segmentation

.travis.yml

@@ -42,10 +42,8 @@ before_install:
 
   # origin weights are from https://github.com/NifTK/NiftyNetModelZoo/tree/5-reorganising-with-lfs/unet_histology
   - megadl 'https://mega.nz/#!RAFU1I4T!xjuol1lZ9t_hA67AxgAxp8w1JIb5FXm53617UXR0NO8' --path data --no-progress  # frozen_unet_histology.pb
-  # TODO: Implement frozen_unet_histology.pb model conversion with OpenVINO
   # (note that TensorFlow accepts input tensor of shape 1,952,1144,1,1,3)
-  - megadl 'https://mega.nz/#!AR1HwIIA!U3p46tM9SFMhKzbSwJIEiugdJDQ5mUSOC3twZL6UYTM' --path data --no-progress  # celeba_generator.xml
-  - megadl 'https://mega.nz/#!hAslCCaL!2vWXjiPq3EbuLZbOvEDOI0y9VorGn5enUm7UMNWGrWo' --path data --no-progress  # celeba_generator.bin
+  - /opt/intel/openvino/deployment_tools/model_optimizer/mo.py --input_model data/frozen_unet_histology.pb --input_shape [1,952,1144,1,1,3] --output_dir data
 
 script:
   - source /opt/intel/openvino/bin/setupvars.sh -pyver 3.5

modules/6_segmentation/include/segmentation.hpp

@@ -23,6 +23,10 @@ class ADAS : public Segmenter {
     ADAS();
 
     virtual void segment(const cv::Mat& image, cv::Mat& mask);
+
+private:
+    InferenceEngine::InferRequest req;
+    std::string outputName;
 };
 
 // Glands segmentation in colon histology images
@@ -53,4 +57,5 @@ class UNetHistology : public Segmenter {
 
 private:
     InferenceEngine::InferRequest req;
+    std::string outputName;
 };

modules/6_segmentation/src/segmentation.cpp

@@ -7,7 +7,7 @@ using namespace cv;
 using namespace cv::utils::fs;
 
 float Dice(const Mat& a, const Mat& b) {
-    CV_Error(Error::StsNotImplemented, "Dice score computation");
+    return  (float)countNonZero(a&b) * 2.0f / (float)(countNonZero(a) + countNonZero(b));
 }
 
 ADAS::ADAS() {
@@ -16,8 +16,42 @@ ADAS::ADAS() {
     // Load deep learning network into memory
     auto net = ie.ReadNetwork(join(DATA_FOLDER, "semantic-segmentation-adas-0001.xml"),
                               join(DATA_FOLDER, "semantic-segmentation-adas-0001.bin"));
+
+    InputInfo::Ptr inputInfo = net.getInputsInfo()["data"];
+    inputInfo->getPreProcess().setResizeAlgorithm(ResizeAlgorithm::RESIZE_BILINEAR);
+    inputInfo->setLayout(Layout::NHWC);
+    inputInfo->setPrecision(Precision::U8);
+    outputName = net.getOutputsInfo().begin()->first;
+
+    // Initialize runnable object on CPU device
+    ExecutableNetwork execNet = ie.LoadNetwork(net, "CPU");
+
+    // Create a single processing thread
+    req = execNet.CreateInferRequest();
 }
+Blob::Ptr wrapMatToBlob2(const Mat& m) {
+    CV_Assert(m.depth() == CV_8U);
+    std::vector<size_t> dims = { 1, (size_t)m.channels(), (size_t)m.rows, (size_t)m.cols };
+    return make_shared_blob<uint8_t>(TensorDesc(Precision::U8, dims, Layout::NHWC),
+        m.data);
+}
+
+
 
 void ADAS::segment(const Mat& image, Mat& mask) {
-    CV_Error(Error::StsNotImplemented, "ADAS semantic segmentation");
+    Blob::Ptr input = wrapMatToBlob2(image);
+
+    req.SetBlob("data", input);
+
+    req.Infer();
+    int* output = req.GetBlob(outputName)->buffer();
+    int Height = 1024;
+    int Width = 2048;
+    Mat res = Mat::zeros(Height, Width, CV_8UC1);
+    for (int i = 0; i < Height; i++) {
+        for (int j = 0; j < Width; j++) {
+            res.at<unsigned char>(i, j) = output[i * Width + j];
+        }
+    }
+    resize(res, mask, image.size());
 }

modules/6_segmentation/src/unet_histology.cpp

@@ -25,15 +25,21 @@ UNetHistology::UNetHistology() {
 
     // Create a single processing thread
     req = execNet.CreateInferRequest();
+    outputName = net.getOutputsInfo().begin()->first;
 }
 
 void UNetHistology::bgr2rgb(const Mat& src, Mat& dst) {
-    CV_Error(Error::StsNotImplemented, "bgr2rgb");
+    cvtColor(src, dst, COLOR_BGR2RGB);
 }
 
 
 void UNetHistology::normalize(const Mat& src, Mat& dst) {
-    CV_Error(Error::StsNotImplemented, "normalize");
+    Scalar mean, std;
+    meanStdDev(src, mean, std);
+    dst = Mat::zeros(src.size(), CV_32FC3);
+    dst += src;
+    dst -= mean;
+    dst /= std;
 }
 
 void UNetHistology::segment(const Mat& image, Mat& mask) {
@@ -64,12 +70,38 @@ void UNetHistology::segment(const Mat& image, Mat& mask) {
     Blob::Ptr inputBlob = wrapMatToBlob(inp);
 
     // TODO: Put inputBlob to the network, perform inference and return mask
-
-    CV_Error(Error::StsNotImplemented, "UNetHistology semantic segmentation");
+    req.SetBlob("worker_0/validation/IteratorGetNext", inputBlob);
+
+    // Launch network
+    req.Infer();
+
+    // Copy output. "prob" is a name of output from .xml file
+    int* output = req.GetBlob(outputName)->buffer();
+    int Height = 772;
+    int Width = 964;
+    Mat res = Mat::zeros(Height, Width, CV_8UC1);
+    for (int i = 0; i < Height; i++) {
+        for (int j = 0; j < Width; j++) {
+            res.at<unsigned char>(i, j) = output[i * Width + j];
+        }
+    }
+    resize(res, mask, image.size());
+
 }
 
 int UNetHistology::countGlands(const cv::Mat& segm) {
-    CV_Error(Error::StsNotImplemented, "countGlands");
+    Mat thresh;
+    threshold(segm, thresh, 0, 255, THRESH_BINARY_INV + THRESH_OTSU);
+    morphologyEx(thresh, thresh, MORPH_CLOSE, Mat::ones(3, 3, CV_8U), Point(-1, -1), 3);
+    dilate(thresh, thresh, 3);
+    distanceTransform(thresh, thresh, DIST_L2, 5);
+    double maxValue;
+    minMaxLoc(thresh, 0 , &maxValue);
+    threshold(thresh, thresh, 0.45*maxValue, 255, 0);
+    thresh.convertTo(thresh, CV_8U);
+    std::vector<std::vector<Point> > contours;
+    findContours(thresh, contours, RETR_EXTERNAL, CHAIN_APPROX_SIMPLE);
+    return contours.size();
 }
 
 void UNetHistology::padMinimum(const Mat& src, int width, int height, Mat& dst) {