kushalvyas

/*
Author : Kushal Vyas
Code for template matching as implemented by opencv.

*/
#include "opencv2/imgproc/imgproc.hpp"
#include "opencv2/opencv.hpp"
#include "iostream"
#include "cmath"

kushalvyas

"""
Template matching using opencv

Run : python file.py <templateImage> <actualImage>


"""

import sys, cv2
import numpy as np

kushalvyas

"""
Using SKLearns API for performing Kmeans clustering.
Using sklearn.datasets.make_blobs for generating randomized gaussians
for clustering.

"""

import numpy as np 
from matplotlib import pyplot as plt 
from sklearn.cluster import KMeans

kushalvyas

n_samples = 1000
n_features = 5;
n_clusters = 3;

# aint this sweet 
X, y = make_blobs(n_samples, n_features) 
# X => array of shape [nsamples,nfeatures] ;;; y => array of shape[nsamples]

# X : generated samples, y : integer labels for cluster membership of each sample

kushalvyas

import cv2
import numpy as np 
from glob import glob 
import argparse
from helpers import *

class BOV:
    def __init__(self, no_clusters):
        self.no_clusters = no_clusters
        self.train_path = None

kushalvyas

import cv2
import numpy as np 
from glob import glob
from sklearn.cluster import KMeans
from sklearn.svm import SVC
from sklearn.preprocessing import StandardScaler
from matplotlib import pyplot as plt

class ImageHelpers:
	def __init__(self):

kushalvyas

def developVocabulary(self,n_images, descriptor_list, kmeans_ret = None):
		
		"""
		Each cluster denotes a particular visual word 
		Every image can be represeted as a combination of multiple 
		visual words. The best method is to generate a sparse histogram
		that contains the frequency of occurence of each visual word 

		Thus the vocabulary comprises of a set of histograms of encompassing
		all descriptions for all images

kushalvyas

def generateChromosome():
	# randomly generates a sequence of board states.
	global nQueens
	init_distribution = np.arange(nQueens)
	np.random.shuffle(init_distribution)
	return init_distribution

def generatePopulation(population_size = 100):
	global POPULATION

kushalvyas

"""
check out http://kushalvyas.github.io/gen_8Q.html#gen_8Q

@file : queens.py 

Illustration of 8 queens using GA - evolution

"""

import numpy as np

kushalvyas

name: "CaltechNET"
layer {
  name: "data"
  type: "Data"
  top: "data"
  top: "label"
  include {
    phase: TRAIN
  }
  transform_param {