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import xml.etree.ElementTree as ET
import pickle
import os
from os import listdir, getcwd
from os.path import join
import random
from shutil import copyfile
classes = [“hat”, “person”] # 自定义类
TRAIN_RATIO = 80 # 8：2 训练集和验证集比率
def clear_hidden_files(path):
| dir_list = os.listdir(path) | |
| for i in dir_list: | |
| abspath = os.path.join(os.path.abspath(path), i) | |
| if os.path.isfile(abspath): | |
| if i.startswith("._"): | |
| os.remove(abspath) | |
| else: | |
| clear_hidden_files(abspath) |
def convert(size, box):
| dw = 1./size[0] | |
| dh = 1./size[1] | |
| x = (box[0] + box[1])/2.0 | |
| y = (box[2] + box[3])/2.0 | |
| w = box[1] - box[0] | |
| h = box[3] - box[2] | |
| x = x*dw | |
| w = w*dw | |
| y = y*dh | |
| h = h*dh | |
| return (x,y,w,h) |
def convert_annotation(image_id):
| in_file = open('VOCdevkit/VOC2007/Annotations/%s.xml' %image_id) | |
| out_file = open('VOCdevkit/VOC2007/YOLOLabels/%s.txt' %image_id, 'w') | |
| tree=ET.parse(in_file) | |
| root = tree.getroot() | |
| size = root.find('size') | |
| w = int(size.find('width').text) | |
| h = int(size.find('height').text) | |
| for obj in root.iter('object'): | |
| difficult = obj.find('difficult').text | |
| cls =[金融期货](https://www.gendan5.com/futures/ff.html) obj.find('name').text | |
| if cls not in classes or int(difficult) == 1: | |
| continue | |
| cls_id = classes.index(cls) | |
| xmlbox = obj.find('bndbox') | |
| b = (float(xmlbox.find('xmin').text), float(xmlbox.find('xmax').text), float(xmlbox.find('ymin').text), float(xmlbox.find('ymax').text)) | |
| bb = convert((w,h), b) | |
| out_file.write(str(cls_id) + "" +" ".join([str(a) for a in bb]) +'\n') | |
| in_file.close() | |
| out_file.close() |
wd = os.getcwd()
wd = os.getcwd()
data_base_dir = os.path.join(wd, “VOCdevkit/”)
if not os.path.isdir(data_base_dir):
os.mkdir(data_base_dir)
work_sapce_dir = os.path.join(data_base_dir, “VOC2007/”)
if not os.path.isdir(work_sapce_dir):
os.mkdir(work_sapce_dir)
annotation_dir = os.path.join(work_sapce_dir, “Annotations/”)
if not os.path.isdir(annotation_dir):
os.mkdir(annotation_dir)
clear_hidden_files(annotation_dir)
image_dir = os.path.join(work_sapce_dir, “JPEGImages/”)
if not os.path.isdir(image_dir):
os.mkdir(image_dir)
clear_hidden_files(image_dir)
yolo_labels_dir = os.path.join(work_sapce_dir, “YOLOLabels/”)
if not os.path.isdir(yolo_labels_dir):
os.mkdir(yolo_labels_dir)
clear_hidden_files(yolo_labels_dir)
yolov5_images_dir = os.path.join(data_base_dir, “images/”)
if not os.path.isdir(yolov5_images_dir):
os.mkdir(yolov5_images_dir)
clear_hidden_files(yolov5_images_dir)
yolov5_labels_dir = os.path.join(data_base_dir, “labels/”)
if not os.path.isdir(yolov5_labels_dir):
os.mkdir(yolov5_labels_dir)
clear_hidden_files(yolov5_labels_dir)
yolov5_images_train_dir = os.path.join(yolov5_images_dir, “train/”)
if not os.path.isdir(yolov5_images_train_dir):
os.mkdir(yolov5_images_train_dir)
clear_hidden_files(yolov5_images_train_dir)
yolov5_images_test_dir = os.path.join(yolov5_images_dir, “val/”)
if not os.path.isdir(yolov5_images_test_dir):
os.mkdir(yolov5_images_test_dir)
clear_hidden_files(yolov5_images_test_dir)
yolov5_labels_train_dir = os.path.join(yolov5_labels_dir, “train/”)
if not os.path.isdir(yolov5_labels_train_dir):
os.mkdir(yolov5_labels_train_dir)
clear_hidden_files(yolov5_labels_train_dir)
yolov5_labels_test_dir = os.path.join(yolov5_labels_dir, “val/”)
if not os.path.isdir(yolov5_labels_test_dir):
os.mkdir(yolov5_labels_test_dir)
clear_hidden_files(yolov5_labels_test_dir)
train_file = open(os.path.join(wd, “yolov5_train.txt”), ‘w’)
test_file = open(os.path.join(wd, “yolov5_val.txt”), ‘w’)
train_file.close()
test_file.close()
train_file = open(os.path.join(wd, “yolov5_train.txt”), ‘a’)
test_file = open(os.path.join(wd, “yolov5_val.txt”), ‘a’)
list_imgs = os.listdir(image_dir) # list image files
prob = random.randint(1, 100)
print(“Probability: %d” % prob)
for i in range(0,len(list_imgs)):
| path = os.path.join(image_dir,list_imgs[i]) | |
| if os.path.isfile(path): | |
| image_path = image_dir + list_imgs[i] | |
| voc_path = list_imgs[i] | |
| (nameWithoutExtention, extention) = os.path.splitext(os.path.basename(image_path)) | |
| (voc_nameWithoutExtention, voc_extention) = os.path.splitext(os.path.basename(voc_path)) | |
| annotation_name = nameWithoutExtention + '.xml' | |
| annotation_path = os.path.join(annotation_dir, annotation_name) | |
| label_name = nameWithoutExtention + '.txt' | |
| label_path = os.path.join(yolo_labels_dir, label_name) | |
| prob = random.randint(1, 100) | |
| print("Probability: %d" % prob) | |
| if(prob < TRAIN_RATIO): # train dataset | |
| if os.path.exists(annotation_path): | |
| train_file.write(image_path + '\n') | |
| convert_annotation(nameWithoutExtention) # convert label | |
| copyfile(image_path, yolov5_images_train_dir + voc_path) | |
| copyfile(label_path, yolov5_labels_train_dir + label_name) | |
| else: # test dataset | |
| if os.path.exists(annotation_path): | |
| test_file.write(image_path + '\n') | |
| convert_annotation(nameWithoutExtention) # convert label | |
| copyfile(image_path, yolov5_images_test_dir + voc_path) | |
| copyfile(label_path, yolov5_labels_test_dir + label_name) |
train_file.close()
test_file.close()
