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-rw-r--r--recipes-support/opencv/opencv/OpenCV_DNN_examples.patch148
1 files changed, 148 insertions, 0 deletions
diff --git a/recipes-support/opencv/opencv/OpenCV_DNN_examples.patch b/recipes-support/opencv/opencv/OpenCV_DNN_examples.patch
new file mode 100644
index 00000000..569d5b02
--- /dev/null
+++ b/recipes-support/opencv/opencv/OpenCV_DNN_examples.patch
@@ -0,0 +1,148 @@
+From 3c4daafb54f961e376104a461ca7ec114ff0331a Mon Sep 17 00:00:00 2001
+From: Ludek Slosarcik <ludek.slosarcik@nxp.com>
+Date: Fri, 14 Feb 2020 15:46:50 +0100
+Subject: [PATCH] opencv_dnn: added video device for 2 examples, and change text labels
+
+Signed-off-by: Ludek Slosarcik <ludek.slosarcik@nxp.com>
+
+Upstream-Status: Pending
+---
+ samples/cpp/logistic_regression.cpp | 2 +-
+ samples/dnn/classification.cpp | 7 ++++---
+ samples/dnn/object_detection.cpp | 10 +++++-----
+ samples/dnn/segmentation.cpp | 2 +-
+ samples/dnn/text_detection.cpp | 5 +++--
+ 5 files changed, 14 insertions(+), 12 deletions(-)
+
+Index: git/samples/cpp/logistic_regression.cpp
+===================================================================
+--- git.orig/samples/cpp/logistic_regression.cpp
++++ git/samples/cpp/logistic_regression.cpp
+@@ -83,7 +83,7 @@ static float calculateAccuracyPercent(co
+
+ int main()
+ {
+- const String filename = samples::findFile("data01.xml");
++ const String filename = samples::findFile("../data/data01.xml");
+ cout << "**********************************************************************" << endl;
+ cout << filename
+ << " contains digits 0 and 1 of 20 samples each, collected on an Android device" << endl;
+Index: git/samples/dnn/classification.cpp
+===================================================================
+--- git.orig/samples/dnn/classification.cpp
++++ git/samples/dnn/classification.cpp
+@@ -11,6 +11,7 @@ std::string keys =
+ "{ help h | | Print help message. }"
+ "{ @alias | | An alias name of model to extract preprocessing parameters from models.yml file. }"
+ "{ zoo | models.yml | An optional path to file with preprocessing parameters }"
++ "{ device | 0 | camera device number. }"
+ "{ input i | | Path to input image or video file. Skip this argument to capture frames from a camera.}"
+ "{ framework f | | Optional name of an origin framework of the model. Detect it automatically if it does not set. }"
+ "{ classes | | Optional path to a text file with names of classes. }"
+@@ -94,7 +95,7 @@ int main(int argc, char** argv)
+ if (parser.has("input"))
+ cap.open(parser.get<String>("input"));
+ else
+- cap.open(0);
++ cap.open(parser.get<int>("device"));
+ //! [Open a video file or an image file or a camera stream]
+
+ // Process frames.
+@@ -131,13 +132,13 @@ int main(int argc, char** argv)
+ double freq = getTickFrequency() / 1000;
+ double t = net.getPerfProfile(layersTimes) / freq;
+ std::string label = format("Inference time: %.2f ms", t);
+- putText(frame, label, Point(0, 15), FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0, 255, 0));
++ putText(frame, label, Point(0, 20), FONT_HERSHEY_SIMPLEX, 0.8, Scalar(0, 0, 255), 2, 8, false);
+
+ // Print predicted class.
+ label = format("%s: %.4f", (classes.empty() ? format("Class #%d", classId).c_str() :
+ classes[classId].c_str()),
+ confidence);
+- putText(frame, label, Point(0, 40), FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0, 255, 0));
++ putText(frame, label, Point(0, 45), FONT_HERSHEY_SIMPLEX, 0.8, Scalar(0, 0, 255), 2, 8, false);
+
+ imshow(kWinName, frame);
+ }
+Index: git/samples/dnn/object_detection.cpp
+===================================================================
+--- git.orig/samples/dnn/object_detection.cpp
++++ git/samples/dnn/object_detection.cpp
+@@ -251,13 +251,13 @@ int main(int argc, char** argv)
+ if (predictionsQueue.counter > 1)
+ {
+ std::string label = format("Camera: %.2f FPS", framesQueue.getFPS());
+- putText(frame, label, Point(0, 15), FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0, 255, 0));
++ putText(frame, label, Point(0, 20), FONT_HERSHEY_SIMPLEX, 0.8, Scalar(0, 0, 255), 2, 8, false);
+
+ label = format("Network: %.2f FPS", predictionsQueue.getFPS());
+- putText(frame, label, Point(0, 30), FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0, 255, 0));
++ putText(frame, label, Point(0, 45), FONT_HERSHEY_SIMPLEX, 0.8, Scalar(0, 0, 255), 2, 8, false);
+
+ label = format("Skipped frames: %d", framesQueue.counter - predictionsQueue.counter);
+- putText(frame, label, Point(0, 45), FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0, 255, 0));
++ putText(frame, label, Point(0, 70), FONT_HERSHEY_SIMPLEX, 0.8, Scalar(0, 0, 255), 2, 8, false);
+ }
+ imshow(kWinName, frame);
+ }
+@@ -293,7 +293,7 @@ int main(int argc, char** argv)
+ double freq = getTickFrequency() / 1000;
+ double t = net.getPerfProfile(layersTimes) / freq;
+ std::string label = format("Inference time: %.2f ms", t);
+- putText(frame, label, Point(0, 15), FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0, 255, 0));
++ putText(frame, label, Point(0, 20), FONT_HERSHEY_SIMPLEX, 0.8, Scalar(0, 0, 255), 2, 8, false);
+
+ imshow(kWinName, frame);
+ }
+@@ -462,7 +462,7 @@ void drawPred(int classId, float conf, i
+ top = max(top, labelSize.height);
+ rectangle(frame, Point(left, top - labelSize.height),
+ Point(left + labelSize.width, top + baseLine), Scalar::all(255), FILLED);
+- putText(frame, label, Point(left, top), FONT_HERSHEY_SIMPLEX, 0.5, Scalar());
++ putText(frame, label, Point(left, top), FONT_HERSHEY_SIMPLEX, 0.8, Scalar());
+ }
+
+ void callback(int pos, void*)
+Index: git/samples/dnn/segmentation.cpp
+===================================================================
+--- git.orig/samples/dnn/segmentation.cpp
++++ git/samples/dnn/segmentation.cpp
+@@ -157,7 +157,7 @@ int main(int argc, char** argv)
+ double freq = getTickFrequency() / 1000;
+ double t = net.getPerfProfile(layersTimes) / freq;
+ std::string label = format("Inference time: %.2f ms", t);
+- putText(frame, label, Point(0, 15), FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0, 255, 0));
++ putText(frame, label, Point(0, 20), FONT_HERSHEY_SIMPLEX, 0.8, Scalar(0, 0, 255), 2, 8, false);
+
+ imshow(kWinName, frame);
+ if (!classes.empty())
+Index: git/samples/dnn/text_detection.cpp
+===================================================================
+--- git.orig/samples/dnn/text_detection.cpp
++++ git/samples/dnn/text_detection.cpp
+@@ -25,6 +25,7 @@ using namespace cv::dnn;
+ const char* keys =
+ "{ help h | | Print help message. }"
+ "{ input i | | Path to input image or video file. Skip this argument to capture frames from a camera.}"
++ "{ device | 0 | camera device number. }"
+ "{ model m | | Path to a binary .pb file contains trained detector network.}"
+ "{ ocr | | Path to a binary .pb or .onnx file contains trained recognition network.}"
+ "{ width | 320 | Preprocess input image by resizing to a specific width. It should be multiple by 32. }"
+@@ -75,7 +76,7 @@ int main(int argc, char** argv)
+
+ // Open a video file or an image file or a camera stream.
+ VideoCapture cap;
+- bool openSuccess = parser.has("input") ? cap.open(parser.get<String>("input")) : cap.open(0);
++ bool openSuccess = parser.has("input") ? cap.open(parser.get<String>("input")) : cap.open(parser.get<int>("device"));
+ CV_Assert(openSuccess);
+
+ static const std::string kWinName = "EAST: An Efficient and Accurate Scene Text Detector";
+@@ -156,7 +157,7 @@ int main(int argc, char** argv)
+
+ // Put efficiency information.
+ std::string label = format("Inference time: %.2f ms", tickMeter.getTimeMilli());
+- putText(frame, label, Point(0, 15), FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0, 255, 0));
++ putText(frame, label, Point(0, 20), FONT_HERSHEY_SIMPLEX, 0.8, Scalar(0, 0, 255), 2, 8, false);
+
+ imshow(kWinName, frame);
+
generated by cgit 1.2.3-korg (git 2.39.0) at 2024-04-26 18:51:46 +0000