168 lines
6.2 KiB
Python
168 lines
6.2 KiB
Python
import xml.etree.ElementTree as ET
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import pickle
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import os
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from os import listdir, getcwd
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from os.path import join
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import random
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from shutil import copyfile
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from PIL import Image
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# 当前程序默认图片是png格式，若是jpg格式记得修改
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# 分类名称 这里改成数据集的分类名称，一定要改！！！请查看数据集目录下的txt文件
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CLASSES = ["helmet", "head", "fire", 'smoke']
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# 数据集目录 这里改成数据集的根目录，根目录下有两个文件夹Annotations和JPEGImages，一定要改！！！
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PATH = 'fire_smoke'
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# 训练集占比80% 训练集:验证集=8:2 这里划分数据集 不用改
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TRAIN_RATIO = 80
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def clear_hidden_files(path):
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dir_list = os.listdir(path)
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for i in dir_list:
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abspath = os.path.join(os.path.abspath(path), i)
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if os.path.isfile(abspath):
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if i.startswith("._"):
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os.remove(abspath)
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else:
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clear_hidden_files(abspath)
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def convert(size, box):
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dw = 1. / size[0]
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dh = 1. / size[1]
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x = (box[0] + box[1]) / 2.0
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y = (box[2] + box[3]) / 2.0
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w = box[1] - box[0]
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h = box[3] - box[2]
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x = x * dw
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w = w * dw
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y = y * dh
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h = h * dh
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return (x, y, w, h)
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save_path = 'fire_smoke/remove/none.txt'
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def convert_annotation(image_id):
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# Assuming the image format is jpg
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image_path = os.path.join(image_dir, f"{image_id}.jpg")
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img = Image.open(image_path)
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w, h = img.size
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in_file = open(PATH+'/annotations/%s.xml' % image_id, encoding='utf-8')
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out_file = open(PATH+'/YOLOLabels/%s.txt' %
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image_id, 'w', encoding='utf-8')
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tree = ET.parse(in_file)
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root = tree.getroot()
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size = root.find('size')
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# w = int(size.find('width').text)
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# h = int(size.find('height').text)
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difficult = 0
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f = 0
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for obj in root.iter('object'):
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if obj.find('difficult'):
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difficult = obj.find('difficult').text
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cls = obj.find('name').text
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if cls not in CLASSES or int(difficult) == 1:
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continue
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cls_id = CLASSES.index(cls)
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xmlbox = obj.find('bndbox')
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b = (float(xmlbox.find('xmin').text), float(xmlbox.find('xmax').text), float(xmlbox.find('ymin').text),
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float(xmlbox.find('ymax').text))
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bb = convert((w, h), b)
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out_file.write(str(cls_id) + " " +
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" ".join([str(a) for a in bb]) + '\n')
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f = 1
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if f == 0:
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with open(save_path, 'a') as f:
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f.write(image_id+'\n')
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# print(image_id)
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in_file.close()
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out_file.close()
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wd = os.getcwd()
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wd = os.getcwd()
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work_sapce_dir = os.path.join(wd, PATH+"/")
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annotation_dir = os.path.join(work_sapce_dir, "annotations/")
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if not os.path.isdir(annotation_dir):
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os.mkdir(annotation_dir)
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clear_hidden_files(annotation_dir)
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image_dir = os.path.join(work_sapce_dir, "images/")
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if not os.path.isdir(image_dir):
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os.mkdir(image_dir)
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clear_hidden_files(image_dir)
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yolo_labels_dir = os.path.join(work_sapce_dir, "YOLOLabels/")
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if not os.path.isdir(yolo_labels_dir):
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os.mkdir(yolo_labels_dir)
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clear_hidden_files(yolo_labels_dir)
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yolov5_train_dir = os.path.join(work_sapce_dir, "train/")
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if not os.path.isdir(yolov5_train_dir):
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os.mkdir(yolov5_train_dir)
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clear_hidden_files(yolov5_train_dir)
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yolov5_images_train_dir = os.path.join(yolov5_train_dir, "images/")
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if not os.path.isdir(yolov5_images_train_dir):
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os.mkdir(yolov5_images_train_dir)
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clear_hidden_files(yolov5_images_train_dir)
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yolov5_labels_train_dir = os.path.join(yolov5_train_dir, "labels/")
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if not os.path.isdir(yolov5_labels_train_dir):
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os.mkdir(yolov5_labels_train_dir)
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clear_hidden_files(yolov5_labels_train_dir)
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yolov5_test_dir = os.path.join(work_sapce_dir, "val/")
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if not os.path.isdir(yolov5_test_dir):
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os.mkdir(yolov5_test_dir)
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clear_hidden_files(yolov5_test_dir)
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yolov5_images_test_dir = os.path.join(yolov5_test_dir, "images/")
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if not os.path.isdir(yolov5_images_test_dir):
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os.mkdir(yolov5_images_test_dir)
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clear_hidden_files(yolov5_images_test_dir)
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yolov5_labels_test_dir = os.path.join(yolov5_test_dir, "labels/")
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if not os.path.isdir(yolov5_labels_test_dir):
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os.mkdir(yolov5_labels_test_dir)
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clear_hidden_files(yolov5_labels_test_dir)
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train_file = open(os.path.join(wd, "yolov5_train.txt"), 'w', encoding='utf-8')
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test_file = open(os.path.join(wd, "yolov5_valid.txt"), 'w', encoding='utf-8')
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train_file.close()
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test_file.close()
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train_file = open(os.path.join(wd, "yolov5_train.txt"), 'a', encoding='utf-8')
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test_file = open(os.path.join(wd, "yolov5_valid.txt"), 'a', encoding='utf-8')
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list_imgs = os.listdir(image_dir) # list image files
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prob = random.randint(1, 100)
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print("数据集: %d个" % len(list_imgs))
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for i in range(0, len(list_imgs)):
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path = os.path.join(image_dir, list_imgs[i])
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if os.path.isfile(path):
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image_path = image_dir + list_imgs[i]
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voc_path = list_imgs[i]
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(nameWithoutExtention, extention) = os.path.splitext(
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os.path.basename(image_path))
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(voc_nameWithoutExtention, voc_extention) = os.path.splitext(
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os.path.basename(voc_path))
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annotation_name = nameWithoutExtention + '.xml'
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annotation_path = os.path.join(annotation_dir, annotation_name)
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label_name = nameWithoutExtention + '.txt'
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label_path = os.path.join(yolo_labels_dir, label_name)
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prob = random.randint(1, 100)
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print("Probability: %d" % prob, i, list_imgs[i])
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if (prob < TRAIN_RATIO):
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# train dataset
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if os.path.exists(annotation_path):
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train_file.write(image_path + '\n')
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convert_annotation(nameWithoutExtention) # convert label
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copyfile(image_path, yolov5_images_train_dir + voc_path)
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copyfile(label_path, yolov5_labels_train_dir + label_name)
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else:
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# test dataset
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if os.path.exists(annotation_path):
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test_file.write(image_path + '\n')
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convert_annotation(nameWithoutExtention) # convert label
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copyfile(image_path, yolov5_images_test_dir + voc_path)
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copyfile(label_path, yolov5_labels_test_dir + label_name)
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train_file.close()
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test_file.close()
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