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-rw-r--r--recipes-support/opencv/opencv/OpenCV_DNN_examples.patch141
1 files changed, 141 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..ef7831a5
--- /dev/null
+++ b/recipes-support/opencv/opencv/OpenCV_DNN_examples.patch
@@ -0,0 +1,141 @@
+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
+@@ -28,7 +28,7 @@ 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
+@@ -12,6 +12,7 @@ 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.}"
+ "{ initial_width | 0 | Preprocess input image by initial resizing to a specific width.}"
+ "{ initial_height | 0 | Preprocess input image by initial resizing to a specific height.}"
+@@ -113,7 +114,7 @@ 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.
+@@ -195,14 +196,14 @@ int main(int argc, char** argv)
+ }
+ std::string label = format("Inference time of 1 round: %.2f ms", t1);
+ std::string label2 = format("Average time of 200 rounds: %.2f ms", timeRecorder.getTimeMilli()/200);
+- putText(frame, label, Point(0, 15), FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0, 255, 0));
+- putText(frame, label2, Point(0, 35), 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);
++ putText(frame, label2, Point(0, 45), 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, 55), 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);
+ }
+Index: git/samples/dnn/object_detection.cpp
+===================================================================
+--- git.orig/samples/dnn/object_detection.cpp
++++ git/samples/dnn/object_detection.cpp
+@@ -260,13 +260,13 @@ 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);
+ }
+@@ -302,7 +302,7 @@ 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);
+ }
+@@ -471,7 +471,7 @@ 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
+@@ -162,7 +162,7 @@ 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
+@@ -30,6 +30,7 @@ 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. }"
+ "{ detModel dmp | | Path to a binary .pb file contains trained detector network.}"
+ "{ width | 320 | Preprocess input image by resizing to a specific width. It should be multiple by 32. }"
+ "{ height | 320 | Preprocess input image by resizing to a specific height. It should be multiple by 32. }"
+@@ -106,7 +107,7 @@ 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";
generated by cgit 1.2.3-korg (git 2.39.0) at 2024-05-02 10:51:10 +0000