*Memos:
- My post explains CenterCrop().
- My post explains RandomCrop() about
sizeargument. - My post explains RandomResizedCrop() about
sizeargument (1). - My post explains OxfordIIITPet().
FiveCrop() can crop an image into 5 parts(Top-left, Top-right, Bottom-left, Bottom-right and Center) as shown below:
*Memos:
- The 1st argument for initialization is
size(Required-Type:intortuple/list(int) or size()): *Memos:- It's
[height, width]. - It must be
1 <= x. - A tuple/list must be the 1D with 1 or 2 elements.
- A single value(
intortuple/list(int)) means[size, size].
- It's
- The 1st argument is
img(Required-Type:PIL Imageortensor(int/float/complex/bool)): *Memos:- A tensor must be 2D or more D.
- Don't use
img=.
-
v2is recommended to use according to V1 or V2? Which one should I use?.
from torchvision.datasets import OxfordIIITPet from torchvision.transforms.v2 import FiveCrop fc = FiveCrop(size=100) fc # FiveCrop(size=(100, 100)) fc.size # (100, 100) origin_data = OxfordIIITPet( root="data", transform=None ) s500_394origin_data = OxfordIIITPet( # `s` is size. root="data", transform=FiveCrop(size=[500, 394]) ) s300_data = OxfordIIITPet( root="data", transform=FiveCrop(size=300) ) s200_data = OxfordIIITPet( root="data", transform=FiveCrop(size=200) ) s100_data = OxfordIIITPet( root="data", transform=FiveCrop(size=100) ) s50_data = OxfordIIITPet( root="data", transform=FiveCrop(size=50) ) s10_data = OxfordIIITPet( root="data", transform=FiveCrop(size=10) ) s1_data = OxfordIIITPet( root="data", transform=FiveCrop(size=1) ) s200_300_data = OxfordIIITPet( root="data", transform=FiveCrop(size=[200, 300]) ) s300_200_data = OxfordIIITPet( root="data", transform=FiveCrop(size=[300, 200]) ) import matplotlib.pyplot as plt def show_images1(fcims, main_title=None): plt.figure(figsize=[10, 5]) plt.suptitle(t=main_title, y=0.8, fontsize=14) titles = ['Top-left', 'Top-right', 'Bottom-left', 'Bottom-right', 'Center'] for i, fcim in zip(range(1, 6), fcims): plt.subplot(1, 5, i) plt.title(label=titles[i-1], fontsize=14) plt.imshow(X=fcim) plt.tight_layout() plt.show() plt.figure(figsize=[7, 9]) plt.title(label="s500_394origin_data", fontsize=14) plt.imshow(X=origin_data[0][0]) show_images1(fcims=s500_394origin_data[0][0], main_title="s500_394origin_data") show_images1(fcims=s300_data[0][0], main_title="s300_data") show_images1(fcims=s200_data[0][0], main_title="s200_data") show_images1(fcims=s100_data[0][0], main_title="s100_data") show_images1(fcims=s50_data[0][0], main_title="s50_data") show_images1(fcims=s10_data[0][0], main_title="s10_data") show_images1(fcims=s1_data[0][0], main_title="s1_data") print() show_images1(fcims=s200_300_data[0][0], main_title="s200_300_data") show_images1(fcims=s300_200_data[0][0], main_title="s300_200_data") # ↓ ↓ ↓ ↓ ↓ ↓ The code below is identical to the code above. ↓ ↓ ↓ ↓ ↓ ↓ def show_images2(im, main_title=None, s=None): plt.figure(figsize=[10, 5]) plt.suptitle(t=main_title, y=0.8, fontsize=14) titles = ['Top-left', 'Top-right', 'Bottom-left', 'Bottom-right', 'Center'] if not s: s = [im.size[1], im.size[0]] fc = FiveCrop(size=s) for i, fcim in zip(range(1, 6), fc(im)): plt.subplot(1, 5, i) plt.title(label=titles[i-1], fontsize=14) plt.imshow(X=fcim) plt.tight_layout() plt.show() plt.figure(figsize=[7, 9]) plt.title(label="s500_394origin_data", fontsize=14) plt.imshow(X=origin_data[0][0]) show_images2(im=origin_data[0][0], main_title="s500_394origin_data") # ↑ show_images2(im=origin_data[0][0], main_title="s500_394origin_data", # s=[500, 394]) show_images2(im=origin_data[0][0], main_title="s300_data", s=300) show_images2(im=origin_data[0][0], main_title="s200_data", s=200) show_images2(im=origin_data[0][0], main_title="s100_data", s=100) show_images2(im=origin_data[0][0], main_title="s50_data", s=50) show_images2(im=origin_data[0][0], main_title="s10_data", s=10) show_images2(im=origin_data[0][0], main_title="s1_data", s=1) print() show_images2(im=origin_data[0][0], main_title="s200_300_data", s=[200, 300]) show_images2(im=origin_data[0][0], main_title="s300_200_data", s=[300, 200]) 
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