Where
C is number of Channels.
3 is for RGB
1 is Black and white channel
W is width of image
H is height of image
N is number of images in a given Batch.
Read Image
from PIL import Image
img = Image.open(r"jk.jpg")
Image Resize
import torchvision.transforms as T
# define transformt o resize
# the image with given size
transform = T.Resize(size = (250,450))
# apply the transform on the input image
img = transform(img)
Convert to Tensor
convert_tensor = transforms.ToTensor()
convert_tensor(img)
Tensor is ready to use with PyTorch
( DataClass and DataLoader)
import torchvision.transforms as transforms
from PIL import Image
# load image in RGB mode (png files contains additional alpha channel)
img = Image.open('golf.png').convert('RGB')
# set up transformation to resize the image
resize = transforms.Resize([224, 224])
img = resize(img)
to_tensor = transforms.ToTensor()
# apply transformation and convert to Pytorch tensor
tensor = to_tensor(img)
# torch.Size([3, 224, 224])
# add another dimension at the front to get NCHW shape
tensor = tensor.unsqueeze(0)
# torch.Size([1, 3, 224, 224])