Updated classification example

modules/dnn/samples/bvlc_alexnet.prototxt

@@ -1,281 +0,0 @@
-name: "AlexNet"
-input: "data"
-input_dim: 10
-input_dim: 3
-input_dim: 227
-input_dim: 227
-
-layer {
-  name: "conv1"
-  type: "Convolution"
-  bottom: "data"
-  top: "conv1"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 96
-    kernel_size: 11
-    stride: 4
-  }
-}
-layer {
-  name: "relu1"
-  type: "ReLU"
-  bottom: "conv1"
-  top: "conv1"
-}
-layer {
-  name: "norm1"
-  type: "LRN"
-  bottom: "conv1"
-  top: "norm1"
-  lrn_param {
-    local_size: 5
-    alpha: 0.0001
-    beta: 0.75
-  }
-}
-layer {
-  name: "pool1"
-  type: "Pooling"
-  bottom: "norm1"
-  top: "pool1"
-  pooling_param {
-    pool: MAX
-    kernel_size: 3
-    stride: 2
-  }
-}
-
-layer {
-  name: "conv2"
-  type: "Convolution"
-  bottom: "pool1"
-  top: "conv2"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 256
-    pad: 2
-    kernel_size: 5
-    group: 2
-  }
-}
-layer {
-  name: "relu2"
-  type: "ReLU"
-  bottom: "conv2"
-  top: "conv2"
-}
-layer {
-  name: "norm2"
-  type: "LRN"
-  bottom: "conv2"
-  top: "norm2"
-  lrn_param {
-    local_size: 5
-    alpha: 0.0001
-    beta: 0.75
-  }
-}
-layer {
-  name: "pool2"
-  type: "Pooling"
-  bottom: "norm2"
-  top: "pool2"
-  pooling_param {
-    pool: MAX
-    kernel_size: 3
-    stride: 2
-  }
-}
-
-layer {
-  name: "conv3"
-  type: "Convolution"
-  bottom: "pool2"
-  top: "conv3"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 384
-    pad: 1
-    kernel_size: 3
-  }
-}
-layer {
-  name: "relu3"
-  type: "ReLU"
-  bottom: "conv3"
-  top: "conv3"
-}
-
-layer {
-  name: "conv4"
-  type: "Convolution"
-  bottom: "conv3"
-  top: "conv4"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 384
-    pad: 1
-    kernel_size: 3
-    group: 2
-  }
-}
-layer {
-  name: "relu4"
-  type: "ReLU"
-  bottom: "conv4"
-  top: "conv4"
-}
-
-layer {
-  name: "conv5"
-  type: "Convolution"
-  bottom: "conv4"
-  top: "conv5"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 256
-    pad: 1
-    kernel_size: 3
-    group: 2
-  }
-}
-layer {
-  name: "relu5"
-  type: "ReLU"
-  bottom: "conv5"
-  top: "conv5"
-}
-layer {
-  name: "pool5"
-  type: "Pooling"
-  bottom: "conv5"
-  top: "pool5"
-  pooling_param {
-    pool: MAX
-    kernel_size: 3
-    stride: 2
-  }
-}
-layer {
-  name: "fc6"
-  type: "InnerProduct"
-  bottom: "pool5"
-  top: "fc6"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  inner_product_param {
-    num_output: 4096
-  }
-}
-layer {
-  name: "relu6"
-  type: "ReLU"
-  bottom: "fc6"
-  top: "fc6"
-}
-layer {
-  name: "drop6"
-  type: "Dropout"
-  bottom: "fc6"
-  top: "fc61"
-  dropout_param {
-    dropout_ratio: 0.5
-  }
-}
-layer {
-  name: "fc7"
-  type: "InnerProduct"
-  bottom: "fc61"
-  top: "fc7"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  inner_product_param {
-    num_output: 4096
-  }
-}
-layer {
-  name: "relu7"
-  type: "ReLU"
-  bottom: "fc7"
-  top: "fc7"
-}
-layer {
-  name: "drop7"
-  type: "Dropout"
-  bottom: "fc7"
-  top: "fc71"
-  dropout_param {
-    dropout_ratio: 0.5
-  }
-}
-layer {
-  name: "fc8"
-  type: "InnerProduct"
-  bottom: "fc71"
-  top: "fc8"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  inner_product_param {
-    num_output: 1000
-  }
-}
-layer {
-  name: "prob"
-  type: "Softmax"
-  bottom: "fc8"
-  top: "prob"
-}

modules/dnn/samples/classify_with_googlenet.cpp

@@ -25,50 +25,49 @@ std::vector<String> CLASES_NAMES;
 
 void initClassesNames()
 {
-    std::ifstream fp("ILSVRC2012_synsets.txt");
+    std::ifstream fp("synset_words.txt");
     CV_Assert(fp.is_open());
 
     std::string name;
     while (!fp.eof())
     {
         std::getline(fp, name);
-        CLASES_NAMES.push_back(name);
+        if (name.length())
+            CLASES_NAMES.push_back( name.substr(name.find(' ')+1) );
     }
 
-    CV_Assert(CLASES_NAMES.size() == 1000);
-
     fp.close();
 }
 
 
-int main(void)
+int main(int argc, char **argv)
 {
     Net net;
     {
-        Ptr<Importer> importer = createCaffeImporter("bvlc_alexnet.prototxt", "bvlc_alexnet.caffemodel");
+        Ptr<Importer> importer = createCaffeImporter("bvlc_googlenet.prototxt", "bvlc_googlenet.caffemodel");
         importer->populateNet(net);
     }
 
-    Mat img = imread("zebra.jpg");
+    String filename = (argc > 1) ? argv[1] : "space_shuttle.jpg";
+
+    Mat img = imread(filename);
     CV_Assert(!img.empty());
     cvtColor(img, img, COLOR_BGR2RGB);
-    img.convertTo(img, CV_32F);
     resize(img, img, Size(227, 227));
-    subtract(img, cv::mean(img), img);
     Blob imgBlob(img);
 
-    net.setBlob("data", imgBlob);
+    net.setBlob(".data", imgBlob);
     net.forward();
 
-    Blob probBlob = net.getBlob("prob");
-    ClassProb bc = getMaxClass(probBlob);
+    Blob prob = net.getBlob("prob");
+    ClassProb bc = getMaxClass(prob);
 
     initClassesNames();
     std::string className = (bc.first < (int)CLASES_NAMES.size()) ? CLASES_NAMES[bc.first] : "unnamed";
 
     std::cout << "Best class:";
     std::cout << " #" << bc.first;
-    std::cout << " (from " << probBlob.total(1) << ")";
+    std::cout << " (from " << prob.total(1) << ")";
     std::cout << " \"" + className << "\"";
     std::cout <<  std::endl;
     std::cout << "Prob: " << bc.second * 100 << "%" << std::endl;

modules/dnn/test/cnpy.cpp

@@ -245,6 +245,3 @@ cnpy::NpyArray cnpy::npy_load(std::string fname) {
     fclose(fp);
     return arr;
 }
-
-
-