#import cv2
import scipy.misc
#import ipdb
import numpy as np
from numpy import shape


#Some helper functions

def OneHot(X, n, negative_class=0.):
    X = np.asarray(X).flatten()
    if n is None:
        n = np.max(X) + 1
    Xoh = np.ones((len(X), n)) * negative_class
    for i in range(len(X)):
        m=X[i]
        Xoh[i,m]=1
    return Xoh


#For saving image-like scenarios map
def save_visualization(X, nh_nw, save_path='./vis/sample.jpg'):
    h,w = X.shape[1], X.shape[2]
    img = np.zeros((h * nh_nw[0], w * nh_nw[1], 3))

    for n,x in enumerate(X):
        j = n // nh_nw[1]
        i = n % nh_nw[1]
        img[j*h:j*h+h, i*w:i*w+w, :] = x

    scipy.misc.imsave(save_path, img)