Merge remote-tracking branch 'upstream/3.4' into merge-3.4

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

@@ -937,6 +937,13 @@ CV__DNN_INLINE_NS_BEGIN
                              CV_OUT std::vector<int>& indices,
                              const float eta = 1.f, const int top_k = 0);
 
+    /** @brief Release a Myriad device is binded by OpenCV.
+     *
+     * Single Myriad device cannot be shared across multiple processes which uses
+     * Inference Engine's Myriad plugin.
+     */
+    CV_EXPORTS_W void resetMyriadDevice();
+
 //! @}
 CV__DNN_INLINE_NS_END
 }

modules/dnn/src/op_inf_engine.cpp

@@ -443,13 +443,14 @@ void InfEngineBackendNet::init(int targetId)
         initPlugin(*this);
 }
 
+static std::map<InferenceEngine::TargetDevice, InferenceEngine::InferenceEnginePluginPtr> sharedPlugins;
+
 void InfEngineBackendNet::initPlugin(InferenceEngine::ICNNNetwork& net)
 {
     CV_Assert(!isInitialized());
 
     try
     {
-        static std::map<InferenceEngine::TargetDevice, InferenceEngine::InferenceEnginePluginPtr> sharedPlugins;
         auto pluginIt = sharedPlugins.find(targetDevice);
         if (pluginIt != sharedPlugins.end())
         {
@@ -589,4 +590,14 @@ void forwardInfEngine(Ptr<BackendNode>& node)
 #endif  // HAVE_INF_ENGINE
 }
 
+CV__DNN_INLINE_NS_BEGIN
+
+void resetMyriadDevice()
+{
+#ifdef HAVE_INF_ENGINE
+    sharedPlugins.erase(InferenceEngine::TargetDevice::eMYRIAD);
+#endif  // HAVE_INF_ENGINE
+}
+
+CV__DNN_INLINE_NS_END
 }}  // namespace dnn, namespace cv

modules/dnn/test/test_ie_models.cpp

@@ -177,6 +177,11 @@ TEST_P(DNNTestOpenVINO, models)
     Target target = (dnn::Target)(int)get<0>(GetParam());
     std::string modelName = get<1>(GetParam());
 
+    if (target == DNN_TARGET_MYRIAD && (modelName == "landmarks-regression-retail-0001" ||
+                                        modelName == "semantic-segmentation-adas-0001" ||
+                                        modelName == "face-reidentification-retail-0001"))
+        throw SkipTestException("");
+
     std::string precision = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? "FP16" : "FP32";
     std::string prefix = utils::fs::join("intel_models",
                          utils::fs::join(modelName,
@@ -186,6 +191,8 @@ TEST_P(DNNTestOpenVINO, models)
 
     std::map<std::string, cv::Mat> inputsMap;
     std::map<std::string, cv::Mat> ieOutputsMap, cvOutputsMap;
+    // Single Myriad device cannot be shared across multiple processes.
+    resetMyriadDevice();
     runIE(target, xmlPath, binPath, inputsMap, ieOutputsMap);
     runCV(target, xmlPath, binPath, inputsMap, cvOutputsMap);
 
@@ -238,8 +245,8 @@ static testing::internal::ParamGenerator<Target> dnnDLIETargets()
         targets.push_back(DNN_TARGET_OPENCL_FP16);
     }
 #endif
-    //if (checkMyriadTarget())
+    if (checkMyriadTarget())
-    //    targets.push_back(DNN_TARGET_MYRIAD);
+        targets.push_back(DNN_TARGET_MYRIAD);
     return testing::ValuesIn(targets);
 }
 

modules/ts/misc/run_android.py

@@ -123,7 +123,7 @@ class AndroidTestSuite(TestSuite):
     def checkPrerequisites(self):
         self.adb.init(self.options.serial)
 
-    def runTest(self, path, logfile, workingDir, args=[]):
+    def runTest(self, module, path, logfile, workingDir, args=[]):
         args = args[:]
         exe = os.path.abspath(path)
 

modules/ts/misc/run_long.py

@@ -7,6 +7,18 @@ from pprint import PrettyPrinter as PP
 LONG_TESTS_DEBUG_VALGRIND = [
     ('calib3d', 'Calib3d_InitUndistortRectifyMap.accuracy', 2017.22),
     ('dnn', 'Reproducibility*', 1000),  # large DNN models
+    ('dnn', '*RCNN*', 1000),  # very large DNN models
+    ('dnn', '*RFCN*', 1000),  # very large DNN models
+    ('dnn', '*EAST*', 1000),  # very large DNN models
+    ('dnn', '*VGG16*', 1000),  # very large DNN models
+    ('dnn', '*ZFNet*', 1000),  # very large DNN models
+    ('dnn', '*ResNet101_DUC_HDC*', 1000),  # very large DNN models
+    ('dnn', '*LResNet100E_IR*', 1000),  # very large DNN models
+    ('dnn', '*read_yolo_voc_stream*', 1000),  # very large DNN models
+    ('dnn', '*eccv16*', 1000),  # very large DNN models
+    ('dnn', '*OpenPose*', 1000),  # very large DNN models
+    ('dnn', '*SSD/*', 1000),  # very large DNN models
+    ('face', 'CV_Face_FacemarkLBF.test_workflow', 10000.0), # >40min on i7
     ('features2d', 'Features2d/DescriptorImage.no_crash/3', 1000),
     ('features2d', 'Features2d/DescriptorImage.no_crash/4', 1000),
     ('features2d', 'Features2d/DescriptorImage.no_crash/5', 1000),
@@ -29,6 +41,8 @@ LONG_TESTS_DEBUG_VALGRIND = [
     ('shape', 'Shape_SCD.regression', 3311.46),
     ('tracking', 'AUKF.br_mean_squared_error', 10764.6),
     ('tracking', 'UKF.br_mean_squared_error', 5228.27),
+    ('tracking', '*DistanceAndOverlap*/1', 1000.0), # dudek
+    ('tracking', '*DistanceAndOverlap*/2', 1000.0), # faceocc2
     ('videoio', 'Videoio_Video.ffmpeg_writebig', 1000),
     ('xfeatures2d', 'Features2d_RotationInvariance_Descriptor_BoostDesc_LBGM.regression', 1124.51),
     ('xfeatures2d', 'Features2d_RotationInvariance_Descriptor_VGG120.regression', 2198.1),
@@ -41,17 +55,21 @@ LONG_TESTS_DEBUG_VALGRIND = [
     ('xfeatures2d', 'Features2d_ScaleInvariance_Descriptor_VGG64.regression', 1163.41),
     ('xfeatures2d', 'Features2d_ScaleInvariance_Descriptor_VGG80.regression', 1179.06),
     ('ximgproc', 'L0SmoothTest.SplatSurfaceAccuracy', 6382.26),
-    ('ximgproc', 'L0SmoothTest_perf.perf/17', 2052.16),
+    ('ximgproc', 'perf*/1*:perf*/2*:perf*/3*:perf*/4*:perf*/5*:perf*/6*:perf*/7*:perf*/8*:perf*/9*', 1000.0),  # only first 10 parameters
-    ('ximgproc', 'RollingGuidanceFilterTest_perf.perf/59', 2760.29),
     ('ximgproc', 'TypicalSet1/RollingGuidanceFilterTest.MultiThreadReproducibility/5', 1086.33),
     ('ximgproc', 'TypicalSet1/RollingGuidanceFilterTest.MultiThreadReproducibility/7', 1405.05),
     ('ximgproc', 'TypicalSet1/RollingGuidanceFilterTest.SplatSurfaceAccuracy/5', 1253.07),
     ('ximgproc', 'TypicalSet1/RollingGuidanceFilterTest.SplatSurfaceAccuracy/7', 1599.98),
+    ('ximgproc', '*MultiThreadReproducibility*/1:*MultiThreadReproducibility*/2:*MultiThreadReproducibility*/3:*MultiThreadReproducibility*/4:*MultiThreadReproducibility*/5:*MultiThreadReproducibility*/6:*MultiThreadReproducibility*/7:*MultiThreadReproducibility*/8:*MultiThreadReproducibility*/9:*MultiThreadReproducibility*/1*', 1000.0),
+    ('ximgproc', '*AdaptiveManifoldRefImplTest*/1:*AdaptiveManifoldRefImplTest*/2:*AdaptiveManifoldRefImplTest*/3', 1000.0),
+    ('ximgproc', '*JointBilateralFilterTest_NaiveRef*', 1000.0),
+    ('ximgproc', '*RollingGuidanceFilterTest_BilateralRef*/1*:*RollingGuidanceFilterTest_BilateralRef*/2*:*RollingGuidanceFilterTest_BilateralRef*/3*', 1000.0),
+    ('ximgproc', '*JointBilateralFilterTest_NaiveRef*', 1000.0),
 ]
 
 
 def longTestFilter(data, module=None):
-    res = ['*', '-'] + [v for _, v, m in data if module is None or m == module]
+    res = ['*', '-'] + [v for m, v, _time in data if module is None or m == module]
     return '--gtest_filter={}'.format(':'.join(res))
 
 

modules/ts/misc/run_suite.py

@@ -77,7 +77,7 @@ class TestSuite(object):
             return False
         return os.access(fullpath, os.X_OK)
 
-    def wrapCommand(self, cmd, env):
+    def wrapCommand(self, module, cmd, env):
         if self.options.valgrind:
             res = ['valgrind']
             supp = self.options.valgrind_supp or []
@@ -88,7 +88,7 @@ class TestSuite(object):
                     print("WARNING: Valgrind suppression file is missing, SKIP: %s" % f)
             res.extend(self.options.valgrind_opt)
             has_gtest_filter = next((True for x in cmd if x.startswith('--gtest_filter=')), False)
-            return res + cmd + ([longTestFilter(LONG_TESTS_DEBUG_VALGRIND)] if not has_gtest_filter else [])
+            return res + cmd + ([longTestFilter(LONG_TESTS_DEBUG_VALGRIND, module)] if not has_gtest_filter else [])
         elif self.options.qemu:
             import shlex
             res = shlex.split(self.options.qemu)
@@ -107,14 +107,14 @@ class TestSuite(object):
             pass
         return False
 
-    def runTest(self, path, logfile, workingDir, args=[]):
+    def runTest(self, module, path, logfile, workingDir, args=[]):
         args = args[:]
         exe = os.path.abspath(path)
-        if path == "java":
+        if module == "java":
             cmd = [self.cache.ant_executable, "-Dopencv.build.type=%s" % self.cache.build_type, "buildAndTest"]
             ret = execute(cmd, cwd=self.cache.java_test_dir)
             return None, ret
-        elif path in ['python2', 'python3']:
+        elif module in ['python2', 'python3']:
             executable = os.getenv('OPENCV_PYTHON_BINARY', None)
             if executable is None:
                 executable = path
@@ -140,7 +140,7 @@ class TestSuite(object):
                 env['OPENCV_TRACE_SYNC_OPENCL'] = '1'
             tempDir = TempEnvDir('OPENCV_TEMP_PATH', "__opencv_temp.")
             tempDir.init()
-            cmd = self.wrapCommand([exe] + args, env)
+            cmd = self.wrapCommand(module, [exe] + args, env)
             log.warning("Run: %s" % " ".join(cmd))
             ret = execute(cmd, cwd=workingDir, env=env)
             try:
@@ -184,7 +184,7 @@ class TestSuite(object):
             if self.options.dry_run:
                 logfile, r = None, 0
             else:
-                logfile, r = self.runTest(exe, logname, workingDir, args + more_args)
+                logfile, r = self.runTest(test, exe, logname, workingDir, args + more_args)
             log.debug("Test returned: %s ==> %s", r, logfile)
 
             if r != 0:

samples/dnn/tf_text_graph_common.py

@@ -302,3 +302,26 @@ def removeUnusedNodesAndAttrs(to_remove, graph_def):
         for i in reversed(range(len(node.input))):
             if node.input[i] in removedNodes:
                 del node.input[i]
+
+
+def writeTextGraph(modelPath, outputPath, outNodes):
+    try:
+        import cv2 as cv
+
+        cv.dnn.writeTextGraph(modelPath, outputPath)
+    except:
+        import tensorflow as tf
+        from tensorflow.tools.graph_transforms import TransformGraph
+
+        with tf.gfile.FastGFile(modelPath, 'rb') as f:
+            graph_def = tf.GraphDef()
+            graph_def.ParseFromString(f.read())
+
+            graph_def = TransformGraph(graph_def, ['image_tensor'], outNodes, ['sort_by_execution_order'])
+
+            for node in graph_def.node:
+                if node.op == 'Const':
+                    if 'value' in node.attr:
+                        del node.attr['value']
+
+        tf.train.write_graph(graph_def, "", outputPath, as_text=True)

samples/dnn/tf_text_graph_faster_rcnn.py

@@ -1,6 +1,5 @@
 import argparse
 import numpy as np
-import cv2 as cv
 from tf_text_graph_common import *
 
 
@@ -42,7 +41,7 @@ def createFasterRCNNGraph(modelPath, configPath, outputPath):
     print('Features stride:   %f' % features_stride)
 
     # Read the graph.
-    cv.dnn.writeTextGraph(modelPath, outputPath)
+    writeTextGraph(modelPath, outputPath, ['num_detections', 'detection_scores', 'detection_boxes', 'detection_classes'])
     graph_def = parseTextGraph(outputPath)
 
     removeIdentity(graph_def)

samples/dnn/tf_text_graph_mask_rcnn.py

@@ -1,6 +1,5 @@
 import argparse
 import numpy as np
-import cv2 as cv
 from tf_text_graph_common import *
 
 parser = argparse.ArgumentParser(description='Run this script to get a text graph of '
@@ -48,7 +47,7 @@ print('Height stride:     %f' % height_stride)
 print('Features stride:   %f' % features_stride)
 
 # Read the graph.
-cv.dnn.writeTextGraph(args.input, args.output)
+writeTextGraph(args.input, args.output, ['num_detections', 'detection_scores', 'detection_boxes', 'detection_classes', 'detection_masks'])
 graph_def = parseTextGraph(args.output)
 
 removeIdentity(graph_def)

samples/dnn/tf_text_graph_ssd.py

@@ -11,7 +11,6 @@
 # See details and examples on the following wiki page: https://github.com/opencv/opencv/wiki/TensorFlow-Object-Detection-API
 import argparse
 from math import sqrt
-import cv2 as cv
 from tf_text_graph_common import *
 
 def createSSDGraph(modelPath, configPath, outputPath):
@@ -52,12 +51,12 @@ def createSSDGraph(modelPath, configPath, outputPath):
     print('Input image size: %dx%d' % (image_width, image_height))
 
     # Read the graph.
-    cv.dnn.writeTextGraph(modelPath, outputPath)
-    graph_def = parseTextGraph(outputPath)
-
     inpNames = ['image_tensor']
     outNames = ['num_detections', 'detection_scores', 'detection_boxes', 'detection_classes']
 
+    writeTextGraph(modelPath, outputPath, outNames)
+    graph_def = parseTextGraph(outputPath)
+
     def getUnconnectedNodes():
         unconnected = []
         for node in graph_def.node: