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pytorch的colorjitter

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pytorch的colorjitter

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colorjitter()可以随机更改图像的亮度,对比度,饱和度和色调,如下所示:

*备忘录:

初始化的第一个参数是亮度(可选默认:0型:int,float或tuple/tuple/list(int或float)):*备忘录:>是亮度[min,max]的范围,因此必须是min 必须为0 元组/列表必须是具有2个元素的1d。单个值表示[max(0,1亮度),1 亮度]。初始化的第二个参数是对比度(可选默认:0型:int,float或tuple/tuple/list(int或float)):*备忘录:这是对比度[min,max]的范围,因此必须是min 必须为0 元组/列表必须是具有2个元素的1d。单个值表示[max(0,1-contrast),1 对比]。>初始化的第三个参数是饱和(可选默认:0型:int,float或tuple/tuple/list(int或float)):*备忘录:>是饱和度[min,max]的范围,因此必须是min 必须为0 元组/列表必须是具有2个元素的1d。单个值表示[max(0,1-饱和),1 饱和]。初始化的第四个参数是色调(可选默认:0型:float或tuple/list(float)):*备忘录:>这是色调的范围[min,max],因此必须是min >必须为-0.5 元组或列表必须是具有2个元素的1d。单个值表示[-hue, hue]。>第一个参数是img(必需类型:pil图像或张量(int)):*备忘录:张量必须为2d或3d。不使用img =。建议根据v1或v2使用v2?我应该使用哪一个?

from torchvision.datasets import OxfordIIITPetfrom torchvision.transforms.v2 import ColorJittercolorjitter = ColorJitter()colorjitter = ColorJitter(brightness=0, contrast=0, saturation=0, hue=0)colorjitter = transform=ColorJitter(brightness=[1, 1]),contrast=[1, 1],saturation=[1, 1],hue=[0, 0])colorjitter# ColorJitter()print(colorjitter.brightness)# Noneprint(colorjitter.contrast)# Noneprint(colorjitter.saturation)# Noneprint(colorjitter.hue)# Noneorigin_data = OxfordIIITPet( root="data", transform=None # transform=ColorJitter() # colorjitter = ColorJitter(brightness=0, # contrast=0, # saturation=0, # hue=0) # transform=ColorJitter(brightness=[1, 1]), # contrast=[1, 1], # saturation=[1, 1], # hue=[0, 0]))brightp2_data = OxfordIIITPet( # `bright` is brightness and `p` is plus. root="data", transform=ColorJitter(brightness=2) # transform=ColorJitter(brightness=[0, 3]))brightp2p2_data = OxfordIIITPet( root="data", transform=ColorJitter(brightness=[2, 2]))brightp05p05_data = OxfordIIITPet( root="data", transform=ColorJitter(brightness=[0.5, 0.5]))contrap2_data = OxfordIIITPet( # `contra` is contrast. root="data", transform=ColorJitter(contrast=2) # transform=ColorJitter(contrast=[0, 3]))contrap2p2_data = OxfordIIITPet( root="data", transform=ColorJitter(contrast=[2, 2]))contrap05p05_data = OxfordIIITPet( root="data", transform=ColorJitter(contrast=[0.5, 0.5]))saturap2_data = OxfordIIITPet( # `satura` is saturation. root="data", transform=ColorJitter(saturation=2) # transform=ColorJitter(saturation=[0, 3]))saturap2p2_data = OxfordIIITPet( root="data", transform=ColorJitter(saturation=[2, 2]))saturap05p05_data = OxfordIIITPet( root="data", transform=ColorJitter(saturation=[0.5, 0.5]))huep05_data = OxfordIIITPet( root="data", transform=ColorJitter(hue=0.5) # transform=ColorJitter(hue=[-0.5, 0.5]))huep025p025_data = OxfordIIITPet( # `m` is minus. root="data", transform=ColorJitter(hue=[0.25, 0.25]))huem025m025_data = OxfordIIITPet( # `m` is minus. root="data", transform=ColorJitter(hue=[-0.25, -0.25]))import matplotlib.pyplot as pltdef 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")show_images1(data=brightp2_data, main_title="brightp2_data")show_images1(data=brightp2p2_data, main_title="brightp2p2_data")show_images1(data=brightp05p05_data, main_title="brightp05p05_data")print()show_images1(data=origin_data, main_title="origin_data")show_images1(data=contrap2_data, main_title="contrap2_data")show_images1(data=contrap2p2_data, main_title="contrap2p2_data")show_images1(data=contrap05p05_data, main_title="contrap05p05_data")print()show_images1(data=origin_data, main_title="origin_data")show_images1(data=saturap2_data, main_title="saturap2_data")show_images1(data=saturap2p2_data, main_title="saturap2p2_data")show_images1(data=saturap05p05_data, main_title="saturap05p05_data")print()show_images1(data=origin_data, main_title="origin_data")show_images1(data=huep05_data, main_title="huep05_data")show_images1(data=huep025p025_data, main_title="huep025p025_data")show_images1(data=huem025m025_data, main_title="huem025m025_data")# ↓ ↓ ↓ ↓ ↓ ↓ The code below is identical to the code above. ↓ ↓ ↓ ↓ ↓ ↓def show_images2(data, main_title=None, b=0, c=0, s=0, h=0): 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) cj = ColorJitter(brightness=b, contrast=c, # Here saturation=s, hue=h) plt.imshow(X=cj(im)) # Here plt.xticks(ticks=[]) plt.yticks(ticks=[]) plt.tight_layout() plt.show()show_images2(data=my_data, main_title="origin_data")show_images2(data=my_data, main_title="brightp2_data", b=2)show_images2(data=my_data, main_title="brightp2p2_data", b=[2, 2])show_images2(data=my_data, main_title="brightp05p05_data", b=[0.5, 0.5])print()show_images2(data=my_data, main_title="origin_data")show_images2(data=my_data, main_title="contrap2_data", c=2)show_images2(data=my_data, main_title="contrap2p2_data", c=[2, 2])show_images2(data=my_data, main_title="contrap05p05_data", c=[0.5, 0.5])print()show_images2(data=my_data, main_title="origin_data")show_images2(data=my_data, main_title="saturap2_data", s=2)show_images2(data=my_data, main_title="saturap2p2_data", s=[2, 2])show_images2(data=my_data, main_title="saturap05p05_data", s=[0.5, 0.5])print()show_images2(data=my_data, main_title="origin_data")show_images2(data=my_data, main_title="huep05_data", h=0.5)show_images2(data=my_data, main_title="huep025p025_data", h=[0.25, 0.25])show_images2(data=my_data, main_title="huem025m025_data", h=[-0.25, -0.25])

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