plot_category.py

import json
import os
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from matplotlib.patches import Patch

num_objects = 0
num_frames = 0
num_masks = 0
category_item = []
json_path = "/Users/gongsitong/Desktop/dataset_processing/Ref_LVOS/video_exp.json"
json_dict = json.load(open(json_path, 'r'))["videos"]
category_list = json.load(open("/Users/gongsitong/Desktop/dataset_processing/Ref_LVOS/category.json", "r"))["category"]
category_dict = dict()

for category in category_list:
    category_dict[category] = 0
    
category_list = []
for video_name, video_dict in json_dict.items():
    new_obj_id = -1
    num_frames += len(video_dict["frames"])
    for exp_id, exp_dict in video_dict["expressions"].items():
        if type(exp_dict["category"]) != list:
            category_list.append(exp_dict["category"])
            category_dict[exp_dict["category"]] += 1
        else:
            for category in exp_dict["category"]:
                category_dict[category] += 1
        
# print(set(category_list))
print(category_dict)

# 分组字典
category_group_map = {
    "Transportation": ['car', 'bus', 'motorcycle', 'helicopter'],
    "Daily Object": [
        'bag', 'electronic', 'furniture', 'sign', 'rope', 'cup', 'bottle', 'can', 'tool', 'box', 'card', 'food', "accessory"
    ],
    # "Food": [  ], 
    "Animal": [
        'bird', 'cattle', 'sheep', 'squirrel', 'cat', 'giraffe', 'elephant',
        'kangaroo', 'rabbit', 'zebra', 'bear', 'dog', 'fish', 'primate'
    ],
    "Sports": [ 'dartboard', 'bowling', 'puck', 'billiards', 'basketball', 'frisbee', 'paddle', "pusher", "kite"],
    "Person": ['pedestrian', 'performer', 'exerciser', 'worker', 'observer', 'athlete', 'player', 'dancer', 'skator']
}

# 创建 DataFrame
df = pd.DataFrame(list(category_dict.items()), columns=['Category', 'Count'])

# 颜色映射
color_map = {
    "Transportation": '#00BFFF',
    "Daily Object": '#F5516F',
    "Animal": '#ADFF2F',
    "Sports": '#BBBBBB',
    "Person": '#FFCD5C'
    # "Other": '#BBBBBB',
    # "Food": '#D94F4F',   
}

# 添加 Group 列
def assign_group(cat):
    for group, members in category_group_map.items():
        if cat in members:
            return group
    return 'Other'

df['Group'] = df['Category'].apply(assign_group)
df['Color'] = df['Group'].map(color_map)

df = df.sort_values(by=['Group', 'Count'], ascending=[True, False])

# 排序
# df = df.sort_values(by='Count', ascending=False)

# 绘图
# plt.rcParams['font.family'] = 'serif'
# plt.figure(figsize=(24, 6))
# bars = plt.bar(df['Category'], df['Count'], color=df['Color']) #, edgecolor='black')
# plt.yscale("log")
# plt.xticks(rotation=90)
# plt.ylabel("Expression Count (log scale)")
# # plt.title("Expression References per Object Category by Group")
# plt.margins(x=0.01)  # ✅ 减少图像两边空白

# # 添加图例
# legend_elements = [Patch(facecolor=color_map[g], label=g) for g in color_map]
# plt.legend(handles=legend_elements, loc='upper left', bbox_to_anchor=(1.01, 1))
# # 保存图像
# plt.tight_layout()
# plt.savefig("category_expression.png", dpi=300, bbox_inches="tight")
# plt.close()


# ================= 修改部分开始 =================

# 1. 设置全局字体大小 (基础大小)
plt.rcParams['font.family'] = 'serif'
plt.rcParams['font.size'] = 14  # 调大基础字体

plt.figure(figsize=(24, 6))
bars = plt.bar(df['Category'], df['Count'], color=df['Color'])

plt.yscale("log")

# 2. 调大 X 轴刻度字体
plt.xticks(rotation=90, fontsize=18) 

# 3. 调大 Y 轴标签字体
plt.ylabel("Expression Count (log scale)", fontsize=16)

plt.margins(x=0.01)

# 4. 调大图例字体
legend_elements = [Patch(facecolor=color_map[g], label=g) for g in color_map]
plt.legend(handles=legend_elements, loc='upper left', bbox_to_anchor=(1.01, 1), fontsize=14)

plt.tight_layout()
plt.savefig("category_expression_large_font.png", dpi=300, bbox_inches="tight")
plt.close() # 显示图片

# ================= 修改部分结束 =================