©2020-2024 ioDraw All rights reserved,
Privacy Policy
<>记录贴：将inria行人检测数据集转化为YOLO可以训练的txt格式
inria行人检测数据集解压后有train和test文件，将里面的标注信息提取出来
<>转化代码
# coding=UTF-8 import os import re from PIL import Image sets=['train']
#需要填写变量image_path、annotations_path、full_path image_path =
r"D:\BaiduNetdiskDownload\59_INRIA Person Dataset\shuju1/" # 图片存放路径，路径固定
annotations_path= r"D:\BaiduNetdiskDownload\59_INRIA Person
Dataset\INRIAPerson\Test\annotations/" #文件夹目录 # INRIA标签存放路径 annotations= os.
listdir(annotations_path) #得到文件夹下的所有文件名称 # 获取文件夹下所有图片的图片名 def get_name(file_dir)
: list_file=[] for root, dirs, files in os.walk(file_dir): for file in files: #
splitext()将路径拆分为文件名+扩展名，例如os.path.splitext(“E:/lena.jpg”)将得到”E:/lena“+".jpg" if
os.path.splitext(file)[1] == '.jpg': list_file.append(os.path.join(root, file))
return list_file # 在labels目录下创建每个图片的标签txt文档 def text_create(name,bnd): full_path
= r"D:\BaiduNetdiskDownload\59_INRIA Person Dataset\labels1/%s.txt"%(name) size
= get_size(name + '.png') convert_size = convert(size, bnd) file = open(
full_path, 'a') file.write('0 ' + str(convert_size[0]) + ' ' + str(convert_size[
1]) + ' ' + str(convert_size[2]) + ' ' + str(convert_size[3]) ) file.write('\n')
# 获取要查询的图片的w,h def get_size(image_id): im = Image.open(
r'D:\BaiduNetdiskDownload\59_INRIA Person Dataset\INRIAPerson\Test\pos/%s'%(
image_id)) # 源图片存放路径 size = im.size w = size[0] h = size[1] return (w,h) #
将Tagphoto的x,y,w,h格式转换成yolo的X,Y,W,H def convert(size, box): dw = 1./size[0] dh =
1./size[1] x = (box[0] + box[2])/2.0 y = (box[1] + box[3])/2.0 w = box[2] - box[
0] h = box[3] - box[1] x = x*dw w = w*dw y = y*dh h = h*dh return (x,y,w,h) #
将处理的图片路径放入一个ｔｘｔ文件夹中 for image_set in sets: if not os.path.exists(
r'D:\BaiduNetdiskDownload\59_INRIA Person Dataset\labels1'): os.makedirs(
r'D:\BaiduNetdiskDownload\59_INRIA Person Dataset\labels1') #
生成的yolo3标签存放路径，路径固定 image_names = get_name(image_path) list_file = open(
'2007_%s.txt'%(image_set), 'w') for image_name in image_names: list_file.write(
'%s\n'%(image_name)) list_file.close() s = [] for file in annotations: #遍历文件夹
str_name= file.replace('.txt', '') if not os.path.isdir(file):
#判断是否是文件夹，不是文件夹才打开 with open(annotations_path+"/"+file) as f : #打开文件 iter_f =
iter(f); #创建迭代器 for line in iter_f: #遍历文件，一行行遍历，读取文本 str_XY = "(Xmax, Ymax)" if
str_XYin line: strlist = line.split(str_XY) strlist1 = "".join(strlist[1:]) #
把list转为str strlist1 = strlist1.replace(':', '') strlist1 = strlist1.replace('-',
'') strlist1 = strlist1.replace('(', '') strlist1 = strlist1.replace(')', '')
strlist1= strlist1.replace(',', '') b = strlist1.split() bnd = (float(b[0]) ,
float(b[1]) ,float(b[2]) ,float(b[3])) text_create(str_name, bnd) else: continue
<>可视化一下
判断转化是否正确，写了一个可视化代码
import os import cv2 img_path = r'D:\BaiduNetdiskDownload\59_INRIA Person
Dataset\INRIAPerson\Train\pos/' label_path = r'D:\BaiduNetdiskDownload\59_INRIA
Person Dataset\labels/' f = os.listdir(img_path) def paint(label_file, img_file)
: #读取照片 img = cv2.imread(img_file) img_h, img_w, _ = img.shape with open(
label_file, 'r') as f: obj_lines = [l.strip() for l in f.readlines()] for
obj_linein obj_lines: cls, cx, cy, nw, nh = [float(item) for item in obj_line.
split(' ')] color = (0, 0, 255) if cls == 0.0 else (0, 255, 0) x_min = int((cx -
(nw / 2.0)) * img_w) y_min = int((cy - (nh / 2.0)) * img_h) x_max = int((cx + (
nw/ 2.0)) * img_w) y_max = int((cy + (nh / 2.0)) * img_h) cv2.rectangle(img, (
x_min, y_min), (x_max, y_max), color, 2) cv2.imshow('Ima', img) cv2.waitKey(0)
for i in f: label_path_name = label_path + i.replace('png','txt') img_path_name
= img_path + i print(label_path_name) print(img_path_name) paint(label_path_name
,img_path_name)
发现这个数据集的多人场景下只标注了几个人