*My post explains OxfordIIITPet().
RandomPosterize() can randomly posterize an image with a given probability as shown below:
*Memos:
- The 1st argument for initialization is
bits(Required-Type:int): *Memos:- It's the number of bits to keep for each channel.
- It must be
x <= 8.
- The 2nd argument for initialization is
p(Optional-Default:0.5-Type:intorfloat): *Memos:- It's the probability of whether an image is posterized 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 RandomPosterize rp = RandomPosterize(bits=1) rp = RandomPosterize(bits=1, p=0.5) rp # RandomPosterize(p=0.5, bits=1) rp.bits # 1 rp.p # 0.5 origin_data = OxfordIIITPet( root="data", transform=None ) b8p1origin_data = OxfordIIITPet( # `b` is bits. root="data", transform=RandomPosterize(bits=8, p=1) ) b7p1_data = OxfordIIITPet( root="data", transform=RandomPosterize(bits=7, p=1) ) b6p1_data = OxfordIIITPet( root="data", transform=RandomPosterize(bits=6, p=1) ) b5p1_data = OxfordIIITPet( root="data", transform=RandomPosterize(bits=5, p=1) ) b4p1_data = OxfordIIITPet( root="data", transform=RandomPosterize(bits=4, p=1) ) b3p1_data = OxfordIIITPet( root="data", transform=RandomPosterize(bits=3, p=1) ) b2p1_data = OxfordIIITPet( root="data", transform=RandomPosterize(bits=2, p=1) ) b1p1_data = OxfordIIITPet( root="data", transform=RandomPosterize(bits=1, p=1) ) b0p1_data = OxfordIIITPet( root="data", transform=RandomPosterize(bits=0, p=1) ) bn1p1_data = OxfordIIITPet( # `n` is negative. root="data", transform=RandomPosterize(bits=-1, p=1) ) bn10p1_data = OxfordIIITPet( root="data", transform=RandomPosterize(bits=-10, p=1) ) bn100p1_data = OxfordIIITPet( root="data", transform=RandomPosterize(bits=-100, p=1) ) b1p0_data = OxfordIIITPet( root="data", transform=RandomPosterize(bits=1, p=0) ) b1p05_data = OxfordIIITPet( root="data", transform=RandomPosterize(bits=1, p=0.5) # transform=RandomPosterize(bits=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=b8p1origin_data, main_title="b8p1origin_data") show_images1(data=b7p1_data, main_title="b7p1_data") show_images1(data=b6p1_data, main_title="b6p1_data") show_images1(data=b5p1_data, main_title="b5p1_data") show_images1(data=b4p1_data, main_title="b4p1_data") show_images1(data=b3p1_data, main_title="b3p1_data") show_images1(data=b2p1_data, main_title="b2p1_data") show_images1(data=b1p1_data, main_title="b1p1_data") show_images1(data=b0p1_data, main_title="b0p1_data") show_images1(data=bn1p1_data, main_title="bn1p1_data") show_images1(data=bn10p1_data, main_title="bn10p1_data") show_images1(data=bn100p1_data, main_title="bn100p1_data") print() show_images1(data=b1p0_data, main_title="b1p0_data") show_images1(data=b1p0_data, main_title="b1p0_data") show_images1(data=b1p0_data, main_title="b1p0_data") print() show_images1(data=b1p05_data, main_title="b1p05_data") show_images1(data=b1p05_data, main_title="b1p05_data") show_images1(data=b1p05_data, main_title="b1p05_data") print() show_images1(data=b1p1_data, main_title="b1p1_data") show_images1(data=b1p1_data, main_title="b1p1_data") show_images1(data=b1p1_data, main_title="b1p1_data") # ↓ ↓ ↓ ↓ ↓ ↓ The code below is identical to the code above. ↓ ↓ ↓ ↓ ↓ ↓ def show_images2(data, main_title=None, b=None, p=0): 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) rp = RandomPosterize(bits=b, p=p) plt.imshow(X=rp(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="b8p1origin_data", b=8, p=1) show_images2(data=origin_data, main_title="b7p1_data", b=7, p=1) show_images2(data=origin_data, main_title="b6p1_data", b=6, p=1) show_images2(data=origin_data, main_title="b5p1_data", b=5, p=1) show_images2(data=origin_data, main_title="b4p1_data", b=4, p=1) show_images2(data=origin_data, main_title="b3p1_data", b=3, p=1) show_images2(data=origin_data, main_title="b2p1_data", b=2, p=1) show_images2(data=origin_data, main_title="b1p1_data", b=1, p=1) show_images2(data=origin_data, main_title="b0p1_data", b=0, p=1) show_images2(data=origin_data, main_title="bn1p1_data", b=-1, p=1) show_images2(data=origin_data, main_title="bn10p1_data", b=-10, p=1) show_images2(data=origin_data, main_title="bn100p1_data", b=-100, p=1) print() show_images2(data=origin_data, main_title="b1p0_data", b=1, p=0) show_images2(data=origin_data, main_title="b1p0_data", b=1, p=0) show_images2(data=origin_data, main_title="b1p0_data", b=1, p=0) print() show_images2(data=origin_data, main_title="b1p05_data", b=1, p=0.5) show_images2(data=origin_data, main_title="b1p05_data", b=1, p=0.5) show_images2(data=origin_data, main_title="b1p05_data", b=1, p=0.5) print() show_images2(data=origin_data, main_title="b1p1_data", b=1, p=1) show_images2(data=origin_data, main_title="b1p1_data", b=1, p=1) show_images2(data=origin_data, main_title="b1p1_data", b=1, p=1) 
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