InterpolationMode in PyTorch (3)

from torchvision.datasets import OxfordIIITPet from torchvision.transforms.v2 import PILToTensor, Resize import numpy as np import cv2 ptt = PILToTensor() pytorchimagetensor = ptt(origin_data[0][0]) r = Resize(size=[50, 50], interpolation=InterpolationMode.NEAREST) pytorch_resize = r(pytorchimagetensor).permute(dims=[1, 2, 0]) pytorch_resize # tensor([[[37, 20, 12], # [36, 17, 10], # ..., # [252, 250, 253]], # [[36, 15, 14], # [24, 8, 9], # ..., # [255, 255, 255]], # [[255, 255, 196], # [253, 255, 206], # ..., # [255, 255, 255]], # ..., # [[14, 16, 54], # [12, 14, 52], # ..., # [254, 254, 254]], # [[8, 11, 44], # [11, 16, 46], # ..., # [255, 254, 255]], # [[4, 9, 31], # [0, 2, 0], # ..., # [57, 109, 231]]], dtype=torch.uint8)  numpyimagearray = np.array(object=origin_data[0][0]) opencv_resize = cv2.resize(src=numpyimagearray, dsize=[50, 50], interpolation=cv2.INTER_NEAREST) opencv_resize # array([[[37, 20, 12], # [36, 17, 10], # ..., # [252, 250, 253]], # [[36, 15, 14], # [24, 8, 9], # ..., # [255, 255, 255]], # [[255, 255, 196], # [253, 255, 206], # ..., # [255, 255, 255]], # ..., # [[14, 16, 54], # [12, 14, 52], # ..., # [254, 254, 254]], # [[8, 11, 44], # [11, 16, 46], # ..., # [255, 254, 255]], # [[4, 9, 31], # [0, 2, 0], # ..., # [57, 109, 231]]], dtype=uint8) 

from torchvision.datasets import OxfordIIITPet from torchvision.transforms.v2 import PILToTensor, Resize import numpy as np from skimage.transform import resize ptt = PILToTensor() pytorchimagetensor = ptt(origin_data[0][0]) r = Resize(size=[50, 50], interpolation=InterpolationMode.NEAREST_EXACT) pytorch_resize = r(pytorchimagetensor).permute(dims=[1, 2, 0]) pytorch_resize # tensor([[[36, 19, 11], # [31, 20, 14], # ..., # [254, 254, 254]] # [[241, 230, 114], # [252, 251, 98], # ..., # [255, 255, 255]], # [[73, 158, 195], # [255, 253, 192], # ..., # [255, 255, 255]] # ..., # [[13, 13, 49], # [12, 12, 50], # ..., # [253, 253, 253]], # [[10, 14, 43], # [177, 176, 172], # ..., # [61, 90, 216]], # [[6, 8, 21], # [14, 16, 41], # ..., # [60, 103, 231]]], dtype=torch.uint8)  numpyimagearray = np.array(object=origin_data[0][0]) scikitimage_resize = resize(image=numpyimagearray, # `0` is Nearest-neighbor.  output_shape=[50, 50], order=0) scikitimage_resize # array([[[36, 19, 11], # [31, 20, 14], # ..., # [254, 254, 254]], # [[241, 230, 114], # [252, 251, 98], # ..., # [255, 255, 255]], # [[73, 158, 195], # [255, 253, 192], # ..., # [255, 255, 255]], # ..., # [[13, 13, 49], # [12, 12, 50], # ..., # [253, 253, 253]], # [[10, 14, 43], # [177, 176, 172], # ..., # [61, 90, 216]], # [[6, 8, 21], # [14, 16, 41], # ..., # [60, 103, 231]]], dtype=uint8)  PILimagearray = np.array(origin_data[0][0].resize(size=[50, 50], resample=0)) # `0` is Nearest. PILimagearray # array([[[36, 19, 11], # [31, 20, 14], # ..., # [254, 254, 254]], # [[241, 230, 114], # [252, 251, 98], # ..., # [255, 255, 255]], # [[73, 158, 195], # [255, 253, 192], # ..., # [255, 255, 255]], # ..., # [[13, 13, 49], # [12, 12, 50], # ..., # [253, 253, 253]], # [[10, 14, 43], # [177, 176, 172], # ..., # [61, 90, 216]], # [[6, 8, 21], # [14, 16, 41], # ..., # [60, 103, 231]]], dtype=uint8)  ptt = PILToTensor() PILimagetensor = ptt(origin_data[0][0].resize(size=[50, 50], resample=0)).permute(dims=[1, 2, 0]) PILimagetensor # `0` is Nearest. # tensor([[[36, 19, 11], # [31, 20, 14], # ..., # [254, 254, 254]], # [[241, 230, 114], # [252, 251, 98], # ..., # [255, 255, 255]], # [[73, 158, 195], # [255, 253, 192], # ..., # [255, 255, 255]], # ..., # [[13, 13, 49], # [12, 12, 50], # ..., # [253, 253, 253]], # [[10, 14, 43], # [177, 176, 172], # ..., # [61, 90, 216]], # [[6, 8, 21], # [14, 16, 41], # ..., # [60, 103, 231]]], dtype=torch.uint8)