DOTA数据集图像及标签裁剪
批量裁剪DOTA数据集图片及标签，只需修改main中的路径，及裁剪的尺寸size_w，size_h，重叠步长step。不区分DOTA版本，都可用。
裁剪完成后的标签格式和DOTA原始标签格式保持一致，若要用yolo之类的进行训练，可再将裁剪后的标签转换为yolo格式
import cv2
import os# 图像宽不足裁剪宽度,填充至裁剪宽度
def fill_right(img, size_w):size = img.shape# 填充值为数据集均值img_fill_right = cv2.copyMakeBorder(img, 0, 0, 0, size_w - size[1],cv2.BORDER_CONSTANT, value=(107, 113, 115))return img_fill_right# 图像高不足裁剪高度,填充至裁剪高度
def fill_bottom(img, size_h):size = img.shapeimg_fill_bottom = cv2.copyMakeBorder(img, 0, size_h - size[0], 0, 0,cv2.BORDER_CONSTANT, value=(107, 113, 115))return img_fill_bottom# 图像宽高不足裁剪宽高度,填充至裁剪宽高度
def fill_right_bottom(img, size_w, size_h):size = img.shapeimg_fill_right_bottom = cv2.copyMakeBorder(img, 0, size_h - size[0], 0, size_w - size[1],cv2.BORDER_CONSTANT, value=(107, 113, 115))return img_fill_right_bottom# 图像切割
# img_floder 图像文件夹
# out_img_floder 图像切割输出文件夹
# size_w 切割图像宽
# size_h 切割图像高
# step 切割步长
def image_split(img_floder, out_img_floder, size_w, size_h, step):print("进行图像的裁剪--------------------------------")img_list = os.listdir(img_floder)count = 0for img_name in img_list:number = 0# 去除.png后缀name = img_name[:-4]img = cv2.imread(img_floder + "" + img_name)size = img.shape# 若图像宽高大于切割宽高if size[0] >= size_h and size[1] >= size_w:count = count + 1for h in range(0, size[0] - 1, step):start_h = hfor w in range(0, size[1] - 1, step):start_w = wend_h = start_h + size_hif end_h > size[0]:start_h = size[0] - size_hend_h = start_h + size_hend_w = start_w + size_wif end_w > size[1]:start_w = size[1] - size_wend_w = start_w + size_wcropped = img[start_h: end_h, start_w: end_w]# 用起始坐标来命名切割得到的图像，为的是方便后续标签数据抓取name_img = name + '_' + str(start_h) + '_' + str(start_w)cv2.imwrite('{}/{}.png'.format(out_img_floder, name_img), cropped)number = number + 1# 若图像高大于切割高,但宽小于切割宽elif size[0] >= size_h and size[1] < size_w:print('图片{}需要在右面补齐'.format(name))count = count + 1img0 = fill_right(img, size_w)for h in range(0, size[0] - 1, step):start_h = hstart_w = 0end_h = start_h + size_hif end_h > size[0]:start_h = size[0] - size_hend_h = start_h + size_hend_w = start_w + size_wcropped = img0[start_h: end_h, start_w: end_w]name_img = name + '_' + str(start_h) + '_' + str(start_w)cv2.imwrite('{}/{}.png'.format(out_img_floder, name_img), cropped)number = number + 1# 若图像宽大于切割宽,但高小于切割高elif size[0] < size_h and size[1] >= size_w:count = count + 1print('图片{}需要在下面补齐'.format(name))img0 = fill_bottom(img, size_h)for w in range(0, size[1] - 1, step):start_h = 0start_w = wend_w = start_w + size_wif end_w > size[1]:start_w = size[1] - size_wend_w = start_w + size_wend_h = start_h + size_hcropped = img0[start_h: end_h, start_w: end_w]name_img = name + '_' + str(start_h) + '_' + str(start_w)cv2.imwrite('{}/{}.png'.format(out_img_floder, name_img), cropped)number = number + 1# 若图像宽高小于切割宽高elif size[0] < size_h and size[1] < size_w:count = count + 1print('图片{}需要在下面和右面补齐'.format(name))img0 = fill_right_bottom(img, size_w, size_h)cropped = img0[0: size_h, 0: size_w]name_img = name + '_' + '0' + '_' + '0'cv2.imwrite('{}/{}.png'.format(out_img_floder, name_img), cropped)number = number + 1print('{}.png切割成{}张.'.format(name, number))print('共完成{}张图片'.format(count))def txt_split(out_img_floder, txt_floder, out_txt_floder, size_h, size_w):print("进行标签文件的裁剪----------------------------")img_list = os.listdir(out_img_floder)for img_name in img_list:# 去除.png后缀name = img_name[:-4]# 得到原图像(也即txt)索引 + 切割高 + 切割宽name_list = name.split('_')txt_name = name_list[0]h = int(name_list[1])w = int(name_list[2])txtpath = txt_floder + "" + txt_name + '.txt'out_txt_path = out_txt_floder + "" + name + '.txt'f = open(out_txt_path, 'a')# 打开txt文件with open(txtpath, 'r') as f_in:lines = f_in.readlines()# 逐行读取for line in lines:splitline = line.split(',')# print("---",splitline[0].split('(')[1])# print("---", splitline[1].split(')')[0])# print("---", splitline[2].split('(')[1])# print("---", splitline[3].split(')')[0])label = splitline[4]x1 = int(splitline[0].split('(')[1])y1 = int(splitline[1].split(')')[0])x2 = int(splitline[2].split('(')[1])y2 = int(splitline[3].split(')')[0])if w <= x1 <= w + size_w and w <= x2 <= w + size_w and h <= y1 <= h + size_h and h <= y2 <= h + size_h:f.write('({},{}),({},{}),{}'.format(int(x1 - w),int(y1 - h), int(x2 - w), int(y2 - h),label))print('{}.txt切割完成.'.format(name))f.close()'''
对裁剪后的影像中的标签实现自动抓取
'''
def tqtxt(path,path_txt,path_out,size_h,size_w):ims_list=os.listdir(path)for im_list in ims_list:name_list = []name = im_list[:-4]name_list = name.split('_')if len(name_list)<2:continueh = int(name_list[1])w = int(name_list[2])txtpath = path_txt + name_list[0] + '.txt'txt_outpath = path_out + name + '.txt'f = open(txt_outpath,'a')with open(txtpath, 'r') as f_in: #打开txt文件i = 0lines = f_in.readlines()for line in lines:if i in [0,1]:f.write(line) #txt前两行直接复制过去i = i+1continuesplitline = line.split(' ')label = splitline[8]kunnan = splitline[9]x1 = int(float(splitline[0]))y1 = int(float(splitline[1]))x2 = int(float(splitline[2]))y2 = int(float(splitline[3]))x3 = int(float(splitline[4]))y3 = int(float(splitline[5]))x4 = int(float(splitline[6]))y4 = int(float(splitline[7]))if w<=x1<=w+size_w and w<=x2<=w+size_w and w<=x3<=w+size_w and w<=x4<=w+size_w and h<=y1<=h+size_h and h<=y2<=h+size_h and h<=y3<=h+size_h and h<=y4<=h+size_h:f.write('{} {} {} {} {} {} {} {} {} {}'.format(float(x1-w),float(y1-h),float(x2-w),float(y2-h),float(x3-w),float(y3-h),float(x4-w),float(y4-h),label,kunnan))f.close()if __name__ == '__main__':ims_path='D:/rxy/data/DOTA/images/val/' # 图像数据集的路径out_img_path='D:/rxy/data/DOTA/images/val_1024/' # 裁剪后图像数据集的路径txt_path = 'D:/rxy/data/DOTA/val/vallabelTxt-v1.5/DOTA-v1.5_val/' #原数据集标签文件out_txt_path = 'D:/rxy/data/DOTA/val/val1.5_1024/' #裁剪后数据集的标签文件存放路径size_w =1024size_h = 1024step=400 #重叠步长#1.图像裁剪image_split(ims_path, out_img_path, size_w=size_w, size_h=size_h, step=step)#2.标签裁剪tqtxt(out_img_path,txt_path,out_txt_path,size_w=size_w,size_h =size_h)
