Python functional.to_grayscale方法代碼示例

本文整理匯總了Python中torchvision.transforms.functional.to_grayscale方法的典型用法代碼示例。如果您正苦於以下問題：Python functional.to_grayscale方法的具體用法？Python functional.to_grayscale怎麽用？Python functional.to_grayscale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.functional的用法示例。

在下文中一共展示了functional.to_grayscale方法的8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: to_grayscale

# 需要導入模塊: from torchvision.transforms import functional [as 別名]
# 或者: from torchvision.transforms.functional import to_grayscale [as 別名]
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

開發者ID:YU-Zhiyang，項目名稱:opencv_transforms_torchvision，代碼行數:24，代碼來源:cvfunctional.py

示例2: cv_transform

# 需要導入模塊: from torchvision.transforms import functional [as 別名]
# 或者: from torchvision.transforms.functional import to_grayscale [as 別名]
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)

開發者ID:YU-Zhiyang，項目名稱:opencv_transforms_torchvision，代碼行數:26，代碼來源:cvfunctional.py

示例3: pil_transform

# 需要導入模塊: from torchvision.transforms import functional [as 別名]
# 或者: from torchvision.transforms.functional import to_grayscale [as 別名]
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)

開發者ID:YU-Zhiyang，項目名稱:opencv_transforms_torchvision，代碼行數:24，代碼來源:cvfunctional.py

示例4: call

# 需要導入模塊: from torchvision.transforms import functional [as 別名]
# 或者: from torchvision.transforms.functional import to_grayscale [as 別名]
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

開發者ID:AlexanderParkin，項目名稱:ChaLearn_liveness_challenge，代碼行數:9，代碼來源:transforms.py

示例5: call

# 需要導入模塊: from torchvision.transforms import functional [as 別名]
# 或者: from torchvision.transforms.functional import to_grayscale [as 別名]
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)

開發者ID:yalesong，項目名稱:pvse，代碼行數:11，代碼來源:video_transforms.py

示例6: call

# 需要導入模塊: from torchvision.transforms import functional [as 別名]
# 或者: from torchvision.transforms.functional import to_grayscale [as 別名]
def __call__(self, img):
        if random.random() < 0.1:
            return  TrF.to_grayscale(img, num_output_channels=3)
        else: 
            return img

開發者ID:gjy3035，項目名稱:NWPU-Crowd-Sample-Code，代碼行數:7，代碼來源:transforms.py

示例7: torchvision_transform

# 需要導入模塊: from torchvision.transforms import functional [as 別名]
# 或者: from torchvision.transforms.functional import to_grayscale [as 別名]
def torchvision_transform(self, img):
        return torchvision.to_grayscale(img, num_output_channels=3)

開發者ID:albumentations-team，項目名稱:albumentations，代碼行數:4，代碼來源:benchmark.py

示例8: transform

# 需要導入模塊: from torchvision.transforms import functional [as 別名]
# 或者: from torchvision.transforms.functional import to_grayscale [as 別名]
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

開發者ID:wonbeomjang，項目名稱:mobile-hair-segmentation-pytorch，代碼行數:46，代碼來源:dataloader.py

注：本文中的torchvision.transforms.functional.to_grayscale方法示例由純淨天空整理自Github/MSDocs等開源代碼及文檔管理平台，相關代碼片段篩選自各路編程大神貢獻的開源項目，源碼版權歸原作者所有，傳播和使用請參考對應項目的License；未經允許，請勿轉載。