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plot_statistic.py
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import os
import json
import matplotlib.pyplot as plt
from collections import defaultdict
# 设置文件夹路径
folder_path = 'statistic'
# 初始化字典来存储面积数据
area_data = defaultdict(int)
size_data = defaultdict(int)
height_data = defaultdict(int)
object_data = defaultdict(int)
# 遍历文件夹中的所有文件
categories_area = ['0-999', '1000-9999', '10000-99999', '100000-999999']
categories_size = ['0-9999', '10000-99999', '100000-999999', '1000000-9999999', '10000000+']
categories_height = ['0-3', '3-10', '11-30', '31-100', '100+']
categories_objnum = ['0-100', '100-500', '500-1000', '1000-3000', '3000+']
for key in categories_area:
area_data[key] = 0
for key in categories_size:
size_data[key] = 0
for key in categories_height:
height_data[key] = 0
for filename in os.listdir(folder_path):
# 检查文件扩展名是否为.json
if filename.endswith('.json'):
file_path = os.path.join(folder_path, filename)
# 打开并读取JSON文件
with open(file_path, 'r') as file:
data = json.load(file)
# 假设每个JSON文件中都有一个'Area'键
# 将面积值添加到字典中
# 根据面积值确定其所属的区间
if 'area' in data:
area = data['area']
if area < 1000: # 31*31 = 961
category = '0-999'
elif area < 10000: # 100*100 = 10000
category = '1000-9999'
elif area < 1000000: # 316*316 = 99856
category = '10000-99999'
elif area < 10000000: # 1000*1000 = 1000000
category = '100000-999999'
else:
category = '1000000+'
print(data['env_name'], data['area'])
area_data[category] += 1
if 'size' in data:
size = data['size']
if size < 10000:
category = '0-9999'
elif size < 100000:
category = '10000-99999'
elif size < 1000000:
category = '100000-999999'
elif size < 10000000:
category = '1000000-9999999'
else:
category = '10000000+'
size_data[category] += 1
if 'bbox' in data:
height = data['bbox'][2]
if height <= 3:
category = '0-3'
elif height <= 10:
category = '3-10'
elif height <= 30:
category = '11-30'
elif height <= 100:
category = '31-100'
else:
category = '100+'
height_data[category] += 1
if 'object_num' in data:
obj_num = data['object_num']
if obj_num <= 100:
category = '0-100'
elif obj_num <= 500:
category = '100-500'
elif obj_num <= 1000:
category = '500-1000'
elif obj_num <= 3000:
category = '1000-3000'
else:
category = '3000+'
object_data[category] += 1
# 准备数据用于绘图
# categories = list(area_data.keys())
# counts = list(area_data.values())
print(area_data, size_data, height_data)
def plot_bar_chart(categories, data, title, xlabel, ylabel):
colormap = plt.get_cmap('viridis')
counts = [data[category] for category in categories if category in data]
bars = plt.bar(categories, counts, color=[colormap(i / len(counts)) for i in range(len(counts))])
plt.xlabel(xlabel)
plt.ylabel(ylabel)
plt.title(title)
plt.show()
plot_bar_chart(categories_area, area_data, 'Distribution of Area Categories', 'Area Categories', 'Count')
plot_bar_chart(categories_size, size_data, 'Distribution of Size Categories', 'Size Categories', 'Count')
plot_bar_chart(categories_height, height_data, 'Distribution of Height Categories', 'Height Categories', 'Count')
plot_bar_chart(categories_objnum, object_data, 'Distribution of Object Number Categories', 'Object Number Categories', 'Count')
# # 创建柱状图，使用颜色映射
# counts = [area_data[category] for category in categories if category in area_data]
# bars = plt.bar(categories, counts, color=[colormap(i / len(counts)) for i in range(len(counts))])
#
# # 添加标题和标签
# plt.xlabel('Area Categories')
# plt.ylabel('Count')
# plt.title('Distribution of Area Categories')
#
#
# # 显示图表
# plt.show()