I was trying to perform some data augmentation in object detection models in tensorflow so I was checking the compatibility of different image representations.
First I was just reading an image file using PIL (Pillow to be precise)
full_path = 'path/to/my/image.jpg' image = PIL.Image.open(full_path) image_np = np.array(image) encoded_jpg_io1 = io.BytesIO(image_np) Then I used the tensorflow version (used to create tfrecords as well):
with tf.gfile.GFile(full_path, 'rb') as fid: encoded_jpg = fid.read() encoded_jpg_io2 = io.BytesIO(encoded_jpg) And then I checked the equality of the above operations:
if encoded_jpg_io1 == encoded_jpg_io2: print('Equal') I was expecting those two to be equal. So, why this is not the case here?
If I use the bytes I get the same result:
v1 = encoded_jpg_io1.getvalue() v2 = encoded_jpg_io2.getvalue() if encoded_jpg_io1.getvalue() == encoded_jpg_io2.getvalue(): print('Equal') if v1.__eq__(v2): print('Equal') I need to manipulate my images with numpy and then create some tfrecords so the equality is required.
Some interesting facts:
1. PIL cannot read the image in np.array format at all:
image1 = PIL.Image.open(encoded_jpg_io1) OSError: cannot identify image file
While using GFile works fine:
image2 = PIL.Image.open(encoded_jpg_io2) 2.PIL image cannot be directly converted to BytesIO:
encoded_jpg_io1 = io.BytesIO(image) TypeError: a bytes-like object is required, not 'JpegImageFile'