Using the OpenCV C API with IplImage* img: Use cvSet(): cvSet(img, CV_RGB(redVal,greenVal,blueVal)); Using the OpenCV C++ API with cv::Mat img, then use either: cv::Mat::operator=(const Scalar& s) as in: img = cv::Scalar(redVal,greenVal,blueVal); or the more general, mask supporting, cv::Mat::setTo(): img.setTo(cv::Scalar(redVal,greenVal,blueVal));
I use always: quality in 85 progressive (comprobed compression) a very tiny gausssian blur to optimize the size (0.05 or 0.5 of radius) depends on the quality and size of the picture, this notably optimizes the size of the jpeg. Strip any comment or EXIF metadata in imagemagick should be convert -strip -interlace Plane -gaussian-blur … Read more
Here is some code I have written to do roughly what you describe. It creates a 3D data set (called cell, but it would be comparable to your array A), populates it with random data, and then computes a resultant 3D output array (called node, but it would be comparable to your array C) based … Read more
Here is my attempt at answering my question after reading the paper Zhengyou Zhang , Li-Wei He, “Whiteboard scanning and image enhancement” http://research.microsoft.com/en-us/um/people/zhang/papers/tr03-39.pdf I manipulated the equations for some time in SAGE, and came up with this pseudo-code in c-style: // in case it matters: licensed under GPLv2 or later // legend: // sqr(x) = … Read more
I am not sure whether all your images are like this. But for this image, below is a simple python-opencv code to crop it. first import libraries : import cv2 import numpy as np Read the image, convert it into grayscale, and make in binary image for threshold value of 1. img = cv2.imread(‘sofwin.png’) gray … Read more
In opencv 2.2, I’d use the C++ interface. cv::Mat in = /* your image goes here, assuming single-channel image with 8bits per pixel */ for(int row = 0; row < in.rows; ++row) { unsigned char* inp = in.ptr<unsigned char>(row); for (int col = 0; col < in.cols; ++col) { if (*inp++ == 0) { std::cout … Read more
New method! It seems that the following ImageMagick command, or maybe a variation of it, depending on looking at a greater selection of your images, will extract the wording at the top of your cards convert aggressiveurge.jpg -crop 80%x10%+10%+10% crop.png which takes the top 10% of your image and 80% of the width (starting at … Read more
It looks similar to a problem I recently had to separate bacterial colonies growing on agar plates. I performed a distance transform on the thresholded image (in your case you will need to invert it). Then found the peaks of the distance map (by calculating the difference between a the dilated distance map and the … Read more
“segmentation” is a partition of an image into several “coherent” parts, but without any attempt at understanding what these parts represent. One of the most famous works (but definitely not the first) is Shi and Malik “Normalized Cuts and Image Segmentation” PAMI 2000. These works attempt to define “coherence” in terms of low-level cues such … Read more
You probably want to convert the colors to an HSL model (Hue, Saturation, Lightness) and then compare the values within thresholds in the order HSL. If the hue is within a tolerance deemed as “close”, then check the “closeness” of the saturation, and then the lightness.