Added NAFNet quantized model for deblurring DNN sample (#295)

* Adding NAFNet deblurring model

* Added version check and made objects git-lfs

* Re-add images and ONNX under Git LFS

* Removed onnx model from git-lfs

* Added deblurring onnx model

CMakeLists.txt

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+cmake_minimum_required(VERSION 3.22.2)
+project(opencv_zoo_deblurring_nafnet)
+
+set(OPENCV_VERSION "5.0.0")
+set(OPENCV_INSTALLATION_PATH "" CACHE PATH "Where to look for OpenCV installation")
+
+# Find OpenCV
+find_package(OpenCV ${OPENCV_VERSION} REQUIRED HINTS ${OPENCV_INSTALLATION_PATH})
+
+add_executable(opencv_zoo_deblurring_nafnet demo.cpp)
+target_link_libraries(opencv_zoo_deblurring_nafnet ${OpenCV_LIBS})

LICENSE

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README.md

@@ -0,0 +1,54 @@
+# NAFNet
+
+NAFNet is a lightweight image deblurring model that eliminates nonlinear activations to achieve state-of-the-art performance with minimal computational cost.
+
+Notes:
+
+- Model source: [.pth](https://drive.google.com/file/d/14D4V4raNYIOhETfcuuLI3bGLB-OYIv6X/view).
+- ONNX Model link: [ONNX](https://drive.google.com/uc?export=dowload&id=1ZLRhkpCekNruJZggVpBgSoCx3k7bJ-5v)
+
+## Requirements 
+Install latest OpenCV >=5.0.0 and CMake >= 3.22.2 to get started with.
+
+## Demo
+
+### Python
+
+Run the following command to try the demo:
+
+```shell
+# deblur the default input image
+python demo.py
+# deblur the user input image
+python demo.py --input /path/to/image
+
+# get help regarding various parameters
+python demo.py --help
+```
+
+### C++
+
+```shell
+# A typical and default installation path of OpenCV is /usr/local
+cmake -B build -D OPENCV_INSTALLATION_PATH=/path/to/opencv/installation .
+cmake --build build
+
+# deblur the default input image
+./build/demo
+# deblur the user input image
+./build/demo --input=/path/to/image
+# get help messages
+./build/demo -h
+```
+
+### Example outputs
+
+![licenseplate_motion](./example_outputs/licenseplate_motion_output.jpg)
+
+## License
+
+All files in this directory are licensed under [MIT License](./LICENSE).
+
+## Reference
+
+- https://github.com/megvii-research/NAFNet

demo.cpp

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+#include <opencv2/dnn.hpp>
+#include <opencv2/imgproc.hpp>
+#include <opencv2/highgui.hpp>
+#include <iostream>
+#include <string>
+#include <cmath>
+#include <vector>
+
+using namespace cv;
+using namespace cv::dnn;
+using namespace std;
+
+class Nafnet {
+public:
+    Nafnet(const string& modelPath) {
+        loadModel(modelPath);
+    }
+
+    // Function to set up the input image and process it
+    void process(const Mat& image, Mat& result) {
+        Mat blob = blobFromImage(image, 0.00392, Size(image.cols, image.rows), Scalar(0, 0, 0), true, false, CV_32F);
+        net.setInput(blob);
+        Mat output = net.forward();
+        postProcess(output, result);
+    }
+
+private:
+    Net net;
+
+    // Load Model
+    void loadModel(const string modelPath) {
+        net = readNetFromONNX(modelPath);
+        net.setPreferableBackend(DNN_BACKEND_DEFAULT);
+        net.setPreferableTarget(DNN_TARGET_CPU);
+    }
+
+    void postProcess(const Mat& output, Mat& result) {
+        Mat output_transposed(3, &output.size[1], CV_32F, const_cast<void*>(reinterpret_cast<const void*>(output.ptr<float>())));
+
+        vector<Mat> channels;
+        for (int i = 0; i < 3; ++i) {
+            channels.push_back(Mat(output_transposed.size[1], output_transposed.size[2], CV_32F,
+                                        output_transposed.ptr<float>(i)));
+        }
+        merge(channels, result);
+        result.convertTo(result, CV_8UC3, 255.0);
+        cvtColor(result, result, COLOR_RGB2BGR);
+    }
+};
+
+int main(int argc, char** argv) {
+    const string about =
+        "This sample demonstrates deblurring with nafnet deblurring model.\n\n";
+    const string keys =
+        "{ help h          |                                         | Print help message. }"
+        "{ input i         | example_outputs/licenseplate_motion.jpg | Path to input image.}"
+        "{ model           |     deblurring_nafnet_2025may.onnx      | Path to the nafnet deblurring onnx model file }";
+
+    CommandLineParser parser(argc, argv, keys);
+    if (parser.has("help"))
+    {
+        cout << about << endl;
+        parser.printMessage();
+        return -1;
+    }
+
+    parser = CommandLineParser(argc, argv, keys);
+    string model = parser.get<String>("model");
+    parser.about(about);
+
+    Mat image = imread(parser.get<String>("input"));
+    if (image.empty()) {
+        cerr << "Error: Input image could not be loaded." << endl;
+        return -1;
+    }
+
+    // Create an instance of Dexined
+    Nafnet nafnet(model);
+
+    Mat result;
+    nafnet.process(image, result);
+
+    imshow("Input", image);
+    imshow("Output", result);
+    waitKey(0);
+
+    destroyAllWindows();
+    return 0;
+}

demo.py

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+import cv2 as cv
+import argparse
+
+# Check OpenCV version
+opencv_python_version = lambda str_version: tuple(map(int, [p.split('-')[0] for p in str_version.split('.')]))
+assert opencv_python_version(cv.__version__) >= opencv_python_version("5.0.0"), \
+       "Please install latest opencv-python for benchmark: python3 -m pip install --upgrade opencv-python"
+
+from nafnet import Nafnet
+
+def get_args_parser(func_args):
+    parser = argparse.ArgumentParser(add_help=False)
+    parser.add_argument('--input', help='Path to input image.', default='example_outputs/licenseplate_motion.jpg', required=False)
+    parser.add_argument('--model', help='Path to nafnet deblurring onnx model', default='deblurring_nafnet_2025may.onnx', required=False)
+
+    args, _ = parser.parse_known_args()
+    parser = argparse.ArgumentParser(parents=[parser],
+                                     description='', formatter_class=argparse.RawTextHelpFormatter)
+    return parser.parse_args(func_args)
+
+def main(func_args=None):
+    args = get_args_parser(func_args)
+
+    nafnet = Nafnet(modelPath=args.model)
+
+    input_image = cv.imread(args.input)
+
+    tm = cv.TickMeter()
+    tm.start()
+    result = nafnet.infer(input_image)
+    tm.stop()
+    label = 'Inference time: {:.2f} ms'.format(tm.getTimeMilli())
+    cv.putText(result, label, (0, 15), cv.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 0))
+
+    cv.imshow("Input image", input_image)
+    cv.imshow("Output image", result)
+    cv.waitKey(0)
+    cv.destroyAllWindows()
+
+if __name__ == '__main__':
+    main()

nafnet.py

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+import cv2 as cv
+import numpy as np
+
+class Nafnet:
+    def __init__(self, modelPath='deblurring_nafnet_2025may.onnx', backendId=0, targetId=0):
+        self._modelPath = modelPath
+        self._backendId = backendId
+        self._targetId = targetId
+        
+        # Load the model
+        self._model = cv.dnn.readNetFromONNX(self._modelPath)
+        self.setBackendAndTarget(self._backendId, self._targetId)
+
+    @property
+    def name(self):
+        return self.__class__.__name__
+
+    def setBackendAndTarget(self, backendId, targetId):
+        self._backendId = backendId
+        self._targetId = targetId
+        self._model.setPreferableBackend(self._backendId)
+        self._model.setPreferableTarget(self._targetId)
+
+    def infer(self, image):
+        image_blob = cv.dnn.blobFromImage(image, 0.00392, (image.shape[1], image.shape[0]), (0,0,0), True, False)
+
+        self._model.setInput(image_blob)
+        output = self._model.forward()
+
+        # Postprocessing
+        result = output[0]
+        result = np.transpose(result, (1, 2, 0))
+        result = np.clip(result * 255.0, 0, 255).astype(np.uint8)
+        result = cv.cvtColor(result, cv.COLOR_RGB2BGR)
+
+        return result