diff --git a/cmake/OpenCVDetectInferenceEngine.cmake b/cmake/OpenCVDetectInferenceEngine.cmake index 2c0296d634..8c6470e7d7 100644 --- a/cmake/OpenCVDetectInferenceEngine.cmake +++ b/cmake/OpenCVDetectInferenceEngine.cmake @@ -138,8 +138,8 @@ if(INF_ENGINE_TARGET) math(EXPR INF_ENGINE_RELEASE "${InferenceEngine_VERSION_MAJOR} * 1000000 + ${InferenceEngine_VERSION_MINOR} * 10000 + ${InferenceEngine_VERSION_PATCH} * 100") endif() if(NOT INF_ENGINE_RELEASE) - message(WARNING "InferenceEngine version has not been set, 2021.3 will be used by default. Set INF_ENGINE_RELEASE variable if you experience build errors.") - set(INF_ENGINE_RELEASE "2021030000") + message(WARNING "InferenceEngine version has not been set, 2021.4 will be used by default. Set INF_ENGINE_RELEASE variable if you experience build errors.") + set(INF_ENGINE_RELEASE "2021040000") endif() set(INF_ENGINE_RELEASE "${INF_ENGINE_RELEASE}" CACHE STRING "Force IE version, should be in form YYYYAABBCC (e.g. 2020.1.0.2 -> 2020010002)") set_target_properties(${INF_ENGINE_TARGET} PROPERTIES diff --git a/modules/core/include/opencv2/core/utils/filesystem.private.hpp b/modules/core/include/opencv2/core/utils/filesystem.private.hpp index ea2591c9de..72b2bb9479 100644 --- a/modules/core/include/opencv2/core/utils/filesystem.private.hpp +++ b/modules/core/include/opencv2/core/utils/filesystem.private.hpp @@ -16,8 +16,8 @@ # define OPENCV_HAVE_FILESYSTEM_SUPPORT 1 # elif defined(__APPLE__) # include -# if (defined(TARGET_OS_OSX) && TARGET_OS_OSX) || (!defined(TARGET_OS_OSX) && !TARGET_OS_IPHONE) -# define OPENCV_HAVE_FILESYSTEM_SUPPORT 1 // OSX only +# if (defined(TARGET_OS_OSX) && TARGET_OS_OSX) || (defined(TARGET_OS_IOS) && TARGET_OS_IOS) +# define OPENCV_HAVE_FILESYSTEM_SUPPORT 1 // OSX, iOS only # endif # else /* unknown */ diff --git a/modules/dnn/src/ie_ngraph.cpp b/modules/dnn/src/ie_ngraph.cpp index 7484032714..6f730a907a 100644 --- a/modules/dnn/src/ie_ngraph.cpp +++ b/modules/dnn/src/ie_ngraph.cpp @@ -657,7 +657,11 @@ void InfEngineNgraphNet::initPlugin(InferenceEngine::CNNNetwork& net) try { InferenceEngine::IExtensionPtr extension = +#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2021_4) + std::make_shared(libName); +#else InferenceEngine::make_so_pointer(libName); +#endif ie.AddExtension(extension, "CPU"); CV_LOG_INFO(NULL, "DNN-IE: Loaded extension plugin: " << libName); @@ -1005,35 +1009,54 @@ void InfEngineNgraphNet::forward(const std::vector >& outBlo reqWrapper->req.SetInput(inpBlobs); reqWrapper->req.SetOutput(outBlobs); +#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2021_4) + InferenceEngine::InferRequest infRequest = reqWrapper->req; + NgraphReqWrapper* wrapperPtr = reqWrapper.get(); + CV_Assert(wrapperPtr && "Internal error"); +#else InferenceEngine::IInferRequest::Ptr infRequestPtr = reqWrapper->req; - infRequestPtr->SetUserData(reqWrapper.get(), 0); + CV_Assert(infRequestPtr); + InferenceEngine::IInferRequest& infRequest = *infRequestPtr.get(); + infRequest.SetUserData(reqWrapper.get(), 0); +#endif - infRequestPtr->SetCompletionCallback( - [](InferenceEngine::IInferRequest::Ptr request, InferenceEngine::StatusCode status) +#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2021_4) + // do NOT capture 'reqWrapper' (smart ptr) in the lambda callback + infRequest.SetCompletionCallback>( + [wrapperPtr](InferenceEngine::InferRequest /*request*/, InferenceEngine::StatusCode status) +#else + infRequest.SetCompletionCallback( + [](InferenceEngine::IInferRequest::Ptr requestPtr, InferenceEngine::StatusCode status) +#endif { CV_LOG_DEBUG(NULL, "DNN(nGraph): completionCallback(" << (int)status << ")"); +#if !INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2021_4) + CV_Assert(requestPtr); + InferenceEngine::IInferRequest& request = *requestPtr.get(); - NgraphReqWrapper* wrapper; - request->GetUserData((void**)&wrapper, 0); - CV_Assert(wrapper && "Internal error"); + NgraphReqWrapper* wrapperPtr; + request.GetUserData((void**)&wrapperPtr, 0); + CV_Assert(wrapperPtr && "Internal error"); +#endif + NgraphReqWrapper& wrapper = *wrapperPtr; size_t processedOutputs = 0; try { - for (; processedOutputs < wrapper->outProms.size(); ++processedOutputs) + for (; processedOutputs < wrapper.outProms.size(); ++processedOutputs) { - const std::string& name = wrapper->outsNames[processedOutputs]; - Mat m = ngraphBlobToMat(wrapper->req.GetBlob(name)); + const std::string& name = wrapper.outsNames[processedOutputs]; + Mat m = ngraphBlobToMat(wrapper.req.GetBlob(name)); try { CV_Assert(status == InferenceEngine::StatusCode::OK); - wrapper->outProms[processedOutputs].setValue(m.clone()); + wrapper.outProms[processedOutputs].setValue(m.clone()); } catch (...) { try { - wrapper->outProms[processedOutputs].setException(std::current_exception()); + wrapper.outProms[processedOutputs].setException(std::current_exception()); } catch(...) { CV_LOG_ERROR(NULL, "DNN: Exception occurred during async inference exception propagation"); } @@ -1043,16 +1066,16 @@ void InfEngineNgraphNet::forward(const std::vector >& outBlo catch (...) { std::exception_ptr e = std::current_exception(); - for (; processedOutputs < wrapper->outProms.size(); ++processedOutputs) + for (; processedOutputs < wrapper.outProms.size(); ++processedOutputs) { try { - wrapper->outProms[processedOutputs].setException(e); + wrapper.outProms[processedOutputs].setException(e); } catch(...) { CV_LOG_ERROR(NULL, "DNN: Exception occurred during async inference exception propagation"); } } } - wrapper->isReady = true; + wrapper.isReady = true; } ); } diff --git a/modules/dnn/src/layers/batch_norm_layer.cpp b/modules/dnn/src/layers/batch_norm_layer.cpp index edd9948db1..e28c964689 100644 --- a/modules/dnn/src/layers/batch_norm_layer.cpp +++ b/modules/dnn/src/layers/batch_norm_layer.cpp @@ -35,6 +35,7 @@ namespace dnn class BatchNormLayerImpl CV_FINAL : public BatchNormLayer { public: + Mat origin_weights, origin_bias; Mat weights_, bias_; UMat umat_weight, umat_bias; mutable int dims; @@ -88,11 +89,11 @@ public: const float* weightsData = hasWeights ? blobs[weightsBlobIndex].ptr() : 0; const float* biasData = hasBias ? blobs[biasBlobIndex].ptr() : 0; - weights_.create(1, (int)n, CV_32F); - bias_.create(1, (int)n, CV_32F); + origin_weights.create(1, (int)n, CV_32F); + origin_bias.create(1, (int)n, CV_32F); - float* dstWeightsData = weights_.ptr(); - float* dstBiasData = bias_.ptr(); + float* dstWeightsData = origin_weights.ptr(); + float* dstBiasData = origin_bias.ptr(); for (size_t i = 0; i < n; ++i) { @@ -100,15 +101,12 @@ public: dstWeightsData[i] = w; dstBiasData[i] = (hasBias ? biasData[i] : 0.0f) - w * meanData[i] * varMeanScale; } - // We will use blobs to store origin weights and bias to restore them in case of reinitialization. - weights_.copyTo(blobs[0].reshape(1, 1)); - bias_.copyTo(blobs[1].reshape(1, 1)); } virtual void finalize(InputArrayOfArrays, OutputArrayOfArrays) CV_OVERRIDE { - blobs[0].reshape(1, 1).copyTo(weights_); - blobs[1].reshape(1, 1).copyTo(bias_); + origin_weights.reshape(1, 1).copyTo(weights_); + origin_bias.reshape(1, 1).copyTo(bias_); } void getScaleShift(Mat& scale, Mat& shift) const CV_OVERRIDE diff --git a/modules/dnn/src/onnx/onnx_importer.cpp b/modules/dnn/src/onnx/onnx_importer.cpp index 98714bbd5c..1f240cae46 100644 --- a/modules/dnn/src/onnx/onnx_importer.cpp +++ b/modules/dnn/src/onnx/onnx_importer.cpp @@ -1954,6 +1954,23 @@ void ONNXImporter::handleNode(const opencv_onnx::NodeProto& node_proto_) addConstant(layerParams.name, concatenated[0]); return; } + else + { + for (int i = 0; i < node_proto.input_size(); ++i) + { + if (constBlobs.find(node_proto.input(i)) != constBlobs.end()) + { + LayerParams constParams; + constParams.name = node_proto.input(i); + constParams.type = "Const"; + constParams.blobs.push_back(getBlob(node_proto, i)); + + opencv_onnx::NodeProto proto; + proto.add_output(constParams.name); + addLayer(constParams, proto); + } + } + } } else if (layer_type == "Resize") { diff --git a/modules/dnn/src/op_inf_engine.hpp b/modules/dnn/src/op_inf_engine.hpp index f52334bc45..ab2f161eaf 100644 --- a/modules/dnn/src/op_inf_engine.hpp +++ b/modules/dnn/src/op_inf_engine.hpp @@ -30,10 +30,11 @@ #define INF_ENGINE_RELEASE_2021_1 2021010000 #define INF_ENGINE_RELEASE_2021_2 2021020000 #define INF_ENGINE_RELEASE_2021_3 2021030000 +#define INF_ENGINE_RELEASE_2021_4 2021040000 #ifndef INF_ENGINE_RELEASE -#warning("IE version have not been provided via command-line. Using 2021.3 by default") -#define INF_ENGINE_RELEASE INF_ENGINE_RELEASE_2021_3 +#warning("IE version have not been provided via command-line. Using 2021.4 by default") +#define INF_ENGINE_RELEASE INF_ENGINE_RELEASE_2021_4 #endif #define INF_ENGINE_VER_MAJOR_GT(ver) (((INF_ENGINE_RELEASE) / 10000) > ((ver) / 10000)) diff --git a/modules/dnn/test/test_backends.cpp b/modules/dnn/test/test_backends.cpp index aab4c6f507..e8c7e700f6 100644 --- a/modules/dnn/test/test_backends.cpp +++ b/modules/dnn/test/test_backends.cpp @@ -204,7 +204,7 @@ TEST_P(DNNTestNetwork, MobileNet_SSD_Caffe) Mat inp = blobFromImage(sample, 1.0f / 127.5, Size(300, 300), Scalar(127.5, 127.5, 127.5), false); float scoreDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 1.5e-2 : 0.0; float iouDiff = (target == DNN_TARGET_MYRIAD) ? 0.063 : 0.0; - float detectionConfThresh = (target == DNN_TARGET_MYRIAD) ? 0.252 : FLT_MIN; + float detectionConfThresh = (target == DNN_TARGET_MYRIAD) ? 0.262 : FLT_MIN; processNet("dnn/MobileNetSSD_deploy.caffemodel", "dnn/MobileNetSSD_deploy.prototxt", inp, "detection_out", "", scoreDiff, iouDiff, detectionConfThresh); expectNoFallbacksFromIE(net); @@ -359,8 +359,8 @@ TEST_P(DNNTestNetwork, OpenPose_pose_coco) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X, CV_TEST_TAG_DNN_SKIP_IE_VERSION); #endif - const float l1 = (target == DNN_TARGET_MYRIAD) ? 0.0056 : 0.0; - const float lInf = (target == DNN_TARGET_MYRIAD) ? 0.072 : 0.0; + const float l1 = (target == DNN_TARGET_MYRIAD) ? 0.009 : 0.0; + const float lInf = (target == DNN_TARGET_MYRIAD) ? 0.09 : 0.0; processNet("dnn/openpose_pose_coco.caffemodel", "dnn/openpose_pose_coco.prototxt", Size(46, 46), "", "", l1, lInf); expectNoFallbacksFromIE(net); @@ -380,8 +380,8 @@ TEST_P(DNNTestNetwork, OpenPose_pose_mpi) #endif // output range: [-0.001, 0.97] - const float l1 = (target == DNN_TARGET_MYRIAD) ? 0.012 : 0.0; - const float lInf = (target == DNN_TARGET_MYRIAD || target == DNN_TARGET_OPENCL_FP16) ? 0.16 : 0.0; + const float l1 = (target == DNN_TARGET_MYRIAD) ? 0.02 : 0.0; + const float lInf = (target == DNN_TARGET_MYRIAD || target == DNN_TARGET_OPENCL_FP16) ? 0.2 : 0.0; processNet("dnn/openpose_pose_mpi.caffemodel", "dnn/openpose_pose_mpi.prototxt", Size(46, 46), "", "", l1, lInf); expectNoFallbacksFromIE(net); diff --git a/modules/dnn/test/test_ie_models.cpp b/modules/dnn/test/test_ie_models.cpp index b285e91d96..760651c5c0 100644 --- a/modules/dnn/test/test_ie_models.cpp +++ b/modules/dnn/test/test_ie_models.cpp @@ -307,6 +307,15 @@ TEST_P(DNNTestOpenVINO, models) ASSERT_FALSE(backendId != DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && backendId != DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) << "Inference Engine backend is required"; +#if INF_ENGINE_VER_MAJOR_EQ(2021040000) + if (targetId == DNN_TARGET_MYRIAD && ( + modelName == "person-detection-retail-0013" || // ncDeviceOpen:1013 Failed to find booted device after boot + modelName == "age-gender-recognition-retail-0013" // ncDeviceOpen:1013 Failed to find booted device after boot + ) + ) + applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_DNN_BACKEND_INFERENCE_ENGINE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); +#endif + #if INF_ENGINE_VER_MAJOR_GE(2020020000) if (targetId == DNN_TARGET_MYRIAD && backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019) { diff --git a/modules/dnn/test/test_onnx_importer.cpp b/modules/dnn/test/test_onnx_importer.cpp index 22a504df69..b8f45a204c 100644 --- a/modules/dnn/test/test_onnx_importer.cpp +++ b/modules/dnn/test/test_onnx_importer.cpp @@ -349,6 +349,7 @@ TEST_P(Test_ONNX_layers, Concatenation) if (target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER); } testONNXModels("concatenation"); + testONNXModels("concat_const_blobs"); } TEST_P(Test_ONNX_layers, Eltwise3D) diff --git a/modules/dnn/test/test_torch_importer.cpp b/modules/dnn/test/test_torch_importer.cpp index f1d636895b..48725f244e 100644 --- a/modules/dnn/test/test_torch_importer.cpp +++ b/modules/dnn/test/test_torch_importer.cpp @@ -290,9 +290,14 @@ TEST_P(Test_Torch_layers, net_padding) TEST_P(Test_Torch_layers, net_non_spatial) { -#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021030000) +#if defined(INF_ENGINE_RELEASE) && ( \ + INF_ENGINE_VER_MAJOR_EQ(2021030000) || \ + INF_ENGINE_VER_MAJOR_EQ(2021040000) \ +) if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD) - applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH); // crash + // 2021.3: crash + // 2021.4: [ GENERAL_ERROR ] AssertionFailed: !out.networkInputs.empty() + applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH); if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH); // exception if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL_FP16) diff --git a/samples/cpp/tutorial_code/Histograms_Matching/MatchTemplate_Demo.cpp b/samples/cpp/tutorial_code/Histograms_Matching/MatchTemplate_Demo.cpp index 5bcc878965..f9abbae945 100644 --- a/samples/cpp/tutorial_code/Histograms_Matching/MatchTemplate_Demo.cpp +++ b/samples/cpp/tutorial_code/Histograms_Matching/MatchTemplate_Demo.cpp @@ -89,7 +89,7 @@ void MatchingMethod( int, void* ) //! [create_result_matrix] /// Create the result matrix - int result_cols = img.cols - templ.cols + 1; + int result_cols = img.cols - templ.cols + 1; int result_rows = img.rows - templ.rows + 1; result.create( result_rows, result_cols, CV_32FC1 ); diff --git a/samples/cpp/tutorial_code/Histograms_Matching/calcBackProject_Demo1.cpp b/samples/cpp/tutorial_code/Histograms_Matching/calcBackProject_Demo1.cpp index 61b6d607ce..bcb547a2fb 100644 --- a/samples/cpp/tutorial_code/Histograms_Matching/calcBackProject_Demo1.cpp +++ b/samples/cpp/tutorial_code/Histograms_Matching/calcBackProject_Demo1.cpp @@ -72,18 +72,18 @@ void Hist_and_Backproj(int, void* ) //! [initialize] int histSize = MAX( bins, 2 ); float hue_range[] = { 0, 180 }; - const float* ranges = { hue_range }; + const float* ranges[] = { hue_range }; //! [initialize] //! [Get the Histogram and normalize it] Mat hist; - calcHist( &hue, 1, 0, Mat(), hist, 1, &histSize, &ranges, true, false ); + calcHist( &hue, 1, 0, Mat(), hist, 1, &histSize, ranges, true, false ); normalize( hist, hist, 0, 255, NORM_MINMAX, -1, Mat() ); //! [Get the Histogram and normalize it] //! [Get Backprojection] Mat backproj; - calcBackProject( &hue, 1, 0, hist, backproj, &ranges, 1, true ); + calcBackProject( &hue, 1, 0, hist, backproj, ranges, 1, true ); //! [Get Backprojection] //! [Draw the backproj] diff --git a/samples/cpp/tutorial_code/Histograms_Matching/calcHist_Demo.cpp b/samples/cpp/tutorial_code/Histograms_Matching/calcHist_Demo.cpp index 86167e519a..a7582e4282 100644 --- a/samples/cpp/tutorial_code/Histograms_Matching/calcHist_Demo.cpp +++ b/samples/cpp/tutorial_code/Histograms_Matching/calcHist_Demo.cpp @@ -37,7 +37,7 @@ int main(int argc, char** argv) //! [Set the ranges ( for B,G,R) )] float range[] = { 0, 256 }; //the upper boundary is exclusive - const float* histRange = { range }; + const float* histRange[] = { range }; //! [Set the ranges ( for B,G,R) )] //! [Set histogram param] @@ -46,9 +46,9 @@ int main(int argc, char** argv) //! [Compute the histograms] Mat b_hist, g_hist, r_hist; - calcHist( &bgr_planes[0], 1, 0, Mat(), b_hist, 1, &histSize, &histRange, uniform, accumulate ); - calcHist( &bgr_planes[1], 1, 0, Mat(), g_hist, 1, &histSize, &histRange, uniform, accumulate ); - calcHist( &bgr_planes[2], 1, 0, Mat(), r_hist, 1, &histSize, &histRange, uniform, accumulate ); + calcHist( &bgr_planes[0], 1, 0, Mat(), b_hist, 1, &histSize, histRange, uniform, accumulate ); + calcHist( &bgr_planes[1], 1, 0, Mat(), g_hist, 1, &histSize, histRange, uniform, accumulate ); + calcHist( &bgr_planes[2], 1, 0, Mat(), r_hist, 1, &histSize, histRange, uniform, accumulate ); //! [Compute the histograms] //! [Draw the histograms for B, G and R] diff --git a/samples/python/camera_calibration_show_extrinsics.py b/samples/python/camera_calibration_show_extrinsics.py index 0118b5b913..d676691f15 100755 --- a/samples/python/camera_calibration_show_extrinsics.py +++ b/samples/python/camera_calibration_show_extrinsics.py @@ -188,7 +188,7 @@ def main(): fig = plt.figure() ax = fig.gca(projection='3d') - ax.set_aspect("equal") + ax.set_aspect("auto") cam_width = args.cam_width cam_height = args.cam_height diff --git a/samples/python/gaussian_mix.py b/samples/python/gaussian_mix.py index 5f2dfcc440..6a656647dd 100755 --- a/samples/python/gaussian_mix.py +++ b/samples/python/gaussian_mix.py @@ -32,7 +32,7 @@ def draw_gaussain(img, mean, cov, color): w, u, _vt = cv.SVDecomp(cov) ang = np.arctan2(u[1, 0], u[0, 0])*(180/np.pi) s1, s2 = np.sqrt(w)*3.0 - cv.ellipse(img, (x, y), (s1, s2), ang, 0, 360, color, 1, cv.LINE_AA) + cv.ellipse(img, (int(x), int(y)), (int(s1), int(s2)), ang, 0, 360, color, 1, cv.LINE_AA) def main(): diff --git a/samples/python/hist.py b/samples/python/hist.py index 4c2c1ad395..157d5ff0ba 100755 --- a/samples/python/hist.py +++ b/samples/python/hist.py @@ -48,7 +48,7 @@ def hist_lines(im): cv.normalize(hist_item,hist_item,0,255,cv.NORM_MINMAX) hist=np.int32(np.around(hist_item)) for x,y in enumerate(hist): - cv.line(h,(x,0),(x,y),(255,255,255)) + cv.line(h,(x,0),(x,y[0]),(255,255,255)) y = np.flipud(h) return y diff --git a/samples/python/lk_homography.py b/samples/python/lk_homography.py index 808f30965f..38a05f63b6 100755 --- a/samples/python/lk_homography.py +++ b/samples/python/lk_homography.py @@ -77,8 +77,8 @@ class App: for (x0, y0), (x1, y1), good in zip(self.p0[:,0], self.p1[:,0], status[:,0]): if good: - cv.line(vis, (x0, y0), (x1, y1), (0, 128, 0)) - cv.circle(vis, (x1, y1), 2, (red, green)[good], -1) + cv.line(vis, (int(x0), int(y0)), (int(x1), int(y1)), (0, 128, 0)) + cv.circle(vis, (int(x1), int(y1)), 2, (red, green)[good], -1) draw_str(vis, (20, 20), 'track count: %d' % len(self.p1)) if self.use_ransac: draw_str(vis, (20, 40), 'RANSAC') @@ -86,7 +86,7 @@ class App: p = cv.goodFeaturesToTrack(frame_gray, **feature_params) if p is not None: for x, y in p[:,0]: - cv.circle(vis, (x, y), 2, green, -1) + cv.circle(vis, (int(x), int(y)), 2, green, -1) draw_str(vis, (20, 20), 'feature count: %d' % len(p)) cv.imshow('lk_homography', vis) diff --git a/samples/python/lk_track.py b/samples/python/lk_track.py index 7b77f1b335..97a8c40241 100755 --- a/samples/python/lk_track.py +++ b/samples/python/lk_track.py @@ -65,7 +65,7 @@ class App: if len(tr) > self.track_len: del tr[0] new_tracks.append(tr) - cv.circle(vis, (x, y), 2, (0, 255, 0), -1) + cv.circle(vis, (int(x), int(y)), 2, (0, 255, 0), -1) self.tracks = new_tracks cv.polylines(vis, [np.int32(tr) for tr in self.tracks], False, (0, 255, 0)) draw_str(vis, (20, 20), 'track count: %d' % len(self.tracks)) diff --git a/samples/python/video_v4l2.py b/samples/python/video_v4l2.py index 61b1e35804..abebb2a2ca 100644 --- a/samples/python/video_v4l2.py +++ b/samples/python/video_v4l2.py @@ -30,7 +30,7 @@ def main(): color = (0, 255, 0) cap = cv.VideoCapture(0) - cap.set(cv.CAP_PROP_AUTOFOCUS, False) # Known bug: https://github.com/opencv/opencv/pull/5474 + cap.set(cv.CAP_PROP_AUTOFOCUS, 0) # Known bug: https://github.com/opencv/opencv/pull/5474 cv.namedWindow("Video") @@ -67,7 +67,7 @@ def main(): break elif k == ord('g'): convert_rgb = not convert_rgb - cap.set(cv.CAP_PROP_CONVERT_RGB, convert_rgb) + cap.set(cv.CAP_PROP_CONVERT_RGB, 1 if convert_rgb else 0) print('Done')