dnn(test): fix optional test data

modules/dnn/misc/python/test/test_dnn.py

@@ -62,6 +62,7 @@ def printParams(backend, target):
     }
     print('%s/%s' % (backendNames[backend], targetNames[target]))
 
+testdata_required = bool(os.environ.get('OPENCV_DNN_TEST_REQUIRE_TESTDATA', False))
 
 class dnn_test(NewOpenCVTests):
 
@@ -87,13 +88,15 @@ class dnn_test(NewOpenCVTests):
                     self.dnnBackendsAndTargets.append([cv.dnn.DNN_BACKEND_INFERENCE_ENGINE, cv.dnn.DNN_TARGET_OPENCL_FP16])
 
     def find_dnn_file(self, filename, required=True):
+        if not required:
+            required = testdata_required
         return self.find_file(filename, [os.environ.get('OPENCV_DNN_TEST_DATA_PATH', os.getcwd()),
                                          os.environ['OPENCV_TEST_DATA_PATH']],
                               required=required)
 
     def checkIETarget(self, backend, target):
-        proto = self.find_dnn_file('dnn/layers/layer_convolution.prototxt', required=True)
-        model = self.find_dnn_file('dnn/layers/layer_convolution.caffemodel', required=True)
+        proto = self.find_dnn_file('dnn/layers/layer_convolution.prototxt')
+        model = self.find_dnn_file('dnn/layers/layer_convolution.caffemodel')
         net = cv.dnn.readNet(proto, model)
         net.setPreferableBackend(backend)
         net.setPreferableTarget(target)
@@ -134,8 +137,11 @@ class dnn_test(NewOpenCVTests):
 
     def test_model(self):
         img_path = self.find_dnn_file("dnn/street.png")
-        weights = self.find_dnn_file("dnn/MobileNetSSD_deploy.caffemodel")
-        config = self.find_dnn_file("dnn/MobileNetSSD_deploy.prototxt")
+        weights = self.find_dnn_file("dnn/MobileNetSSD_deploy.caffemodel", required=False)
+        config = self.find_dnn_file("dnn/MobileNetSSD_deploy.prototxt", required=False)
+        if weights is None or config is None:
+            raise unittest.SkipTest("Missing DNN test files (dnn/MobileNetSSD_deploy.{prototxt/caffemodel}). Verify OPENCV_DNN_TEST_DATA_PATH configuration parameter.")
+
         frame = cv.imread(img_path)
         model = cv.dnn_DetectionModel(weights, config)
         model.setInputParams(size=(300, 300), mean=(127.5, 127.5, 127.5), scale=1.0/127.5)
@@ -163,9 +169,11 @@ class dnn_test(NewOpenCVTests):
 
     def test_classification_model(self):
         img_path = self.find_dnn_file("dnn/googlenet_0.png")
-        weights = self.find_dnn_file("dnn/squeezenet_v1.1.caffemodel")
+        weights = self.find_dnn_file("dnn/squeezenet_v1.1.caffemodel", required=False)
         config = self.find_dnn_file("dnn/squeezenet_v1.1.prototxt")
         ref = np.load(self.find_dnn_file("dnn/squeezenet_v1.1_prob.npy"))
+        if weights is None or config is None:
+            raise unittest.SkipTest("Missing DNN test files (dnn/squeezenet_v1.1.{prototxt/caffemodel}). Verify OPENCV_DNN_TEST_DATA_PATH configuration parameter.")
 
         frame = cv.imread(img_path)
         model = cv.dnn_ClassificationModel(config, weights)
@@ -177,9 +185,8 @@ class dnn_test(NewOpenCVTests):
 
 
     def test_face_detection(self):
-        testdata_required = bool(os.environ.get('OPENCV_DNN_TEST_REQUIRE_TESTDATA', False))
-        proto = self.find_dnn_file('dnn/opencv_face_detector.prototxt', required=testdata_required)
-        model = self.find_dnn_file('dnn/opencv_face_detector.caffemodel', required=testdata_required)
+        proto = self.find_dnn_file('dnn/opencv_face_detector.prototxt')
+        model = self.find_dnn_file('dnn/opencv_face_detector.caffemodel', required=False)
         if proto is None or model is None:
             raise unittest.SkipTest("Missing DNN test files (dnn/opencv_face_detector.{prototxt/caffemodel}). Verify OPENCV_DNN_TEST_DATA_PATH configuration parameter.")
 
@@ -216,9 +223,8 @@ class dnn_test(NewOpenCVTests):
 
     def test_async(self):
         timeout = 10*1000*10**6  # in nanoseconds (10 sec)
-        testdata_required = bool(os.environ.get('OPENCV_DNN_TEST_REQUIRE_TESTDATA', False))
-        proto = self.find_dnn_file('dnn/layers/layer_convolution.prototxt', required=testdata_required)
-        model = self.find_dnn_file('dnn/layers/layer_convolution.caffemodel', required=testdata_required)
+        proto = self.find_dnn_file('dnn/layers/layer_convolution.prototxt')
+        model = self.find_dnn_file('dnn/layers/layer_convolution.caffemodel')
         if proto is None or model is None:
             raise unittest.SkipTest("Missing DNN test files (dnn/layers/layer_convolution.{prototxt/caffemodel}). Verify OPENCV_DNN_TEST_DATA_PATH configuration parameter.")
 

modules/dnn/test/test_model.cpp

@@ -8,10 +8,10 @@
 namespace opencv_test { namespace {
 
 template<typename TString>
-static std::string _tf(TString filename)
+static std::string _tf(TString filename, bool required = true)
 {
     String rootFolder = "dnn/";
-    return findDataFile(rootFolder + filename);
+    return findDataFile(rootFolder + filename, required);
 }
 
 
@@ -96,7 +96,7 @@ TEST_P(Test_Model, Classify)
 
     std::string img_path = _tf("grace_hopper_227.png");
     std::string config_file = _tf("bvlc_alexnet.prototxt");
-    std::string weights_file = _tf("bvlc_alexnet.caffemodel");
+    std::string weights_file = _tf("bvlc_alexnet.caffemodel", false);
 
     Size size{227, 227};
     float norm = 1e-4;
@@ -127,7 +127,7 @@ TEST_P(Test_Model, DetectRegion)
                                     Rect2d(58, 141, 117, 249)};
 
     std::string img_path = _tf("dog416.png");
-    std::string weights_file = _tf("yolo-voc.weights");
+    std::string weights_file = _tf("yolo-voc.weights", false);
     std::string config_file = _tf("yolo-voc.cfg");
 
     double scale = 1.0 / 255.0;
@@ -160,7 +160,7 @@ TEST_P(Test_Model, DetectionOutput)
                                     Rect2d(132, 223, 207, 344)};
 
     std::string img_path = _tf("dog416.png");
-    std::string weights_file = _tf("resnet50_rfcn_final.caffemodel");
+    std::string weights_file = _tf("resnet50_rfcn_final.caffemodel", false);
     std::string config_file = _tf("rfcn_pascal_voc_resnet50.prototxt");
 
     Scalar mean = Scalar(102.9801, 115.9465, 122.7717);
@@ -203,7 +203,7 @@ TEST_P(Test_Model, DetectionMobilenetSSD)
         refBoxes.emplace_back(left, top, width, height);
     }
 
-    std::string weights_file = _tf("MobileNetSSD_deploy.caffemodel");
+    std::string weights_file = _tf("MobileNetSSD_deploy.caffemodel", false);
     std::string config_file = _tf("MobileNetSSD_deploy.prototxt");
 
     Scalar mean = Scalar(127.5, 127.5, 127.5);
@@ -228,7 +228,7 @@ TEST_P(Test_Model, Detection_normalized)
     std::vector<float> refConfidences = {0.999222f};
     std::vector<Rect2d> refBoxes = {Rect2d(0, 4, 227, 222)};
 
-    std::string weights_file = _tf("MobileNetSSD_deploy.caffemodel");
+    std::string weights_file = _tf("MobileNetSSD_deploy.caffemodel", false);
     std::string config_file = _tf("MobileNetSSD_deploy.prototxt");
 
     Scalar mean = Scalar(127.5, 127.5, 127.5);
@@ -247,7 +247,7 @@ TEST_P(Test_Model, Segmentation)
 {
     std::string inp = _tf("dog416.png");
     std::string weights_file = _tf("fcn8s-heavy-pascal.prototxt");
-    std::string config_file = _tf("fcn8s-heavy-pascal.caffemodel");
+    std::string config_file = _tf("fcn8s-heavy-pascal.caffemodel", false);
     std::string exp = _tf("segmentation_exp.png");
 
     Size size{128, 128};