[人工智能]深度学习物体检测实战算法3——检测数据集处理

检测数据集介绍

商品检测数据集处理

from xml.etree import ElementTree as ET
import os
import numpy as np
import pickle

class XmlProcess(object):
    def __init__(self,file_path):
        self.xml_path = file_path
        self.num_classes = 8
        self.data = {}

    def process_xml(self):
        """
        处理图片的标注信息，解析图片大小，图片中所有物体位置，类别
        存入序列化的pkl文件
        :return:
        """

        #1.找到路径对应的图片
        for filename in os.listdir(self.xml_path):
            et = ET.parse(self.xml_path+filename)
            root = et.getroot()
            #print(root)
            #获取图片基础属性
            #获取size
            size = root.find('size')
            width = float(size.find('width').text)
            height =float(size.find('height').text)
            depth = float(size.find('depth').text)
            print(width,height,depth)
            #2.对于每张图片，解析其中的多个物体
            bounding_boxes = []
            one_hots = []
            for object_tree in root.findall('object'):
                for res in object_tree.iter('bndbox'):
                    #xmin,ymin,xmax,ymax---->标准化的结果去保存值
                    xmin = float(res.find('xmin').text) / width
                    ymin = float(res.find('ymin').text) / height
                    xmax = float(res.find('xmax').text) / width
                    ymax = float(res.find('ymax').text) / height
                    #print(xmin,ymin,xmax,ymax)
                bounding_boxes.append([xmin,ymin,xmax,ymax])
                #每个object都会有一个名称，目标值保存成one_hot编码
                object_name = object_tree.find('name').text
                object_onehot = self.one_hot(object_name)
                one_hots.append(object_onehot)
                #print(bounding_boxes,one_hots)
            #进行物体位置和目标值的one_hot编码进行拼接
            bounding_boxes = np.asarray(bounding_boxes)
            one_hots = np.asarray(one_hots)
            image_data = np.hstack((bounding_boxes,one_hots))
            print(image_data)
            self.data[filename] = image_data
        return None
    def one_hot(self,name):
        one_hot_vector = [0]*self.num_classes
        if name == 'clothes':
            one_hot_vector[0] = 1
        elif name == 'pants':
            one_hot_vector[1] = 1
        elif name == 'shoes':
            one_hot_vector[2] = 1
        elif name == 'watch':
            one_hot_vector[3] = 1
        elif name == 'phone':
            one_hot_vector[4] = 1
        elif name == 'audio':
            one_hot_vector[5] = 1
        elif name == 'computer':
            one_hot_vector[6] = 1
        elif name == 'books':
            one_hot_vector[7] = 1
        else:
            print('unknown label:%s' % name)
        return one_hot_vector



if __name__=='__name':
    xp=XmlProcess()
    xp.process_xml()
    print(xp.data)
    pickle.dump(xp.data,open(".pkl",'wb'))