Added lama quantized model for inpainting DNN sample. (#284)

models/inpainting_lama/CMakeLists.txt

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+cmake_minimum_required(VERSION 3.22.1)
+project(opencv_zoo_inpainting_lama)
+
+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(demo demo.cpp)
+target_link_libraries(demo ${OpenCV_LIBS})

models/inpainting_lama/LICENSE

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models/inpainting_lama/README.md

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+# Lama
+
+LaMa is a very lightweight yet powerful image inpainting model.
+
+Notes:
+
+- Model source: [ONNX](https://huggingface.co/Carve/LaMa-ONNX/blob/main/lama_fp32.onnx).
+
+## Requirements 
+Install latest OpenCV >=5.0.0 and CMake >= 3.22.1 to get started with.
+
+## Demo
+
+### Python
+
+Run the following command to try the demo:
+
+```shell
+# usage
+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
+
+# usage
+./build/demo --input=/path/to/image
+# get help messages
+./build/demo -h
+```
+
+### Example outputs
+
+![chicky](./example_outputs/squirrel_output.jpg)
+
+## License
+
+All files in this directory are licensed under [Apache License](./LICENSE).
+
+## Reference
+
+- https://github.com/advimman/lama

models/inpainting_lama/demo.cpp

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+/*
+This sample inpaints the masked area in the given image.
+
+Copyright (C) 2025, Bigvision LLC.
+*/
+
+#include <iostream>
+#include <fstream>
+
+#include <opencv2/imgproc.hpp>
+#include <opencv2/highgui.hpp>
+#include <opencv2/dnn.hpp>
+
+using namespace cv;
+using namespace dnn;
+using namespace std;
+
+class Lama {
+public:
+    Lama(const string& modelPath) {
+        loadModel(modelPath);
+    }
+
+    // Function to set up the input image and process it
+    void process(const Mat& image, const Mat& mask, Mat& result) {
+        double aspectRatio = static_cast<double>(image.rows) / static_cast<double>(image.cols);
+
+        Mat image_blob = blobFromImage(image, 1.0/255.0, Size(512, 512), Scalar(0, 0, 0), false, false, CV_32F);
+        Mat mask_blob = blobFromImage(mask, 1.0, Size(512, 512), Scalar(0), false, false);
+
+        mask_blob = (mask_blob > 0);
+        mask_blob.convertTo(mask_blob, CV_32F);
+        mask_blob = mask_blob/255.0;
+
+        net.setInput(image_blob, "image");
+        net.setInput(mask_blob, "mask");
+
+        Mat output = net.forward();
+
+        postProcess(output, result, aspectRatio);
+    }
+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, double aspectRatio) {
+        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_8U);
+
+        int h = static_cast<int>(512 * aspectRatio);
+        resize(result, result, Size(512, h));
+    }
+};
+
+
+const string about = "This sample demonstrates image inpainting with lama inpainting technique.\n\n";
+
+const string keys =
+    "{help    h  |                              | show help message}"
+    "{input   i  |                              | Path to input image}"
+    "{ model     | inpainting_lama_2024jan.onnx | Path to the lama onnx model file }";
+
+bool drawing = false;
+Mat maskGray;
+int brush_size = 25;
+
+static void drawMask(int event, int x, int y, int, void*) {
+    if (event == EVENT_LBUTTONDOWN) {
+        drawing = true;
+    } else if (event == EVENT_MOUSEMOVE) {
+        if (drawing) {
+            circle(maskGray, Point(x, y), brush_size, Scalar(255), -1);
+        }
+    } else if (event == EVENT_LBUTTONUP) {
+        drawing = false;
+    }
+}
+
+int main(int argc, char **argv)
+{
+    CommandLineParser parser(argc, argv, keys);
+
+    if (parser.has("help"))
+    {
+        cout<<about<<endl;
+        parser.printMessage();
+        return 0;
+    }
+    parser = CommandLineParser(argc, argv, keys);
+    parser.about(about);
+
+    const string model = parser.get<String>("model");
+
+    int height = 512;
+    int width = 512;
+    int stdSize = 20;
+    int stdWeight = 400;
+    int stdImgSize = 512;
+    int imgWidth = -1; // Initialization
+    int fontSize = 50;
+    int fontWeight = 500;
+
+    FontFace fontFace("sans");
+    Lama lama(model);
+
+    Mat image = imread(parser.get<String>("input"));
+    if (image.empty()) {
+        cerr << "Error: Input image could not be loaded." << endl;
+        return -1;
+    }
+
+    imgWidth = min(image.rows, image.cols);
+    fontSize = min(fontSize, (stdSize*imgWidth)/stdImgSize);
+    fontWeight = min(fontWeight, (stdWeight*imgWidth)/stdImgSize);
+
+    maskGray = Mat::zeros(image.size(), CV_8U);
+
+    namedWindow("Draw Mask");
+    setMouseCallback("Draw Mask", drawMask);
+
+    const string label = "Draw the mask on the image. Press space bar when done ";
+
+    for(;;) {
+        Mat displayImage = image.clone();
+        Mat overlay = image.clone();
+
+        double alpha = 0.5;
+        Rect r = getTextSize(Size(), label, Point(), fontFace, fontSize, fontWeight);
+        r.height += 2 * fontSize; // padding
+        r.width += 10; // padding
+        rectangle(overlay, r, Scalar::all(255), FILLED);
+        addWeighted(overlay, alpha, displayImage, 1 - alpha, 0, displayImage);
+        putText(displayImage, label, Point(10, fontSize), Scalar(0,0,0), fontFace, fontSize, fontWeight);
+        putText(displayImage, "Press 'i' to increase and 'd' to decrease brush size", Point(10, 2*fontSize), Scalar(0,0,0), fontFace, fontSize, fontWeight);
+
+        displayImage.setTo(Scalar(255, 255, 255), maskGray > 0); // Highlight mask area
+        imshow("Draw Mask", displayImage);
+
+        char key = waitKey(1);
+        if (key == 'i') {
+            brush_size += 1;
+            cout << "Brush size increased to " << brush_size << endl;
+        } else if (key == 'd') {
+            brush_size = max(1, brush_size - 1);
+            cout << "Brush size decreased to " << brush_size << endl;
+        } else if (key == ' ') {
+            break;
+        } else if (key == 27){
+            return -1;
+        }
+    }
+    destroyAllWindows();
+
+    Mat result;
+    lama.process(image, maskGray, result);
+
+    imshow("Inpainted Output", result);
+    waitKey(0);
+
+    return 0;
+}

models/inpainting_lama/demo.py

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+import cv2 as cv
+import numpy as np
+import argparse
+from lama import Lama
+
+def get_args_parser(func_args):
+    parser = argparse.ArgumentParser(add_help=False)
+    parser.add_argument('--input', help='Path to input image', default=0, required=False)
+    parser.add_argument('--model', help='Path to lama onnx', default='inpainting_lama_2025jan.onnx', required=False)
+
+    parser = argparse.ArgumentParser(parents=[parser],
+                                     description='', formatter_class=argparse.RawTextHelpFormatter)
+    return parser.parse_args(func_args)
+
+drawing = False
+mask_gray = None
+brush_size = 15
+
+def draw_mask(event, x, y, flags, param):
+    global drawing, mask_gray, brush_size
+    if event == cv.EVENT_LBUTTONDOWN:
+        drawing = True
+    elif event == cv.EVENT_MOUSEMOVE:
+        if drawing:
+            cv.circle(mask_gray, (x, y), brush_size, (255), thickness=-1)
+    elif event == cv.EVENT_LBUTTONUP:
+        drawing = False
+
+def main(func_args=None):
+    global mask_gray, brush_size
+    args = get_args_parser(func_args)
+
+    lama = Lama(modelPath=args.model)
+    input_image = cv.imread(args.input)
+    mask_gray = np.zeros((input_image.shape[0], input_image.shape[1]), dtype=np.uint8)
+
+    stdSize = 0.6
+    stdWeight = 2
+    stdImgSize = 512
+    imgWidth = min(input_image.shape[:2])
+    fontSize = min(1.5, (stdSize*imgWidth)/stdImgSize)
+    fontThickness = max(1,(stdWeight*imgWidth)//stdImgSize)
+
+    cv.namedWindow("Draw Mask")
+    cv.setMouseCallback("Draw Mask", draw_mask)
+
+    label = "Draw the mask on the image. Press space bar when done."
+    labelSize, _ = cv.getTextSize(label, cv.FONT_HERSHEY_SIMPLEX, fontSize, fontThickness)
+    while True:
+        display_image = input_image.copy()
+        overlay = input_image.copy()
+
+        alpha = 0.5
+        cv.rectangle(overlay, (0, 0), (labelSize[0]+10, labelSize[1]+int(30*fontSize)), (255, 255, 255), cv.FILLED)
+        cv.addWeighted(overlay, alpha, display_image, 1 - alpha, 0, display_image)
+
+        cv.putText(display_image, label, (10, int(25*fontSize)), cv.FONT_HERSHEY_SIMPLEX, fontSize, (0, 0, 0), fontThickness)
+        cv.putText(display_image, "Press 'i' to increase and 'd' to decrease brush size.", (10, int(50*fontSize)), cv.FONT_HERSHEY_SIMPLEX, fontSize, (0, 0, 0), fontThickness)
+        display_image[mask_gray > 0] = [255, 255, 255]
+        cv.imshow("Draw Mask", display_image)
+
+        key = cv.waitKey(1) & 0xFF
+        if key == ord('i'):  # Increase brush size
+            brush_size += 1
+            print(f"Brush size increased to {brush_size}")
+        elif key == ord('d'):  # Decrease brush size
+            brush_size = max(1, brush_size - 1)
+            print(f"Brush size decreased to {brush_size}")
+        elif key == ord(' '): # Press space bar to finish drawing
+            break
+        elif key == 27:
+            exit()
+    cv.destroyAllWindows()
+
+    tm = cv.TickMeter()
+    tm.start()
+    result = lama.infer(input_image, mask_gray)
+    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("Inpainted Output", result)
+    cv.waitKey(0)
+    cv.destroyAllWindows()
+
+if __name__ == '__main__':
+    main()

models/inpainting_lama/lama.py

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+import cv2 as cv
+import numpy as np
+
+class Lama:
+    def __init__(self, modelPath='inpainting_lama_2025jan.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, mask):
+        image_blob = cv.dnn.blobFromImage(image, 0.00392, (512, 512), (0,0,0), False, False)
+        mask_blob = cv.dnn.blobFromImage(mask, scalefactor=1.0, size=(512, 512), mean=(0,), swapRB=False, crop=False)
+        mask_blob = (mask_blob > 0).astype(np.float32)
+        
+        self._model.setInput(image_blob, "image")
+        self._model.setInput(mask_blob, "mask")
+
+        output = self._model.forward()
+
+        # Postprocessing
+        aspect_ratio = image.shape[0]/image.shape[1]
+        result = output[0]
+        result = np.transpose(result, (1, 2, 0))
+        result = (result).astype(np.uint8)
+        width = result.shape[1]
+        height = int(width*aspect_ratio)
+        result = cv.resize(result, (width, height))
+
+        return result