本文整理汇总了Python中torchvision.transforms.functional.affine方法的典型用法代码示例。如果您正苦于以下问题：Python functional.affine方法的具体用法？Python functional.affine怎么用？Python functional.affine使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类torchvision.transforms.functional的用法示例。
在下文中一共展示了functional.affine方法的6个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: get_params
# 需要导入模块: from torchvision.transforms import functional [as 别名]
# 或者: from torchvision.transforms.functional import affine [as 别名]
def get_params(degrees, translate, scale_ranges, shears, img_size):
"""Get parameters for affine transformation
Returns:
sequence: params to be passed to the affine transformation
"""
angle = np.random.uniform(degrees[0], degrees[1])
if translate is not None:
max_dx = translate[0] * img_size[0]
max_dy = translate[1] * img_size[1]
translations = (np.round(np.random.uniform(-max_dx, max_dx)),
np.round(np.random.uniform(-max_dy, max_dy)))
else:
translations = (0, 0)
if scale_ranges is not None:
scale = np.random.uniform(scale_ranges[0], scale_ranges[1])
else:
scale = 1.0
if shears is not None:
shear = np.random.uniform(shears[0], shears[1])
else:
shear = 0.0
return angle, translations, scale, shear 示例2: cv_transform
# 需要导入模块: from torchvision.transforms import functional [as 别名]
# 或者: from torchvision.transforms.functional import affine [as 别名]
def cv_transform(img):
# img = resize(img, size=(100, 300))
# img = to_tensor(img)
# img = normalize(img, mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
# img = pad(img, padding=(10, 10, 20, 20), fill=(255, 255, 255), padding_mode='constant')
# img = pad(img, padding=(100, 100, 100, 100), fill=5, padding_mode='symmetric')
# img = crop(img, -40, -20, 1000, 1000)
# img = center_crop(img, (310, 300))
# img = resized_crop(img, -10.3, -20, 330, 220, (500, 500))
# img = hflip(img)
# img = vflip(img)
# tl, tr, bl, br, center = five_crop(img, 100)
# img = adjust_brightness(img, 2.1)
# img = adjust_contrast(img, 1.5)
# img = adjust_saturation(img, 2.3)
# img = adjust_hue(img, 0.5)
# img = adjust_gamma(img, gamma=3, gain=0.1)
# img = rotate(img, 10, resample='BILINEAR', expand=True, center=None)
# img = to_grayscale(img, 3)
# img = affine(img, 10, (0, 0), 1, 0, resample='BICUBIC', fillcolor=(255,255,0))
# img = gaussion_noise(img)
# img = poisson_noise(img)
img = salt_and_pepper(img)
return to_tensor(img) 示例3: pil_transform
# 需要导入模块: from torchvision.transforms import functional [as 别名]
# 或者: from torchvision.transforms.functional import affine [as 别名]
def pil_transform(img):
# img = functional.resize(img, size=(100, 300))
# img = functional.to_tensor(img)
# img = functional.normalize(img, mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
# img = functional.pad(img, padding=(10, 10, 20, 20), fill=(255, 255, 255), padding_mode='constant')
# img = functional.pad(img, padding=(100, 100, 100, 100), padding_mode='symmetric')
# img = functional.crop(img, -40, -20, 1000, 1000)
# img = functional.center_crop(img, (310, 300))
# img = functional.resized_crop(img, -10.3, -20, 330, 220, (500, 500))
# img = functional.hflip(img)
# img = functional.vflip(img)
# tl, tr, bl, br, center = functional.five_crop(img, 100)
# img = functional.adjust_brightness(img, 2.1)
# img = functional.adjust_contrast(img, 1.5)
# img = functional.adjust_saturation(img, 2.3)
# img = functional.adjust_hue(img, 0.5)
# img = functional.adjust_gamma(img, gamma=3, gain=0.1)
# img = functional.rotate(img, 10, resample=PIL.Image.BILINEAR, expand=True, center=None)
# img = functional.to_grayscale(img, 3)
# img = functional.affine(img, 10, (0, 0), 1, 0, resample=PIL.Image.BICUBIC, fillcolor=(255,255,0))
return functional.to_tensor(img) 示例4: __call__
# 需要导入模块: from torchvision.transforms import functional [as 别名]
# 或者: from torchvision.transforms.functional import affine [as 别名]
def __call__(self, img, mask):
rotate_degree = random.random() * 2 * self.degree - self.degree
return (
tf.affine(img,
translate=(0, 0),
scale=1.0,
angle=rotate_degree,
resample=Image.BILINEAR,
fillcolor=(0, 0, 0),
shear=0.0),
tf.affine(mask,
translate=(0, 0),
scale=1.0,
angle=rotate_degree,
resample=Image.NEAREST,
fillcolor=250,
shear=0.0)) 示例5: __call__
# 需要导入模块: from torchvision.transforms import functional [as 别名]
# 或者: from torchvision.transforms.functional import affine [as 别名]
def __call__(self, img, mask):
rotate_degree = random.random() * 2 * self.degree - self.degree
return (
tf.affine(
img,
translate=(0, 0),
scale=1.0,
angle=rotate_degree,
resample=Image.BILINEAR,
fillcolor=(0, 0, 0),
shear=0.0,
),
tf.affine(
mask,
translate=(0, 0),
scale=1.0,
angle=rotate_degree,
resample=Image.NEAREST,
fillcolor=250,
shear=0.0,
),
) 示例6: __call__
# 需要导入模块: from torchvision.transforms import functional [as 别名]
# 或者: from torchvision.transforms.functional import affine [as 别名]
def __call__(self, img, mask):
rotate_degree = random.random() * 2 * self.degree - self.degree
return (
tf.affine(img,
translate=(0, 0),
scale=1.0,
angle=rotate_degree,
resample=Image.BILINEAR,
fillcolor=(0, 0, 0),
shear=0.0),
tf.affine(mask,
translate=(0, 0),
scale=1.0,
angle=rotate_degree,
resample=Image.NEAREST,
fillcolor=250,
shear=0.0))