PIL.UnidentifiedImageError：无法识别图像文件'/NGC-DL-CONTAINER-LICENSE'

Question

I have some errors with PILLOW. first I am import images from PIL after that working with a pillow

but after some time I got some error line PIL.UnidentifiedImageError: cannot identify image file '/NGC-DL-CONTAINER-LICENSE'

code:

from PIL import Image, ImageFilter

from PIL import Image, ImageFilter, ImageOps, ImageChops
...
...
inputs = torch.Tensor(1, 3, 16, 240, 320)
x_time = [jj for jj in range(len(img))]
y_pred = [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]
for num, i in enumerate(img):
    if num < 16:
        inputs[:,:,num,:,:] = ToTensor(1)(Image.open(i))
        cv_img = cv2.imread(i)
        print(cv_img.shape)
        h,w,_ =cv_img.shape
        cv_img = cv2.putText(cv_img, 'FPS : 0.0, Pred : 0.0', (5,15), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255,200,240), 2)

error:

Traceback (most recent call last):
  File "vis.py", line 135, in <module>
    inputs[:,:,num,:,:] = ToTensor(1)(Image.open(i))
  File "/usr/local/lib/python3.7/dist-packages/PIL/Image.py", line 3148, in open
    "cannot identify image file %r" % (filename if filename else fp)
PIL.UnidentifiedImageError: cannot identify image file '/NGC-DL-CONTAINER-LICENSE'

How to resolve this issue any answers and suggestions are highly recommended.

for more code: https://github.com/seominseok0429/Real-world-Anomaly-Detection-in-Surveillance-Videos-pytorch/blob/main/vis.py

Answer 1

It seems like your list of images, img contain file names that are NOT images, eg, '/NGC-DL-CONTAINER-LICENSE' .

How did you construct this list?

You can avoid this in your code:

for num, i in enumerate(img):
    if num < 16:
        try:
            inputs[:,:,num,:,:] = ToTensor(1)(Image.open(i))
        except PIL.UnidentifiedImageError:
            print(f'Got non-image file {i} - skipping...')
            continue
        cv_img = cv2.imread(i)
        print(cv_img.shape)
        h,w,_ =cv_img.shape
        cv_img = cv2.putText(cv_img, 'FPS : 0.0, Pred : 0.0', (5,15), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255,200,240), 2)