Chiebot-Mirror
/
opencv
mirror of https://github.com/opencv/opencv.git
8
0
Fork 0
Code Issues Projects Releases Wiki Activity

Merge pull request #15639 from alalek:dnn_test_fix_data

Browse Source
pull/15651/head
Alexander Alekhin 5 years ago
parent c9b2cedebd f0058bbed3
commit 8115b004b9
2 changed files with 25 additions and 19 deletions
Whitespace
Show all changes Ignore whitespace when comparing lines Ignore changes in amount of whitespace Ignore changes in whitespace at EOL
Unified View
Diff Options
Show Stats Download Patch File Download Diff File
  1. 28
      modules/dnn/misc/python/test/test_dnn.py
  2. 16
      modules/dnn/test/test_model.cpp

28
modules/dnn/misc/python/test/test_dnn.py
Unescape View File

@ -62,6 +62,7 @@ def printParams(backend, target):
} }
print('%s/%s' % (backendNames[backend], targetNames[target])) print('%s/%s' % (backendNames[backend], targetNames[target]))
testdata_required = bool(os.environ.get('OPENCV_DNN_TEST_REQUIRE_TESTDATA', False))
class dnn_test(NewOpenCVTests): 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]) self.dnnBackendsAndTargets.append([cv.dnn.DNN_BACKEND_INFERENCE_ENGINE, cv.dnn.DNN_TARGET_OPENCL_FP16])
def find_dnn_file(self, filename, required=True): 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()), return self.find_file(filename, [os.environ.get('OPENCV_DNN_TEST_DATA_PATH', os.getcwd()),
os.environ['OPENCV_TEST_DATA_PATH']], os.environ['OPENCV_TEST_DATA_PATH']],
required=required) required=required)
def checkIETarget(self, backend, target): def checkIETarget(self, backend, target):
proto = self.find_dnn_file('dnn/layers/layer_convolution.prototxt', required=True) proto = self.find_dnn_file('dnn/layers/layer_convolution.prototxt')
model = self.find_dnn_file('dnn/layers/layer_convolution.caffemodel', required=True) model = self.find_dnn_file('dnn/layers/layer_convolution.caffemodel')
net = cv.dnn.readNet(proto, model) net = cv.dnn.readNet(proto, model)
net.setPreferableBackend(backend) net.setPreferableBackend(backend)
net.setPreferableTarget(target) net.setPreferableTarget(target)
@ -134,8 +137,11 @@ class dnn_test(NewOpenCVTests):
def test_model(self): def test_model(self):
img_path = self.find_dnn_file("dnn/street.png") img_path = self.find_dnn_file("dnn/street.png")
weights = self.find_dnn_file("dnn/MobileNetSSD_deploy.caffemodel") weights = self.find_dnn_file("dnn/MobileNetSSD_deploy.caffemodel", required=False)
config = self.find_dnn_file("dnn/MobileNetSSD_deploy.prototxt") 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) frame = cv.imread(img_path)
model = cv.dnn_DetectionModel(weights, config) model = cv.dnn_DetectionModel(weights, config)
model.setInputParams(size=(300, 300), mean=(127.5, 127.5, 127.5), scale=1.0/127.5) 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): def test_classification_model(self):
img_path = self.find_dnn_file("dnn/googlenet_0.png") 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") config = self.find_dnn_file("dnn/squeezenet_v1.1.prototxt")
ref = np.load(self.find_dnn_file("dnn/squeezenet_v1.1_prob.npy")) 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) frame = cv.imread(img_path)
model = cv.dnn_ClassificationModel(config, weights) model = cv.dnn_ClassificationModel(config, weights)
@ -177,9 +185,8 @@ class dnn_test(NewOpenCVTests):
def test_face_detection(self): 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')
proto = self.find_dnn_file('dnn/opencv_face_detector.prototxt', required=testdata_required) model = self.find_dnn_file('dnn/opencv_face_detector.caffemodel', required=False)
model = self.find_dnn_file('dnn/opencv_face_detector.caffemodel', required=testdata_required)
if proto is None or model is None: 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.") 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): def test_async(self):
timeout = 10*1000*10**6 # in nanoseconds (10 sec) 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')
proto = self.find_dnn_file('dnn/layers/layer_convolution.prototxt', required=testdata_required) model = self.find_dnn_file('dnn/layers/layer_convolution.caffemodel')
model = self.find_dnn_file('dnn/layers/layer_convolution.caffemodel', required=testdata_required)
if proto is None or model is None: 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.") raise unittest.SkipTest("Missing DNN test files (dnn/layers/layer_convolution.{prototxt/caffemodel}). Verify OPENCV_DNN_TEST_DATA_PATH configuration parameter.")

16
modules/dnn/test/test_model.cpp
Unescape View File

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

Write Preview
Loading…
Cancel
Save