本文整理汇总了Python中torchvision.transforms.functional.to_grayscale方法的典型用法代码示例。如果您正苦于以下问题：Python functional.to_grayscale方法的具体用法？Python functional.to_grayscale怎么用？Python functional.to_grayscale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.to_grayscale方法的8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: to_grayscale
# 需要导入模块: from torchvision.transforms import functional [as 别名]
# 或者: from torchvision.transforms.functional import to_grayscale [as 别名]
def to_grayscale(img, num_output_channels=1):
"""Convert image to grayscale version of image.
Args:
img (np.ndarray): Image to be converted to grayscale.
Returns:
CV Image: Grayscale version of the image.
if num_output_channels == 1 : returned image is single channel
if num_output_channels == 3 : returned image is 3 channel with r == g == b
"""
if not _is_numpy_image(img):
raise TypeError('img should be CV Image. Got {}'.format(type(img)))
if num_output_channels == 1:
img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
elif num_output_channels == 3:
img = cv2.cvtColor(cv2.cvtColor(img, cv2.COLOR_RGB2GRAY), cv2.COLOR_GRAY2RGB)
else:
raise ValueError('num_output_channels should be either 1 or 3')
return img 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 to_grayscale [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 to_grayscale [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 to_grayscale [as 别名]
def __call__(self, img_dict):
if random.random() < self.p:
keys = ['rgb']
for key in keys:
num_output_channels = 1 if img_dict[key].mode == 'L' else 3
img_dict[key] = F.to_grayscale(img_dict[key], num_output_channels=num_output_channels)
return img_dict 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 to_grayscale [as 别名]
def __call__(self, img):
"""
Args:
img (PIL Image): Image to be converted to grayscale.
Returns:
PIL Image: Randomly grayscaled image.
"""
return F.to_grayscale(img, num_output_channels=self.num_output_channels) 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 to_grayscale [as 别名]
def __call__(self, img):
if random.random() < 0.1:
return TrF.to_grayscale(img, num_output_channels=3)
else:
return img 示例7: torchvision_transform
# 需要导入模块: from torchvision.transforms import functional [as 别名]
# 或者: from torchvision.transforms.functional import to_grayscale [as 别名]
def torchvision_transform(self, img):
return torchvision.to_grayscale(img, num_output_channels=3) 示例8: transform
# 需要导入模块: from torchvision.transforms import functional [as 别名]
# 或者: from torchvision.transforms.functional import to_grayscale [as 别名]
def transform(image, mask, image_size=224):
# Resize
resized_num = int(random.random() * image_size)
resize = transforms.Resize(size=(image_size + resized_num, image_size + resized_num))
image = resize(image)
mask = resize(mask)
# num_pad = int(random.random() * image_size)
# image = TF.pad(image, num_pad, padding_mode='edge')
# mask = TF.pad(mask, num_pad)
# # Random crop
# i, j, h, w = transforms.RandomCrop.get_params(
# image, output_size=(image_size, image_size))
# image = TF.crop(image, i, j, h, w)
# mask = TF.crop(mask, i, j, h, w)
# # Random horizontal flipping
# if random.random() > 0.5:
# image = TF.hflip(image)
# mask = TF.hflip(mask)
#
# # Random vertical flipping
# if random.random() > 0.5:
# image = TF.vflip(image)
# mask = TF.vflip(mask)
resize = transforms.Resize(size=(image_size, image_size))
image = resize(image)
mask = resize(mask)
# Make gray scale image
gray_image = TF.to_grayscale(image)
# Transform to tensor
image = TF.to_tensor(image)
mask = TF.to_tensor(mask)
gray_image = TF.to_tensor(gray_image)
# Normalize Data
image = TF.normalize(image, (0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
return image, gray_image, mask