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.shape
img_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.shape
img_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 = 0
for 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 + 1
for h in range(0, size[0] - 1, step):
start_h = h
for w in range(0, size[1] - 1, step):
start_w = w
end_h = start_h + size_h
if end_h > size[0]:
start_h = size[0] - size_h
end_h = start_h + size_h
end_w = start_w + size_w
if end_w > size[1]:
start_w = size[1] - size_w
end_w = start_w + size_w
cropped = 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 + 1
img0 = fill_right(img, size_w)
for h in range(0, size[0] - 1, step):
start_h = h
start_w = 0
end_h = start_h + size_h
if end_h > size[0]:
start_h = size[0] - size_h
end_h = start_h + size_h
end_w = start_w + size_w
cropped = 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 + 1
print('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 = 0
start_w = w
end_w = start_w + size_w
if end_w > size[1]:
start_w = size[1] - size_w
end_w = start_w + size_w
end_h = start_h + size_h
cropped = 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 + 1
print('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 + 1
print('{}.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:
continue
h = 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 = 0
lines = 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+1
continue
splitline = 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:/data/DOTA/images/val/' # 图像数据集的路径
out_img_path='D:/data/DOTA/images/val_1024/' # 裁剪后图像数据集的路径
txt_path = 'D:/data/DOTA/val/vallabelTxt-v1.5/DOTA-v1.5_val/' #原数据集标签文件
out_txt_path = 'D:/data/DOTA/val/val1.5_1024/' #裁剪后数据集的标签文件存放路径
size_w =1024
size_h = 1024
step=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)
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