import glob
import PIL.Image as Image
from matplotlib import pyplot as plt
from matplotlib import cm as CM
import os
import numpy as np
import time

from PIL import Image


def save_density_map(density_map, name):
    plt.figure(dpi=600)
    plt.axis('off')
    plt.margins(0, 0)
    plt.imshow(density_map, cmap=CM.jet)
    plt.savefig(name, dpi=600, bbox_inches='tight', pad_inches=0)
    plt.close()


def save_density_map_normalize(density_map, name):
    den = density_map / density_map.max(density_map + 1e-20)
    plt.figure(dpi=600)
    plt.axis('off')
    plt.margins(0, 0)
    plt.imshow(den, cmap=CM.jet)
    plt.savefig(name, dpi=600, bbox_inches='tight', pad_inches=0)
    plt.close()



def save_density_map_with_colorrange(density_map, name, vmin, vmax):
    plt.figure(dpi=600)
    plt.axis('off')
    plt.margins(0, 0)
    plt.imshow(density_map, cmap=CM.jet)
    plt.clim(vmin, vmax)
    plt.savefig(name, dpi=600, bbox_inches='tight', pad_inches=0)
    plt.close()

def save_density_map_with_colorrange_max(density_map, name, vmin, vmax):
    den = density_map/np.max(density_map+1e-20)
    plt.figure(dpi=600)
    plt.axis('off')
    plt.margins(0, 0)
    plt.imshow(den, cmap=CM.jet)
    plt.clim(vmin, vmax)
    plt.savefig(name, dpi=600, bbox_inches='tight', pad_inches=0)
    plt.close()


def save_img(imgnp, name):
    # plt.imshow(imgnp[0].permute(1, 2, 0).numpy())
    plt.imsave(name, imgnp[0].permute(1, 2, 0).numpy())
    # plt.show()
    # im = Image.fromarray(imgnp[0].permute(1, 2, 0).numpy())
    # im.save(name)

def get_readable_time():
    """
    make human readable time with format year-month-day hour-minute
    :return: a string of human readable time (ex: '2020-02-24 10:31' )
    """
    return time.strftime('%Y-%m-%d %H:%M')