Om Prakash iamprakashom
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iamprakashom
/ main.cpp
Created
June 29, 2017 13:14
— forked from nikotan/main.cpp
face detection sample code for OpenCV
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| #include <opencv/cv.h> | |
| #include <opencv/highgui.h> | |
| #include <opencv/ml.h> | |
| void doMosaic(IplImage* in, int x, int y, | |
| int width, int height, int size); | |
| int main (int argc, char **argv) | |
| { | |
| int i, c; |
iamprakashom
/ Makefile.install_opencv_fedora
Last active
June 29, 2017 04:32
— forked from wuerges/Makefile.install_opencv_fedora
Makefile for Installing OpenCV in Fedora 23 with Python 3 support.
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| install_opencv: | |
| sudo dnf install --best --allowerasing \ | |
| cmake python-devel numpy gcc gcc-c++ \ | |
| python3-devel python3-numpy \ | |
| gtk2-devel libdc1394-devel libv4l-devel ffmpeg-devel \ | |
| gstreamer-plugins-base-devel libpng-devel libjpeg-turbo-devel \ | |
| jasper-devel openexr-devel libtiff-devel libwebp-devel \ | |
| tbb-devel eigen3-devel python-sphinx texlive git | |
| if [ ! -d opencv/ ]; then \ |
iamprakashom
/ md5.coffee
Created
April 17, 2017 10:08
— forked from amscotti/md5.coffee
MD5 hashing
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| crypto = require('crypto'); | |
| #Quick MD5 of text | |
| text = "MD5 this text!" | |
| md5hash1 = crypto.createHash('md5').update(text).digest("hex") | |
| #MD5 of text with updates | |
| m = crypto.createHash('md5') | |
| m.update("MD5 ") | |
| m.update("this ") |
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iamprakashom
/ unsupervised_alt.py
Created
January 7, 2017 05:06
— forked from AlexandreAbraham/unsupervised_alt.py
These are two implementations of the silhouette score. They are compatible with the scikit learn implementation but offers different drawbacks in term of complexity and memory usage. The slow version needs no memory but is painfully slow and should, I think, not be used. The second one is based on a block strategy: distance between samples and c…
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| """ Unsupervised evaluation metrics. """ | |
| # License: BSD Style. | |
| from itertools import combinations | |
| import numpy as np | |
| from sklearn.utils import check_random_state | |
| from sklearn.metrics.pairwise import distance_metrics | |
| from sklearn.metrics.pairwise import pairwise_distances |
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