from __future__ import print_function
import torch
import numpy as np
from PIL import Image
import os


# Converts a Tensor into a Numpy array
# |imtype|: the desired type of the converted numpy array
def tensor2im(image_tensor, imtype=np.uint8):
    image_numpy = image_tensor[0].cpu().float().numpy()
    if image_numpy.shape[0] == 1:
        image_numpy = np.tile(image_numpy, (3, 1, 1))
    image_numpy = (np.transpose(image_numpy, (1, 2, 0)) + 1) / 2.0 * 255.0
    return image_numpy.astype(imtype)


def diagnose_network(net, name='network'):
    mean = 0.0
    count = 0
    for param in net.parameters():
        if param.grad is not None:
            mean += torch.mean(torch.abs(param.grad.data))
            count += 1
    if count > 0:
        mean = mean / count
    print(name)
    print(mean)


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


def print_numpy(x, val=True, shp=False):
    x = x.astype(np.float64)
    if shp:
        print('shape,', x.shape)
    if val:
        x = x.flatten()
        print('mean = %3.3f, min = %3.3f, max = %3.3f, median = %3.3f, std=%3.3f' % (
            np.mean(x), np.min(x), np.max(x), np.median(x), np.std(x)))


def mkdirs(paths):
    if isinstance(paths, list) and not isinstance(paths, str):
        for path in paths:
            mkdir(path)
    else:
        mkdir(paths)


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

def crop_image(img, xy, scale_factor):
    '''Crop the image around the tuple xy

    Inputs:
    -------
    img: Image opened with PIL.Image
    xy: tuple with relative (x,y) position of the center of the cropped image
        x and y shall be between 0 and 1
    scale_factor: the ratio between the original image's size and the cropped image's size
    '''
    center = (img.size[0] * xy[0], img.size[1] * xy[1])
    new_size = (img.size[0] / scale_factor, img.size[1] / scale_factor)
    left = max (0, (int) (center[0] - new_size[0] / 2))
    right = min (img.size[0], (int) (center[0] + new_size[0] / 2))
    upper = max (0, (int) (center[1] - new_size[1] / 2))
    lower = min (img.size[1], (int) (center[1] + new_size[1] / 2))
    cropped_img = img.crop((left, upper, right, lower))
    return cropped_img