int main(int argc, char * argv[])
{
if (argc != 3)
{
printf("SubdivisionSurfaces <mesh_name> <image_name>\n");
return 0;
}
else
{
Visualization(argv[1], argv[2]);
}
//Mesh * mesh = ConstructMeshFromPlyFile(argv[1]);
return 0;
}
Texture::Visualization::Visualization(const Any& a) {
*this = Visualization();
if (a.type() == Any::ARRAY) {
if (a.nameEquals("bumpInAlpha")) {
*this = bumpInAlpha();
} else if (a.nameEquals("defaults")) {
*this = defaults();
} else if (a.nameEquals("linearRGB")) {
*this = linearRGB();
} else if (a.nameEquals("depthBuffer")) {
*this = depthBuffer();
} else if (a.nameEquals("packedUnitVector")) {
*this = packedUnitVector();
} else if (a.nameEquals("radiance")) {
*this = radiance();
} else if (a.nameEquals("reflectivity")) {
*this = reflectivity();
} else if (a.nameEquals("sRGB")) {
*this = sRGB();
} else if (a.nameEquals("unitVector")) {
*this = unitVector();
} else {
a.verify(false, "Unrecognized Visualization factory method");
}
} else {
a.verifyName("Texture::Visualization", "Visualization");
AnyTableReader r(a);
String c;
if (r.getIfPresent("channels", c)) {
channels = toChannels(c);
}
r.getIfPresent("documentGamma", documentGamma);
r.getIfPresent("invertIntensity", invertIntensity);
r.getIfPresent("max", max);
r.getIfPresent("min", min);
r.getIfPresent("layer", layer);
r.getIfPresent("mipLevel", mipLevel);
r.verifyDone();
}
}
void ENetSegmenter::Predict(const cv::Mat& in_image_mat, cv::Mat& out_segmented)
{
caffe::Blob<float>* input_layer = net_->input_blobs()[0];
input_layer->Reshape(1, num_channels_, input_geometry_.height,
input_geometry_.width);
/* Forward dimension change to all layers. */
net_->Reshape();
std::vector<cv::Mat> input_channels;
WrapInputLayer(&input_channels);
Preprocess(in_image_mat, &input_channels);
net_->Forward();
/* Copy the output layer to a std::vector */
caffe::Blob<float>* output_layer = net_->output_blobs()[0];
int width = output_layer->width();
int height = output_layer->height();
int channels = output_layer->channels();
// compute argmax
cv::Mat class_each_row(channels, width * height, CV_32FC1,
const_cast<float *>(output_layer->cpu_data()));
class_each_row = class_each_row.t(); // transpose to make each row with all probabilities
cv::Point maxId; // point [x,y] values for index of max
double maxValue; // the holy max value itself
cv::Mat prediction_map(height, width, CV_8UC1);
for (int i = 0; i < class_each_row.rows; i++)
{
minMaxLoc(class_each_row.row(i), 0, &maxValue, 0, &maxId);
prediction_map.at<uchar>(i) = maxId.x;
}
out_segmented = Visualization(prediction_map, lookuptable_file_);
cv::resize(out_segmented, out_segmented, cv::Size(in_image_mat.cols, in_image_mat.rows));
}
