Category : computer-vision

I am a newbie in the computer vision field and I am trying to write a module tracking by feature detection. As you know detection starts from scratch for each frame but tracking searches for a particular area/predicted path. My main goal is to parallelize my detection algorithm, which uses ORB with a tracker to ..

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i want to change brightness of image, only accessing pixel value. not using opencv function(ex. convertTo) input : image , num num means constant value for brightness here is my code and result looks wierd. Is there any problem? original result cv::Mat function(cv::Mat img, int num){ cv::Mat output; output = cv::Mat::zeros(img.rows, img.cols, img.type()); for (int ..

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I am making an object tracker using eigen features and KLT tracker. And I could track some features from both frames. Now I want to transform the bounding box according to the transformation of these features. This is my first try:- cv::Mat affine = cv::estimateAffinePartial2D(cvFrom, cvTo, inliers, method, ransacReprojThreshold, maxIters, confidence, refineIters); // affine = ..

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When I run this code, code outputs sometimes, do you have any idea, why it is happening? ? #include <iostream> #include <pcl/io/pcd_io.h> #include <pcl/point_types.h> #include <cuda.h> #include <cuda_runtime.h> #include <stdio.h> __global__ void voxelGrid(int *d_x) { int a = d_x[0]; printf("%d",a); } int main() { pcl::PointCloud<pcl::PointXYZ>::Ptr cloud(new pcl::PointCloud<pcl::PointXYZ>); if (pcl::io::loadPCDFile<pcl::PointXYZ> ("C:Usersb84193943Desktopbatuprojectdatabatu.pcd", *cloud) == -1) //* load ..

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Source code lines for bilinear interpolation as implemented by OpenCV: https://github.com/opencv/opencv/blob/6fbfc58602e719b9d58099a3c43e5c764718c4ba/modules/imgproc/src/resize.cpp#L3318 A more basic idea of this implementation in Python: def bilinear_interpolation(image, dimension): height = image.shape[0] width = image.shape[1] scale_x = (width)/(dimension[1]) scale_y = (height)/(dimension[0]) new_image = np.zeros((dimension[0], dimension[1], image.shape[2])) for k in range(3): for i in range(dimension[0]): for j in range(dimension[1]): x = (j+0.5) ..

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