“If it ain’t broke, don’t fix it.” See if you can find anyone telling you why Simplex is better. “It’s faster and extends to multiple dimensions” and “simplex noise attempts to reduce the complexity of higher dimensional noise functions” were what I found. Most of us work in 2 or 3 dimensions, maybe 4 if … 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
I just added filters to my open source GPUImage framework that perform three of the four processing tasks you describe (swirling, sketch filtering, and converting to an oil painting). While I don’t yet have colorspace transforms as filters, I do have the ability to apply a matrix to transform colors. As examples of these filters … Read more
Comparing histograms is quite a subject in itself. You’ve got two big classes of comparison functions : bin-to-bin comparison and cross-bin comparison. Bin-to-bin comparison : As you stated, standard sum of differences is quite bad. There’s an improvement: the Chi-squared distance. If H1.red[0] = 0.001 and H2.red[0] = 0.011, then H2.red[0] is much more important … Read more
All you need to do is install pillow: pip install pillow Then you should be all set. Found this after hours of searching.
You can try this: ffmpeg -i input.jpg -vf scale=320:240 output_320x240.png I got this from source Note: The scale filter can also automatically calculate a dimension while preserving the aspect ratio: scale=320:-1, or scale=-1:240
Occlusion means that there is something you want to see, but can’t due to some property of your sensor setup, or some event. Exactly how it manifests itself or how you deal with the problem will vary due to the problem at hand. Some examples: If you are developing a system which tracks objects (people, … Read more
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));
You can view compress level using the identify tool in ImageMagick. Download and installation instructions can be found at the official website. After you install it, run the following command from the command line: identify -format ‘%Q’ yourimage.jpg This will return a value from 0 (low quality, small filesize) to 100 (high quality, large filesize). … 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