VOC2012数据集的探索性数据分析（EDA）
对VOC2012数据集的探索性数据分析。文章结尾有EDA python代码和Github EDA代码仓库（包含多个数据集EDA代码）。
运行结果日志：包括训练集\验证集的图片数，目标数，各尺寸目标数以及占比：
analyze voc train dataset...
--------------------------------------------------
loading annotations into memory...
Done (t=0.06s)
creating index...
index created!
voc train images num: 5717
voc train objects num: 15774
voc small objects num: 1895
voc medium objects num: 4561
voc large objects num: 9318
voc small objects percent: 0.12013439837707621
voc medium objects percent: 0.2891466970964879
voc large objects percent: 0.5907189045264359
--------------------------------------------------
analyze voc val dataset...
--------------------------------------------------
loading annotations into memory...
Done (t=0.12s)
creating index...
index created!
voc val images num: 5823
voc val objects num: 15787
voc small objects num: 1651
voc medium objects num: 4565
voc large objects num: 9571
voc small objects percent: 0.10457971748907328
voc medium objects percent: 0.28916196870843097
voc large objects percent: 0.6062583138024957
--------------------------------------------------
训练集所有类别的数量分布情况：
训练集所有类别的尺寸分布情况：
验证集所有类别的数量分布情况：
验证集所有类别的尺寸分布情况：
Github EDA代码仓库（包含多个数据集EDA代码）：
gy-7/EDA: Exploratory Data Analysis (github.com)
EDA代码：
先将voc数据集格式转换为coco数据集格式，我用的是mmdetection的工具转换的。有些人可能没有mmdetection，github仓库里有我转换成功后的json文件。
在 mmdetection 根目录下运行命令即可，具体转换命令如下（将VOCdevkit文件夹路径切换为自己的路径）：
python tools/dataset_converters/pascal_voc.py /home/user1/data/voc2012/VOCdevkit/ -o /home/user1/data/voc2012/coco_anns/ --out-format coco
将train_ann_fp，val_ann_fp改为自己的路径即可。
import os
import seaborn as sns
import pycocotools.coco as coco
import matplotlib.pyplot as plt
root_dir = os.getcwd()
train_ann_fp = os.path.join(root_dir, 'annotations', 'voc12_train.json')
val_ann_fp = os.path.join(root_dir, 'annotations', 'voc12_val.json')
class VOC_EDA:
def __init__(self, json_file, type='train'):
self.COCO_SMALL_SCALE = 32
self.COCO_MEDIUM_SCALE = 96
self.json_file = json_file
voc = coco.COCO(json_file)
self.type = type
self.imgs = voc.dataset['images']
self.anns = voc.dataset['annotations']
self.cats = voc.dataset['categories']
self.img_ids = voc.getImgIds()
self.ann_ids = voc.getAnnIds()
self.cat_ids = voc.getCatIds()
self.cat2imgs = voc.catToImgs
self.img2anns = voc.imgToAnns
self.imgs_num = len(self.imgs)
self.objs_num = len(self.anns)
# data to be collected
self.small_objs_num = 0
self.medium_objss_num = 0
self.large_objss_num = 0
self.small_objs = []
self.medium_objs = []
self.large_objs = []
self.cat2objs = {}
self.small_cat2objs = {} # small objects classes distribution
self.medium_cat2objs = {} # medium objects classes distribution
self.large_cat2objs = {} # large objects classes distribution
self.cat2objs_num = {} # objects classes distribution
self.small_cat2objs_num = {} # small objects classes distribution
self.medium_cat2objs_num = {} # medium objects classes distribution
self.large_cat2objs_num = {} # large objects classes distribution
# plot use data
self.catid2name = {} # 用于绘图中显示类别名字
self.cats_plot = [] # voc 所有尺寸目标的类别分布
self.small_cats_plot = [] # 小目标中每个类的分布情况
self.medium_cats_plot = [] # 中目标中每个类的分布情况
self.large_cats_plot = [] # 大目标中每个类的分布情况
# 每个类的小，中，大目标的数量
self.size_distribution = {}
def collect_data(voc):
# collect small, medium, large objects
for ann in voc.anns:
if ann['area'] < voc.COCO_SMALL_SCALE ** 2:
voc.small_objs_num += 1
voc.small_objs.append(ann)
elif ann['area'] < voc.COCO_MEDIUM_SCALE ** 2:
voc.medium_objs.append(ann)
voc.medium_objss_num += 1
else:
voc.large_objs.append(ann)
voc.large_objss_num += 1
for i in voc.cat_ids:
voc.cat2objs[i] = []
voc.small_cat2objs[i] = []
voc.medium_cat2objs[i] = []
voc.large_cat2objs[i] = []
voc.cat2objs_num[i] = 0
voc.small_cat2objs_num[i] = 0
voc.medium_cat2objs_num[i] = 0
voc.large_cat2objs_num[i] = 0
voc.size_distribution[i] = []
for i in voc.cats:
voc.catid2name[i['id']] = i['name']
# collect small, medium, large class distribution
for i in voc.anns:
voc.cat2objs[i['category_id']].append(i)
voc.cat2objs_num[i['category_id']] += 1
voc.cats_plot.append(voc.catid2name[i['category_id']])
if i['area'] < voc.COCO_SMALL_SCALE ** 2:
voc.small_cat2objs[i['category_id']].append(i)
voc.small_cat2objs_num[i['category_id']] += 1
voc.small_cats_plot.append(voc.catid2name[i['category_id']])
voc.size_distribution[i['category_id']].append('s')
elif i['area'] < voc.COCO_MEDIUM_SCALE ** 2:
voc.medium_cat2objs[i['category_id']].append(i)
voc.medium_cat2objs_num[i['category_id']] += 1
voc.medium_cats_plot.append(voc.catid2name[i['category_id']])
voc.size_distribution[i['category_id']].append('m')
else:
voc.large_cat2objs[i['category_id']].append(i)
voc.large_cat2objs_num[i['category_id']] += 1
voc.large_cats_plot.append(voc.catid2name[i['category_id']])
voc.size_distribution[i['category_id']].append('l')
assert len(voc.small_objs) == voc.small_objs_num == sum(voc.small_cat2objs_num.values())
assert len(voc.medium_objs) == voc.medium_objss_num == sum(voc.medium_cat2objs_num.values())
assert len(voc.large_objs) == voc.large_objss_num == sum(voc.large_cat2objs_num.values())
assert len(voc.anns) == voc.objs_num == sum(voc.cat2objs_num.values())
def plot_voc_class_distribution(plot_data, plot_order, save_fp, plot_title, plot_y_heigh,
plot_y_heigh_residual=[20, 20]):
# 绘制voc数据集的类别分布
sns.set_style("whitegrid")
plt.figure(figsize=(15, 8)) # 图片的宽和高，单位为inch
plt.title(plot_title, fontsize=9) # 标题
plt.xlabel('class', fontsize=8) # x轴名称
plt.ylabel('counts', fontsize=8) # y轴名称
plt.xticks(rotation=90, fontsize=8) # x轴标签竖着显示
plt.yticks(fontsize=8)
for x, y in enumerate(plot_y_heigh):
if 'train' in save_fp:
plt.text(x, y + plot_y_heigh_residual[0], '%s' % y, ha='center', fontsize=7, rotation=0)
else:
plt.text(x, y + plot_y_heigh_residual[1], '%s' % y, ha='center', fontsize=7, rotation=0)
ax = sns.countplot(x=plot_data, palette="PuBu_r", order=plot_order) # 绘制直方图，palette调色板，蓝色由浅到深渐变。
# palette样式：https://blog.csdn.net/panlb1990/article/details/103851983
plt.savefig(os.path.join(save_fp), dpi=500)
plt.show()
def plot_size_distribution(plot_data, save_fp, plot_title, plot_order=['s', 'm', 'l']):
sns.set_style("whitegrid")
plt.figure(figsize=(15, 12)) # 图片的宽和高，单位为inch
plt.subplots_adjust(left=0.1, bottom=0.1, right=0.9, top=0.9, wspace=1, hspace=1.5) # 调整子图间距
for idx, size_data in enumerate(plot_data.values()):
plt.subplot(4, 5, idx + 1)
plt.xticks(rotation=0, fontsize=18) # x轴标签竖着显示
plt.yticks(fontsize=18)
plt.xlabel('size', fontsize=20) # x轴名称
plt.ylabel('count', fontsize=20) # y轴名称
plt.title(plot_title[idx], fontsize=24) # 标题
sns.countplot(x=size_data, palette="PuBu_r", order=plot_order) # 绘制直方图，palette调色板，蓝色由浅到深渐变。
plt.savefig(save_fp, dpi=500, pad_inches=0)
plt.show()
def run_plot_voc_class_distribution(voc, save_dir):
# # 绘制voc数据集的类别分布
plot_order = [i for i in voc.catid2name.values()]
plot_heigh = [i for i in voc.cat2objs_num.values()]
save_fp = os.path.join(save_dir, f'voc_{voc.type}_class_distribution.png')
plot_voc_class_distribution(voc.cats_plot, plot_order, save_fp, 'VOC train2012 class distribution', plot_heigh,
plot_y_heigh_residual=[20, 20])
plot_heigh = [i for i in voc.small_cat2objs_num.values()]
save_fp = os.path.join(save_dir, f'voc_{voc.type}_small_class_distribution.png')
plot_voc_class_distribution(voc.small_cats_plot, plot_order, save_fp, 'VOC train2012 small class distribution',
plot_heigh,
plot_y_heigh_residual=[20, 20])
plot_heigh = [i for i in voc.medium_cat2objs_num.values()]
save_fp = os.path.join(save_dir, f'voc_{voc.type}_medium_class_distribution.png')
plot_voc_class_distribution(voc.medium_cats_plot, plot_order, save_fp, 'VOC train2012 medium class distribution',
plot_heigh, plot_y_heigh_residual=[20, 20])
plot_heigh = [i for i in voc.large_cat2objs_num.values()]
save_fp = os.path.join(save_dir, f'voc_{voc.type}_large_class_distribution.png')
plot_voc_class_distribution(voc.large_cats_plot, plot_order, save_fp, 'VOC train2012 large class distribution',
plot_heigh,
plot_y_heigh_residual=[20, 20])
def run_plot_voc_size_distribution(voc, save_dir):
# 绘制voc数据集各类别的尺寸分布
plot_order = [i for i in voc.catid2name.values()]
save_fp = os.path.join(save_dir, f'voc_{voc.type}_size_distribution.png')
plot_size_distribution(voc.size_distribution, save_fp, plot_order)
if __name__ == '__main__':
cwd = os.getcwd()
voc_res_dir = os.path.join(cwd, "voc_results")
if not os.path.exists(voc_res_dir):
os.makedirs(voc_res_dir)
print("analyze voc train dataset...")
print("-" * 50)
voc_train = VOC_EDA(train_ann_fp, type='train')
collect_data(voc_train)
print("voc train images num:", voc_train.imgs_num)
print("voc train objects num:", voc_train.objs_num)
print("voc small objects num:", voc_train.small_objs_num)
print("voc medium objects num:", voc_train.medium_objss_num)
print("voc large objects num:", voc_train.large_objss_num)
print("voc small objects percent:", voc_train.small_objs_num / voc_train.objs_num)
print("voc medium objects percent:", voc_train.medium_objss_num / voc_train.objs_num)
print("voc large objects percent:", voc_train.large_objss_num / voc_train.objs_num)
run_plot_voc_class_distribution(voc_train, voc_res_dir)
run_plot_voc_size_distribution(voc_train, voc_res_dir)
print("-" * 50)
print()
print("analyze voc val dataset...")
print("-" * 50)
voc_val = VOC_EDA(val_ann_fp, type='val')
collect_data(voc_val)
print("voc val images num:", voc_val.imgs_num)
print("voc val objects num:", voc_val.objs_num)
print("voc small objects num:", voc_val.small_objs_num)
print("voc medium objects num:", voc_val.medium_objss_num)
print("voc large objects num:", voc_val.large_objss_num)
print("voc small objects percent:", voc_val.small_objs_num / voc_val.objs_num)
print("voc medium objects percent:", voc_val.medium_objss_num / voc_val.objs_num)
print("voc large objects percent:", voc_val.large_objss_num / voc_val.objs_num)
run_plot_voc_class_distribution(voc_val, voc_res_dir)
run_plot_voc_size_distribution(voc_val, voc_res_dir)
print("-" * 50)