import os
import sys
import errno
import torch
import math
import numpy as np
import cv2
from skimage import io
from skimage import color
from numba import jit

from urllib.parse import urlparse
from torch.hub import download_url_to_file, HASH_REGEX
try:
    from torch.hub import get_dir
except BaseException:
    from torch.hub import _get_torch_home as get_dir

gauss_kernel = None


def _gaussian(
        size=3, sigma=0.25, amplitude=1, normalize=False, width=None,
        height=None, sigma_horz=None, sigma_vert=None, mean_horz=0.5,
        mean_vert=0.5):
    # handle some defaults
    if width is None:
        width = size
    if height is None:
        height = size
    if sigma_horz is None:
        sigma_horz = sigma
    if sigma_vert is None:
        sigma_vert = sigma
    center_x = mean_horz * width + 0.5
    center_y = mean_vert * height + 0.5
    gauss = np.empty((height, width), dtype=np.float32)
    # generate kernel
    for i in range(height):
        for j in range(width):
            gauss[i][j] = amplitude * math.exp(-(math.pow((j + 1 - center_x) / (
                sigma_horz * width), 2) / 2.0 + math.pow((i + 1 - center_y) / (sigma_vert * height), 2) / 2.0))
    if normalize:
        gauss = gauss / np.sum(gauss)
    return gauss


def draw_gaussian(image, point, sigma):
    global gauss_kernel
    # Check if the gaussian is inside
    ul = [math.floor(point[0] - 3 * sigma), math.floor(point[1] - 3 * sigma)]
    br = [math.floor(point[0] + 3 * sigma), math.floor(point[1] + 3 * sigma)]
    if (ul[0] > image.shape[1] or ul[1] > image.shape[0] or br[0] < 1 or br[1] < 1):
        return image
    size = 6 * sigma + 1
    if gauss_kernel is None:
        g = _gaussian(size)
        gauss_kernel = g
    else:
        g = gauss_kernel
    g_x = [int(max(1, -ul[0])), int(min(br[0], image.shape[1])) - int(max(1, ul[0])) + int(max(1, -ul[0]))]
    g_y = [int(max(1, -ul[1])), int(min(br[1], image.shape[0])) - int(max(1, ul[1])) + int(max(1, -ul[1]))]
    img_x = [int(max(1, ul[0])), int(min(br[0], image.shape[1]))]
    img_y = [int(max(1, ul[1])), int(min(br[1], image.shape[0]))]
    assert (g_x[0] > 0 and g_y[1] > 0)
    image[img_y[0] - 1:img_y[1], img_x[0] - 1:img_x[1]
          ] = image[img_y[0] - 1:img_y[1], img_x[0] - 1:img_x[1]] + g[g_y[0] - 1:g_y[1], g_x[0] - 1:g_x[1]]
    image[image > 1] = 1
    return image


def transform(point, center, scale, resolution, invert=False):
    """Generate and affine transformation matrix.

    Given a set of points, a center, a scale and a targer resolution, the
    function generates and affine transformation matrix. If invert is ``True``
    it will produce the inverse transformation.

    Arguments:
        point {torch.tensor} -- the input 2D point
        center {torch.tensor or numpy.array} -- the center around which to perform the transformations
        scale {float} -- the scale of the face/object
        resolution {float} -- the output resolution

    Keyword Arguments:
        invert {bool} -- define wherever the function should produce the direct or the
        inverse transformation matrix (default: {False})
    """
    _pt = torch.ones(3)
    _pt[0] = point[0]
    _pt[1] = point[1]

    h = 200.0 * scale
    t = torch.eye(3)
    t[0, 0] = resolution / h
    t[1, 1] = resolution / h
    t[0, 2] = resolution * (-center[0] / h + 0.5)
    t[1, 2] = resolution * (-center[1] / h + 0.5)

    if invert:
        t = torch.inverse(t)

    new_point = (torch.matmul(t, _pt))[0:2]

    return new_point.int()


def crop(image, center, scale, resolution=256.0):
    """Center crops an image or set of heatmaps

    Arguments:
        image {numpy.array} -- an rgb image
        center {numpy.array} -- the center of the object, usually the same as of the bounding box
        scale {float} -- scale of the face

    Keyword Arguments:
        resolution {float} -- the size of the output cropped image (default: {256.0})

    Returns:
        [type] -- [description]
    """  # Crop around the center point
    """ Crops the image around the center. Input is expected to be an np.ndarray """
    ul = transform([1, 1], center, scale, resolution, True)
    br = transform([resolution, resolution], center, scale, resolution, True)
    # pad = math.ceil(torch.norm((ul - br).float()) / 2.0 - (br[0] - ul[0]) / 2.0)
    if image.ndim > 2:
        newDim = np.array([br[1] - ul[1], br[0] - ul[0],
                           image.shape[2]], dtype=np.int32)
        newImg = np.zeros(newDim, dtype=np.uint8)
    else:
        newDim = np.array([br[1] - ul[1], br[0] - ul[0]], dtype=np.int)
        newImg = np.zeros(newDim, dtype=np.uint8)
    ht = image.shape[0]
    wd = image.shape[1]
    newX = np.array(
        [max(1, -ul[0] + 1), min(br[0], wd) - ul[0]], dtype=np.int32)
    newY = np.array(
        [max(1, -ul[1] + 1), min(br[1], ht) - ul[1]], dtype=np.int32)
    oldX = np.array([max(1, ul[0] + 1), min(br[0], wd)], dtype=np.int32)
    oldY = np.array([max(1, ul[1] + 1), min(br[1], ht)], dtype=np.int32)
    newImg[newY[0] - 1:newY[1], newX[0] - 1:newX[1]
           ] = image[oldY[0] - 1:oldY[1], oldX[0] - 1:oldX[1], :]
    newImg = cv2.resize(newImg, dsize=(int(resolution), int(resolution)),
                        interpolation=cv2.INTER_LINEAR)
    return newImg


@jit(nopython=True)
def transform_np(point, center, scale, resolution, invert=False):
    """Generate and affine transformation matrix.

    Given a set of points, a center, a scale and a targer resolution, the
    function generates and affine transformation matrix. If invert is ``True``
    it will produce the inverse transformation.

    Arguments:
        point {numpy.array} -- the input 2D point
        center {numpy.array} -- the center around which to perform the transformations
        scale {float} -- the scale of the face/object
        resolution {float} -- the output resolution

    Keyword Arguments:
        invert {bool} -- define wherever the function should produce the direct or the
        inverse transformation matrix (default: {False})
    """
    _pt = np.ones(3)
    _pt[0] = point[0]
    _pt[1] = point[1]

    h = 200.0 * scale
    t = np.eye(3)
    t[0, 0] = resolution / h
    t[1, 1] = resolution / h
    t[0, 2] = resolution * (-center[0] / h + 0.5)
    t[1, 2] = resolution * (-center[1] / h + 0.5)

    if invert:
        t = np.ascontiguousarray(np.linalg.pinv(t))

    new_point = np.dot(t, _pt)[0:2]

    return new_point.astype(np.int32)


def get_preds_fromhm(hm, center=None, scale=None):
    """Obtain (x,y) coordinates given a set of N heatmaps. If the center
    and the scale is provided the function will return the points also in
    the original coordinate frame.

    Arguments:
        hm {torch.tensor} -- the predicted heatmaps, of shape [B, N, W, H]

    Keyword Arguments:
        center {torch.tensor} -- the center of the bounding box (default: {None})
        scale {float} -- face scale (default: {None})
    """
    B, C, H, W = hm.shape
    idx = np.argmax(hm.reshape(B, C, H * W), axis=2)
    preds, preds_orig = _get_preds_fromhm(hm, idx, center, scale)

    return preds, preds_orig


@jit(nopython=True)
def _get_preds_fromhm(hm, idx, center=None, scale=None):
    """Obtain (x,y) coordinates given a set of N heatmaps and the
    coresponding locations of the maximums. If the center
    and the scale is provided the function will return the points also in
    the original coordinate frame.

    Arguments:
        hm {torch.tensor} -- the predicted heatmaps, of shape [B, N, W, H]

    Keyword Arguments:
        center {torch.tensor} -- the center of the bounding box (default: {None})
        scale {float} -- face scale (default: {None})
    """
    B, C, H, W = hm.shape
    idx += 1
    preds = idx.repeat(2).reshape(B, C, 2).astype(np.float32)
    preds[:, :, 0] = (preds[:, :, 0] - 1) % W + 1
    preds[:, :, 1] = np.floor((preds[:, :, 1] - 1) / H) + 1

    for i in range(B):
        for j in range(C):
            hm_ = hm[i, j, :]
            pX, pY = int(preds[i, j, 0]) - 1, int(preds[i, j, 1]) - 1
            if pX > 0 and pX < 63 and pY > 0 and pY < 63:
                diff = np.array(
                    [hm_[pY, pX + 1] - hm_[pY, pX - 1],
                     hm_[pY + 1, pX] - hm_[pY - 1, pX]])
                preds[i, j] += np.sign(diff) * 0.25

    preds -= 0.5

    preds_orig = np.zeros_like(preds)
    if center is not None and scale is not None:
        for i in range(B):
            for j in range(C):
                preds_orig[i, j] = transform_np(
                    preds[i, j], center, scale, H, True)

    return preds, preds_orig


def create_target_heatmap(target_landmarks, centers, scales):
    heatmaps = np.zeros((target_landmarks.shape[0], 68, 64, 64), dtype=np.float32)
    for i in range(heatmaps.shape[0]):
        for p in range(68):
            landmark_cropped_coor = transform(target_landmarks[i, p] + 1, centers[i], scales[i], 64, invert=False)
            heatmaps[i, p] = draw_gaussian(heatmaps[i, p], landmark_cropped_coor + 1, 2)
    return torch.tensor(heatmaps)


def create_bounding_box(target_landmarks, expansion_factor=0.0):
    """
    gets a batch of landmarks and calculates a bounding box that includes all the landmarks per set of landmarks in
    the batch
    :param target_landmarks: batch of landmarks of dim (n x 68 x 2). Where n is the batch size
    :param expansion_factor: expands the bounding box by this factor. For example, a `expansion_factor` of 0.2 leads
    to 20% increase in width and height of the boxes
    :return: a batch of bounding boxes of dim (n x 4) where the second dim is (x1,y1,x2,y2)
    """
    # Calc bounding box
    x_y_min, _ = target_landmarks.reshape(-1, 68, 2).min(dim=1)
    x_y_max, _ = target_landmarks.reshape(-1, 68, 2).max(dim=1)
    # expanding the bounding box
    expansion_factor /= 2
    bb_expansion_x = (x_y_max[:, 0] - x_y_min[:, 0]) * expansion_factor
    bb_expansion_y = (x_y_max[:, 1] - x_y_min[:, 1]) * expansion_factor
    x_y_min[:, 0] -= bb_expansion_x
    x_y_max[:, 0] += bb_expansion_x
    x_y_min[:, 1] -= bb_expansion_y
    x_y_max[:, 1] += bb_expansion_y
    return torch.cat([x_y_min, x_y_max], dim=1)


def shuffle_lr(parts, pairs=None):
    """Shuffle the points left-right according to the axis of symmetry
    of the object.

    Arguments:
        parts {torch.tensor} -- a 3D or 4D object containing the
        heatmaps.

    Keyword Arguments:
        pairs {list of integers} -- [order of the flipped points] (default: {None})
    """
    if pairs is None:
        pairs = [16, 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0,
                 26, 25, 24, 23, 22, 21, 20, 19, 18, 17, 27, 28, 29, 30, 35,
                 34, 33, 32, 31, 45, 44, 43, 42, 47, 46, 39, 38, 37, 36, 41,
                 40, 54, 53, 52, 51, 50, 49, 48, 59, 58, 57, 56, 55, 64, 63,
                 62, 61, 60, 67, 66, 65]
    if parts.ndimension() == 3:
        parts = parts[pairs, ...]
    else:
        parts = parts[:, pairs, ...]

    return parts


def flip(tensor, is_label=False):
    """Flip an image or a set of heatmaps left-right

    Arguments:
        tensor {numpy.array or torch.tensor} -- [the input image or heatmaps]

    Keyword Arguments:
        is_label {bool} -- [denote wherever the input is an image or a set of heatmaps ] (default: {False})
    """
    if not torch.is_tensor(tensor):
        tensor = torch.from_numpy(tensor)

    if is_label:
        tensor = shuffle_lr(tensor).flip(tensor.ndimension() - 1)
    else:
        tensor = tensor.flip(tensor.ndimension() - 1)

    return tensor


def get_image(image_or_path):
    """Reads an image from file or array/tensor and converts it to RGB (H,W,3).

    Arguments:
        tensor {Sstring, numpy.array or torch.tensor} -- [the input image or path to it]
    """
    if isinstance(image_or_path, str):
        try:
            image = io.imread(image_or_path)
        except IOError:
            print("error opening file :: ", image_or_path)
            return None
    elif isinstance(image_or_path, torch.Tensor):
        image = image_or_path.detach().cpu().numpy()
    else:
        image = image_or_path

    if image.ndim == 2:
        image = color.gray2rgb(image)
    elif image.ndim == 4:
        image = image[..., :3]

    return image


# Pytorch load supports only pytorch models
def load_file_from_url(url, model_dir=None, progress=True, check_hash=False, file_name=None):
    if model_dir is None:
        hub_dir = get_dir()
        model_dir = os.path.join(hub_dir, 'checkpoints')

    try:
        os.makedirs(model_dir)
    except OSError as e:
        if e.errno == errno.EEXIST:
            # Directory already exists, ignore.
            pass
        else:
            # Unexpected OSError, re-raise.
            raise

    parts = urlparse(url)
    filename = os.path.basename(parts.path)
    if file_name is not None:
        filename = file_name
    cached_file = os.path.join(model_dir, filename)
    if not os.path.exists(cached_file):
        sys.stderr.write('Downloading: "{}" to {}\n'.format(url, cached_file))
        hash_prefix = None
        if check_hash:
            r = HASH_REGEX.search(filename)  # r is Optional[Match[str]]
            hash_prefix = r.group(1) if r else None
        download_url_to_file(url, cached_file, hash_prefix, progress=progress)

    return cached_file
