Dota数据集切割和保存yolo和voc格式——HBB
- 从DOTA1.生成5个标记YOLO格式标注——HBB
- 根据得到的YOLO格式转换成PASCAL VOC格式
- 验证VOC格式 的正确性
- 显示可视化结果
- 总结
从DOTA1.生成5个标记YOLO格式标注——HBB
位置需要修改
from PIL import Image,ImageDraw import numpy as np import os import matplotlib.pyplot as plt def get_anno(x1,y1,x2,y2,a,c,file): l1 = a[:,0] < x2-5 l2 = a[:,1] < y2-5 l3 = a[:,2] > x1 5 l4 = a[:,3] > y1 5 l12 = np.logical_and(l1,l2) l23 = np.logical_and(l3,l4) l = np.logical_and(l23,l12) a = a[l] c = c[l] a[:,::2] -= x1 a[:,1::2] -= y1
a[:,:2] = (a[:,:2] + a[:,2:])/2
a[:,2:] = (a[:,2:] - a[:,:2])*2
a[:,::2] /= (x2-x1)
a[:, 1::2] /= (y2 - y1)
with open(file,'w+')as f:
for c_,a_ in zip(c,a):
c_ = classnames_v1_5.index(c_)
f.write(str(c_)+' ')
for a__ in a_:
f.write(str(a__)+' ')
f.write('\n')
def read_txt(txt):
with open(txt,'r')as f:
a = f.readlines()[2:]
c = [i.split(' ')[8] for i in a]
a = [i.split(' ')[:6] for i in a]
a = np.array(a,dtype=float)
a = np.concatenate([a[:,:2],a[:,-2:]],axis=1)
return a,np.array(c,dtype=object)
classnames_v1_5 = ['plane', 'baseball-diamond', 'bridge', 'ground-track-field', 'small-vehicle',
'large-vehicle', 'ship', 'tennis-court','basketball-court', 'storage-tank',
'soccer-ball-field', 'roundabout', 'harbor', 'swimming-pool', 'helicopter',
'container-crane']
ann_file = r'F:\work\zsj\dota\val\images\DOTA-v1.5_val_hbb'
yolo_file = r'F:\work\zsj\dota\val\images\txt'
png_file = r'F:\work\zsj\dota\val\images\images'
jpg_file = r'F:\work\zsj\dota\val\images\jpg'
for file,img in zip(os.listdir(ann_file),os.listdir(png_file)):
jpg = jpg_file + '\\' + img.replace('png','jpg')
png = png_file + '\\' + img
yolo = yolo_file + '\\' + file
ann = ann_file + '\\' + file
try:
a, c = read_txt(ann)
image = Image.open(png)
q = 0
if image.size[0] > 1920 or image.size[1] > 1920:
for i in range(image.size[0] // 1600):
for j in range(image.size[1] // 1600):
img_ = image.crop((i * 1600, j * 1600, i * 1600 + 1920, j * 1600 + 1920))
jpg_ = jpg.replace('.jpg', '%d.jpg' % q)
yolo_ = yolo.replace('.txt', '%d.txt' % q)
get_anno(i * 1600, j * 1600, i * 1600 + 1920, j * 1600 + 1920, a, c, yolo_)
img_.save(jpg_)
q += 1
img_ = image.crop((i * 1600, image.size[1] - 1920, i * 1600 + 1920, image.size[1]))
jpg_ = jpg.replace('.jpg', '%d.jpg' % q)
yolo_ = yolo.replace('.txt', '%d.txt' % q)
get_anno(i * 1600, image.size[1] - 1920, i * 1600 + 1920, image.size[1], a, c, yolo_)
img_.save(jpg_)
q += 1
for j in range(image.size[1] // 1600):
img_ = image.crop((image.size[0] - 1920, j * 1600, image.size[0], j * 1600 + 1920))
jpg_ = jpg.replace('.jpg', '%d.jpg' % q)
yolo_ = yolo.replace('.txt', '%d.txt' % q)
get_anno(image.size[0] - 1920, j * 1600, image.size[0], j * 1600 + 1920, a, c, yolo_)
img_.save(jpg_)
q += 1
img_ = image.crop((image.size[0] - 1920, image.size[1] - 1920, image.size[0], image.size[1]))
jpg_ = jpg.replace('.jpg', '%d.jpg' % q)
yolo_ = yolo.replace('.txt', '%d.txt' % q)
get_anno(image.size[0] - 1920, image.size[1] - 1920, image.size[0], image.size[1], a, c, yolo_)
img_.save(jpg_)
q += 1
else:
# jpg_ = jpg.replace('.jpg', '%d.jpg' % q)
# yolo_ = yolo.replace('.txt', '%d.txt' % q)
get_anno(0, 0, image.size[0], image.size[1], a, c, yolo)
image.save(jpg)
# print(img + ' finished!')
except:
print(img + 'failed')
根据得到的YOLO格式转换成PASCAL VOC格式
需要进行修改的位置
__Author__ = "Shliang" __Email__ = "shliang0603@gmail.com" import os import xml.etree.ElementTree as ET from xml.dom.minidom import Document import cv2 import multiprocessing from tqdm import tqdm ''' import xml xml.dom.minidom.Document().writexml() def writexml(self, writer: Any, indent: str = "", addindent: str = "", newl: str = "", encoding: Any = None) -> None ''' class YOLO2VOCConvert: def __init__(self, txts_path, xmls_path, imgs_path): self.txts_path = txts_path # 标注的yolo格式
标签文件路径 self.xmls_path = xmls_path # 转化为voc格式标签之后保存路径 self.imgs_path = imgs_path # 读取读片的路径个图片名字,存储到xml标签文件中 self.classes = ['granulocyte', 'mitotic figure', 'tumor cell', 'other/ambigous cells', 'binucleated cell', 'multinukleated cell', 'Mitotic figure lookalike'] # 从所有的txt文件中提取出所有的类别, yolo格式的标签格式类别为数字 0,1,... # writer为True时,把提取的类别保存到'./Annotations/classes.txt'文件中 def search_all_classes(self, writer=False): # 读取每一个txt标签文件,取出每个目标的标注信息 all_names = set() txts = os.listdir(self.txts_path) # 使用列表生成式过滤出只有后缀名为txt的标签文件 txts = [txt for txt in txts if txt.split('.')[-1] == 'txt'] print(len(txts), txts) # 11 ['0002030.txt', '0002031.txt', ... '0002039.txt', '0002040.txt'] for txt in txts: txt_file = os.path.join(self.txts_path, txt) with open(txt_file, 'r') as f: objects = f.readlines() for object in objects: object = object.strip().split(' ') print(object) # ['2', '0.506667', '0.553333', '0.490667', '0.658667'] all_names.add(int(object[0])) # print(objects) # ['2 0.506667 0.553333 0.490667 0.658667\n', '0 0.496000 0.285333 0.133333 0.096000\n', '8 0.501333 0.412000 0.074667 0.237333\n'] print("所有的类别标签:", all_names, "共标注数据集:%d张" % len(txts)) return list(all_names) def yolo2voc(self): # 创建一个保存xml标签文件的文件夹 if not os.path.exists(self.xmls_path): os.mkdir(self.xmls_path) # 把上面的两个循环改写成为一个循环: imgs = os.listdir(self.imgs_path) txts = os.listdir(self.txts_path) txts = [txt for txt in txts if not txt.split('.')[0] == "classes"] # 过滤掉classes.txt文件 print(txts) # 注意,这里保持图片的数量和标签txt文件数量相等,且要保证名字是一一对应的 (后面改进,通过判断txt文件名是否在imgs中即可) if len(imgs) == len(txts): # 注意:./Annotation_txt 不要把classes.txt文件放进去 map_imgs_txts = [(img, txt) for img, txt in zip(imgs, txts)] txts = [txt for txt in txts if txt.split('.')[-1] == 'txt'] print(len(txts), txts) for img_name, txt_name in map_imgs_txts: # 读取图片的尺度信息 print("读取图片:", img_name) img = cv2.imread(os.path.join(self.imgs_path, img_name)) height_img, width_img, depth_img = img.shape print(height_img, width_img, depth_img) # h 就是多少行(对应图片的高度), w就是多少列(对应图片的宽度) # 获取标注文件txt中的标注信息 all_objects = [] txt_file = os.path.join(self.txts_path, txt_name) with open(txt_file, 'r') as f: objects = f.readlines() for object in objects: object = object.strip().split(' ') all_objects.append(object) print(object) # ['2', '0.506667', '0.553333', '0.490667', '0.658667'] # 创建xml标签文件中的标签 xmlBuilder = Document() # 创建annotation标签,也是根标签 annotation = xmlBuilder.createElement("annotation") # 给标签annotation添加一个子标签 xmlBuilder.appendChild(annotation) # 创建子标签folder folder = xmlBuilder.createElement("folder") # 给子标签folder中存入内容,folder标签中的内容是存放图片的文件夹,例如:JPEGImages folderContent = xmlBuilder.createTextNode(self.imgs_path.split('/')[-1]) # 标签内存 folder.appendChild(folderContent) # 把内容存入标签 annotation.appendChild(folder) # 把存好内容的folder标签放到 annotation根标签下 # 创建子标签filename filename = xmlBuilder.createElement("filename") # 给子标签filename中存入内容,filename标签中的内容是图片的名字,例如:000250.jpg filenameContent = xmlBuilder.createTextNode(txt_name.split('.')[0] + '.jpg') # 标签内容 filename.appendChild(filenameContent) annotation.appendChild(filename) # 把图片的shape存入xml标签中 size = xmlBuilder.createElement("size") # 给size标签创建子标签width width = xmlBuilder.createElement("width") # size子标签width widthContent = xmlBuilder.createTextNode(str(width_img)) width.appendChild(widthContent) size.appendChild(width) # 把width添加为size的子标签 # 给size标签创建子标签height height = xmlBuilder.createElement("height") # size子标签height heightContent = xmlBuilder.createTextNode(str(height_img)) # xml标签中存入的内容都是字符串 height.appendChild(heightContent) size.appendChild(height) # 把width添加为size的子标签 # 给size标签创建子标签depth depth = xmlBuilder.createElement("depth") # size子标签width depthContent = xmlBuilder.createTextNode(str(depth_img)) depth.appendChild(depthContent) size.appendChild(depth) # 把width添加为size的子标签 annotation.appendChild(size) # 把size添加为annotation的子标签 # 每一个object中存储的都是['2', '0.506667', '0.553333', '0.490667', '0.658667']一个标注目标 for object_info in all_objects: # 开始创建标注目标的label信息的标签 object = xmlBuilder.createElement("object") # 创建object标签 # 创建label类别标签 # 创建name标签 imgName = xmlBuilder.createElement("name") # 创建name标签 imgNameContent = xmlBuilder.createTextNode(self.classes[int(object_info[0])]) imgName.appendChild(imgNameContent) object.appendChild(imgName) # 把name添加为object的子标签 # 创建pose标签 pose = xmlBuilder.createElement("pose") poseContent = xmlBuilder.createTextNode("Unspecified") pose.appendChild(poseContent) object.appendChild(pose) # 把pose添加为object的标签 # 创建truncated标签 truncated = xmlBuilder.createElement("truncated") truncatedContent = xmlBuilder.createTextNode("0") truncated.appendChild(truncatedContent) object.appendChild(truncated) # 创建difficult标签 difficult = xmlBuilder.createElement("difficult") difficultContent = xmlBuilder.createTextNode("0") difficult.appendChild(difficultContent) object.appendChild(difficult) # 先转换一下坐标 # (objx_center, objy_center, obj_width, obj_height)->(xmin,ymin, xmax,ymax) x_center = float(object_info[1])*width_img + 1 y_center = float(object_info[2])*height_img + 1 xminVal = int(x_center - 0.5*float(object_info[3])*width_img) # object_info列表中的元素都是字符串类型 yminVal = int(y_center - 0.5