import glob
import math
import os
import random
from sys import platform

import cv2
import numpy as np
import torch

# from torch.utils.data import Dataset
from utils.utils import xyxy2xywh


class load_images():  # for inference
    def __init__(self, path, batch_size=1, img_size=416):
        if os.path.isdir(path):
            self.files = sorted(glob.glob('%s/*.*' % path))
        elif os.path.isfile(path):
            self.files = [path]

        self.nF = len(self.files)  # number of image files
        self.nB = math.ceil(self.nF / batch_size)  # number of batches
        self.batch_size = batch_size
        self.height = img_size

        assert self.nF > 0, 'No images found in path %s' % path

        # RGB normalization values
        # self.rgb_mean = np.array([60.134, 49.697, 40.746], dtype=np.float32).reshape((3, 1, 1))
        # self.rgb_std = np.array([29.99, 24.498, 22.046], dtype=np.float32).reshape((3, 1, 1))

    def __iter__(self):
        self.count = -1
        return self

    def __next__(self):
        self.count += 1
        if self.count == self.nB:
            raise StopIteration
        img_path = self.files[self.count]

        # Read image
        img = cv2.imread(img_path)  # BGR

        # Padded resize
        img, _, _, _ = resize_square(img, height=self.height, color=(127.5, 127.5, 127.5))

        # Normalize RGB
        img = img[:, :, ::-1].transpose(2, 0, 1)
        img = np.ascontiguousarray(img, dtype=np.float32)
        # img -= self.rgb_mean
        # img /= self.rgb_std
        img /= 255.0

        return [img_path], img

    def __len__(self):
        return self.nB  # number of batches


class load_images_and_labels():  # for training
    def __init__(self, path, batch_size=1, img_size=608, multi_scale=False, augment=False):
        self.path = path
        # self.img_files = sorted(glob.glob('%s/*.*' % path))
        with open(path, 'r') as file:
            self.img_files = file.readlines()

        if platform == 'darwin':  # MacOS (local)
            self.img_files = [path.replace('\n', '').replace('/images', '/Users/glennjocher/Downloads/data/coco/images')
                              for path in self.img_files]
        else:  # linux (gcp cloud)
            self.img_files = [path.replace('\n', '').replace('/images', '../coco/images') for path in self.img_files]

        self.label_files = [path.replace('images', 'labels').replace('.png', '.txt').replace('.jpg', '.txt') for path in
                            self.img_files]

        self.nF = len(self.img_files)  # number of image files
        self.nB = math.ceil(self.nF / batch_size)  # number of batches
        self.batch_size = batch_size
        self.height = img_size
        self.multi_scale = multi_scale
        self.augment = augment

        assert self.nB > 0, 'No images found in path %s' % path

        # RGB normalization values
        # self.rgb_mean = np.array([60.134, 49.697, 40.746], dtype=np.float32).reshape((1, 3, 1, 1))
        # self.rgb_std = np.array([29.99, 24.498, 22.046], dtype=np.float32).reshape((1, 3, 1, 1))

    def __iter__(self):
        self.count = -1
        self.shuffled_vector = np.random.permutation(self.nF) if self.augment else np.arange(self.nF)
        return self

    def __next__(self):
        self.count += 1
        if self.count == self.nB:
            raise StopIteration

        ia = self.count * self.batch_size
        ib = min((self.count + 1) * self.batch_size, self.nF)

        if self.multi_scale:
            # Multi-Scale YOLO Training
            height = random.choice(range(10, 20)) * 32  # 320 - 608 pixels
        else:
            # Fixed-Scale YOLO Training
            height = self.height

        img_all = []
        labels_all = []
        for index, files_index in enumerate(range(ia, ib)):
            img_path = self.img_files[self.shuffled_vector[files_index]]
            label_path = self.label_files[self.shuffled_vector[files_index]]

            img = cv2.imread(img_path)  # BGR
            if img is None:
                continue

            augment_hsv = True
            if self.augment and augment_hsv:
                # SV augmentation by 50%
                fraction = 0.50
                img_hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
                S = img_hsv[:, :, 1].astype(np.float32)
                V = img_hsv[:, :, 2].astype(np.float32)

                a = (random.random() * 2 - 1) * fraction + 1
                S *= a
                if a > 1:
                    np.clip(S, a_min=0, a_max=255, out=S)

                a = (random.random() * 2 - 1) * fraction + 1
                V *= a
                if a > 1:
                    np.clip(V, a_min=0, a_max=255, out=V)

                img_hsv[:, :, 1] = S.astype(np.uint8)
                img_hsv[:, :, 2] = V.astype(np.uint8)
                cv2.cvtColor(img_hsv, cv2.COLOR_HSV2BGR, dst=img)

            h, w, _ = img.shape
            img, ratio, padw, padh = resize_square(img, height=height, color=(127.5, 127.5, 127.5))

            # Load labels
            if os.path.isfile(label_path):
                labels0 = np.loadtxt(label_path, dtype=np.float32).reshape(-1, 5)

                # Normalized xywh to pixel xyxy format
                labels = labels0.copy()
                labels[:, 1] = ratio * w * (labels0[:, 1] - labels0[:, 3] / 2) + padw
                labels[:, 2] = ratio * h * (labels0[:, 2] - labels0[:, 4] / 2) + padh
                labels[:, 3] = ratio * w * (labels0[:, 1] + labels0[:, 3] / 2) + padw
                labels[:, 4] = ratio * h * (labels0[:, 2] + labels0[:, 4] / 2) + padh
            else:
                labels = np.array([])

            # Augment image and labels
            if self.augment:
                img, labels, M = random_affine(img, labels, degrees=(-5, 5), translate=(0.2, 0.2), scale=(0.8, 1.2))

            plotFlag = False
            if plotFlag:
                import matplotlib.pyplot as plt
                plt.figure(figsize=(10, 10)) if index == 0 else None
                plt.subplot(4, 4, index + 1).imshow(img[:, :, ::-1])
                plt.plot(labels[:, [1, 3, 3, 1, 1]].T, labels[:, [2, 2, 4, 4, 2]].T, '.-')
                plt.axis('off')

            nL = len(labels)
            if nL > 0:
                # convert xyxy to xywh
                labels[:, 1:5] = xyxy2xywh(labels[:, 1:5].copy()) / height

            if self.augment:
                # random left-right flip
                lr_flip = True
                if lr_flip & (random.random() > 0.5):
                    img = np.fliplr(img)
                    if nL > 0:
                        labels[:, 1] = 1 - labels[:, 1]

                # random up-down flip
                ud_flip = False
                if ud_flip & (random.random() > 0.5):
                    img = np.flipud(img)
                    if nL > 0:
                        labels[:, 2] = 1 - labels[:, 2]

            img_all.append(img)
            labels_all.append(torch.from_numpy(labels))

        # Normalize
        img_all = np.stack(img_all)[:, :, :, ::-1].transpose(0, 3, 1, 2)  # BGR to RGB and cv2 to pytorch
        img_all = np.ascontiguousarray(img_all, dtype=np.float32)
        # img_all -= self.rgb_mean
        # img_all /= self.rgb_std
        img_all /= 255.0

        return torch.from_numpy(img_all), labels_all

    def __len__(self):
        return self.nB  # number of batches


def resize_square(img, height=416, color=(0, 0, 0)):  # resize a rectangular image to a padded square
    shape = img.shape[:2]  # shape = [height, width]
    ratio = float(height) / max(shape)  # ratio  = old / new
    new_shape = [round(shape[0] * ratio), round(shape[1] * ratio)]
    dw = height - new_shape[1]  # width padding
    dh = height - new_shape[0]  # height padding
    top, bottom = dh // 2, dh - (dh // 2)
    left, right = dw // 2, dw - (dw // 2)
    img = cv2.resize(img, (new_shape[1], new_shape[0]), interpolation=cv2.INTER_AREA)  # resized, no border
    return cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color), ratio, dw // 2, dh // 2


def random_affine(img, targets=None, degrees=(-10, 10), translate=(.1, .1), scale=(.9, 1.1), shear=(-3, 3),
                  borderValue=(127.5, 127.5, 127.5)):
    # torchvision.transforms.RandomAffine(degrees=(-10, 10), translate=(.1, .1), scale=(.9, 1.1), shear=(-10, 10))
    # https://medium.com/uruvideo/dataset-augmentation-with-random-homographies-a8f4b44830d4

    border = 0  # width of added border (optional)
    height = max(img.shape[0], img.shape[1]) + border * 2

    # Rotation and Scale
    R = np.eye(3)
    a = random.random() * (degrees[1] - degrees[0]) + degrees[0]
    # a += random.choice([-180, -90, 0, 90])  # 90deg rotations added to small rotations
    s = random.random() * (scale[1] - scale[0]) + scale[0]
    R[:2] = cv2.getRotationMatrix2D(angle=a, center=(img.shape[1] / 2, img.shape[0] / 2), scale=s)

    # Translation
    T = np.eye(3)
    T[0, 2] = (random.random() * 2 - 1) * translate[0] * img.shape[0] + border  # x translation (pixels)
    T[1, 2] = (random.random() * 2 - 1) * translate[1] * img.shape[1] + border  # y translation (pixels)

    # Shear
    S = np.eye(3)
    S[0, 1] = math.tan((random.random() * (shear[1] - shear[0]) + shear[0]) * math.pi / 180)  # x shear (deg)
    S[1, 0] = math.tan((random.random() * (shear[1] - shear[0]) + shear[0]) * math.pi / 180)  # y shear (deg)

    M = S @ T @ R  # Combined rotation matrix. ORDER IS IMPORTANT HERE!!
    imw = cv2.warpPerspective(img, M, dsize=(height, height), flags=cv2.INTER_LINEAR,
                              borderValue=borderValue)  # BGR order borderValue

    # Return warped points also
    if targets is not None:
        if len(targets) > 0:
            n = targets.shape[0]
            points = targets[:, 1:5].copy()
            area0 = (points[:, 2] - points[:, 0]) * (points[:, 3] - points[:, 1])

            # warp points
            xy = np.ones((n * 4, 3))
            xy[:, :2] = points[:, [0, 1, 2, 3, 0, 3, 2, 1]].reshape(n * 4, 2)  # x1y1, x2y2, x1y2, x2y1
            xy = (xy @ M.T)[:, :2].reshape(n, 8)

            # create new boxes
            x = xy[:, [0, 2, 4, 6]]
            y = xy[:, [1, 3, 5, 7]]
            xy = np.concatenate((x.min(1), y.min(1), x.max(1), y.max(1))).reshape(4, n).T

            # apply angle-based reduction
            radians = a * math.pi / 180
            reduction = max(abs(math.sin(radians)), abs(math.cos(radians))) ** 0.5
            x = (xy[:, 2] + xy[:, 0]) / 2
            y = (xy[:, 3] + xy[:, 1]) / 2
            w = (xy[:, 2] - xy[:, 0]) * reduction
            h = (xy[:, 3] - xy[:, 1]) * reduction
            xy = np.concatenate((x - w / 2, y - h / 2, x + w / 2, y + h / 2)).reshape(4, n).T

            # reject warped points outside of image
            np.clip(xy, 0, height, out=xy)
            w = xy[:, 2] - xy[:, 0]
            h = xy[:, 3] - xy[:, 1]
            area = w * h
            ar = np.maximum(w / (h + 1e-16), h / (w + 1e-16))
            i = (w > 4) & (h > 4) & (area / (area0 + 1e-16) > 0.1) & (ar < 10)

            targets = targets[i]
            targets[:, 1:5] = xy[i]

        return imw, targets, M
    else:
        return imw


def convert_tif2bmp(p='/Users/glennjocher/Downloads/DATA/xview/val_images_bmp'):
    import glob
    import cv2
    files = sorted(glob.glob('%s/*.tif' % p))
    for i, f in enumerate(files):
        print('%g/%g' % (i + 1, len(files)))

        img = cv2.imread(f)

        cv2.imwrite(f.replace('.tif', '.bmp'), img)
        os.system('rm -rf ' + f)