import os
import cv2
import numpy as np
import seaborn as sns
from scipy.ndimage.interpolation import map_coordinates
from scipy.ndimage.filters import gaussian_filter
import random
from tqdm import tqdm
import imutils
import math


def bluring(img_in, kind):
    if kind == 'gauss':
        img_blur = cv2.GaussianBlur(img_in, (5, 5), 0)
    elif kind == "median":
        img_blur = cv2.medianBlur(img_in, 5)
    elif kind == 'blur':
        img_blur = cv2.blur(img_in, (5, 5))
    return img_blur


def elastic_transform(image, alpha, sigma, seedj, random_state=None):
    """Elastic deformation of images as described in [Simard2003]_.
    .. [Simard2003] Simard, Steinkraus and Platt, "Best Practices for
       Convolutional Neural Networks applied to Visual Document Analysis", in
       Proc. of the International Conference on Document Analysis and
       Recognition, 2003.
    """
    if random_state is None:
        random_state = np.random.RandomState(seedj)

    shape = image.shape
    dx = gaussian_filter((random_state.rand(*shape) * 2 - 1), sigma, mode="constant", cval=0) * alpha
    dy = gaussian_filter((random_state.rand(*shape) * 2 - 1), sigma, mode="constant", cval=0) * alpha
    dz = np.zeros_like(dx)

    x, y, z = np.meshgrid(np.arange(shape[1]), np.arange(shape[0]), np.arange(shape[2]))
    indices = np.reshape(y + dy, (-1, 1)), np.reshape(x + dx, (-1, 1)), np.reshape(z, (-1, 1))

    distored_image = map_coordinates(image, indices, order=1, mode='reflect')
    return distored_image.reshape(image.shape)


def rotation_90(img):
    img_rot = np.zeros((img.shape[1], img.shape[0], img.shape[2]))
    img_rot[:, :, 0] = img[:, :, 0].T
    img_rot[:, :, 1] = img[:, :, 1].T
    img_rot[:, :, 2] = img[:, :, 2].T
    return img_rot


def rotatedRectWithMaxArea(w, h, angle):
    """
  Given a rectangle of size wxh that has been rotated by 'angle' (in
  radians), computes the width and height of the largest possible
  axis-aligned rectangle (maximal area) within the rotated rectangle.
  """
    if w <= 0 or h <= 0:
        return 0, 0

    width_is_longer = w >= h
    side_long, side_short = (w, h) if width_is_longer else (h, w)

    # since the solutions for angle, -angle and 180-angle are all the same,
    # if suffices to look at the first quadrant and the absolute values of sin,cos:
    sin_a, cos_a = abs(math.sin(angle)), abs(math.cos(angle))
    if side_short <= 2. * sin_a * cos_a * side_long or abs(sin_a - cos_a) < 1e-10:
        # half constrained case: two crop corners touch the longer side,
        #   the other two corners are on the mid-line parallel to the longer line
        x = 0.5 * side_short
        wr, hr = (x / sin_a, x / cos_a) if width_is_longer else (x / cos_a, x / sin_a)
    else:
        # fully constrained case: crop touches all 4 sides
        cos_2a = cos_a * cos_a - sin_a * sin_a
        wr, hr = (w * cos_a - h * sin_a) / cos_2a, (h * cos_a - w * sin_a) / cos_2a

    return wr, hr


def rotate_max_area(image, rotated, rotated_label, angle):
    """ image: cv2 image matrix object
        angle: in degree
    """
    wr, hr = rotatedRectWithMaxArea(image.shape[1], image.shape[0],
                                    math.radians(angle))
    h, w, _ = rotated.shape
    y1 = h // 2 - int(hr / 2)
    y2 = y1 + int(hr)
    x1 = w // 2 - int(wr / 2)
    x2 = x1 + int(wr)
    return rotated[y1:y2, x1:x2], rotated_label[y1:y2, x1:x2]


def rotation_not_90_func(img, label, thetha):
    rotated = imutils.rotate(img, thetha)
    rotated_label = imutils.rotate(label, thetha)
    return rotate_max_area(img, rotated, rotated_label, thetha)


def color_images(seg, n_classes):
    ann_u = range(n_classes)
    if len(np.shape(seg)) == 3:
        seg = seg[:, :, 0]

    seg_img = np.zeros((np.shape(seg)[0], np.shape(seg)[1], 3)).astype(float)
    colors = sns.color_palette("hls", n_classes)

    for c in ann_u:
        c = int(c)
        segl = (seg == c)
        seg_img[:, :, 0] += segl * (colors[c][0])
        seg_img[:, :, 1] += segl * (colors[c][1])
        seg_img[:, :, 2] += segl * (colors[c][2])
    return seg_img


def resize_image(seg_in, input_height, input_width):
    return cv2.resize(seg_in, (input_width, input_height), interpolation=cv2.INTER_NEAREST)


def get_one_hot(seg, input_height, input_width, n_classes):
    seg = seg[:, :, 0]
    seg_f = np.zeros((input_height, input_width, n_classes))
    for j in range(n_classes):
        seg_f[:, :, j] = (seg == j).astype(int)
    return seg_f


def IoU(Yi, y_predi):
    ## mean Intersection over Union
    ## Mean IoU = TP/(FN + TP + FP)

    IoUs = []
    classes_true = np.unique(Yi)
    for c in classes_true:
        TP = np.sum((Yi == c) & (y_predi == c))
        FP = np.sum((Yi != c) & (y_predi == c))
        FN = np.sum((Yi == c) & (y_predi != c))
        IoU = TP / float(TP + FP + FN)
        print("class {:02.0f}: #TP={:6.0f}, #FP={:6.0f}, #FN={:5.0f}, IoU={:4.3f}".format(c, TP, FP, FN, IoU))
        IoUs.append(IoU)
    mIoU = np.mean(IoUs)
    print("_________________")
    print("Mean IoU: {:4.3f}".format(mIoU))
    return mIoU


def data_gen(img_folder, mask_folder, batch_size, input_height, input_width, n_classes):
    c = 0
    n = [f for f in os.listdir(img_folder) if not f.startswith('.')]  # os.listdir(img_folder) #List of training images
    random.shuffle(n)
    while True:
        img = np.zeros((batch_size, input_height, input_width, 3)).astype('float')
        mask = np.zeros((batch_size, input_height, input_width, n_classes)).astype('float')

        for i in range(c, c + batch_size):  # initially from 0 to 16, c = 0.
            # print(img_folder+'/'+n[i])

            try:
                filename = n[i].split('.')[0]

                train_img = cv2.imread(img_folder + '/' + n[i]) / 255.
                train_img = cv2.resize(train_img, (input_width, input_height),
                                       interpolation=cv2.INTER_NEAREST)  # Read an image from folder and resize

                img[i - c] = train_img  # add to array - img[0], img[1], and so on.
                train_mask = cv2.imread(mask_folder + '/' + filename + '.png')
                # print(mask_folder+'/'+filename+'.png')
                # print(train_mask.shape)
                train_mask = get_one_hot(resize_image(train_mask, input_height, input_width), input_height, input_width,
                                         n_classes)
                # train_mask = train_mask.reshape(224, 224, 1) # Add extra dimension for parity with train_img size [512 * 512 * 3]

                mask[i - c] = train_mask
            except:
                img[i - c] = np.ones((input_height, input_width, 3)).astype('float')
                mask[i - c] = np.zeros((input_height, input_width, n_classes)).astype('float')

        c += batch_size
        if c + batch_size >= len(os.listdir(img_folder)):
            c = 0
            random.shuffle(n)
        yield img, mask


def otsu_copy(img):
    img_r = np.zeros(img.shape)
    img1 = img[:, :, 0]
    img2 = img[:, :, 1]
    img3 = img[:, :, 2]
    _, threshold1 = cv2.threshold(img1, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
    _, threshold2 = cv2.threshold(img2, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
    _, threshold3 = cv2.threshold(img3, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
    img_r[:, :, 0] = threshold1
    img_r[:, :, 1] = threshold1
    img_r[:, :, 2] = threshold1
    return img_r


def get_patches(dir_img_f, dir_seg_f, img, label, height, width, indexer):
    if img.shape[0] < height or img.shape[1] < width:
        img, label = do_padding(img, label, height, width)

    img_h = img.shape[0]
    img_w = img.shape[1]

    nxf = img_w / float(width)
    nyf = img_h / float(height)

    if nxf > int(nxf):
        nxf = int(nxf) + 1
    if nyf > int(nyf):
        nyf = int(nyf) + 1

    nxf = int(nxf)
    nyf = int(nyf)

    for i in range(nxf):
        for j in range(nyf):
            index_x_d = i * width
            index_x_u = (i + 1) * width

            index_y_d = j * height
            index_y_u = (j + 1) * height

            if index_x_u > img_w:
                index_x_u = img_w
                index_x_d = img_w - width
            if index_y_u > img_h:
                index_y_u = img_h
                index_y_d = img_h - height

            img_patch = img[index_y_d:index_y_u, index_x_d:index_x_u, :]
            label_patch = label[index_y_d:index_y_u, index_x_d:index_x_u, :]

            cv2.imwrite(dir_img_f + '/img_' + str(indexer) + '.png', img_patch)
            cv2.imwrite(dir_seg_f + '/img_' + str(indexer) + '.png', label_patch)
            indexer += 1

    return indexer


def do_padding(img, label, height, width):
    height_new = img.shape[0]
    width_new = img.shape[1]

    h_start = 0
    w_start = 0

    if img.shape[0] < height:
        h_start = int(abs(height - img.shape[0]) / 2.)
        height_new = height

    if img.shape[1] < width:
        w_start = int(abs(width - img.shape[1]) / 2.)
        width_new = width

    img_new = np.ones((height_new, width_new, img.shape[2])).astype(float) * 255
    label_new = np.zeros((height_new, width_new, label.shape[2])).astype(float)

    img_new[h_start:h_start + img.shape[0], w_start:w_start + img.shape[1], :] = np.copy(img[:, :, :])
    label_new[h_start:h_start + label.shape[0], w_start:w_start + label.shape[1], :] = np.copy(label[:, :, :])

    return img_new, label_new


def get_patches_num_scale(dir_img_f, dir_seg_f, img, label, height, width, indexer, n_patches, scaler):
    if img.shape[0] < height or img.shape[1] < width:
        img, label = do_padding(img, label, height, width)

    img_h = img.shape[0]
    img_w = img.shape[1]

    height_scale = int(height * scaler)
    width_scale = int(width * scaler)

    nxf = img_w / float(width_scale)
    nyf = img_h / float(height_scale)

    if nxf > int(nxf):
        nxf = int(nxf) + 1
    if nyf > int(nyf):
        nyf = int(nyf) + 1

    nxf = int(nxf)
    nyf = int(nyf)

    for i in range(nxf):
        for j in range(nyf):
            index_x_d = i * width_scale
            index_x_u = (i + 1) * width_scale

            index_y_d = j * height_scale
            index_y_u = (j + 1) * height_scale

            if index_x_u > img_w:
                index_x_u = img_w
                index_x_d = img_w - width_scale
            if index_y_u > img_h:
                index_y_u = img_h
                index_y_d = img_h - height_scale

            img_patch = img[index_y_d:index_y_u, index_x_d:index_x_u, :]
            label_patch = label[index_y_d:index_y_u, index_x_d:index_x_u, :]

            img_patch = resize_image(img_patch, height, width)
            label_patch = resize_image(label_patch, height, width)

            cv2.imwrite(dir_img_f + '/img_' + str(indexer) + '.png', img_patch)
            cv2.imwrite(dir_seg_f + '/img_' + str(indexer) + '.png', label_patch)
            indexer += 1

    return indexer


def get_patches_num_scale_new(dir_img_f, dir_seg_f, img, label, height, width, indexer, scaler):
    img = resize_image(img, int(img.shape[0] * scaler), int(img.shape[1] * scaler))
    label = resize_image(label, int(label.shape[0] * scaler), int(label.shape[1] * scaler))

    if img.shape[0] < height or img.shape[1] < width:
        img, label = do_padding(img, label, height, width)

    img_h = img.shape[0]
    img_w = img.shape[1]

    height_scale = int(height * 1)
    width_scale = int(width * 1)

    nxf = img_w / float(width_scale)
    nyf = img_h / float(height_scale)

    if nxf > int(nxf):
        nxf = int(nxf) + 1
    if nyf > int(nyf):
        nyf = int(nyf) + 1

    nxf = int(nxf)
    nyf = int(nyf)

    for i in range(nxf):
        for j in range(nyf):
            index_x_d = i * width_scale
            index_x_u = (i + 1) * width_scale

            index_y_d = j * height_scale
            index_y_u = (j + 1) * height_scale

            if index_x_u > img_w:
                index_x_u = img_w
                index_x_d = img_w - width_scale
            if index_y_u > img_h:
                index_y_u = img_h
                index_y_d = img_h - height_scale

            img_patch = img[index_y_d:index_y_u, index_x_d:index_x_u, :]
            label_patch = label[index_y_d:index_y_u, index_x_d:index_x_u, :]

            # img_patch=resize_image(img_patch,height,width)
            # label_patch=resize_image(label_patch,height,width)

            cv2.imwrite(dir_img_f + '/img_' + str(indexer) + '.png', img_patch)
            cv2.imwrite(dir_seg_f + '/img_' + str(indexer) + '.png', label_patch)
            indexer += 1

    return indexer


def provide_patches(dir_img, dir_seg, dir_flow_train_imgs,
                    dir_flow_train_labels,
                    input_height, input_width, blur_k, blur_aug,
                    flip_aug, binarization, scaling, scales, flip_index,
                    scaling_bluring, scaling_binarization, rotation,
                    rotation_not_90, thetha, scaling_flip,
                    augmentation=False, patches=False):
    imgs_cv_train = np.array(os.listdir(dir_img))
    segs_cv_train = np.array(os.listdir(dir_seg))

    indexer = 0
    for im, seg_i in tqdm(zip(imgs_cv_train, segs_cv_train)):
        img_name = im.split('.')[0]
        if not patches:
            cv2.imwrite(dir_flow_train_imgs + '/img_' + str(indexer) + '.png',
                        resize_image(cv2.imread(dir_img + '/' + im), input_height, input_width))
            cv2.imwrite(dir_flow_train_labels + '/img_' + str(indexer) + '.png',
                        resize_image(cv2.imread(dir_seg + '/' + img_name + '.png'), input_height, input_width))
            indexer += 1

            if augmentation:
                if flip_aug:
                    for f_i in flip_index:
                        cv2.imwrite(dir_flow_train_imgs + '/img_' + str(indexer) + '.png',
                                    resize_image(cv2.flip(cv2.imread(dir_img + '/' + im), f_i), input_height,
                                                 input_width))

                        cv2.imwrite(dir_flow_train_labels + '/img_' + str(indexer) + '.png',
                                    resize_image(cv2.flip(cv2.imread(dir_seg + '/' + img_name + '.png'), f_i),
                                                 input_height, input_width))
                        indexer += 1

                if blur_aug:
                    for blur_i in blur_k:
                        cv2.imwrite(dir_flow_train_imgs + '/img_' + str(indexer) + '.png',
                                    (resize_image(bluring(cv2.imread(dir_img + '/' + im), blur_i), input_height,
                                                  input_width)))

                        cv2.imwrite(dir_flow_train_labels + '/img_' + str(indexer) + '.png',
                                    resize_image(cv2.imread(dir_seg + '/' + img_name + '.png'), input_height,
                                                 input_width))
                        indexer += 1

                if binarization:
                    cv2.imwrite(dir_flow_train_imgs + '/img_' + str(indexer) + '.png',
                                resize_image(otsu_copy(cv2.imread(dir_img + '/' + im)), input_height, input_width))

                    cv2.imwrite(dir_flow_train_labels + '/img_' + str(indexer) + '.png',
                                resize_image(cv2.imread(dir_seg + '/' + img_name + '.png'), input_height, input_width))
                    indexer += 1

        if patches:

            indexer = get_patches(dir_flow_train_imgs, dir_flow_train_labels,
                                  cv2.imread(dir_img + '/' + im), cv2.imread(dir_seg + '/' + img_name + '.png'),
                                  input_height, input_width, indexer=indexer)

            if augmentation:

                if rotation:
                    indexer = get_patches(dir_flow_train_imgs, dir_flow_train_labels,
                                          rotation_90(cv2.imread(dir_img + '/' + im)),
                                          rotation_90(cv2.imread(dir_seg + '/' + img_name + '.png')),
                                          input_height, input_width, indexer=indexer)

                if rotation_not_90:

                    for thetha_i in thetha:
                        img_max_rotated, label_max_rotated = rotation_not_90_func(cv2.imread(dir_img + '/' + im),
                                                                                  cv2.imread(
                                                                                      dir_seg + '/' + img_name + '.png'),
                                                                                  thetha_i)
                        indexer = get_patches(dir_flow_train_imgs, dir_flow_train_labels,
                                              img_max_rotated,
                                              label_max_rotated,
                                              input_height, input_width, indexer=indexer)
                if flip_aug:
                    for f_i in flip_index:
                        indexer = get_patches(dir_flow_train_imgs, dir_flow_train_labels,
                                              cv2.flip(cv2.imread(dir_img + '/' + im), f_i),
                                              cv2.flip(cv2.imread(dir_seg + '/' + img_name + '.png'), f_i),
                                              input_height, input_width, indexer=indexer)
                if blur_aug:
                    for blur_i in blur_k:
                        indexer = get_patches(dir_flow_train_imgs, dir_flow_train_labels,
                                              bluring(cv2.imread(dir_img + '/' + im), blur_i),
                                              cv2.imread(dir_seg + '/' + img_name + '.png'),
                                              input_height, input_width, indexer=indexer)

                if scaling:
                    for sc_ind in scales:
                        indexer = get_patches_num_scale_new(dir_flow_train_imgs, dir_flow_train_labels,
                                                            cv2.imread(dir_img + '/' + im),
                                                            cv2.imread(dir_seg + '/' + img_name + '.png'),
                                                            input_height, input_width, indexer=indexer, scaler=sc_ind)
                if binarization:
                    indexer = get_patches(dir_flow_train_imgs, dir_flow_train_labels,
                                          otsu_copy(cv2.imread(dir_img + '/' + im)),
                                          cv2.imread(dir_seg + '/' + img_name + '.png'),
                                          input_height, input_width, indexer=indexer)

                if scaling_bluring:
                    for sc_ind in scales:
                        for blur_i in blur_k:
                            indexer = get_patches_num_scale_new(dir_flow_train_imgs, dir_flow_train_labels,
                                                                bluring(cv2.imread(dir_img + '/' + im), blur_i),
                                                                cv2.imread(dir_seg + '/' + img_name + '.png'),
                                                                input_height, input_width, indexer=indexer,
                                                                scaler=sc_ind)

                if scaling_binarization:
                    for sc_ind in scales:
                        indexer = get_patches_num_scale_new(dir_flow_train_imgs, dir_flow_train_labels,
                                                            otsu_copy(cv2.imread(dir_img + '/' + im)),
                                                            cv2.imread(dir_seg + '/' + img_name + '.png'),
                                                            input_height, input_width, indexer=indexer, scaler=sc_ind)

                if scaling_flip:
                    for sc_ind in scales:
                        for f_i in flip_index:
                            indexer = get_patches_num_scale_new(dir_flow_train_imgs, dir_flow_train_labels,
                                                                cv2.flip(cv2.imread(dir_img + '/' + im), f_i),
                                                                cv2.flip(cv2.imread(dir_seg + '/' + img_name + '.png'),
                                                                         f_i),
                                                                input_height, input_width, indexer=indexer,
                                                                scaler=sc_ind)