == 0);
//CHECK(laplacian_gpu(src.data, width, height, data2.get(), &elapsed_time2) == 0);
//fprintf(stdout, "gray image edge detection: laplacian: cpu run time: %f ms, gpu run time: %f ms\n", elapsed_time1, elapsed_time2);
cv::Mat dst;
cv::Laplacian(src, dst, src.depth(), ksize);
#ifdef __linux__
cv::imwrite("test_data/images/laplacian.png", dst);
#else
cv::imwrite("E:/GitCode/CUDA_Test/test_data/images/laplacian.png", dst);
#endif
CHECK(compare_result(data1.get(), dst.data, width*height) == 0);
//CHECK(compare_result(data1.get(), data2.get(), width*height) == 0);
#ifdef __linux__
save_image(src, dst, width, height / 2, "test_data/images/laplacian_result.png");
#else
save_image(src, dst, width, height / 2, "E:/GitCode/CUDA_Test/test_data/images/laplacian_result.png");
#endif
return 0;
}
static void test_load_training_images()
{
char filename[256];
unsigned char ** images=NULL;
char ** classifications=NULL;
int * numbers;
int im;
int no_of_images = 3;
int no_of_images2;
int width=40,height=40;
char commandstr[256],str[256];
printf("test_load_training_images...");
/* create a directory for the images */
sprintf(commandstr,"mkdir %sdeeplearn_test_images",
DEEPLEARN_TEMP_DIRECTORY);
system(commandstr);
/* save a tests images */
for (im = 0; im < no_of_images; im++) {
sprintf(filename,"%sdeeplearn_test_images/img%d.%d.png",
DEEPLEARN_TEMP_DIRECTORY, im%2, im);
save_image(filename);
}
sprintf(str,"%sdeeplearn_test_images",
DEEPLEARN_TEMP_DIRECTORY);
/* load the images */
no_of_images2 =
deeplearn_load_training_images(str,
&images,
&classifications,
&numbers,
width, height);
assert(no_of_images == no_of_images2);
assert(images!=NULL);
/* free memory */
for (im = 0; im < no_of_images; im++) {
free(images[im]);
free(classifications[im]);
}
free(images);
free(classifications);
free(numbers);
/* remove the images */
sprintf(commandstr,"rm -rf %sdeeplearn_test_images",
DEEPLEARN_TEMP_DIRECTORY);
system(commandstr);
printf("Ok\n");
}
void
kbd_hit(char key) {
set_active();
switch (key) {
case 'd':
dump_screen(0);
break;
case 'w':
case 'W':
save_image();
break;
case 'q':
case 'Q':
case 'x':
case 'X': /* exit the program */
end_display();
exit(0);
case 'v':
show_video_settings = !show_video_settings;
refresh_screen = 1;
break;
case 'h':
set_hue(get_hue() - 1);
refresh_screen = 1;
break;
case 'H':
set_hue(get_hue() + 1);
refresh_screen = 1;
break;
case 's':
set_saturation(get_saturation() - 1);
refresh_screen = 1;
break;
case 'S':
set_saturation(get_saturation() + 1);
refresh_screen = 1;
break;
case 'b':
set_brightness(get_brightness() - 1);
refresh_screen = 1;
break;
case 'B':
set_brightness(get_brightness() + 1);
refresh_screen = 1;
break;
case 'c':
set_contrast(get_contrast() - 1);
refresh_screen = 1;
break;
case 'C':
set_contrast(get_contrast() + 1);
refresh_screen = 1;
break;
}
}
static void
test_ui_file (GFile *file)
{
char *ui_file, *reference_file;
cairo_surface_t *ui_image, *reference_image, *diff_image;
GtkStyleProvider *provider;
ui_file = g_file_get_path (file);
provider = add_extra_css (ui_file, ".css");
ui_image = reftest_snapshot_ui_file (ui_file);
reference_file = get_test_file (ui_file, ".ref.ui", TRUE);
if (reference_file)
reference_image = reftest_snapshot_ui_file (reference_file);
else
{
reference_image = cairo_image_surface_create (CAIRO_FORMAT_ARGB32, 1, 1);
g_test_message ("No reference image.");
g_test_fail ();
}
g_free (reference_file);
diff_image = reftest_compare_surfaces (ui_image, reference_image);
save_image (ui_image, ui_file, ".out.png");
save_image (reference_image, ui_file, ".ref.png");
if (diff_image)
{
save_image (diff_image, ui_file, ".diff.png");
if (known_fail(ui_file))
{
printf("KNOWN FAILURE - %s\n", ui_file);
g_test_message ("KNOWN FAIL: %s", ui_file);
}
else
g_test_fail ();
}
remove_extra_css (provider);
}
void display(void) {
glLoadIdentity();
glClearColor(0.0f,0.0f,0.0f,1.0f); //set the background colour
// OK, now clear the screen with the background colour
glClear(GL_COLOR_BUFFER_BIT | GL_DEPTH_BUFFER_BIT | GL_STENCIL_BUFFER_BIT);
/* Place the camera in various positions in the secene */
if(curTime >= 0 && curTime <= 3){
gluLookAt(-12,5,30,0,5,0,0,1,0);
}
else if(curTime > 3 && curTime <= 6){
gluLookAt(-12,5, 10, 0, 7, 0, 0, 1, 0);
}
else if(curTime > 6 && curTime <= 10.75){
gluLookAt(0, 5, 30, 0, 5, 0, 0, 1, 0);
}
else if(curTime > 10.75 && curTime <= 11.5){
gluLookAt(-12, 7, -8, 0, 4, -2, 0, 1, 0);
}
else if(curTime > 11.5 && curTime <= 12){
gluLookAt(11, 25, -30, -2,5, 0, 0, 1, 0);
}
else {
gluLookAt(-12,5, 10, 0, 7, 0, 0, 1, 0);
}
drawWalls();
drawColumnSet();
drawStairs();
drawCube();
drawSphere();
drawPlatform();
drawPyramid();
glPushMatrix();
drawMan();
glPopMatrix();
drawHammer();
glFlush();
glutSwapBuffers();
if (recording) {
int frame = (int)(curTime / tStep + 0.5);
save_image(frame);
}
}
static void
create_image_blend_functor(
const surface& dst
, const std::string root
, const Uint8 amount
, const Uint32 color)
{
const std::string filename = blend_get_filename(root, amount, color);
save_image(dst, filename);
}
void image_bmp::geometry_spin()
{
int i,j,new_Height,new_Width;
double rad,old_x,old_y,angle,x[4],y[4],min_x,max_x,min_y,max_y;
angle=30;
rad=angle/180*pi;
x[0]=0;y[0]=0;
x[1]=0-Width*sin(rad);y[1]=0+Width*cos(rad);
x[2]=Height*cos(rad)-0;y[2]=Height*sin(rad)+0;
x[3]=Height*cos(rad)-Width*sin(rad);y[3]=Height*sin(rad)+Width*cos(rad);
min_x=x[0];max_x=x[0];min_y=y[0];max_y=y[0];
for(i=0;i<3;i++)
{
min_x=x[i+1]<min_x?x[i+1]:min_x;
max_x=x[i+1]>max_x?x[i+1]:max_x;
min_y=y[i+1]<min_y?y[i+1]:min_y;
max_y=y[i+1]>max_y?y[i+1]:max_y;
}
new_Height=ceil(max_x-min_x);
new_Width=ceil(max_y-min_y);
datatemp=new imagedata *[new_Height];
for(i=0;i<new_Height;i++)
datatemp[i]=new imagedata [new_Width];
for(i=0;i<new_Height;i++)
{
for(j=0;j<new_Width;j++)
{
old_x=(i+min_x)*cos(-rad)-(j+min_y)*sin(-rad);
old_y=(i+min_x)*sin(-rad)+(j+min_y)*cos(-rad);
Biliner(i,j,old_x,old_y,Height,Width,data,datatemp);
}
}
infoHeader_temp.biHeight=new_Height;
infoHeader_temp.biWidth=new_Width;
path="±£´æµÄͼƬ\\geometry_spin.bmp";
save_image();
for(i=0;i<new_Height;i++)
delete[] datatemp[i];
delete[] datatemp;
}
void factor_vm::primitive_save_image()
{
/* do a full GC to push everything into tenured space */
primitive_compact_gc();
data_root<byte_array> path2(ctx->pop(),this);
path2.untag_check(this);
data_root<byte_array> path1(ctx->pop(),this);
path1.untag_check(this);
save_image((vm_char *)(path1.untagged() + 1 ),(vm_char *)(path2.untagged() + 1));
}
Error ResourceSaverPNG::save(const String &p_path,const RES& p_resource,uint32_t p_flags) {
Ref<ImageTexture> texture=p_resource;
ERR_FAIL_COND_V(!texture.is_valid(),ERR_INVALID_PARAMETER);
ERR_EXPLAIN("Can't save empty texture as PNG");
ERR_FAIL_COND_V(!texture->get_width() || !texture->get_height(),ERR_INVALID_PARAMETER);
Image img = texture->get_data();
Error err = save_image(p_path, img);
if (err == OK) {
bool global_filter = Globals::get_singleton()->get("image_loader/filter");
bool global_mipmaps = Globals::get_singleton()->get("image_loader/gen_mipmaps");
bool global_repeat = Globals::get_singleton()->get("image_loader/repeat");
String text;
if (global_filter!=bool(texture->get_flags()&Texture::FLAG_FILTER)) {
text+=bool(texture->get_flags()&Texture::FLAG_FILTER)?"filter=true\n":"filter=false\n";
}
if (global_mipmaps!=bool(texture->get_flags()&Texture::FLAG_MIPMAPS)) {
text+=bool(texture->get_flags()&Texture::FLAG_MIPMAPS)?"gen_mipmaps=true\n":"gen_mipmaps=false\n";
}
if (global_repeat!=bool(texture->get_flags()&Texture::FLAG_REPEAT)) {
text+=bool(texture->get_flags()&Texture::FLAG_REPEAT)?"repeat=true\n":"repeat=false\n";
}
if (bool(texture->get_flags()&Texture::FLAG_ANISOTROPIC_FILTER)) {
text+="anisotropic=true\n";
}
if (bool(texture->get_flags()&Texture::FLAG_CONVERT_TO_LINEAR)) {
text+="tolinear=true\n";
}
if (bool(texture->get_flags()&Texture::FLAG_MIRRORED_REPEAT)) {
text+="mirroredrepeat=true\n";
}
if (text!="" || FileAccess::exists(p_path+".flags")) {
FileAccess* f = FileAccess::open(p_path+".flags",FileAccess::WRITE);
if (f) {
f->store_string(text);
memdelete(f);
}
}
}
return err;
};
/**
* \callgraph
*/
int main () {
//make_platon_scene (LARGEUR, HAUTEUR);
make_default_scene(LARGEUR, HAUTEUR);
compute_image();
save_image ("monimage.exr");
return 0;
}
int test_image_process_histogram_equalization()
{
// Blog: http://blog.csdn.net/fengbingchun/article/details/79188021
#ifdef __linux__
const std::string image_name{ "test_data/images/lena.png" };
#else
const std::string image_name{ "E:/GitCode/CUDA_Test/test_data/images/lena.png" };
#endif
cv::Mat mat = cv::imread(image_name, 0);
CHECK(mat.data);
const int width{ mat.cols/*1513*/ }, height{ mat.rows/*1473*/ };
cv::resize(mat, mat, cv::Size(width, height));
std::unique_ptr<unsigned char[]> data1(new unsigned char[width * height]), data2(new unsigned char[width * height]);
float elapsed_time1{ 0.f }, elapsed_time2{ 0.f }; // milliseconds
CHECK(histogram_equalization_cpu(mat.data, width, height, data1.get(), &elapsed_time1) == 0);
//CHECK(histogram_equalization_gpu(mat.data, width, height, data2.get(), &elapsed_time2) == 0);
//fprintf(stdout, "image histogram equalization: cpu run time: %f ms, gpu run time: %f ms\n", elapsed_time1, elapsed_time2);
cv::Mat dst;
cv::equalizeHist(mat, dst);
#ifdef __linux__
cv::imwrite("test_data/images/histogram_equalization.png", dst);
#else
cv::imwrite("E:/GitCode/CUDA_Test/test_data/images/histogram_equalization.png", dst);
#endif
CHECK(compare_result(data1.get(), dst.data, width*height) == 0);
//CHECK(compare_result(data1.get(), data2.get(), width*height) == 0);
#ifdef __linux__
save_image(mat, dst, width, height/2, "test_data/images/histogram_equalization_result.png");
#else
save_image(mat, dst, width, height/2, "E:/GitCode/CUDA_Test/test_data/images/histogram_equalization_result.png");
#endif
return 0;
}