if not os.path.exists(output_folder): os.makedirs(output_folder)
时间: 2023-06-18 09:02:19 浏览: 90
这段代码的作用是判断是否存在指定的文件夹路径,如果不存在则创建该文件夹。具体解释如下:
- `os.path.exists(output_folder)` 判断 `output_folder` 文件夹路径是否存在,如果存在返回 `True`,否则返回 `False`。
- `if not os.path.exists(output_folder)` 判断 `output_folder` 文件夹路径是否不存在,如果不存在则执行下面的代码。
- `os.makedirs(output_folder)` 创建 `output_folder` 文件夹路径。如果该文件夹的上级文件夹不存在,则同时创建上级文件夹。
相关问题
from PIL import Image import os import numpy as np input_folder = 'D://with ground truth/train/disp' # 输入文件夹路径 output_folder = 'D://with ground truth/train/disp2' # 输出文件夹路径 if not os.path.exists(output_folder): os.makedirs(output_folder) for filename in os.listdir(input_folder): if filename.endswith('.tif') or filename.endswith('.tiff'): # 仅对tif或tiff格式的文件进行转换 with Image.open(os.path.join(input_folder, filename)) as im: im.save(os.path.join(output_folder, os.path.splitext(filename)[0] + '.png'))报错raise OSError(msg) from e OSError: cannot write mode F as PNG
这个错误通常会出现在尝试以模式 "F" 保存 PNG 图像时。模式 "F" 表示浮点数像素,但 PNG 格式不支持浮点数像素。因此,你需要将图像转换为支持的模式(例如 "RGB" 或 "RGBA")才能将其保存为 PNG。以下是一个示例代码片段,可以将图像转换为 "RGB" 模式并保存为 PNG:
```
from PIL import Image
import os
import numpy as np
input_folder = 'D://with ground truth/train/disp' # 输入文件夹路径
output_folder = 'D://with ground truth/train/disp2' # 输出文件夹路径
if not os.path.exists(output_folder):
os.makedirs(output_folder)
for filename in os.listdir(input_folder):
if filename.endswith('.tif') or filename.endswith('.tiff'): # 仅对tif或tiff格式的文件进行转换
with Image.open(os.path.join(input_folder, filename)) as im:
# 将图像转换为 "RGB" 模式
im = im.convert("RGB")
# 保存为 PNG
im.save(os.path.join(output_folder, os.path.splitext(filename)[0] + '.png'))
```
如果你仍然遇到问题,请提供完整的错误消息。
将下面代码简洁化:def split_dataset(img_path, target_folder_path, output_path): filename = [] total_imgs = os.listdir(img_path) #for root, dirs, files in os.walk(img_path): for img in total_imgs: filename.append(img) np.random.shuffle(filename) train = filename[:int(len(filename) * 0.9)] test = filename[int(len(filename) * 0.9):] out_images = os.path.join(output_path, 'imgs') if not os.path.exists(out_images): os.makedirs(out_images) out_images_train = os.path.join(out_images, 'training') if not os.path.exists(out_images_train): os.makedirs(out_images_train) out_images_test = os.path.join(out_images, 'test') if not os.path.exists(out_images_test): os.makedirs(out_images_test) out_annotations = os.path.join(output_path, 'annotations') if not os.path.exists(out_annotations): os.makedirs(out_annotations) out_annotations_train = os.path.join(out_annotations, 'training') if not os.path.exists(out_annotations_train): os.makedirs(out_annotations_train) out_annotations_test = os.path.join(out_annotations, 'test') if not os.path.exists(out_annotations_test): os.makedirs(out_annotations_test) for i in train: print(os.path.join(img_path, i)) print(os.path.join(out_images_train, i)) shutil.copyfile(os.path.join(img_path, i), os.path.join(out_images_train, i)) annotations_name = "gt_" + i[:-3] + 'txt' shutil.copyfile(os.path.join(target_folder_path, annotations_name), os.path.join(out_annotations_train, annotations_name)) for i in test: shutil.copyfile(os.path.join(img_path, i), os.path.join(out_images_test, i)) annotations_name = "gt_" + i[:-3] + 'txt' shutil.copyfile(os.path.join(target_folder_path, annotations_name), os.path.join(out_annotations_test, annotations_name))
def split_dataset(img_path, target_folder_path, output_path):
filename = os.listdir(img_path)
np.random.shuffle(filename)
train = filename[:int(len(filename) * 0.9)]
test = filename[int(len(filename) * 0.9):]
out_images = os.path.join(output_path, 'imgs')
os.makedirs(out_images, exist_ok=True)
out_images_train = os.path.join(out_images, 'training')
os.makedirs(out_images_train, exist_ok=True)
out_images_test = os.path.join(out_images, 'test')
os.makedirs(out_images_test, exist_ok=True)
out_annotations = os.path.join(output_path, 'annotations')
os.makedirs(out_annotations, exist_ok=True)
out_annotations_train = os.path.join(out_annotations, 'training')
os.makedirs(out_annotations_train, exist_ok=True)
out_annotations_test = os.path.join(out_annotations, 'test')
os.makedirs(out_annotations_test, exist_ok=True)
for i in train:
img_src = os.path.join(img_path, i)
img_dst = os.path.join(out_images_train, i)
shutil.copyfile(img_src, img_dst)
annotations_name = "gt_" + i[:-3] + 'txt'
annotations_src = os.path.join(target_folder_path, annotations_name)
annotations_dst = os.path.join(out_annotations_train, annotations_name)
shutil.copyfile(annotations_src, annotations_dst)
for i in test:
img_src = os.path.join(img_path, i)
img_dst = os.path.join(out_images_test, i)
shutil.copyfile(img_src, img_dst)
annotations_name = "gt_" + i[:-3] + 'txt'
annotations_src = os.path.join(target_folder_path, annotations_name)
annotations_dst = os.path.join(out_annotations_test, annotations_name)
shutil.copyfile(annotations_src, annotations_dst)