*Memos:
- My post explains RandomSolarize().
- My post explains OxfordIIITPet().
RandomInvert() can randomly invert an image as shown below:
*Memos:
- The 1st argument for initialization is
p(Optional-Default:0.5-Type:intorfloat): *Memos:- It's the probability of whether an image is inverted or not.
- It must be
0 <= x <= 1.
- The 1st argument is
img(Required-Type:PIL Imageortensor(int/float)): *Memos:- A tensor must be 0D 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 RandomInvert ri = RandomInvert() ri = RandomInvert(p=0.5) ri # RandomInvert(p=0.5) ri.p # 0.5 origin_data = OxfordIIITPet( root="data", transform=None ) p0_data = OxfordIIITPet( root="data", transform=RandomInvert(p=0) ) p05_data = OxfordIIITPet( root="data", transform=RandomInvert(p=0.5) # transform=RandomInvert() ) p1_data = OxfordIIITPet( root="data", transform=RandomInvert(p=1) ) import matplotlib.pyplot as plt def show_images1(data, main_title=None): plt.figure(figsize=[10, 5]) plt.suptitle(t=main_title, y=0.8, fontsize=14) for i, (im, _) in zip(range(1, 6), data): plt.subplot(1, 5, i) plt.imshow(X=im) plt.xticks(ticks=[]) plt.yticks(ticks=[]) plt.tight_layout() plt.show() show_images1(data=origin_data, main_title="origin_data") print() show_images1(data=p0_data, main_title="p0_data") show_images1(data=p0_data, main_title="p0_data") show_images1(data=p0_data, main_title="p0_data") print() show_images1(data=p05_data, main_title="p05_data") show_images1(data=p05_data, main_title="p05_data") show_images1(data=p05_data, main_title="p05_data") print() show_images1(data=p1_data, main_title="p1_data") show_images1(data=p1_data, main_title="p1_data") show_images1(data=p1_data, main_title="p1_data") # ↓ ↓ ↓ ↓ ↓ ↓ The code below is identical to the code above. ↓ ↓ ↓ ↓ ↓ ↓ def show_images2(data, main_title=None, p=0.5): plt.figure(figsize=[10, 5]) plt.suptitle(t=main_title, y=0.8, fontsize=14) if main_title != "origin_data": for i, (im, _) in zip(range(1, 6), data): plt.subplot(1, 5, i) ri = RandomInvert(p=p) plt.imshow(X=ri(im)) plt.xticks(ticks=[]) plt.yticks(ticks=[]) else: for i, (im, _) in zip(range(1, 6), data): plt.subplot(1, 5, i) plt.imshow(X=im) plt.xticks(ticks=[]) plt.yticks(ticks=[]) plt.tight_layout() plt.show() show_images2(data=origin_data, main_title="origin_data") print() show_images2(data=origin_data, main_title="p0_data", p=0) show_images2(data=origin_data, main_title="p0_data", p=0) show_images2(data=origin_data, main_title="p0_data", p=0) print() show_images2(data=origin_data, main_title="p05_data", p=0.5) show_images2(data=origin_data, main_title="p05_data", p=0.5) show_images2(data=origin_data, main_title="p05_data", p=0.5) print() show_images2(data=origin_data, main_title="p1_data", p=1) show_images2(data=origin_data, main_title="p1_data", p=1) show_images2(data=origin_data, main_title="p1_data", p=1) 
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