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Merge pull request #20476 from TolyaTalamanov:at/support-unet-camvid-0001-segm-sample

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[G-API] Support postprocessing for not argmaxed outputs

* Support postprocessing for not argmaxed outputs

* Fix typo

* Add assert

* Remove static cast

* CamelCast to snake_case

* Fix windows warning

* Add static_cast to uint8_t

* Add const to variables
This commit is contained in:
Anatoliy Talamanov 2021-08-06 13:26:49 +03:00 committed by GitHub
parent 0c2741f7ad
commit 24de676a64
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1 changed files with 63 additions and 20 deletions
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83
modules/gapi/samples/semantic_segmentation.cpp
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@ -47,6 +47,53 @@ std::string get_weights_path(const std::string &model_path) {
CV_Assert(ext == ".xml");
return model_path.substr(0u, sz - EXT_LEN) + ".bin";
}
void classesToColors(const cv::Mat &out_blob,
cv::Mat &mask_img) {
const int H = out_blob.size[0];
const int W = out_blob.size[1];
mask_img.create(H, W, CV_8UC3);
GAPI_Assert(out_blob.type() == CV_8UC1);
const uint8_t* const classes = out_blob.ptr<uint8_t>();
for (int rowId = 0; rowId < H; ++rowId) {
for (int colId = 0; colId < W; ++colId) {
uint8_t class_id = classes[rowId * W + colId];
mask_img.at<cv::Vec3b>(rowId, colId) =
class_id < colors.size()
? colors[class_id]
: cv::Vec3b{0, 0, 0}; // NB: sample supports 20 classes
}
}
}
void probsToClasses(const cv::Mat& probs, cv::Mat& classes) {
const int C = probs.size[1];
const int H = probs.size[2];
const int W = probs.size[3];
classes.create(H, W, CV_8UC1);
GAPI_Assert(probs.depth() == CV_32F);
float* out_p = reinterpret_cast<float*>(probs.data);
uint8_t* classes_p = reinterpret_cast<uint8_t*>(classes.data);
for (int h = 0; h < H; ++h) {
for (int w = 0; w < W; ++w) {
double max = 0;
int class_id = 0;
for (int c = 0; c < C; ++c) {
int idx = c * H * W + h * W + w;
if (out_p[idx] > max) {
max = out_p[idx];
class_id = c;
}
}
classes_p[h * W + w] = static_cast<uint8_t>(class_id);
}
}
}
} // anonymous namespace
namespace custom {
@ -57,25 +104,21 @@ G_API_OP(PostProcessing, <cv::GMat(cv::GMat, cv::GMat)>, "sample.custom.post_pro
};
GAPI_OCV_KERNEL(OCVPostProcessing, PostProcessing) {
static void run(const cv::Mat &in, const cv::Mat &detected_classes, cv::Mat &out) {
// This kernel constructs output image by class table and colors vector
// The semantic-segmentation-adas-0001 output a blob with the shape
// [B, C=1, H=1024, W=2048]
const int outHeight = 1024;
const int outWidth = 2048;
cv::Mat maskImg(outHeight, outWidth, CV_8UC3);
const int* const classes = detected_classes.ptr<int>();
for (int rowId = 0; rowId < outHeight; ++rowId) {
for (int colId = 0; colId < outWidth; ++colId) {
size_t classId = static_cast<size_t>(classes[rowId * outWidth + colId]);
maskImg.at<cv::Vec3b>(rowId, colId) =
classId < colors.size()
? colors[classId]
: cv::Vec3b{0, 0, 0}; // sample detects 20 classes
}
static void run(const cv::Mat &in, const cv::Mat &out_blob, cv::Mat &out) {
cv::Mat classes;
// NB: If output has more than single plane, it contains probabilities
// otherwise class id.
if (out_blob.size[1] > 1) {
probsToClasses(out_blob, classes);
} else {
out_blob.convertTo(classes, CV_8UC1);
classes = classes.reshape(1, out_blob.size[2]);
}
cv::resize(maskImg, out, in.size());
cv::Mat mask_img;
classesToColors(classes, mask_img);
cv::resize(mask_img, out, in.size());
const float blending = 0.3f;
out = in * blending + out * (1 - blending);
}
@ -104,8 +147,8 @@ int main(int argc, char *argv[]) {
// Now build the graph
cv::GMat in;
cv::GMat detected_classes = cv::gapi::infer<SemSegmNet>(in);
cv::GMat out = custom::PostProcessing::on(in, detected_classes);
cv::GMat out_blob = cv::gapi::infer<SemSegmNet>(in);
cv::GMat out = custom::PostProcessing::on(in, out_blob);
cv::GStreamingCompiled pipeline = cv::GComputation(cv::GIn(in), cv::GOut(out))
.compileStreaming(cv::compile_args(kernels, networks));