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vendor: OpenCV 5.0.0 snapshot at 40738fb16ceddb5fb3fea747585f7ce6abb0605b

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2026-08-22 00:10:33 +08:00
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#include <fstream>
#include <sstream>
#include <iostream>
#include <opencv2/dnn.hpp>
#include <opencv2/imgproc.hpp>
#include <opencv2/highgui.hpp>
#include <opencv2/core/utils/logger.hpp>
#include "common.hpp"
using namespace cv;
using namespace std;
using namespace dnn;
const string about =
"Use this script to run semantic segmentation deep learning networks using OpenCV.\n\n"
"Firstly, download required models using `download_models.py` (if not already done). Set environment variable OPENCV_DOWNLOAD_CACHE_DIR to specify where models should be downloaded. Also, point OPENCV_SAMPLES_DATA_PATH to opencv/samples/data.\n"
"To run:\n"
"\t ./example_dnn_classification modelName(e.g. u2netp) --input=$OPENCV_SAMPLES_DATA_PATH/butterfly.jpg (or ignore this argument to use device camera)\n"
"Model path can also be specified using --model argument.";
const string param_keys =
"{ help h | | Print help message. }"
"{ @alias | | 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 }"
"{ device | 0 | camera device number. }"
"{ input i | | Path to input image or video file. Skip this argument to capture frames from a camera. }"
"{ colors | | Optional path to a text file with colors for an every class. "
"Every color is represented with three values from 0 to 255 in BGR channels order. }";
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;
vector<string> labels;
vector<Vec3b> colors;
static void colorizeSegmentation(const Mat &score, Mat &segm)
{
const int rows = score.size[2];
const int cols = score.size[3];
const int chns = score.size[1];
if (colors.empty())
{
// Generate colors.
colors.push_back(Vec3b());
for (int i = 1; i < chns; ++i)
{
Vec3b color;
for (int j = 0; j < 3; ++j)
color[j] = (colors[i - 1][j] + rand() % 256) / 2;
colors.push_back(color);
}
}
else if (chns != (int)colors.size())
{
CV_Error(Error::StsError, format("Number of output labels does not match "
"number of colors (%d != %zu)",
chns, colors.size()));
}
Mat maxCl = Mat::zeros(rows, cols, CV_8UC1);
Mat maxVal(rows, cols, CV_32FC1, score.data);
for (int ch = 1; ch < chns; ch++)
{
for (int row = 0; row < rows; row++)
{
const float *ptrScore = score.ptr<float>(0, ch, row);
uint8_t *ptrMaxCl = maxCl.ptr<uint8_t>(row);
float *ptrMaxVal = maxVal.ptr<float>(row);
for (int col = 0; col < cols; col++)
{
if (ptrScore[col] > ptrMaxVal[col])
{
ptrMaxVal[col] = ptrScore[col];
ptrMaxCl[col] = (uchar)ch;
}
}
}
}
segm.create(rows, cols, CV_8UC3);
for (int row = 0; row < rows; row++)
{
const uchar *ptrMaxCl = maxCl.ptr<uchar>(row);
Vec3b *ptrSegm = segm.ptr<Vec3b>(row);
for (int col = 0; col < cols; col++)
{
ptrSegm[col] = colors[ptrMaxCl[col]];
}
}
}
static void showLegend(FontFace fontFace)
{
static const int kBlockHeight = 30;
static Mat legend;
if (legend.empty())
{
const int numClasses = (int)labels.size();
if ((int)colors.size() != numClasses)
{
CV_Error(Error::StsError, format("Number of output labels does not match "
"number of labels (%zu != %zu)",
colors.size(), labels.size()));
}
legend.create(kBlockHeight * numClasses, 200, CV_8UC3);
for (int i = 0; i < numClasses; i++)
{
Mat block = legend.rowRange(i * kBlockHeight, (i + 1) * kBlockHeight);
block.setTo(colors[i]);
Rect r = getTextSize(Size(), labels[i], Point(), fontFace, 15, 400);
r.height += 15; // padding
r.width += 10; // padding
rectangle(block, r, Scalar::all(255), FILLED);
putText(block, labels[i], Point(10, kBlockHeight/2), Scalar(0,0,0), fontFace, 15, 400);
}
namedWindow("Legend", WINDOW_AUTOSIZE);
imshow("Legend", legend);
}
}
int main(int argc, char **argv)
{
utils::logging::setLogLevel(utils::logging::LOG_LEVEL_INFO);
CommandLineParser parser(argc, argv, keys);
const string modelName = parser.get<String>("@alias");
const string zooFile = findFile(parser.get<String>("zoo"));
keys += genPreprocArguments(modelName, zooFile);
parser = CommandLineParser(argc, argv, keys);
parser.about(about);
if (!parser.has("@alias") || parser.has("help"))
{
parser.printMessage();
return 0;
}
string sha1 = parser.get<String>("sha1");
float scale = parser.get<float>("scale");
Scalar mean = parser.get<Scalar>("mean");
bool swapRB = parser.get<bool>("rgb");
int inpWidth = parser.get<int>("width");
int inpHeight = parser.get<int>("height");
String model = findModel(parser.get<String>("model"), sha1);
const string backend = parser.get<String>("backend");
const string target = parser.get<String>("target");
int stdSize = 20;
int stdWeight = 400;
int stdImgSize = 512;
int imgWidth = -1; // Initialization
int fontSize = 50;
int fontWeight = 500;
FontFace fontFace("sans");
// Open file with labels names.
if (parser.has("labels"))
{
string file = findFile(parser.get<String>("labels"));
ifstream ifs(file.c_str());
if (!ifs.is_open())
CV_Error(Error::StsError, "File " + file + " not found");
string line;
while (getline(ifs, line))
{
labels.push_back(line);
}
}
// Open file with colors.
if (parser.has("colors"))
{
string file = findFile(parser.get<String>("colors"));
ifstream ifs(file.c_str());
if (!ifs.is_open())
CV_Error(Error::StsError, "File " + file + " not found");
string line;
while (getline(ifs, line))
{
istringstream colorStr(line.c_str());
Vec3b color;
for (int i = 0; i < 3 && !colorStr.eof(); ++i)
colorStr >> color[i];
colors.push_back(color);
}
}
if (!parser.check())
{
parser.printErrors();
return 1;
}
CV_Assert(!model.empty());
//! [Read and initialize network]
EngineType engine = ENGINE_AUTO;
if (backend != "default" || target != "cpu"){
engine = ENGINE_CLASSIC;
}
Net net = readNetFromONNX(model, engine);
net.setPreferableBackend(getBackendID(backend));
net.setPreferableTarget(getTargetID(target));
net.setProfilingMode(DNN_PROFILE_SUMMARY);
//! [Read and initialize network]
// Create a window
static const string kWinName = "Deep learning semantic segmentation in OpenCV";
namedWindow(kWinName, WINDOW_AUTOSIZE);
//! [Open a video file or an image file or a camera stream]
VideoCapture cap;
if (parser.has("input"))
cap.open(findFile(parser.get<String>("input")));
else
cap.open(parser.get<int>("device"));
if (!cap.isOpened()) {
cerr << "Error: Video could not be opened." << endl;
return -1;
}
//! [Open a video file or an image file or a camera stream]
// Process frames.
Mat frame, blob;
while (waitKey(1) < 0)
{
cap >> frame;
if (frame.empty())
{
waitKey();
break;
}
if (imgWidth == -1){
imgWidth = max(frame.rows, frame.cols);
fontSize = min(fontSize, (stdSize*imgWidth)/stdImgSize);
fontWeight = min(fontWeight, (stdWeight*imgWidth)/stdImgSize);
}
imshow("Original Image", frame);
//! [Create a 4D blob from a frame]
blobFromImage(frame, blob, scale, Size(inpWidth, inpHeight), mean, swapRB, false);
//! [Set input blob]
net.setInput(blob);
//! [Set input blob]
int64 t0 = getTickCount();
if (modelName == "u2netp")
{
vector<Mat> output;
net.forward(output, net.getUnconnectedOutLayersNames());
net.printPerfProfile();
Mat pred = output[0].reshape(1, output[0].size[2]);
pred.convertTo(pred, CV_8U, 255.0);
Mat mask;
resize(pred, mask, Size(frame.cols, frame.rows), 0, 0, INTER_AREA);
// Create overlays for foreground and background
Mat foreground_overlay;
// Set foreground (object) to red
Mat all_zeros = Mat::zeros(frame.size(), CV_8UC1);
vector<Mat> channels = {all_zeros, all_zeros, mask};
merge(channels, foreground_overlay);
// Blend the overlays with the original frame
addWeighted(frame, 0.25, foreground_overlay, 0.75, 0, frame);
}
else
{
//! [Make forward pass]
Mat score = net.forward();
net.printPerfProfile();
//! [Make forward pass]
Mat segm;
colorizeSegmentation(score, segm);
resize(segm, segm, frame.size(), 0, 0, INTER_NEAREST);
addWeighted(frame, 0.1, segm, 0.9, 0.0, frame);
}
// Put efficiency information.
double t = (getTickCount() - t0) * 1000.0 / getTickFrequency();
string label = format("Inference time: %.2f ms", t);
Rect r = getTextSize(Size(), label, Point(), fontFace, fontSize, fontWeight);
r.height += fontSize; // padding
r.width += 10; // padding
rectangle(frame, r, Scalar::all(255), FILLED);
putText(frame, label, Point(10, fontSize), Scalar(0,0,0), fontFace, fontSize, fontWeight);
imshow(kWinName, frame);
if (!labels.empty())
showLegend(fontFace);
}
return 0;
}