给我买咖啡☕
*备忘录：
我的帖子解释了randomrotation（）。>我的帖子解释了randomaffine（）。我的帖子解释了牛津iiitpet（）。
> randomperspective（）可以对图像进行随机的透视转换，如下所示：
*备忘录：
初始化的第一个参数是distortion_scale（可选默认：0.5型：int或float）：*备忘录：它可以进行透视转换。>必须为0 >初始化的第二个参数为p（可选默认：0.5-type：int或float）：*备忘录：>是图像是否通过透视转换完成的概率。必须为0 初始化的第三个参数是插值（可选默认：interpolationmode.binear-type：interpolationmode）。初始化的第四个参数是填充（可选默认：0型：int，float或tuple/tuple/list（int或float））：*备忘录：>它可以更改图像的背景。 *在为图像进行透视转换时，可以看到背景。>元组/列表必须是具有1或3个元素的1d。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 RandomPerspectivefrom torchvision.transforms.functional import InterpolationModerandompers = RandomPerspective()randompers = RandomPerspective(distortion_scale=0.5, p=0.5, interpolation=InterpolationMode.BILINEAR, fill=0)randompers# RandomPerspective(p=0.5,# distortion_scale=0.5,# interpolation=InterpolationMode.BILINEAR,# fill=0)randompers.distortion_scale# 0.5randompers.p# 0.5randompers.interpolation# randompers.fill# 0origin_data = OxfordIIITPet( root="data", transform=None)ds0p1origin_data = OxfordIIITPet( # `ds` is distortion_scale. root="data", transform=RandomPerspective(distortion_scale=0, p=1))ds01p1_data = OxfordIIITPet( root="data", transform=RandomPerspective(distortion_scale=0.1, p=1))ds02p1_data = OxfordIIITPet( root="data", transform=RandomPerspective(distortion_scale=0.2, p=1))ds03p1_data = OxfordIIITPet( root="data", transform=RandomPerspective(distortion_scale=0.3, p=1))ds04p1_data = OxfordIIITPet( root="data", transform=RandomPerspective(distortion_scale=0.4, p=1))ds05p1_data = OxfordIIITPet( root="data", transform=RandomPerspective(distortion_scale=0.5, p=1))ds06p1_data = OxfordIIITPet( root="data", transform=RandomPerspective(distortion_scale=0.6, p=1))ds07p1_data = OxfordIIITPet( root="data", transform=RandomPerspective(distortion_scale=0.7, p=1))ds08p1_data = OxfordIIITPet( root="data", transform=RandomPerspective(distortion_scale=0.8, p=1))ds09p1_data = OxfordIIITPet( root="data", transform=RandomPerspective(distortion_scale=0.9, p=1))ds1p1_data = OxfordIIITPet( root="data", transform=RandomPerspective(distortion_scale=1, p=1))p0_data = OxfordIIITPet( root="data", transform=RandomPerspective(p=0))p05_data = OxfordIIITPet( root="data", transform=RandomPerspective(p=0.5))p1_data = OxfordIIITPet( root="data", transform=RandomPerspective(p=1))p1fgray_data = OxfordIIITPet( # `f` is fill. root="data", transform=RandomPerspective(p=1, fill=150))p1fpurple_data = OxfordIIITPet( root="data", transform=RandomPerspective(p=1, fill=[160, 32, 240]))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")print()show_images1(data=ds0p1origin_data, main_title="ds0p1origin_data")show_images1(data=ds01p1_data, main_title="ds01p1_data")show_images1(data=ds02p1_data, main_title="ds02p1_data")show_images1(data=ds03p1_data, main_title="ds03p1_data")show_images1(data=ds04p1_data, main_title="ds04p1_data")show_images1(data=ds05p1_data, main_title="ds05p1_data")show_images1(data=ds06p1_data, main_title="ds06p1_data")show_images1(data=ds07p1_data, main_title="ds07p1_data")show_images1(data=ds08p1_data, main_title="ds08p1_data")show_images1(data=ds09p1_data, main_title="ds09p1_data")show_images1(data=ds1p1_data, main_title="ds1p1_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")print()show_images1(data=p1fgray_data, main_title="p1fgray_data")show_images1(data=p1fpurple_data, main_title="p1fpurple_data")# ↓ ↓ ↓ ↓ ↓ ↓ The code below is identical to the code above. ↓ ↓ ↓ ↓ ↓ ↓def show_images2(data, main_title=None, ds=0.5, prob=0.5, ip=InterpolationMode.BILINEAR, f=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) rp = RandomPerspective(distortion_scale=ds, p=prob, # Here interpolation=ip, fill=f) plt.imshow(X=rp(im)) # Here plt.xticks(ticks=[]) plt.yticks(ticks=[]) plt.tight_layout() plt.show()show_images2(data=origin_data, main_title="origin_data", ds=0)print()show_images2(data=origin_data, main_title="ds0p1origin_data", ds=0, prob=1)show_images2(data=origin_data, main_title="ds01p1_data", ds=0.1, prob=1)show_images2(data=origin_data, main_title="ds02p1_data", ds=0.2, prob=1)show_images2(data=origin_data, main_title="ds03p1_data", ds=0.3, prob=1)show_images2(data=origin_data, main_title="ds04p1_data", ds=0.4, prob=1)show_images2(data=origin_data, main_title="ds05p1_data", ds=0.5, prob=1)show_images2(data=origin_data, main_title="ds06p1_data", ds=0.6, prob=1)show_images2(data=origin_data, main_title="ds07p1_data", ds=0.7, prob=1)show_images2(data=origin_data, main_title="ds08p1_data", ds=0.8, prob=1)show_images2(data=origin_data, main_title="ds09p1_data", ds=0.9, prob=1)show_images2(data=origin_data, main_title="ds1p1_data", ds=1, prob=1)print()show_images2(data=origin_data, main_title="p0_data", prob=0)show_images2(data=origin_data, main_title="p0_data", prob=0)show_images2(data=origin_data, main_title="p0_data", prob=0)print()show_images2(data=origin_data, main_title="p05_data", prob=0.5)show_images2(data=origin_data, main_title="p05_data", prob=0.5)show_images2(data=origin_data, main_title="p05_data", prob=0.5)print()show_images2(data=origin_data, main_title="p1_data", prob=1)show_images2(data=origin_data, main_title="p1_data", prob=1)show_images2(data=origin_data, main_title="p1_data", prob=1)print()show_images2(data=origin_data, main_title="p1fgray_data", prob=1, f=150)show_images2(data=origin_data, main_title="p1fpurple_data", prob=1, f=[160, 32, 240])
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