本文整理匯總了Python中torchvision.transforms.transforms.ColorJitter方法的典型用法代碼示例。如果您正苦於以下問題：Python transforms.ColorJitter方法的具體用法？Python transforms.ColorJitter怎麽用？Python transforms.ColorJitter使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類torchvision.transforms.transforms的用法示例。
在下文中一共展示了transforms.ColorJitter方法的2個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: call_image
# 需要導入模塊: from torchvision.transforms import transforms [as 別名]
# 或者: from torchvision.transforms.transforms import ColorJitter [as 別名]
def call_image(self, img):
return torch_transforms.ColorJitter(self.brightness, self.contrast, self.saturation, self.hue)(img) 示例2: preprocessImage
# 需要導入模塊: from torchvision.transforms import transforms [as 別名]
# 或者: from torchvision.transforms.transforms import ColorJitter [as 別名]
def preprocessImage(img, use_color_jitter, image_size_dict, img_norm_info, use_caffe_pretrained_model):
# calculate target_size and scale_factor, target_size's format is (h, w)
w_ori, h_ori = img.width, img.height
if w_ori > h_ori:
target_size = (image_size_dict.get('SHORT_SIDE'), image_size_dict.get('LONG_SIDE'))
else:
target_size = (image_size_dict.get('LONG_SIDE'), image_size_dict.get('SHORT_SIDE'))
h_t, w_t = target_size
scale_factor = min(w_t/w_ori, h_t/h_ori)
target_size = (round(scale_factor*h_ori), round(scale_factor*w_ori))
# define and do transform
if use_caffe_pretrained_model:
means_norm = img_norm_info['caffe'].get('mean_rgb')
stds_norm = img_norm_info['caffe'].get('std_rgb')
if use_color_jitter:
transform = transforms.Compose([transforms.Resize(target_size),
transforms.ColorJitter(brightness=0.5, contrast=0.5, saturation=0.5, hue=0.1),
transforms.ToTensor(),
transforms.Normalize(mean=means_norm, std=stds_norm)])
else:
transform = transforms.Compose([transforms.Resize(target_size),
transforms.ToTensor(),
transforms.Normalize(mean=means_norm, std=stds_norm)])
img = transform(img) * 255
img = img[(2, 1, 0), :, :]
else:
means_norm = img_norm_info['pytorch'].get('mean_rgb')
stds_norm = img_norm_info['pytorch'].get('std_rgb')
if use_color_jitter:
transform = transforms.Compose([transforms.Resize(target_size),
transforms.ColorJitter(brightness=0.5, contrast=0.5, saturation=0.5, hue=0.1),
transforms.ToTensor(),
transforms.Normalize(mean=means_norm, std=stds_norm)])
else:
transform = transforms.Compose([transforms.Resize(target_size),
transforms.ToTensor(),
transforms.Normalize(mean=means_norm, std=stds_norm)])
img = transform(img)
# return necessary data
return img, scale_factor, target_size