# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
"""
Plotting utils
"""

import os
from pathlib import Path

import cv2
import matplotlib
import numpy as np
import torch
from PIL import Image, ImageDraw, ImageFont

from .general import (LOGGER, clip_coords, increment_path, is_ascii, is_chinese,
             user_config_dir, xywh2xyxy, xyxy2xywh)

# Settings
CONFIG_DIR = user_config_dir()  # Ultralytics settings dir
RANK = int(os.getenv('RANK', -1))
matplotlib.rc('font', **{'size': 11})
matplotlib.use('Agg')  # for writing to files only


class Colors:
    # Ultralytics color palette https://ultralytics.com/
    def __init__(self):
        # hex = matplotlib.colors.TABLEAU_COLORS.values()
        hex = ('FF3838', 'FF9D97', 'FF701F', 'FFB21D', 'CFD231', '48F90A', '92CC17', '3DDB86', '1A9334', '00D4BB',
               '2C99A8', '00C2FF', '344593', '6473FF', '0018EC', '8438FF', '520085', 'CB38FF', 'FF95C8', 'FF37C7')
        self.palette = [self.hex2rgb('#' + c) for c in hex]
        self.n = len(self.palette)

    def __call__(self, i, bgr=False):
        c = self.palette[int(i) % self.n]
        return (c[2], c[1], c[0]) if bgr else c

    @staticmethod
    def hex2rgb(h):  # rgb order (PIL)
        return tuple(int(h[1 + i:1 + i + 2], 16) for i in (0, 2, 4))


colors = Colors()  # create instance for 'from utils.plots import colors'


def check_font(font='Arial.ttf', size=10):
    # Return a PIL TrueType Font, downloading to CONFIG_DIR if necessary
    font = Path(font)
    font = font if font.exists() else (CONFIG_DIR / font.name)
    try:
        return ImageFont.truetype(str(font) if font.exists() else font.name, size)
    except Exception as e:  # download if missing
        url = "https://ultralytics.com/assets/" + font.name
        LOGGER.info(f'Downloading {url} to {font}...')
        torch.hub.download_url_to_file(url, str(font), progress=False)
        try:
            return ImageFont.truetype(str(font), size)
        except TypeError:
            pass

class Annotator:
    if RANK in (-1, 0):
        check_font()  # download TTF if necessary

    # YOLOv5 Annotator for train/val mosaics and jpgs and detect/hub inference annotations
    def __init__(self, im, line_width=None, font_size=None, font='Arial.ttf', pil=False, example='abc'):
        assert im.data.contiguous, 'Image not contiguous. Apply np.ascontiguousarray(im) to Annotator() input images.'
        self.pil = pil or not is_ascii(example) or is_chinese(example)
        if self.pil:  # use PIL
            self.im = im if isinstance(im, Image.Image) else Image.fromarray(im)
            self.draw = ImageDraw.Draw(self.im)
            self.font = check_font(font='Arial.Unicode.ttf' if is_chinese(example) else font,
                                   size=font_size or max(round(sum(self.im.size) / 2 * 0.035), 12))
        else:  # use cv2
            self.im = im
        self.lw = line_width or max(round(sum(im.shape) / 2 * 0.003), 2)  # line width

    def box_label(self, box, label='', color=(128, 128, 128), txt_color=(255, 255, 255)):
        # Add one xyxy box to image with label
        if self.pil or not is_ascii(label):
            self.draw.rectangle(box, width=self.lw, outline=color)  # box
            if label:
                w, h = self.font.getsize(label)  # text width, height
                outside = box[1] - h >= 0  # label fits outside box
                self.draw.rectangle([box[0],
                                     box[1] - h if outside else box[1],
                                     box[0] + w + 1,
                                     box[1] + 1 if outside else box[1] + h + 1], fill=color)
                # self.draw.text((box[0], box[1]), label, fill=txt_color, font=self.font, anchor='ls')  # for PIL>8.0
                self.draw.text((box[0], box[1] - h if outside else box[1]), label, fill=txt_color, font=self.font)
        else:  # cv2
            p1, p2 = (int(box[0]), int(box[1])), (int(box[2]), int(box[3]))
            cv2.rectangle(self.im, p1, p2, color, thickness=self.lw, lineType=cv2.LINE_AA)
            if label:
                tf = max(self.lw - 1, 1)  # font thickness
                w, h = cv2.getTextSize(label, 0, fontScale=self.lw / 3, thickness=tf)[0]  # text width, height
                outside = p1[1] - h - 3 >= 0  # label fits outside box
                p2 = p1[0] + w, p1[1] - h - 3 if outside else p1[1] + h + 3
                cv2.rectangle(self.im, p1, p2, color, -1, cv2.LINE_AA)  # filled
                cv2.putText(self.im, label, (p1[0], p1[1] - 2 if outside else p1[1] + h + 2), 0, self.lw / 3, txt_color,
                            thickness=tf, lineType=cv2.LINE_AA)

    def rectangle(self, xy, fill=None, outline=None, width=1):
        # Add rectangle to image (PIL-only)
        self.draw.rectangle(xy, fill, outline, width)

    def text(self, xy, text, txt_color=(255, 255, 255)):
        # Add text to image (PIL-only)
        w, h = self.font.getsize(text)  # text width, height
        self.draw.text((xy[0], xy[1] - h + 1), text, fill=txt_color, font=self.font)

    def result(self):
        # Return annotated image as array
        return np.asarray(self.im)


def save_one_box(xyxy, im, file='image.jpg', gain=1.02, pad=10, square=False, BGR=False, save=True):
    # Save image crop as {file} with crop size multiple {gain} and {pad} pixels. Save and/or return crop
    xyxy = torch.tensor(xyxy).view(-1, 4)
    b = xyxy2xywh(xyxy)  # boxes
    if square:
        b[:, 2:] = b[:, 2:].max(1)[0].unsqueeze(1)  # attempt rectangle to square
    b[:, 2:] = b[:, 2:] * gain + pad  # box wh * gain + pad
    xyxy = xywh2xyxy(b).long()
    clip_coords(xyxy, im.shape)
    crop = im[int(xyxy[0, 1]):int(xyxy[0, 3]), int(xyxy[0, 0]):int(xyxy[0, 2]), ::(1 if BGR else -1)]
    if save:
        file.parent.mkdir(parents=True, exist_ok=True)  # make directory
        cv2.imwrite(str(increment_path(file).with_suffix('.jpg')), crop)
    return crop
