Histogram Equalization, Stretching, origin histo compare and example source code

Example source code for histogram equalization
and compare with origin histogram and stretching histogram.

result is like that:

origin image & histogram




stretching image and histogram






Equalization image and histogram
 

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tags:
equalizeHist, cvtColor, normalize, calcHist

Gray image histogram without opencv function calHist

As you known, there is function for making histogram in Opencv, that is calcHist function.
But at this time, let's try get histogram without use calcHist.

Input image, we are going to convert from rgb to gray.


And this is result of histogram


So.. see the source code. I will be not difficult. ^^

Background Subtraction and Blob labeling and FREAK feature extraction

This code is the source of this video.
https://www.youtube.com/watch?v=txOaulCPzSM

The source code included 2 separable routine.
One is blob labeling.
Another is FREAK feature extraction and draw.

Blob labeling is using MOG2 algorithm.
MOG2 is introduced in past on my blog.
Refer to this page.
http://study.marearts.com/search/label/MOG2
http://study.marearts.com/2014/04/opencv-study-background-subtractor-mog.html
By the way, this code included blur routine.
I thought low frequency image is more useful for background learning.
This rgb blur code on GPU mode is referenced from here.
http://study.marearts.com/2014/11/opencv-gpu-3-channel-blur-example.html

Another routine is FREAK feature extraction.
Firstly, find feature using FAST_GPU, and extract FREAK descriptor.
After extraction, 2 descriptor can compare same image or not.

This code is made for processing time check.
The process is enough to processing in real time, because image is resized and use GPU.

Hope helping to you.
Thank you.




...code start...

...code end...

OpenCV face detection using adaboost example source code and cpu vs gpu detection speed compare (CascadeClassifier, CascadeClassifier_GPU, detectMultiScale)

OpenCV has AdaBoost algorithm function.
And gpu version also is provided.

For using detection, we prepare the trained xml file.
Although we can train some target using adaboost algorithm in opencv functions, there are several trained xml files in the opencv folder. (mostly in opencv/sources/data/haarcascades )

I will use "haarcascade_frontalface_alt.xml" file for face detection example.

gpu and cpu both versions use xml file.

more detail refer to this source code.
The source code is included 2 version of cpu and gpu.

result is ..
gpu is faster than cpu version (but exactly they may not be same condition..)
blue boxes are result of cpu.
red boxes are results of gpu.
The results are not important because it can be different by parameters values.

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Github
https://github.com/MareArts/AdaBoost-Face-Detection-test-using-OpenCV


#Tags
cvtColor, CascadeClassifier, CascadeClassifier_GPU, detectMultiScale,