将VOC2007数据集里的person数据提取出来

1、首先只保留penson的文件

2、删除标签为-1 和 0 的行

删除前：

删除后：

3、显示图片和矩形框

代码：

import xml.etree.ElementTree as ET
import pickle
import os
from os import listdir, getcwd
from os.path import join
import cv2
import matplotlib.pyplot as plt
from math import sqrt as sqrt

# 需要检查的数据
sets=[('2007', 'person_trainval')]
# 需要检查的类别
classes = ['person']
# 输入分辨率
input_size = 320

if __name__ == '__main__':
    VOCRoot = 'F:/data/VOC2007/VOCdevkit'
    # GT框宽高统计
    width = []
    height = []

    for year, image_set in sets:
        for line in open(VOCRoot + '/VOC%s/ImageSets/Main/%s.txt'%(year, image_set)):
            image_id, value = line.split()
            if value != '1':
                continue
            # 图片的路径
            img_path = 'F:/data/VOC2007/VOCdevkit/VOC%s/JPEGImages/%s.jpg'%(year, image_id)
            # 这张图片的XML标注路径
            label_file = open('F:/data/VOC2007/VOCdevkit/VOC%s/Annotations/%s.xml' % (year, image_id))
            tree = ET.parse(label_file)
            root = tree.getroot()
            size = root.find('size')
            img_w = int(size.find('width').text)  # 原始图片的width
            img_h = int(size.find('height').text) # 原始图片的height
            img = cv2.imread(img_path)
            for obj in root.iter('object'):
                difficult = obj.find('difficult').text
                cls = obj.find('name').text
                # 如果标注不是需要的类别或者标注为difficult，就忽略
                if cls not in classes or int(difficult) == 1:
                    continue
                cls_id = classes.index(cls)

                xmlbox = obj.find('bndbox')
                xmin = int(xmlbox.find('xmin').text)
                ymin = int(xmlbox.find('ymin').text)
                xmax = int(xmlbox.find('xmax').text)
                ymax = int(xmlbox.find('ymax').text)
                w = xmax - xmin
                h = ymax - ymin

                img = cv2.rectangle(img, (xmin, ymin), (xmax, ymax), (0, 255, 0), 2)
                w_change = (w / img_w) * input_size
                h_change = (h / img_h) * input_size
                s = w_change * h_change # 得到了GT框面积
                width.append(sqrt(s))
                height.append(w_change / h_change)
            print(img_path)
            cv2.imshow('result', img)
            cv2.waitKey()

    plt.plot(width, height, 'ro')
    plt.show()

4、GT框的统计

import xml.etree.ElementTree as ET
import cv2
import matplotlib.pyplot as plt
from math import sqrt as sqrt

# 需要检查的数据
sets=[('2007', 'person_trainval')]
# 需要检查的类别
classes = ['person']
# 输入分辨率
input_size = 320

if __name__ == '__main__':
    VOCRoot = 'F:/data/VOC2007/VOCdevkit'
    # GT框宽高统计
    width = []
    height = []

    for year, image_set in sets:
        for line in open(VOCRoot + '/VOC%s/ImageSets/Main/%s.txt'%(year, image_set)):
            image_id, value = line.split()
            if value != '1':
                continue
            # 图片的路径
            img_path = 'F:/data/VOC2007/VOCdevkit/VOC%s/JPEGImages/%s.jpg'%(year, image_id)
            # 这张图片的XML标注路径
            label_file = open('F:/data/VOC2007/VOCdevkit/VOC%s/Annotations/%s.xml' % (year, image_id))
            tree = ET.parse(label_file)
            root = tree.getroot()
            size = root.find('size')
            img_w = int(size.find('width').text)  # 原始图片的width
            img_h = int(size.find('height').text) # 原始图片的height
            img = cv2.imread(img_path)
            for obj in root.iter('object'):
                difficult = obj.find('difficult').text
                cls = obj.find('name').text
                # 如果标注不是需要的类别或者标注为difficult，就忽略
                if cls not in classes or int(difficult) == 1:
                    continue
                cls_id = classes.index(cls)

                xmlbox = obj.find('bndbox')
                xmin = int(xmlbox.find('xmin').text)
                ymin = int(xmlbox.find('ymin').text)
                xmax = int(xmlbox.find('xmax').text)
                ymax = int(xmlbox.find('ymax').text)
                w = xmax - xmin
                h = ymax - ymin

                # img = cv2.rectangle(img, (xmin, ymin), (xmax, ymax), (0, 255, 0), 2)
                w_change = (w / img_w) * input_size
                h_change = (h / img_h) * input_size
                s = w_change * h_change # 得到了GT框面积
                width.append(sqrt(s))
                height.append(w_change / h_change)
            print(img_path)
            cv2.imshow('result', img)
            cv2.waitKey()

    plt.plot(width, height, 'ro')
    plt.show()

Y轴表示宽高比
X轴表示面积