yolo的数据可视化很详细，coco格式没有。所以写了一个接口。
输入：coco格式的instances.json
输出：生成像yolo那样的标注文件统计并可视化

import os
import random
import numpy as np
import pandas as pd
import matplotlib
import matplotlib.pyplot as plt
import seaborn as sn
from glob import glob
from PIL import Image, ImageDraw
import json
"""功能：读取instances.json生成像yolo那样的标注文件统计并可视化"""def convert(size, box): # size(img_width, img_height)# box=[x_min, y_min, width, height]# coco转yolo   dw = 1. / (size[0])dh = 1. / (size[1])x = box[0] + box[2] / 2.0y = box[1] + box[3] / 2.0w = box[2]h = box[3]#round函数确定(xmin, ymin, xmax, ymax)的小数位数x = round(x * dw, 6)w = round(w * dw, 6)y = round(y * dh, 6)h = round(h * dh, 6)return (x, y, w, h)def plot_labels(labels, names=(), save_dir='',colors=[0,0,255]):# plot dataset labelsprint('Plotting labels... ')c, b = labels[:, 0], labels[:, 1:].transpose()  # classes, boxesnc = int(c.max() + 1)  # number of classesx = pd.DataFrame(b.transpose(), columns=['x', 'y', 'width', 'height'])# seaborn correlogramsn.pairplot(x, corner=True, diag_kind='auto', kind='hist', diag_kws=dict(bins=50), plot_kws=dict(pmax=0.9))plt.savefig(os.path.join(save_dir, 'labels_correlogram.jpg'), dpi=200)plt.close()# matplotlib labelsmatplotlib.use('svg')  # fasterax = plt.subplots(2, 2, figsize=(8, 8), tight_layout=True)[1].ravel()y = ax[0].hist(c, bins=np.linspace(0, nc, nc + 1) - 0.5, rwidth=0.8)# [y[2].patches[i].set_color([x / 255 for x in colors(i)]) for i in range(nc)]  # update colors bug #3195ax[0].set_ylabel('instances')if 0 < len(names) < 30:ax[0].set_xticks(range(len(names)))ax[0].set_xticklabels(names, rotation=90, fontsize=10)else:ax[0].set_xlabel('classes')sn.histplot(x, x='x', y='y', ax=ax[2], bins=50, pmax=0.9)sn.histplot(x, x='width', y='height', ax=ax[3], bins=50, pmax=0.9)# rectangleslabels[:, 1:3] = 0.5  # centerlabels[:, 1:] = xywh2xyxy(labels[:, 1:]) * 2000img = Image.fromarray(np.ones((2000, 2000, 3), dtype=np.uint8) * 255)for cls, *box in labels[:1000]:ImageDraw.Draw(img).rectangle(box, width=1, outline=colors[int(cls)-1])  # plotax[1].imshow(img)ax[1].axis('off')for a in [0, 1, 2, 3]:for s in ['top', 'right', 'left', 'bottom']:ax[a].spines[s].set_visible(False)plt.savefig(os.path.join(save_dir, 'labels.jpg'), dpi=200)matplotlib.use('Agg')plt.close()def xywh2xyxy(x):# Convert nx4 boxes from [x, y, w, h] to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-righty = np.copy(x)y[:, 0] = x[:, 0] - x[:, 2] / 2  # top left xy[:, 1] = x[:, 1] - x[:, 3] / 2  # top left yy[:, 2] = x[:, 0] + x[:, 2] / 2  # bottom right xy[:, 3] = x[:, 1] + x[:, 3] / 2  # bottom right yreturn ydef main(json_name,save_root,data_name):# 获取当前数据集中所有json文件with open(json_name, 'r', encoding='utf-8') as file:result = json.load(file)# 每个类别一个颜色category=[]for i in result['categories']:category.append(i['name'])# 类别num_classes = len(category)  # 类别数colors = [(random.randint(0,255),random.randint(0,255),random.randint(0,255)) for _ in range(num_classes)]  # 每个类别生成一个随机颜色# 统计标注信息shapes = []  # 标注框ids = []  # 类别名的索引for i in result['annotations']:img_height=result['images'][i['image_id']-1]['height']img_width=result['images'][i['image_id']-1]['width']label_id=i['category_id']ids.append([label_id])(x, y, w, h)=convert([img_width, img_height], i['bbox']) shapes.append([x, y, w, h])shapes = np.array(shapes)ids = np.array(ids)lbs = np.hstack((ids, shapes))plot_labels(labels=lbs, names=np.array(category),save_dir=os.path.join(save_root,data_name),colors=colors)print("可视化已保存：", os.path.join(save_root,data_name, "label.jpg"))if __name__ == "__main__":json_name = os.path.join(path,data_name,'annotations','instances.json')save_root='保存路径'data_name='数据集的名称'main(json_name,save_root,data_name)

labels.jpg

labels_correlogram.jpg