import os
import numpy as np
# import cv2
from PIL import Image
from utils import paramUtil
import math
import time
import matplotlib.pyplot as plt
from scipy.ndimage import gaussian_filter


def mkdir(path):
    if not os.path.exists(path):
        os.makedirs(path)

COLORS = [[255, 0, 0], [255, 85, 0], [255, 170, 0], [255, 255, 0], [170, 255, 0], [85, 255, 0], [0, 255, 0],
          [0, 255, 85], [0, 255, 170], [0, 255, 255], [0, 170, 255], [0, 85, 255], [0, 0, 255], [85, 0, 255],
          [170, 0, 255], [255, 0, 255], [255, 0, 170], [255, 0, 85]]

MISSING_VALUE = -1

def save_image(image_numpy, image_path):
    img_pil = Image.fromarray(image_numpy)
    img_pil.save(image_path)


def save_logfile(log_loss, save_path):
    with open(save_path, 'wt') as f:
        for k, v in log_loss.items():
            w_line = k
            for digit in v:
                w_line += ' %.3f' % digit
            f.write(w_line + '\n')


def print_current_loss(start_time, niter_state, losses, epoch=None, inner_iter=None):

    def as_minutes(s):
        m = math.floor(s / 60)
        s -= m * 60
        return '%dm %ds' % (m, s)

    def time_since(since, percent):
        now = time.time()
        s = now - since
        es = s / percent
        rs = es - s
        return '%s (- %s)' % (as_minutes(s), as_minutes(rs))

    if epoch is not None:
        print('epoch: %3d niter: %6d  inner_iter: %4d' % (epoch, niter_state, inner_iter), end=" ")

    now = time.time()
    message = '%s'%(as_minutes(now - start_time))

    for k, v in losses.items():
        message += ' %s: %.4f ' % (k, v)
    print(message)


def compose_gif_img_list(img_list, fp_out, duration):
    img, *imgs = [Image.fromarray(np.array(image)) for image in img_list]
    img.save(fp=fp_out, format='GIF', append_images=imgs, optimize=False,
             save_all=True, loop=0, duration=duration)


def save_images(visuals, image_path):
    if not os.path.exists(image_path):
        os.makedirs(image_path)

    for i, (label, img_numpy) in enumerate(visuals.items()):
        img_name = '%d_%s.jpg' % (i, label)
        save_path = os.path.join(image_path, img_name)
        save_image(img_numpy, save_path)


def save_images_test(visuals, image_path, from_name, to_name):
    if not os.path.exists(image_path):
        os.makedirs(image_path)

    for i, (label, img_numpy) in enumerate(visuals.items()):
        img_name = "%s_%s_%s" % (from_name, to_name, label)
        save_path = os.path.join(image_path, img_name)
        save_image(img_numpy, save_path)


def compose_and_save_img(img_list, save_dir, img_name, col=4, row=1, img_size=(256, 200)):
    # print(col, row)
    compose_img = compose_image(img_list, col, row, img_size)
    if not os.path.exists(save_dir):
        os.makedirs(save_dir)
    img_path = os.path.join(save_dir, img_name)
    # print(img_path)
    compose_img.save(img_path)


def compose_image(img_list, col, row, img_size):
    to_image = Image.new('RGB', (col * img_size[0], row * img_size[1]))
    for y in range(0, row):
        for x in range(0, col):
            from_img = Image.fromarray(img_list[y * col + x])
            # print((x * img_size[0], y*img_size[1],
            #                           (x + 1) * img_size[0], (y + 1) * img_size[1]))
            paste_area = (x * img_size[0], y*img_size[1],
                                      (x + 1) * img_size[0], (y + 1) * img_size[1])
            to_image.paste(from_img, paste_area)
            # to_image[y*img_size[1]:(y + 1) * img_size[1], x * img_size[0] :(x + 1) * img_size[0]] = from_img
    return to_image


def list_cut_average(ll, intervals):
    if intervals == 1:
        return ll

    bins = math.ceil(len(ll) * 1.0 / intervals)
    ll_new = []
    for i in range(bins):
        l_low = intervals * i
        l_high = l_low + intervals
        l_high = l_high if l_high < len(ll) else len(ll)
        ll_new.append(np.mean(ll[l_low:l_high]))
    return ll_new


def motion_temporal_filter(motion, sigma=1):
    motion = motion.reshape(motion.shape[0], -1)
    # print(motion.shape)
    for i in range(motion.shape[1]):
        motion[:, i] = gaussian_filter(motion[:, i], sigma=sigma, mode="nearest")
    return motion.reshape(motion.shape[0], -1, 3)