PaddleX/paddlex/modules/object_detection/dataset_checker/dataset_src/analyse_dataset.py

# 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 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 pycocotools.coco import COCO

from .....utils.fonts import PINGFANG_FONT_FILE_PATH


def deep_analyse(dataset_dir, output):
    """class analysis for dataset"""
    tags = ['train', 'val']
    all_instances = 0
    for tag in tags:
        annotations_path = os.path.abspath(
            os.path.join(dataset_dir, f'annotations/instance_{tag}.json'))
        labels_cnt = defaultdict(list)
        coco = COCO(annotations_path)
        cat_ids = coco.getCatIds()
        for cat_id in cat_ids:
            cat_name = coco.loadCats(ids=cat_id)[0]["name"]
            labels_cnt[cat_name] = labels_cnt[cat_name] + coco.getAnnIds(
                catIds=cat_id)
            all_instances += len(labels_cnt[cat_name])
        if tag == 'train':
            cnts_train = [
                len(cat_ids) for cat_name, cat_ids in labels_cnt.items()
            ]
        elif tag == 'val':
            cnts_val = [
                len(cat_ids) for cat_name, cat_ids in labels_cnt.items()
            ]
    classes = [cat_name for cat_name, cat_ids in labels_cnt.items()]
    sorted_id = sorted(
        range(len(cnts_train)), key=lambda k: cnts_train[k], reverse=True)
    cnts_train_sorted = sorted(cnts_train, reverse=True)
    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)
    fig, ax = plt.subplots(figsize=(max(8, int(len(classes) / 5)), 5), dpi=120)
    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_ylabel('Counts')
    plt.legend()
    fig.tight_layout()
    fig_path = os.path.join(output, "histogram.png")
    fig.savefig(fig_path)
    return {"histogram": os.path.join("check_dataset", "histogram.png")}