/*! @brief visualize the candidate part locations overlaid on an image
*
* @param im the image
* @param candidates a vector of type Candidate, representing potential
* part locations
* @param N the number of candidates to render. If the candidates have been sorted,
* this is equivalent to displaying only the 'N best' candidates
* @param display_confidence display the detection confidence above each bounding box
* for each part
*/
void Visualize::candidates(const Mat& im, const vectorCandidate& candidates,
unsigned int N, Mat& canvas,
bool display_confidence) const {
// create a new canvas that we can modify
cvtColor(im, canvas, CV_RGB2BGR);
if (candidates.size() == 0) return;
// generate a set of colors to display. Do this in HSV then convert it
const unsigned int ncolors = candidates[0].parts().size();
vector<Scalar> colors;
for (unsigned int n = 0; n < ncolors; ++n) {
Mat color(Size(1,3), CV_32FC3);
// Hue is in degrees, not radians (because consistency is over-rated)
color.at<float>(0) = (360) / ncolors * n;
color.at<float>(1) = 1.0;
color.at<float>(2) = 0.7;
//cvtColor(color, color, CV_HSV2BGR);
color = color * 255;
colors.push_back(Scalar(color.at<float>(0),
color.at<float>(1),
color.at<float>(2)));
}
if(candidates.size() < N) {
N = candidates.size();
}
// draw each candidate to the canvas
const int LINE_THICKNESS = 4;
Scalar black(0,0,0);
for (unsigned int n = 0; n < N; ++n) {
Candidate candidate = candidates[n];
for (unsigned int p = 0; p < candidate.parts().size(); ++p) {
Rect box = candidate.parts()[p];
std::cout << candidate.confidence()[p] << std::endl;
if(candidate.confidence()[p] >= 0.0) {
return;
}
string confidence = boost::lexical_cast<string>(candidate.confidence()[p]);
rectangle(canvas, box, colors[p], LINE_THICKNESS);
if (display_confidence && p == 0) {
putText(canvas, confidence, Point(box.x, box.y-5),
FONT_HERSHEY_SIMPLEX, 0.5f, black, 2);
}
}
//rectangle(canvas, candidate.boundingBox(), Scalar(255, 0, 0), LINE_THICKNESS);
}
}
