Added python scripts to estimating accuracy

modules/dnn/CMakeLists.txt

@@ -9,7 +9,7 @@ endif()
 
 set(the_description "Deep neural network module. It allows to load models from different frameworks and to make forward pass")
 
-ocv_add_module(dnn opencv_core opencv_imgproc)
+ocv_add_module(dnn opencv_core opencv_imgproc WRAP python matlab)
 ocv_warnings_disable(CMAKE_CXX_FLAGS -Wno-shadow -Wno-parentheses -Wmaybe-uninitialized -Wsign-promo
                                      -Wmissing-declarations -Wmissing-prototypes
 )

modules/dnn/include/opencv2/dnn/dnn.hpp

@@ -304,6 +304,16 @@ namespace dnn //! This namespace is used for dnn module functionlaity.
       */
     CV_EXPORTS_W Net readNetFromCaffe(const String &prototxt, const String &caffeModel = String());
 
+    /** @brief Reads a network model stored in Tensorflow model file.
+      * @details This is shortcut consisting from createTensorflowImporter and Net::populateNet calls.
+      */
+    CV_EXPORTS_W Net readNetFromTensorflow(const String &model);
+
+    /** @brief Reads a network model stored in Torch model file.
+      * @details This is shortcut consisting from createTorchImporter and Net::populateNet calls.
+      */
+    CV_EXPORTS_W Net readNetFromTorch(const String &model, bool isBinary = true);
+
     /** @brief Creates the importer of <a href="http://www.tensorflow.org">TensorFlow</a> framework network.
      *  @param model   path to the .pb file with binary protobuf description of the network architecture.
      *  @returns Pointer to the created importer, NULL in failure cases.

modules/dnn/misc/python/pyopencv_dnn.hpp

@@ -26,4 +26,10 @@ bool pyopencv_to(PyObject *o, dnn::DictValue &dv, const char *name)
         return false;
 }
 
+template<>
+bool pyopencv_to(PyObject *o, std::vector<Mat> &blobs, const char *name) //required for Layer::blobs RW
+{
+  return pyopencvVecConverter<Mat>::to(o, blobs, ArgInfo(name, false));
+}
+
 #endif

modules/dnn/samples/enet-classes.txt

@@ -0,0 +1,20 @@
+Unlabeled    0   0   0
+Road         128  64 128
+Sidewalk     244  35 232
+Building     70  70  70
+Wall         102 102 156
+Fence        190 153 153
+Pole         153 153 153
+TrafficLight 250 170  30
+TrafficSign  220 220   0
+Vegetation   107 142  35
+Terrain      152 251 152
+Sky          70 130 180
+Person       220  20  60
+Rider        255   0   0
+Car          0   0 142
+Truck        0   0  70
+Bus          0  60 100
+Train        0  80 100
+Motorcycle   0   0 230
+Bicycle      119  11  32

modules/dnn/samples/tf_inception.cpp

@@ -99,7 +99,7 @@ int main(int argc, char **argv)
 
     Mat inputBlob = blobFromImage(img);   //Convert Mat to image batch
     //! [Prepare blob]
-
+    inputBlob -= 117.0;
     //! [Set input blob]
     net.setBlob(inBlobName, inputBlob);        //set the network input
     //! [Set input blob]

modules/dnn/samples/torch_enet.cpp

@@ -26,9 +26,9 @@ const String keys =
         "{o_blob    || output blob's name. If empty, last blob's name in net is used}"
         ;
 
-std::vector<String> readClassNames(const char *filename);
 static void colorizeSegmentation(const Mat &score, Mat &segm,
-                                 Mat &legend, vector<String> &classNames);
+                                 Mat &legend, vector<String> &classNames, vector<Vec3b> &colors);
+static vector<Vec3b> readColors(const String &filename, vector<String>& classNames);
 
 int main(int argc, char **argv)
 {
@@ -52,43 +52,21 @@ int main(int argc, char **argv)
     String classNamesFile = parser.get<String>("c_names");
     String resultFile = parser.get<String>("result");
 
-    //! [Create the importer of TensorFlow model]
-    Ptr<dnn::Importer> importer;
-    try                                     //Try to import TensorFlow AlexNet model
-    {
-        importer = dnn::createTorchImporter(modelFile);
-    }
-    catch (const cv::Exception &err)        //Importer can throw errors, we will catch them
-    {
-        std::cerr << err.msg << std::endl;
-    }
-    //! [Create the importer of Caffe model]
-
-    if (!importer)
-    {
-        std::cerr << "Can't load network by using the mode file: " << std::endl;
-        std::cerr << modelFile << std::endl;
-        exit(-1);
-    }
-
-    //! [Initialize network]
-    dnn::Net net;
-    importer->populateNet(net);
-    importer.release();                     //We don't need importer anymore
-    //! [Initialize network]
+    //! [Read model and initialize network]
+    dnn::Net net = dnn::readNetFromTorch(modelFile);
 
     //! [Prepare blob]
-    Mat img = imread(imageFile, 1);
-
+    Mat img = imread(imageFile), input;
     if (img.empty())
     {
         std::cerr << "Can't read image from the file: " << imageFile << std::endl;
         exit(-1);
     }
 
-    Size inputImgSize(512, 512);
+    Size origSize = img.size();
+    Size inputImgSize = cv::Size(1024, 512);
 
-    if (inputImgSize != img.size())
+    if (inputImgSize != origSize)
         resize(img, img, inputImgSize);       //Resize image to input size
 
     Mat inputBlob = blobFromImage(img, 1./255, true);   //Convert Mat to image batch
@@ -130,20 +108,18 @@ int main(int argc, char **argv)
 
     if (parser.has("show"))
     {
-        size_t nclasses = result.size[1];
         std::vector<String> classNames;
+        vector<cv::Vec3b> colors;
         if(!classNamesFile.empty()) {
-            classNames = readClassNames(classNamesFile.c_str());
-            if (classNames.size() > nclasses)
-                classNames = std::vector<String>(classNames.begin() + classNames.size() - nclasses,
-                                                 classNames.end());
+            colors = readColors(classNamesFile, classNames);
         }
         Mat segm, legend;
-        colorizeSegmentation(result, segm, legend, classNames);
+        colorizeSegmentation(result, segm, legend, classNames, colors);
 
         Mat show;
-        addWeighted(img, 0.2, segm, 0.8, 0.0, show);
+        addWeighted(img, 0.1, segm, 0.9, 0.0, show);
 
+        cv::resize(show, show, origSize, 0, 0, cv::INTER_NEAREST);
         imshow("Result", show);
         if(classNames.size())
             imshow("Legend", legend);
@@ -153,44 +129,16 @@ int main(int argc, char **argv)
     return 0;
 } //main
 
-
-std::vector<String> readClassNames(const char *filename)
-{
-    std::vector<String> classNames;
-
-    std::ifstream fp(filename);
-    if (!fp.is_open())
-    {
-        std::cerr << "File with classes labels not found: " << filename << std::endl;
-        exit(-1);
-    }
-
-    std::string name;
-    while (!fp.eof())
-    {
-        std::getline(fp, name);
-        if (name.length())
-            classNames.push_back(name);
-    }
-
-    fp.close();
-    return classNames;
-}
-
-static void colorizeSegmentation(const Mat &score, Mat &segm, Mat &legend, vector<String> &classNames)
+static void colorizeSegmentation(const Mat &score, Mat &segm, Mat &legend, vector<String> &classNames, vector<Vec3b> &colors)
 {
     const int rows = score.size[2];
     const int cols = score.size[3];
     const int chns = score.size[1];
 
-    vector<Vec3i> colors;
-    RNG rng(12345678);
-
     cv::Mat maxCl(rows, cols, CV_8UC1);
     cv::Mat maxVal(rows, cols, CV_32FC1);
     for (int ch = 0; ch < chns; ch++)
     {
-        colors.push_back(Vec3i(rng.uniform(0, 256), rng.uniform(0, 256), rng.uniform(0, 256)));
         for (int row = 0; row < rows; row++)
         {
             const float *ptrScore = score.ptr<float>(0, ch, row);
@@ -230,3 +178,38 @@ static void colorizeSegmentation(const Mat &score, Mat &segm, Mat &legend, vecto
         }
     }
 }
+
+static vector<Vec3b> readColors(const String &filename, vector<String>& classNames)
+{
+    vector<cv::Vec3b> colors;
+    classNames.clear();
+
+    ifstream fp(filename.c_str());
+    if (!fp.is_open())
+    {
+        cerr << "File with colors not found: " << filename << endl;
+        exit(-1);
+    }
+
+    string line;
+    while (!fp.eof())
+    {
+        getline(fp, line);
+        if (line.length())
+        {
+            stringstream ss(line);
+
+            string name; ss >> name;
+            int temp;
+            cv::Vec3b color;
+            ss >> temp; color[0] = temp;
+            ss >> temp; color[1] = temp;
+            ss >> temp; color[2] = temp;
+            classNames.push_back(name);
+            colors.push_back(color);
+        }
+    }
+
+    fp.close();
+    return colors;
+}

modules/dnn/src/dnn.cpp

@@ -604,7 +604,10 @@ void Net::setBlob(String outputName, const Mat &blob_)
 
     LayerData &ld = impl->layers[pin.lid];
     ld.outputBlobs.resize( std::max(pin.oid+1, (int)ld.requiredOutputs.size()) );
+    MatSize prevShape = ld.outputBlobs[pin.oid].size;
     ld.outputBlobs[pin.oid] = blob_.clone();
+
+    impl->netWasAllocated = prevShape == blob_.size;
 }
 
 Mat Net::getBlob(String outputName)

modules/dnn/src/tensorflow/tf_importer.cpp

@@ -736,6 +736,23 @@ void TFImporter::populateNet(Net dstNet)
 
 } // namespace
 
+Net cv::dnn::readNetFromTensorflow(const String &model)
+{
+    Ptr<Importer> importer;
+    try
+    {
+        importer = createTensorflowImporter(model);
+    }
+    catch(...)
+    {
+    }
+
+    Net net;
+    if (importer)
+        importer->populateNet(net);
+    return net;
+}
+
 Ptr<Importer> cv::dnn::createTensorflowImporter(const String &model)
 {
     return Ptr<Importer>(new TFImporter(model.c_str()));

modules/dnn/src/torch/torch_importer.cpp

@@ -970,6 +970,24 @@ Mat readTorchBlob(const String &filename, bool isBinary)
 
     return importer->tensors.begin()->second;
 }
+
+Net readNetFromTorch(const String &model, bool isBinary)
+{
+    Ptr<Importer> importer;
+    try
+    {
+        importer = createTorchImporter(model, isBinary);
+    }
+    catch(...)
+    {
+    }
+
+    Net net;
+    if (importer)
+        importer->populateNet(net);
+    return net;
+}
+
 #else
 
 Ptr<Importer> createTorchImporter(const String &filename, bool isBinary)

modules/dnn/test/cityscapes_semsegm_test_enet.py

@@ -0,0 +1,142 @@
+import numpy as np
+import sys
+import os
+import fnmatch
+import argparse
+
+# sys.path.append('<path to opencv_build_dir/lib>')
+sys.path.append('/home/arrybn/build/opencv_w_contrib/lib')
+try:
+    import cv2 as cv
+except ImportError:
+    raise ImportError('Can\'t find opencv. If you\'ve built it from sources without installation, '
+                      'uncomment the line before and insert there path to opencv_build_dir/lib dir')
+try:
+    import torch
+except ImportError:
+    raise ImportError('Can\'t find pytorch. Please intall it by following instructions on the official site')
+
+from torch.utils.serialization import load_lua
+from pascal_semsegm_test_fcn import eval_segm_result, get_conf_mat, get_metrics, DatasetImageFetch, SemSegmEvaluation
+from imagenet_cls_test_alexnet import Framework, DnnCaffeModel
+
+
+class NormalizePreproc:
+    def __init__(self):
+        pass
+
+    @staticmethod
+    def process(img):
+        image_data = np.array(img).transpose(2, 0, 1).astype(np.float32)
+        image_data = np.expand_dims(image_data, 0)
+        image_data /= 255.0
+        return image_data
+
+
+class CityscapesDataFetch(DatasetImageFetch):
+    img_dir = ''
+    segm_dir = ''
+    segm_files = []
+    colors = []
+    i = 0
+
+    def __init__(self, img_dir, segm_dir, preproc):
+        self.img_dir = img_dir
+        self.segm_dir = segm_dir
+        self.segm_files = sorted([img for img in self.locate('*_color.png', segm_dir)])
+        self.colors = self.get_colors()
+        self.data_prepoc = preproc
+        self.i = 0
+
+    @staticmethod
+    def get_colors():
+        result = []
+        colors_list = (
+         (0, 0, 0), (128, 64, 128), (244, 35, 232), (70, 70, 70), (102, 102, 156), (190, 153, 153), (153, 153, 153),
+         (250, 170, 30), (220, 220, 0), (107, 142, 35), (152, 251, 152), (70, 130, 180), (220, 20, 60), (255, 0, 0),
+         (0, 0, 142), (0, 0, 70), (0, 60, 100), (0, 80, 100), (0, 0, 230), (119, 11, 32))
+
+        for c in colors_list:
+            result.append(DatasetImageFetch.pix_to_c(c))
+        return result
+
+    def __iter__(self):
+        return self
+
+    def next(self):
+        if self.i < len(self.segm_files):
+            segm_file = self.segm_files[self.i]
+            segm = cv.imread(segm_file, cv.IMREAD_COLOR)[:, :, ::-1]
+            segm = cv.resize(segm, (1024, 512), interpolation=cv.INTER_NEAREST)
+
+            img_file = self.rreplace(self.img_dir + segm_file[len(self.segm_dir):], 'gtFine_color', 'leftImg8bit')
+            assert os.path.exists(img_file)
+            img = cv.imread(img_file, cv.IMREAD_COLOR)[:, :, ::-1]
+            img = cv.resize(img, (1024, 512))
+
+            self.i += 1
+            gt = self.color_to_gt(segm, self.colors)
+            img = self.data_prepoc.process(img)
+            return img, gt
+        else:
+            self.i = 0
+            raise StopIteration
+
+    def get_num_classes(self):
+        return len(self.colors)
+
+    @staticmethod
+    def locate(pattern, root_path):
+        for path, dirs, files in os.walk(os.path.abspath(root_path)):
+            for filename in fnmatch.filter(files, pattern):
+                yield os.path.join(path, filename)
+
+    @staticmethod
+    def rreplace(s, old, new, occurrence=1):
+        li = s.rsplit(old, occurrence)
+        return new.join(li)
+
+
+class TorchModel(Framework):
+    net = object
+
+    def __init__(self, model_file):
+        self.net = load_lua(model_file)
+
+    def get_name(self):
+        return 'Torch'
+
+    def get_output(self, input_blob):
+        tensor = torch.FloatTensor(input_blob)
+        out = self.net.forward(tensor).numpy()
+        return out
+
+
+class DnnTorchModel(DnnCaffeModel):
+    net = cv.dnn.Net()
+
+    def __init__(self, model_file):
+        self.net = cv.dnn.readNetFromTorch(model_file)
+
+    def get_output(self, input_blob):
+        self.net.setBlob("", input_blob)
+        self.net.forward()
+        return self.net.getBlob(self.net.getLayerNames()[-1])
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--imgs_dir", help="path to Cityscapes validation images dir, imgsfine/leftImg8bit/val")
+    parser.add_argument("--segm_dir", help="path to Cityscapes dir with segmentation, gtfine/gtFine/val")
+    parser.add_argument("--model", help="path to torch model, download it here: "
+                        "https://www.dropbox.com/sh/dywzk3gyb12hpe5/AAD5YkUa8XgMpHs2gCRgmCVCa")
+    parser.add_argument("--log", help="path to logging file")
+    args = parser.parse_args()
+
+    prep = NormalizePreproc()
+    df = CityscapesDataFetch(args.imgs_dir, args.segm_dir, prep)
+
+    fw = [TorchModel(args.model),
+          DnnTorchModel(args.model)]
+
+    segm_eval = SemSegmEvaluation(args.log)
+    segm_eval.process(fw, df)

modules/dnn/test/imagenet_cls_test_alexnet.py

@@ -0,0 +1,246 @@
+from abc import ABCMeta, abstractmethod
+import numpy as np
+import sys
+import os
+import argparse
+import time
+
+# sys.path.append('<path to git/caffe/python dir>')
+sys.path.append('/home/arrybn/git/caffe/python')
+try:
+    import caffe
+except ImportError:
+    raise ImportError('Can\'t find caffe. If you\'ve built it from sources without installation, '
+                      'uncomment the line before and insert there path to git/caffe/python dir')
+# sys.path.append('<path to opencv_build_dir/lib>')
+sys.path.append('/home/arrybn/build/opencv_w_contrib/lib')
+try:
+    import cv2 as cv
+except ImportError:
+    raise ImportError('Can\'t find opencv. If you\'ve built it from sources without installation, '
+                      'uncomment the line before and insert there path to opencv_build_dir/lib dir')
+
+
+class DataFetch(object):
+    imgs_dir = ''
+    frame_size = 0
+    bgr_to_rgb = False
+    __metaclass__ = ABCMeta
+
+    @abstractmethod
+    def preprocess(self, img):
+        pass
+
+    def get_batch(self, imgs_names):
+        assert type(imgs_names) is list
+        batch = np.zeros((len(imgs_names), 3, self.frame_size, self.frame_size)).astype(np.float32)
+        for i in range(len(imgs_names)):
+            img_name = imgs_names[i]
+            img_file = self.imgs_dir + img_name
+            assert os.path.exists(img_file)
+            img = cv.imread(img_file, cv.IMREAD_COLOR)
+            min_dim = min(img.shape[-3], img.shape[-2])
+            resize_ratio = self.frame_size / float(min_dim)
+            img = cv.resize(img, (0, 0), fx=resize_ratio, fy=resize_ratio)
+            cols = img.shape[1]
+            rows = img.shape[0]
+            y1 = (rows - self.frame_size) / 2
+            y2 = y1 + self.frame_size
+            x1 = (cols - self.frame_size) / 2
+            x2 = x1 + self.frame_size
+            img = img[y1:y2, x1:x2]
+            if self.bgr_to_rgb:
+                img = img[..., ::-1]
+            image_data = img[:, :, 0:3].transpose(2, 0, 1)
+            batch[i] = self.preprocess(image_data)
+        return batch
+
+
+class MeanBlobFetch(DataFetch):
+    mean_blob = np.ndarray(())
+
+    def __init__(self, frame_size, mean_blob_path, imgs_dir):
+        self.imgs_dir = imgs_dir
+        self.frame_size = frame_size
+        blob = caffe.proto.caffe_pb2.BlobProto()
+        data = open(mean_blob_path, 'rb').read()
+        blob.ParseFromString(data)
+        self.mean_blob = np.array(caffe.io.blobproto_to_array(blob))
+        start = (self.mean_blob.shape[2] - self.frame_size) / 2
+        stop = start + self.frame_size
+        self.mean_blob = self.mean_blob[:, :, start:stop, start:stop][0]
+
+    def preprocess(self, img):
+        return img - self.mean_blob
+
+
+class MeanChannelsFetch(MeanBlobFetch):
+    def __init__(self, frame_size, imgs_dir):
+        self.imgs_dir = imgs_dir
+        self.frame_size = frame_size
+        self.mean_blob = np.ones((3, self.frame_size, self.frame_size)).astype(np.float32)
+        self.mean_blob[0] *= 104
+        self.mean_blob[1] *= 117
+        self.mean_blob[2] *= 123
+
+
+class MeanValueFetch(MeanBlobFetch):
+    def __init__(self, frame_size, imgs_dir, bgr_to_rgb):
+        self.imgs_dir = imgs_dir
+        self.frame_size = frame_size
+        self.mean_blob = np.ones((3, self.frame_size, self.frame_size)).astype(np.float32)
+        self.mean_blob *= 117
+        self.bgr_to_rgb = bgr_to_rgb
+
+
+def get_correct_answers(img_list, img_classes, net_output_blob):
+    correct_answers = 0
+    for i in range(len(img_list)):
+        indexes = np.argsort(net_output_blob[i])[-5:]
+        correct_index = img_classes[img_list[i]]
+        if correct_index in indexes:
+            correct_answers += 1
+    return correct_answers
+
+
+class Framework(object):
+    in_blob_name = ''
+    out_blob_name = ''
+
+    __metaclass__ = ABCMeta
+
+    @abstractmethod
+    def get_name(self):
+        pass
+
+    @abstractmethod
+    def get_output(self, input_blob):
+        pass
+
+
+class CaffeModel(Framework):
+    net = caffe.Net
+    need_reshape = False
+
+    def __init__(self, prototxt, caffemodel, in_blob_name, out_blob_name, need_reshape=False):
+        caffe.set_mode_cpu()
+        self.net = caffe.Net(prototxt, caffemodel, caffe.TEST)
+        self.in_blob_name = in_blob_name
+        self.out_blob_name = out_blob_name
+        self.need_reshape = need_reshape
+
+    def get_name(self):
+        return 'Caffe'
+
+    def get_output(self, input_blob):
+        if self.need_reshape:
+            self.net.blobs[self.in_blob_name].reshape(*input_blob.shape)
+        return self.net.forward_all(**{self.in_blob_name: input_blob})[self.out_blob_name]
+
+
+class DnnCaffeModel(Framework):
+    net = object
+
+    def __init__(self, prototxt, caffemodel, in_blob_name, out_blob_name):
+        self.net = cv.dnn.readNetFromCaffe(prototxt, caffemodel)
+        self.in_blob_name = in_blob_name
+        self.out_blob_name = out_blob_name
+
+    def get_name(self):
+        return 'DNN'
+
+    def get_output(self, input_blob):
+        self.net.setBlob(self.in_blob_name, input_blob)
+        self.net.forward()
+        return self.net.getBlob(self.out_blob_name)
+
+
+class ClsAccEvaluation:
+    log = file
+    img_classes = {}
+    batch_size = 0
+
+    def __init__(self, log_path, img_classes_file, batch_size):
+        self.log = open(log_path, 'w')
+        self.img_classes = self.read_classes(img_classes_file)
+        self.batch_size = batch_size
+
+    @staticmethod
+    def read_classes(img_classes_file):
+        result = {}
+        with open(img_classes_file) as file:
+            for l in file.readlines():
+                result[l.split()[0]] = int(l.split()[1])
+        return result
+
+    def process(self, frameworks, data_fetcher):
+        sorted_imgs_names = sorted(self.img_classes.keys())
+        correct_answers = [0] * len(frameworks)
+        samples_handled = 0
+        blobs_l1_diff = [0] * len(frameworks)
+        blobs_l1_diff_count = [0] * len(frameworks)
+        blobs_l_inf_diff = [sys.float_info.min] * len(frameworks)
+        inference_time = [0.0] * len(frameworks)
+
+        for x in xrange(0, len(sorted_imgs_names), self.batch_size):
+            sublist = sorted_imgs_names[x:x + self.batch_size]
+            batch = data_fetcher.get_batch(sublist)
+
+            samples_handled += len(sublist)
+
+            frameworks_out = []
+            fw_accuracy = []
+            for i in range(len(frameworks)):
+                start = time.time()
+                out = frameworks[i].get_output(batch)
+                end = time.time()
+                correct_answers[i] += get_correct_answers(sublist, self.img_classes, out)
+                fw_accuracy.append(100 * correct_answers[i] / float(samples_handled))
+                frameworks_out.append(out)
+                inference_time[i] += end - start
+                print >> self.log, samples_handled, 'Accuracy for', frameworks[i].get_name() + ':', fw_accuracy[i]
+                print >> self.log, "Inference time, ms ", \
+                    frameworks[i].get_name(), inference_time[i] / samples_handled * 1000
+
+            for i in range(1, len(frameworks)):
+                log_str = frameworks[0].get_name() + " vs " + frameworks[i].get_name() + ':'
+                diff = np.abs(frameworks_out[0] - frameworks_out[i])
+                l1_diff = np.sum(diff) / diff.size
+                print >> self.log, samples_handled, "L1 difference", log_str, l1_diff
+                blobs_l1_diff[i] += l1_diff
+                blobs_l1_diff_count[i] += 1
+                if np.max(diff) > blobs_l_inf_diff[i]:
+                    blobs_l_inf_diff[i] = np.max(diff)
+                print >> self.log, samples_handled, "L_INF difference", log_str, blobs_l_inf_diff[i]
+
+            self.log.flush()
+
+        for i in range(1, len(blobs_l1_diff)):
+            log_str = frameworks[0].get_name() + " vs " + frameworks[i].get_name() + ':'
+            print >> self.log, 'Final l1 diff', log_str, blobs_l1_diff[i] / blobs_l1_diff_count[i]
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--imgs_dir", help="path to ImageNet validation subset images dir, ILSVRC2012_img_val dir")
+    parser.add_argument("--img_cls_file", help="path to file with classes ids for images, val.txt file from this "
+                                               "archive: http://dl.caffe.berkeleyvision.org/caffe_ilsvrc12.tar.gz")
+    parser.add_argument("--prototxt", help="path to caffe prototxt, download it here: "
+                                        "https://github.com/BVLC/caffe/blob/master/models/bvlc_alexnet/deploy.prototxt")
+    parser.add_argument("--caffemodel", help="path to caffemodel file, download it here: "
+                                             "http://dl.caffe.berkeleyvision.org/bvlc_alexnet.caffemodel")
+    parser.add_argument("--log", help="path to logging file")
+    parser.add_argument("--mean", help="path to ImageNet mean blob caffe file, imagenet_mean.binaryproto file from"
+                                       "this archive: http://dl.caffe.berkeleyvision.org/caffe_ilsvrc12.tar.gz")
+    parser.add_argument("--batch_size", help="size of images in batch", default=1000)
+    parser.add_argument("--frame_size", help="size of input image", default=227)
+    parser.add_argument("--in_blob", help="name for input blob", default='data')
+    parser.add_argument("--out_blob", help="name for output blob", default='prob')
+    args = parser.parse_args()
+
+    data_fetcher = MeanBlobFetch(args.frame_size, args.mean, args.imgs_dir)
+
+    frameworks = [CaffeModel(args.prototxt, args.caffemodel, args.in_blob, args.out_blob),
+                  DnnCaffeModel(args.prototxt, args.caffemodel, '', args.out_blob)]
+
+    acc_eval = ClsAccEvaluation(args.log, args.img_cls_file, args.batch_size)
+    acc_eval.process(frameworks, data_fetcher)

modules/dnn/test/imagenet_cls_test_googlenet.py

@@ -0,0 +1,43 @@
+import numpy as np
+import sys
+import os
+import argparse
+from imagenet_cls_test_alexnet import MeanChannelsFetch, CaffeModel, DnnCaffeModel, ClsAccEvaluation
+# sys.path.append('<path to git/caffe/python dir>')
+sys.path.append('/home/arrybn/git/caffe/python')
+try:
+    import caffe
+except ImportError:
+    raise ImportError('Can\'t find caffe. If you\'ve built it from sources without installation, '
+                      'uncomment the line before and insert there path to git/caffe/python dir')
+# sys.path.append('<path to opencv_build_dir/lib>')
+sys.path.append('/home/arrybn/build/opencv_w_contrib/lib')
+try:
+    import cv2 as cv
+except ImportError:
+    raise ImportError('Can\'t find opencv. If you\'ve built it from sources without installation, '
+                      'uncomment the line before and insert there path to opencv_build_dir/lib dir')
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--imgs_dir", help="path to ImageNet validation subset images dir, ILSVRC2012_img_val dir")
+    parser.add_argument("--img_cls_file", help="path to file with classes ids for images, val.txt file from this "
+                                               "archive: http://dl.caffe.berkeleyvision.org/caffe_ilsvrc12.tar.gz")
+    parser.add_argument("--prototxt", help="path to caffe prototxt, download it here: "
+                                        "https://github.com/BVLC/caffe/blob/master/models/bvlc_alexnet/deploy.prototxt")
+    parser.add_argument("--caffemodel", help="path to caffemodel file, download it here: "
+                                             "http://dl.caffe.berkeleyvision.org/bvlc_alexnet.caffemodel")
+    parser.add_argument("--log", help="path to logging file")
+    parser.add_argument("--batch_size", help="size of images in batch", default=500, type=int)
+    parser.add_argument("--frame_size", help="size of input image", default=224, type=int)
+    parser.add_argument("--in_blob", help="name for input blob", default='data')
+    parser.add_argument("--out_blob", help="name for output blob", default='prob')
+    args = parser.parse_args()
+
+    data_fetcher = MeanChannelsFetch(args.frame_size, args.imgs_dir)
+
+    frameworks = [CaffeModel(args.prototxt, args.caffemodel, args.in_blob, args.out_blob),
+                  DnnCaffeModel(args.prototxt, args.caffemodel, '', args.out_blob)]
+
+    acc_eval = ClsAccEvaluation(args.log, args.img_cls_file, args.batch_size)
+    acc_eval.process(frameworks, data_fetcher)

modules/dnn/test/imagenet_cls_test_inception.py

@@ -0,0 +1,79 @@
+import numpy as np
+import sys
+import os
+import argparse
+import tensorflow as tf
+from tensorflow.python.platform import gfile
+from imagenet_cls_test_alexnet import MeanValueFetch, DnnCaffeModel, Framework, ClsAccEvaluation
+# sys.path.append('<path to opencv_build_dir/lib>')
+sys.path.append('/home/arrybn/build/opencv_w_contrib/lib')
+try:
+    import cv2 as cv
+except ImportError:
+    raise ImportError('Can\'t find opencv. If you\'ve built it from sources without installation, '
+                      'uncomment the line before and insert there path to opencv_build_dir/lib dir')
+
+# If you've got an exception "Cannot load libmkl_avx.so or libmkl_def.so" or similar, try to export next variable
+# before runnigng the script:
+# LD_PRELOAD=/opt/intel/mkl/lib/intel64/libmkl_core.so:/opt/intel/mkl/lib/intel64/libmkl_sequential.so
+
+
+class TensorflowModel(Framework):
+    sess = tf.Session
+    output = tf.Graph
+
+    def __init__(self, model_file, in_blob_name, out_blob_name):
+        self.in_blob_name = in_blob_name
+        self.sess = tf.Session()
+        with gfile.FastGFile(model_file, 'rb') as f:
+            graph_def = tf.GraphDef()
+            graph_def.ParseFromString(f.read())
+            self.sess.graph.as_default()
+            tf.import_graph_def(graph_def, name='')
+        self.output = self.sess.graph.get_tensor_by_name(out_blob_name + ":0")
+
+    def get_name(self):
+        return 'Tensorflow'
+
+    def get_output(self, input_blob):
+        assert len(input_blob.shape) == 4
+        batch_tf = input_blob.transpose(0, 2, 3, 1)
+        out = self.sess.run(self.output,
+                       {self.in_blob_name+':0': batch_tf})
+        out = out[..., 1:1001]
+        return out
+
+
+class DnnTfInceptionModel(DnnCaffeModel):
+    net = cv.dnn.Net()
+
+    def __init__(self, model_file, in_blob_name, out_blob_name):
+        self.net = cv.dnn.readNetFromTensorflow(model_file)
+        self.in_blob_name = in_blob_name
+        self.out_blob_name = out_blob_name
+
+    def get_output(self, input_blob):
+        return super(DnnTfInceptionModel, self).get_output(input_blob)[..., 1:1001]
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--imgs_dir", help="path to ImageNet validation subset images dir, ILSVRC2012_img_val dir")
+    parser.add_argument("--img_cls_file", help="path to file with classes ids for images, download it here:"
+                            "https://github.com/opencv/opencv_extra/tree/master/testdata/dnn/img_classes_inception.txt")
+    parser.add_argument("--model", help="path to tensorflow model, download it here:"
+                                        "https://storage.googleapis.com/download.tensorflow.org/models/inception5h.zip")
+    parser.add_argument("--log", help="path to logging file")
+    parser.add_argument("--batch_size", help="size of images in batch", default=1)
+    parser.add_argument("--frame_size", help="size of input image", default=224)
+    parser.add_argument("--in_blob", help="name for input blob", default='input')
+    parser.add_argument("--out_blob", help="name for output blob", default='softmax2')
+    args = parser.parse_args()
+
+    data_fetcher = MeanValueFetch(args.frame_size, args.imgs_dir, True)
+
+    frameworks = [TensorflowModel(args.model, args.in_blob, args.out_blob),
+                  DnnTfInceptionModel(args.model, '', args.out_blob)]
+
+    acc_eval = ClsAccEvaluation(args.log, args.img_cls_file, args.batch_size)
+    acc_eval.process(frameworks, data_fetcher)

modules/dnn/test/pascal_semsegm_test_fcn.py

@@ -0,0 +1,225 @@
+from abc import ABCMeta, abstractmethod
+import numpy as np
+import sys
+import argparse
+import time
+
+from imagenet_cls_test_alexnet import CaffeModel, DnnCaffeModel
+sys.path.append('/home/arrybn/build/opencv_w_contrib/lib')
+try:
+    import cv2 as cv
+except ImportError:
+    raise ImportError('Can\'t find opencv. If you\'ve built it from sources without installation, '
+                      'uncomment the line before and insert there path to opencv_build_dir/lib dir')
+
+
+def get_metrics(conf_mat):
+    pix_accuracy = np.trace(conf_mat) / np.sum(conf_mat)
+    t = np.sum(conf_mat, 1)
+    num_cl = np.count_nonzero(t)
+    assert num_cl
+    mean_accuracy = np.sum(np.nan_to_num(np.divide(np.diagonal(conf_mat), t))) / num_cl
+    col_sum = np.sum(conf_mat, 0)
+    mean_iou = np.sum(
+        np.nan_to_num(np.divide(np.diagonal(conf_mat), (t + col_sum - np.diagonal(conf_mat))))) / num_cl
+    return pix_accuracy, mean_accuracy, mean_iou
+
+
+def eval_segm_result(net_out):
+    assert type(net_out) is np.ndarray
+    assert len(net_out.shape) == 4
+
+    channels_dim = 1
+    y_dim = channels_dim + 1
+    x_dim = y_dim + 1
+    res = np.zeros(net_out.shape).astype(np.int)
+    for i in range(net_out.shape[y_dim]):
+        for j in range(net_out.shape[x_dim]):
+            max_ch = np.argmax(net_out[..., i, j])
+            res[0, max_ch, i, j] = 1
+    return res
+
+
+def get_conf_mat(gt, prob):
+    assert type(gt) is np.ndarray
+    assert type(prob) is np.ndarray
+
+    conf_mat = np.zeros((gt.shape[0], gt.shape[0]))
+    for ch_gt in range(conf_mat.shape[0]):
+        gt_channel = gt[ch_gt, ...]
+        for ch_pr in range(conf_mat.shape[1]):
+            prob_channel = prob[ch_pr, ...]
+            conf_mat[ch_gt][ch_pr] = np.count_nonzero(np.multiply(gt_channel, prob_channel))
+    return conf_mat
+
+
+class MeanChannelsPreproc:
+    def __init__(self):
+        pass
+
+    @staticmethod
+    def process(img):
+        image_data = np.array(img).transpose(2, 0, 1).astype(np.float32)
+        mean = np.ones(image_data.shape)
+        mean[0] *= 104
+        mean[1] *= 117
+        mean[2] *= 123
+        image_data -= mean
+        image_data = np.expand_dims(image_data, 0)
+        return image_data
+
+
+class DatasetImageFetch(object):
+    __metaclass__ = ABCMeta
+    data_prepoc = object
+
+    @abstractmethod
+    def __iter__(self):
+        pass
+
+    @abstractmethod
+    def next(self):
+        pass
+
+    @staticmethod
+    def pix_to_c(pix):
+        return pix[0] * 256 * 256 + pix[1] * 256 + pix[2]
+
+    @staticmethod
+    def color_to_gt(color_img, colors):
+        num_classes = len(colors)
+        gt = np.zeros((num_classes, color_img.shape[0], color_img.shape[1])).astype(np.int)
+        for img_y in range(color_img.shape[0]):
+            for img_x in range(color_img.shape[1]):
+                c = DatasetImageFetch.pix_to_c(color_img[img_y][img_x])
+                if c in colors:
+                    cls = colors.index(c)
+                    gt[cls][img_y][img_x] = 1
+        return gt
+
+
+class PASCALDataFetch(DatasetImageFetch):
+    img_dir = ''
+    segm_dir = ''
+    names = []
+    colors = []
+    i = 0
+
+    def __init__(self, img_dir, segm_dir, names_file, segm_cls_colors_file, preproc):
+        self.img_dir = img_dir
+        self.segm_dir = segm_dir
+        self.colors = self.read_colors(segm_cls_colors_file)
+        self.data_prepoc = preproc
+        self.i = 0
+
+        with open(names_file) as f:
+            for l in f.readlines():
+                self.names.append(l.rstrip())
+
+    @staticmethod
+    def read_colors(img_classes_file):
+        result = []
+        with open(img_classes_file) as f:
+            for l in f.readlines():
+                color = np.array(map(int, l.split()[1:]))
+                result.append(DatasetImageFetch.pix_to_c(color))
+        return result
+
+    def __iter__(self):
+        return self
+
+    def next(self):
+        if self.i < len(self.names):
+            name = self.names[self.i]
+            self.i += 1
+            segm_file = self.segm_dir + name + ".png"
+            img_file = self.img_dir + name + ".jpg"
+            gt = self.color_to_gt(cv.imread(segm_file, cv.IMREAD_COLOR)[:, :, ::-1], self.colors)
+            img = self.data_prepoc.process(cv.imread(img_file, cv.IMREAD_COLOR)[:, :, ::-1])
+            return img, gt
+        else:
+            self.i = 0
+            raise StopIteration
+
+    def get_num_classes(self):
+        return len(self.colors)
+
+
+class SemSegmEvaluation:
+    log = file
+
+    def __init__(self, log_path,):
+        self.log = open(log_path, 'w')
+
+    def process(self, frameworks, data_fetcher):
+        samples_handled = 0
+
+        conf_mats = [np.zeros((data_fetcher.get_num_classes(), data_fetcher.get_num_classes())) for i in range(len(frameworks))]
+        blobs_l1_diff = [0] * len(frameworks)
+        blobs_l1_diff_count = [0] * len(frameworks)
+        blobs_l_inf_diff = [sys.float_info.min] * len(frameworks)
+        inference_time = [0.0] * len(frameworks)
+
+        for in_blob, gt in data_fetcher:
+            frameworks_out = []
+            samples_handled += 1
+            for i in range(len(frameworks)):
+                start = time.time()
+                out = frameworks[i].get_output(in_blob)
+                end = time.time()
+                segm = eval_segm_result(out)
+                conf_mats[i] += get_conf_mat(gt, segm[0])
+                frameworks_out.append(out)
+                inference_time[i] += end - start
+
+                pix_acc, mean_acc, miou = get_metrics(conf_mats[i])
+
+                name = frameworks[i].get_name()
+                print >> self.log, samples_handled, 'Pixel accuracy, %s:' % name, 100 * pix_acc
+                print >> self.log, samples_handled, 'Mean accuracy, %s:' % name, 100 * mean_acc
+                print >> self.log, samples_handled, 'Mean IOU, %s:' % name, 100 * miou
+                print >> self.log, "Inference time, ms ", \
+                    frameworks[i].get_name(), inference_time[i] / samples_handled * 1000
+
+            for i in range(1, len(frameworks)):
+                log_str = frameworks[0].get_name() + " vs " + frameworks[i].get_name() + ':'
+                diff = np.abs(frameworks_out[0] - frameworks_out[i])
+                l1_diff = np.sum(diff) / diff.size
+                print >> self.log, samples_handled, "L1 difference", log_str, l1_diff
+                blobs_l1_diff[i] += l1_diff
+                blobs_l1_diff_count[i] += 1
+                if np.max(diff) > blobs_l_inf_diff[i]:
+                    blobs_l_inf_diff[i] = np.max(diff)
+                print >> self.log, samples_handled, "L_INF difference", log_str, blobs_l_inf_diff[i]
+
+            self.log.flush()
+
+        for i in range(1, len(blobs_l1_diff)):
+            log_str = frameworks[0].get_name() + " vs " + frameworks[i].get_name() + ':'
+            print >> self.log, 'Final l1 diff', log_str, blobs_l1_diff[i] / blobs_l1_diff_count[i]
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--imgs_dir", help="path to PASCAL VOC 2012 images dir, data/VOC2012/JPEGImages")
+    parser.add_argument("--segm_dir", help="path to PASCAL VOC 2012 segmentation dir, data/VOC2012/SegmentationClass/")
+    parser.add_argument("--val_names", help="path to file with validation set image names, download it here: "
+                        "https://github.com/shelhamer/fcn.berkeleyvision.org/blob/master/data/pascal/seg11valid.txt")
+    parser.add_argument("--cls_file", help="path to file with colors for classes, download it here: "
+                        "https://github.com/opencv/opencv_contrib/blob/master/modules/dnn/samples/pascal-classes.txt")
+    parser.add_argument("--prototxt", help="path to caffe prototxt, download it here: "
+                        "https://github.com/opencv/opencv_contrib/blob/master/modules/dnn/samples/fcn8s-heavy-pascal.prototxt")
+    parser.add_argument("--caffemodel", help="path to caffemodel file, download it here: "
+                                             "http://dl.caffe.berkeleyvision.org/fcn8s-heavy-pascal.caffemodel")
+    parser.add_argument("--log", help="path to logging file")
+    parser.add_argument("--in_blob", help="name for input blob", default='data')
+    parser.add_argument("--out_blob", help="name for output blob", default='score')
+    args = parser.parse_args()
+
+    prep = MeanChannelsPreproc()
+    df = PASCALDataFetch(args.imgs_dir, args.segm_dir, args.val_names, args.cls_file, prep)
+
+    fw = [CaffeModel(args.prototxt, args.caffemodel, args.in_blob, args.out_blob, True),
+          DnnCaffeModel(args.prototxt, args.caffemodel, '', args.out_blob)]
+
+    segm_eval = SemSegmEvaluation(args.log)
+    segm_eval.process(fw, df)