【发布时间】：2016-04-17 11:12:04
【问题描述】：
我们实际上正在开展一个图像分析项目，我们需要识别场景中消失/出现的对象。这里有 2 张图像，一张是在外科医生采取行动之前拍摄的，另一张是之后拍摄的。
首先，我们刚刚计算了两张图片之间的差异，这是结果（请注意，我在结果中添加了 128 Mat 只是为了获得更好的图片）：
目标是检测杯子（红色箭头）已经从场景中消失并且注射器（黑色箭头）已经进入场景，换句话说，我们应该只检测与离开/进入的对象相对应的区域现场。此外，很明显，场景左上角的物体从它们的初始位置偏移了一点。我想到了Optical flow，所以我使用OpenCV C++ 来计算Farneback 的值，看看它是否足以满足我们的情况，这是我们得到的结果，然后是我们编写的代码：
void drawOptFlowMap(const Mat& flow, Mat& cflowmap, int step, double, const Scalar& color)
{
cout << flow.channels() << " / " << flow.rows << " / " << flow.cols << endl;
for(int y = 0; y < cflowmap.rows; y += step)
for(int x = 0; x < cflowmap.cols; x += step)
{
const Point2f& fxy = flow.at<Point2f>(y, x);
line(cflowmap, Point(x,y), Point(cvRound(x+fxy.x), cvRound(y+fxy.y)), color);
circle(cflowmap, Point(x,y), 1, color, -1);
}
}
void MainProcessorTrackingObjects::diffBetweenImagesToTestTrackObject(string pathOfImageCaptured, string pathOfImagesAfterOneAction, string pathOfResultsFolder)
{
//Preprocessing step...
string pathOfImageBefore = StringUtils::concat(pathOfImageCaptured, imageCapturedFileName);
string pathOfImageAfter = StringUtils::concat(pathOfImagesAfterOneAction, *it);
Mat imageBefore = imread(pathOfImageBefore);
Mat imageAfter = imread(pathOfImageAfter);
Mat imageResult = (imageAfter - imageBefore) + 128;
// absdiff(imageAfter, imageBefore, imageResult);
string imageResultPath = StringUtils::stringFormat("%s%s-color.png",pathOfResultsFolder.c_str(), fileNameWithoutFrameIndex.c_str());
imwrite(imageResultPath, imageResult);
Mat imageBeforeGray, imageAfterGray;
cvtColor( imageBefore, imageBeforeGray, CV_RGB2GRAY );
cvtColor( imageAfter, imageAfterGray, CV_RGB2GRAY );
Mat imageResultGray = (imageAfterGray - imageBeforeGray) + 128;
// absdiff(imageAfterGray, imageBeforeGray, imageResultGray);
string imageResultGrayPath = StringUtils::stringFormat("%s%s-gray.png",pathOfResultsFolder.c_str(), fileNameWithoutFrameIndex.c_str());
imwrite(imageResultGrayPath, imageResultGray);
//*** Compute FarneBack optical flow
Mat opticalFlow;
calcOpticalFlowFarneback(imageBeforeGray, imageAfterGray, opticalFlow, 0.5, 3, 15, 3, 5, 1.2, 0);
drawOptFlowMap(opticalFlow, imageBefore, 5, 1.5, Scalar(0, 255, 255));
string flowPath = StringUtils::stringFormat("%s%s-flow.png",pathOfResultsFolder.c_str(), fileNameWithoutFrameIndex.c_str());
imwrite(flowPath, imageBefore);
break;
}
为了知道这个光流有多准确，我写了一小段代码来计算 (IMAGEAFTER + FLOW) - IMAGEBEFORE:
//Reference method just to see the accuracy of the optical flow calculation
Mat accuracy = Mat::zeros(imageBeforeGray.rows, imageBeforeGray.cols, imageBeforeGray.type());
strinfor(int y = 0; y < imageAfter.rows; y ++)
for(int x = 0; x < imageAfter.cols; x ++)
{
Point2f& fxy = opticalFlow.at<Point2f>(y, x);
uchar intensityPointCalculated = imageAfterGray.at<uchar>(cvRound(y+fxy.y), cvRound(x+fxy.x));
uchar intensityPointBefore = imageBeforeGray.at<uchar>(y,x);
uchar intensityResult = ((intensityPointCalculated - intensityPointBefore) / 2) + 128;
accuracy.at<uchar>(y, x) = intensityResult;
}
validationPixelBased = StringUtils::stringFormat("%s%s-validationPixelBased.png",pathOfResultsFolder.c_str(), fileNameWithoutFrameIndex.c_str());
imwrite(validationPixelBased, accuracy);
拥有这个((intensityPointCalculated - intensityPointBefore) / 2) + 128; 的目的只是为了拥有一个易于理解的图像。
图像结果：
由于它检测到所有已移动/进入/离开场景的区域，我们认为OpticalFlow 不足以仅检测表示对象在场景中消失/出现的区域。有什么方法可以忽略opticalFlow 检测到的稀疏运动？或者有没有其他方法可以检测我们需要什么？
【问题讨论】：
标签： c++ opencv motion-detection opticalflow