import hashlib
import urllib
import cv2
from sklearn.metrics.pairwise import cosine_similarity
import numpy as np
from scipy.stats import pearsonr
from matplotlib import pyplot

#HOG parameters
winSize = (32,32)
blockSize = (16,16)
blockStride = (8,8)
cellSize = (8,8)
nbins = 9
derivAperture = 1
winSigma = 4.
histogramNormType = 0
L2HysThreshold = 2.0000000000000001e-01
gammaCorrection = 0
nlevels = 64
winStride = (8,8)
padding = (8,8)
locations = ((10,20),)
hog = cv2.HOGDescriptor(winSize,blockSize,blockStride,cellSize,nbins,derivAperture,winSigma,
                        histogramNormType,L2HysThreshold,gammaCorrection,nlevels)
#compute(img[, winStride[, padding[, locations]]]) -> descriptors

# time it

outdir='images/'
root='https://upload.wikimedia.org/wikipedia/commons/thumb/'
filename_good='correctpairs.csv'
filename_bad='wrongpairs.csv'



def download_image(filename):
#this function downloads an image from the commons repository
	m = hashlib.md5()
	m.update(filename)
	sumfilename=m.hexdigest()
	first=sumfilename[0]
	second=sumfilename[:2]
	#format is: first char of md5 sum / second char / filename / resolutions - filename
	url=root+first+'/'+second+'/'+filename+'/600px-'+filename
	urllib.urlretrieve(url, outdir+filename)

def evaluate_distance(imagename1,imagename2):
	#downloadImage(img1)
	#downloadImage(img2)
	filename1=outdir+imagename1
	filename2=outdir+imagename2
	#read images
	try:
		img1 = cv2.imread(filename1,0)
		img2 = cv2.imread(filename2,0)
	except:
		return None
	if img1 is None or img2 is None:
		return None
	#compute hog
	hist1 = hog.compute(img1,winStride,padding,locations)
	hist2 = hog.compute(img2,winStride,padding,locations)
	#compute distance
	#dist=cosine_similarity(hist1,hist2)
	dist=pearsonr(hist1,hist2)
	correlation=0 if np.isnan(dist[0]) else dist[0]
	return correlation

def read_file_and_compute(filename):
	distances=[]
	count=0
	with open(filename) as f:
		for line in f:
			row=line[:-1].split('\t')
			img1=row[1]
			img2=row[2]
			d=evaluate_distance(img1,img2)
			if d is not None:
				distances.append(d)
			else:
				count+=1
				print count
	return distances

distances_good=read_file_and_compute(filename_good)
distances_bad=read_file_and_compute(filename_bad)

print 'good'+str(np.mean(np.asarray(distances_good)))

print 'bad'+str(np.mean(np.asarray(distances_bad)))