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vendor: OpenCV 5.0.0 snapshot at 40738fb16ceddb5fb3fea747585f7ce6abb0605b

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/*
This file is part of OpenCV project.
It is subject to the license terms in the LICENSE file found in the top-level directory
of this distribution and at http://opencv.org/license.html.
This sample inpaints the masked area in the given image.
Copyright (C) 2025, Bigvision LLC.
How to use:
Sample command to run:
./example_dnn_inpainting
The system will ask you to draw the mask on area to be inpainted
You can download lama inpainting model using:
`python download_models.py lama`
References:
Github: https://github.com/advimman/lama
ONNX model: https://huggingface.co/Carve/LaMa-ONNX/blob/main/lama_fp32.onnx
ONNX model was further quantized using block quantization from [opencv_zoo](https://github.com/opencv/opencv_zoo)
Set environment variable OPENCV_DOWNLOAD_CACHE_DIR to point to the directory where models are downloaded. Also, point OPENCV_SAMPLES_DATA_PATH to opencv/samples/data.
*/
#include <iostream>
#include <fstream>
#include <opencv2/imgproc.hpp>
#include <opencv2/highgui.hpp>
#include <opencv2/dnn.hpp>
#include "common.hpp"
using namespace cv;
using namespace dnn;
using namespace std;
const string about = "Use this script for image inpainting using OpenCV. \n\n"
"Firstly, download required models i.e. lama using `download_models.py` (if not already done). Set environment variable OPENCV_DOWNLOAD_CACHE_DIR to point to the directory where models are downloaded. Also, point OPENCV_SAMPLES_DATA_PATH to opencv/samples/data.\n"
"To run:\n"
"\t Example: ./example_dnn_inpainting [--input=<image_name>] \n\n"
"Inpainting model path can also be specified using --model argument.\n\n";
const string keyboard_shorcuts = "Keyboard Shorcuts: \n\n"
"Press 'i' to increase brush size.\n"
"Press 'd' to decrease brush size.\n"
"Press 'r' to reset mask.\n"
"Press ' ' (space bar) after selecting area to be inpainted.\n"
"Press ESC to terminate the program.\n\n";
const string param_keys =
"{ help h | | show help message}"
"{ @alias | lama | An alias name of model to extract preprocessing parameters from models.yml file. }"
"{ zoo | ../dnn/models.yml | An optional path to file with preprocessing parameters }"
"{ input i | rubberwhale1.png | image file path}";
const string backend_keys = format(
"{ backend | default | Choose one of computation backends: "
"default: automatically (by default), "
"openvino: Intel's Deep Learning Inference Engine (https://software.intel.com/openvino-toolkit), "
"opencv: OpenCV implementation, "
"vkcom: VKCOM, "
"cuda: CUDA, "
"webnn: WebNN }");
const string target_keys = format(
"{ target | cpu | Choose one of target computation devices: "
"cpu: CPU target (by default), "
"opencl: OpenCL, "
"opencl_fp16: OpenCL fp16 (half-float precision), "
"vpu: VPU, "
"vulkan: Vulkan, "
"cuda: CUDA, "
"cuda_fp16: CUDA fp16 (half-float preprocess) }");
string keys = param_keys + backend_keys + target_keys;
bool drawing = false;
Mat maskGray;
int brush_size = 15;
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("@alias") || parser.has("help"))
{
cout<<about<<endl;
parser.printMessage();
return 0;
}
string modelName = parser.get<String>("@alias");
string zooFile = findFile(parser.get<String>("zoo"));
keys += genPreprocArguments(modelName, zooFile);
parser = CommandLineParser(argc, argv, keys);
parser.about("Use this script to run image inpainting using OpenCV.");
const string sha1 = parser.get<String>("sha1");
const string modelPath = findModel(parser.get<String>("model"), sha1);
string imgPath = parser.get<String>("input");
const string backend = parser.get<String>("backend");
const string target = parser.get<String>("target");
int height = parser.get<int>("height");
int width = parser.get<int>("width");
float scale = parser.get<float>("scale");
bool swapRB = parser.get<bool>("rgb");
Scalar mean_v = parser.get<Scalar>("mean");
int stdSize = 20;
int stdWeight = 400;
int stdImgSize = 512;
int imgWidth = -1; // Initialization
int fontSize = 60;
int fontWeight = 500;
cout<<"Model loading..."<<endl;
EngineType engine = ENGINE_AUTO;
if (backend != "default" || target != "cpu"){
engine = ENGINE_CLASSIC;
}
Net net = readNetFromONNX(modelPath, engine);
net.setPreferableBackend(getBackendID(backend));
net.setPreferableTarget(getTargetID(target));
FontFace fontFace("sans");
Mat input_image = imread(findFile(imgPath));
if (input_image.empty()) {
cerr << "Error: Input image could not be loaded." << endl;
return -1;
}
double aspectRatio = static_cast<double>(input_image.rows) / static_cast<double>(input_image.cols);
int h = static_cast<int>(width * aspectRatio);
resize(input_image, input_image, Size(width, h));
Mat image = input_image.clone();
imgWidth = min(input_image.rows, input_image.cols);
fontSize = min(fontSize, (stdSize*imgWidth)/stdImgSize);
fontWeight = min(fontWeight, (stdWeight*imgWidth)/stdImgSize);
cout<<keyboard_shorcuts<<endl;
const string label = "Press 'i' to increase, 'd' to decrease brush size. And 'r' to reset mask. ";
double alpha = 0.5;
Rect r = getTextSize(Size(), label, Point(), fontFace, fontSize, fontWeight);
r.height += 2 * fontSize; // padding
r.width += 10; // padding
// Setting up window
namedWindow("Draw Mask");
setMouseCallback("Draw Mask", drawMask);
Mat tempImage = input_image.clone();
Mat overlay = input_image.clone();
rectangle(overlay, r, Scalar::all(255), FILLED);
addWeighted(overlay, alpha, tempImage, 1 - alpha, 0, tempImage);
putText(tempImage, "Draw the mask on the image. Press space bar when done", Point(10, fontSize), Scalar(0,0,0), fontFace, fontSize, fontWeight);
putText(tempImage, label, Point(10, 2*fontSize), Scalar(0,0,0), fontFace, fontSize, fontWeight);
Mat displayImage = tempImage.clone();
for (;;) {
maskGray = Mat::zeros(input_image.size(), CV_8U);
displayImage = tempImage.clone();
for(;;) {
displayImage.setTo(Scalar(255, 255, 255), maskGray > 0); // Highlight mask area
imshow("Draw Mask", displayImage);
int key = waitKey(30) & 255;
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 == 'r') {
maskGray = Mat::zeros(image.size(), CV_8U);
displayImage = tempImage.clone();
cout << "Mask cleared." << endl;
} else if (key == ' ') {
break;
} else if (key == 27){
return -1;
}
}
cout<<"Processing image..."<<endl;
// Inference block
Mat image_blob = blobFromImage(image, scale, Size(width, height), mean_v, swapRB, false);
Mat mask_blob;
mask_blob = blobFromImage(maskGray, 1.0, Size(width, height), 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();
// Post Processing
Mat output_transposed(3, &output.size[1], CV_32F, 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)));
}
Mat output_image;
merge(channels, output_image);
output_image.convertTo(output_image, CV_8U);
resize(output_image, output_image, Size(width, h));
image = output_image;
imshow("Inpainted Output", output_image);
}
return 0;
}