"""
Author: Khanh Phan
Date: 2023-11-01
"""

import math
import string
from pathlib import Path

import cv2
import numpy as np
from PIL import (
    Image,
    ImageDraw,
    ImageFont,
)

from src.markdown import Markdown
from src.settings import (
    FONTPATH,
    OUT_DIR,
)


def count_characters(str: str) -> int:
    """
    Count the number of Japanese characters,
    a single English character and a single number
    equal to half the length of Japanese characters.
    args:
        s(string): the input of string
    return(int):
        the number of Japanese characters
    """

    count_zh = count_pu = 0
    s_len = len(str)
    en_dg_count = 0
    for c in str:
        if c in string.ascii_letters or c.isdigit() or c.isspace():
            en_dg_count += 1
        elif c.isalpha():
            count_zh += 1
        else:
            count_pu += 1
    return s_len - math.ceil(en_dg_count / 2)


def create_blank_img(img_h: int, img_w: int) -> Image:
    """
    create new blank img
    args:
        img_h(int): the height of blank img
        img_w(int): the width of blank img
    return(Image|array):
        blank image
    """
    blank_img = np.ones(shape=[img_h, img_w], dtype=np.int8) * 255
    blank_img[:, img_w - 1 :] = 0
    blank_img = Image.fromarray(blank_img).convert("RGB")
    # draw_txt = ImageDraw.Draw(blank_img)
    return blank_img


def text_visual(
    texts: list[str],
    scores: list[float],
    img_h: int = 400,
    img_w: int = 600,
    threshold: float = 0.0,
    font_path: str = FONTPATH,
) -> np.array:
    """
    Create new img with recognized text
    args:
        texts(list): the text will be draw
        scores(list|None): corresponding score of each txt
        img_h(int): the height of blank img
        img_w(int): the width of blank img
        font_path: the path of font which is used to draw text
    return(Image|array): image with recognized text
    """
    if scores is not None:
        assert len(texts) == len(
            scores,
        ), "The number of txts and corresponding scores must match"

    blank_img = create_blank_img()
    draw_txt = ImageDraw.Draw(blank_img)

    font_size = 20
    txt_color = (0, 0, 0)
    font = ImageFont.truetype(font_path, font_size, encoding="utf-8")

    gap = font_size + 5
    txt_img_list = []
    count, index = 1, 0
    for idx, txt in enumerate(texts):
        index += 1
        if scores[idx] < threshold or math.isnan(scores[idx]):
            index -= 1
            continue
        first_line = True
        while count_characters(txt) >= img_w // font_size - 4:
            tmp = txt
            txt = tmp[: img_w // font_size - 4]
            if first_line:
                new_txt = str(index) + ": " + txt
                first_line = False
            else:
                new_txt = "    " + txt
            draw_txt.text((0, gap * count), new_txt, txt_color, font=font)
            txt = tmp[img_w // font_size - 4 :]
            if count >= img_h // gap - 1:
                txt_img_list.append(np.array(blank_img))
                blank_img = create_blank_img()
                draw_txt = ImageDraw.Draw(blank_img)
                count = 0
            count += 1
        if first_line:
            new_txt = str(index) + ": " + txt + "   " + "%.3f" % (scores[idx])
        else:
            new_txt = "  " + txt + "  " + "%.3f" % (scores[idx])
        draw_txt.text((0, gap * count), new_txt, txt_color, font=font)
        # whether add new blank img or not
        if count >= img_h // gap - 1 and idx + 1 < len(texts):
            txt_img_list.append(np.array(blank_img))
            blank_img = create_blank_img()
            draw_txt = ImageDraw.Draw(blank_img)
            count = 0
        count += 1
    txt_img_list.append(np.array(blank_img))
    if len(txt_img_list) == 1:
        blank_img = np.array(txt_img_list[0])
    else:
        blank_img = np.concatenate(txt_img_list, axis=1)
    return np.array(blank_img)


def resize_img(img: np.array, input_size: int = 600) -> np.array:
    """
    Resize img and limit the longest side of the image to input_size
    args:
        img(np.array): original image
        input_size(int): new size of the longest side of the image
    return(Image|array):
        a new-size image
    """
    img = np.array(img)
    im_shape = img.shape
    im_size_max = np.max(im_shape[0:2])
    im_scale = float(input_size) / float(im_size_max)
    img = cv2.resize(img, None, None, fx=im_scale, fy=im_scale)
    return img


def draw_ocr(
    image: np.array,
    boxes: list,
    txts: list[str] = None,
    scores: list[float] = None,
    drop_score: float = 0.0,
    font_path: str = FONTPATH,
) -> np.array:
    """
    Visualize the results of OCR detection and recognition
    args:
        image(Image|array): RGB image
        boxes(list): boxes with shape(N, 4, 2)
        txts(list): the texts
        scores(list): txxs corresponding scores
        drop_score(float): only scores > drop_threshold will be visualized
        font_path: the path of font which is used to draw text
    return(Image|array):
        the visualized img
    """
    if scores is None:
        scores = [1] * len(boxes)
    box_num = len(boxes)
    for i in range(box_num):
        if scores is not None and (
            scores[i] < drop_score or math.isnan(scores[i])
        ):
            continue
        box = np.reshape(np.array(boxes[i]), [-1, 1, 2]).astype(np.int64)
        image = cv2.polylines(np.array(image), [box], True, (255, 0, 0), 2)
    if txts is not None:
        img = np.array(image)
        txt_img = text_visual(
            txts,
            scores,
            img_h=img.shape[0],
            img_w=600,
            threshold=drop_score,
            font_path=font_path,
        )
        img = np.concatenate([np.array(img), np.array(txt_img)], axis=1)
        return img
    return image


def draw_ocr_2(
    img: np.array,
    results: list[list, tuple([str, float])],
) -> list[np.array, np.array]:
    """
    Visualize the results of OCR detection and recognition
    args:
        image(Image|array): RGB image
        results(list): boxes with shape(N, 4, 2), texts and scores
    return(Image|array):
        the visualized img
    """
    img = np.asarray(img)
    img_text = np.ones((img.shape[0], img.shape[1], 3), np.uint8) * 255

    for line in results:
        text = line[1][0]
        # score = line[1][1]

        top = int(min(line[0][0][1], line[0][1][1]))
        bottom = int(max(line[0][2][1], line[0][3][1]))
        left = int(min(line[0][0][0], line[0][3][0]))
        # right = int(max(line[0][1][0], line[0][2][0]))

        # text_size = max(1, int((right - left) / len(text)))
        text_size = max(1, int(bottom - top))
        color = (
            np.random.randint(0, 255),
            np.random.randint(0, 255),
            np.random.randint(0, 255),
        )

        box = np.reshape(np.array(line[0]), [-1, 1, 2]).astype(np.int64)
        img = cv2.polylines(np.array(img), [box], True, color, 2)

        img_text = place_text(img_text, text, (left, top), text_size, color)

    return [img, img_text]


def place_text(
    img: np.array,
    text: str,
    top_left_point: list[int, int],
    text_size: int,
    text_color: tuple([int, int, int]),
) -> np.array:
    """
    Put text into image
    args:
        img(Image|array): RGB image
        text(list): text to be put
        text_size(int): size of text
        top_left_point(array): top-left point to start the text
        text_color(tuple): text color in ()
    return(Image|array):
        the visualized img
    """
    font = ImageFont.truetype(FONTPATH, text_size, encoding="utf-8")
    img_pil = Image.fromarray(img)
    draw = ImageDraw.Draw(img_pil)
    draw.text(top_left_point, text, fill=text_color, font=font)
    return np.array(img_pil)


def visualize_result(
    result: list[list, tuple([str, float])],
    img: str,
) -> list[np.array, np.array]:
    """
    make visualization in image foramt
    args:
        result(array): RGB image
        img_path(str): path to input image
    return(Image|array):
        the visualized img
    """
    result = result[0]
    if isinstance(img, str):
        img_path = img
        image = Image.open(img_path).convert("RGB")
        img_name = Path(img_path).stem
        """
        boxes = [line[0] for line in result]
        txts = [line[1][0] for line in result]
        scores = [line[1][1] for line in result]
        """
    else:
        image = Image.fromarray(img)
        img_name = Path("gradio_input")

    # Write results to markdown file
    format_md = Markdown(result, image)
    md_path = (Path(OUT_DIR) / img_name).with_suffix(".out.md")
    markdown = format_md.write(md_path)

    # Write results to image file
    [img_boxes, img_text] = draw_ocr_2(image, result)
    img_combination = np.concatenate(
        [np.array(img_boxes), np.array(img_text)],
        axis=1,
    )
    img_out_path = (Path(OUT_DIR) / img_name).with_suffix(".out.jpg")
    cv2.imwrite(str(img_out_path), img_combination)

    return [img_boxes, img_text, markdown]