Image Rotate

Documentation

• Class name: Image Rotate
• Category: WAS Suite/Image/Transform
• Output node: False

The Image Rotate node is designed to rotate a batch of images to a specified angle, with options for different rotation modes and sampling methods. It ensures that rotations are constrained within a 360-degree range and supports adjustments to the rotation angle for more precise control. This node caters to both internal and external rotation modes, offering flexibility in how images are transformed.

Input types

Required

• images
  • A batch of images to be rotated. This parameter is crucial as it provides the raw visual data that the node will process and transform according to the specified rotation parameters.
  • Comfy dtype: IMAGE
  • Python dtype: List[torch.Tensor]
• mode
  • Specifies the rotation mode, either 'internal' for direct rotation or an alternative mode that involves transposing the image. This affects how the image is rotated and transformed.
  • Comfy dtype: COMBO[STRING]
  • Python dtype: str
• rotation
  • The angle in degrees to rotate the images. This parameter directly influences the final orientation of the processed images.
  • Comfy dtype: INT
  • Python dtype: int
• sampler
  • Defines the sampling method used during rotation, such as 'nearest', 'bicubic', or 'bilinear'. This affects the quality and appearance of the rotated images.
  • Comfy dtype: COMBO[STRING]
  • Python dtype: str

Output types

• images
  • Comfy dtype: IMAGE
  • The batch of rotated images, returned as a tensor. This output represents the transformed visual data after applying the specified rotation and sampling methods.
  • Python dtype: torch.Tensor

Usage tips

• Infra type: GPU
• Common nodes: unknown

Source code

class WAS_Image_Rotate:
    def __init__(self):
        pass

    @classmethod
    def INPUT_TYPES(cls):
        return {
            "required": {
                "images": ("IMAGE",),
                "mode": (["transpose", "internal",],),
                "rotation": ("INT", {"default": 0, "min": 0, "max": 360, "step": 90}),
                "sampler": (["nearest", "bilinear", "bicubic"],),
            },
        }

    RETURN_TYPES = ("IMAGE",)
    RETURN_NAMES = ("images",)
    FUNCTION = "image_rotate"

    CATEGORY = "WAS Suite/Image/Transform"

    def image_rotate(self, images, mode, rotation, sampler):

        batch_tensor = []
        for image in images:
            # PIL Image
            image = tensor2pil(image)

            # Check rotation
            if rotation > 360:
                rotation = int(360)
            if (rotation % 90 != 0):
                rotation = int((rotation//90)*90)

            # Set Sampler
            if sampler:
                if sampler == 'nearest':
                    sampler = Image.NEAREST
                elif sampler == 'bicubic':
                    sampler = Image.BICUBIC
                elif sampler == 'bilinear':
                    sampler = Image.BILINEAR
                else:
                    sampler == Image.BILINEAR

            # Rotate Image
            if mode == 'internal':
                image = image.rotate(rotation, sampler)
            else:
                rot = int(rotation / 90)
                for _ in range(rot):
                    image = image.transpose(2)

            batch_tensor.append(pil2tensor(image))

        batch_tensor = torch.cat(batch_tensor, dim=0)

        return (batch_tensor, )