import code
import numpy as np
import math
from skimage.transform import ProjectiveTransform
from sklearn.metrics import roc_auc_score
from skimage import io
import scipy
import matplotlib.pyplot as plt
import random
from random import randint
import scipy.ndimage.filters as fi
import scipy.stats as st

BINS = 15
SIGMA = 1.5

def gkern(kernlen=21, nsig=2):
    interval = (2*nsig+1.)/(kernlen)
    x = np.linspace(-nsig-interval/2., nsig+interval/2., kernlen+1)
    kern1d = np.diff(st.norm.cdf(x))
    kernel_raw = np.sqrt(np.outer(kern1d, kern1d))
    kernel = kernel_raw/kernel_raw.sum()
    return kernel

def extract(image, keypoints):
    image = image / np.max(image)
    image = image * 255
    image = image.astype(np.uint8)
    tab = []
    imgs = []
    angles = []
    for point in keypoints:
        img = get_img_around(image, point)
        tab.append(descriptor(img))
        imgs.append(img)
    return tab

def get_img_around(image, point):
    x, y = point
    img = image[x-31:x+32, y-31:y+32]
    return img

def split_image(image):
    imgs = []
    for i in range(4, 60, 8):
        for j in range(4, 60, 8):
            imgs.append(image[i:i+8, j:j+8])
    return imgs

def normalize(img):
    img = img - np.min(img)
    img = (img / float(np.max(img))) * 255
    return img

def descriptor(img):
    desc = []
    img = fi.gaussian_filter(img, sigma=SIGMA)
    img = normalize(img)
    for i in split_image(img):
        desc.append(small_descriptor(i))
    desc = np.array(desc)
    return desc.flatten()

def small_descriptor(image):
    image = image.flatten()
    return np.histogram(image, bins = BINS, range = (0, 256))[0]

def distance(d1, d2):
    return np.sum(np.absolute(d1 - d2))