# Documentation
- Class name: ColorMatch
- Category: KJNodes/image
- Output node: False
- Repo Ref: https://github.com/comfyanonymous/ComfyUI

ColorMatch节点专门用于在图像之间转移色彩方案，运用多种色彩匹配技术。它支持多种颜色转移方法，包括MKL和直方图匹配，以便将目标图像的调色板调整为与参考图像相匹配。

# Input types
## Required
- image_ref
    - 作为色彩参考的图像，其调色板将被转移。这对于决定目标图像的最终外观起着至关重要的作用。
    - Comfy dtype: IMAGE
    - Python dtype: torch.Tensor
- image_target
    - 将接收参考图像调色板的目标图像。此图像将被转换以匹配参考图像的色彩方案。
    - Comfy dtype: IMAGE
    - Python dtype: torch.Tensor
- method
    - 指定要使用的颜色转移方法，如MKL或直方图匹配，这会影响色彩适配的风格。
    - Comfy dtype: COMBO[STRING]
    - Python dtype: str

# Output types
- image
    - 颜色转移后的结果图像，其颜色已调整以匹配参考图像的调色板。
    - Comfy dtype: IMAGE
    - Python dtype: torch.Tensor


## Usage tips
- Infra type: `CPU`
- Common nodes:
    - [ImageBatch](../../Comfy/Nodes/ImageBatch.md)
    - [VHS_SplitImages](../../ComfyUI-VideoHelperSuite/Nodes/VHS_SplitImages.md)



## Source code
```python
class ColorMatch:
    @classmethod
    def INPUT_TYPES(cls):
        return {
            "required": {
                "image_ref": ("IMAGE",),
                "image_target": ("IMAGE",),
                "method": (
            [   
                'mkl',
                'hm', 
                'reinhard', 
                'mvgd', 
                'hm-mvgd-hm', 
                'hm-mkl-hm',
            ], {
               "default": 'mkl'
            }),
                
            },
        }
    
    CATEGORY = "KJNodes/image"

    RETURN_TYPES = ("IMAGE",)
    RETURN_NAMES = ("image",)
    FUNCTION = "colormatch"
    DESCRIPTION = """
color-matcher enables color transfer across images which comes in handy for automatic  
color-grading of photographs, paintings and film sequences as well as light-field  
and stopmotion corrections.  

The methods behind the mappings are based on the approach from Reinhard et al.,  
the Monge-Kantorovich Linearization (MKL) as proposed by Pitie et al. and our analytical solution  
to a Multi-Variate Gaussian Distribution (MVGD) transfer in conjunction with classical histogram   
matching. As shown below our HM-MVGD-HM compound outperforms existing methods.   
https://github.com/hahnec/color-matcher/

"""
    
    def colormatch(self, image_ref, image_target, method):
        try:
            from color_matcher import ColorMatcher
        except:
            raise Exception("Can't import color-matcher, did you install requirements.txt? Manual install: pip install color-matcher")
        cm = ColorMatcher()
        image_ref = image_ref.cpu()
        image_target = image_target.cpu()
        batch_size = image_target.size(0)
        out = []
        images_target = image_target.squeeze()
        images_ref = image_ref.squeeze()

        image_ref_np = images_ref.numpy()
        images_target_np = images_target.numpy()

        if image_ref.size(0) > 1 and image_ref.size(0) != batch_size:
            raise ValueError("ColorMatch: Use either single reference image or a matching batch of reference images.")

        for i in range(batch_size):
            image_target_np = images_target_np if batch_size == 1 else images_target[i].numpy()
            image_ref_np_i = image_ref_np if image_ref.size(0) == 1 else images_ref[i].numpy()
            try:
                image_result = cm.transfer(src=image_target_np, ref=image_ref_np_i, method=method)
            except BaseException as e:
                print(f"Error occurred during transfer: {e}")
                break
            out.append(torch.from_numpy(image_result))
        return (torch.stack(out, dim=0).to(torch.float32), )

```