# copyright (c) 2024 PaddlePaddle Authors. All Rights Reserve.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#    http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import os
import json
import math
import platform
from pathlib import Path

from collections import defaultdict
from PIL import Image
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import font_manager
from matplotlib.backends.backend_agg import FigureCanvasAgg

from .....utils.file_interface import custom_open
from .....utils.fonts import PINGFANG_FONT_FILE_PATH


def deep_analyse(dataset_path, output):
    """class analysis for dataset"""
    tags = ["train", "val"]
    labels_cnt = defaultdict(str)
    label_path = os.path.join(dataset_path, "label_map.txt")
    with custom_open(label_path, "r") as f:
        lines = f.readlines()
    for line in lines:
        line = line.strip().split()
        labels_cnt[line[1]] = " ".join(line[0])
    for tag in tags:
        anno_path = os.path.join(dataset_path, f"{tag}.txt")
        classes_num = defaultdict(int)
        for i in range(len(labels_cnt)):
            classes_num[labels_cnt[str(i)]] = 0
        with custom_open(anno_path, "r") as f:
            lines = f.readlines()
        for line in lines:
            line = line.strip().split()
            label_file_path = os.path.join(dataset_path, line[0])
            with custom_open(label_file_path, "r") as f:
                label_lines = f.readlines()
                for label_line in label_lines:
                    label_info = label_line.strip().split(" ")
                    classes_num[labels_cnt[label_info[0]]] += 1

        if tag == "train":
            cnts_train = [cat_ids for cat_name, cat_ids in classes_num.items()]
        elif tag == "val":
            cnts_val = [cat_ids for cat_name, cat_ids in classes_num.items()]

    classes = [cat_name for cat_name, cat_ids in classes_num.items()]
    sorted_id = sorted(
        range(len(cnts_train)), key=lambda k: cnts_train[k], reverse=True
    )
    cnts_train_sorted = [cnts_train[index] for index in sorted_id]
    cnts_val_sorted = [cnts_val[index] for index in sorted_id]
    classes_sorted = [classes[index] for index in sorted_id]
    x = np.arange(len(classes))
    width = 0.5

    # bar
    os_system = platform.system().lower()
    if os_system == "windows":
        plt.rcParams["font.sans-serif"] = "FangSong"
    else:
        font = font_manager.FontProperties(fname=PINGFANG_FONT_FILE_PATH, size=10)
    fig, ax = plt.subplots(figsize=(max(8, int(len(classes) / 5)), 5), dpi=300)
    ax.bar(x, cnts_train_sorted, width=0.5, label="train")
    ax.bar(x + width, cnts_val_sorted, width=0.5, label="val")
    plt.xticks(
        x + width / 2,
        classes_sorted,
        rotation=90,
        fontproperties=None if os_system == "windows" else font,
    )
    ax.set_xlabel(
        "类别名称", fontproperties=None if os_system == "windows" else font, fontsize=12
    )
    ax.set_ylabel(
        "样本框数量",
        fontproperties=None if os_system == "windows" else font,
        fontsize=12,
    )
    plt.legend(loc=1)
    fig.tight_layout()
    file_path = os.path.join(output, "histogram.png")
    fig.savefig(file_path, dpi=300)

    return {"histogram": os.path.join("check_dataset", "histogram.png")}