Commit ImageScaleByAspectRatio node.

2024-02-20 00:24:16 +08:00
parent 409f315aef
commit 975c415b36
6 changed files with 210 additions and 0 deletions
@@ -17,6 +17,7 @@ Nodes are divided into 5 groups according to their functions: LayerStyle, LayerC

 ## Update
 **If the dependency package error after updating, please reinstall the relevant dependency packages. for details, please refer to [here](https://github.com/chflame163/ComfyUI_LayerStyle/issues/5).
+* Commit [ImageScaleByAspectRatio](#ImageScaleByAspectRatio) node, it can be scaled image or mask according to frame ratio.
 * Fix the bug of color gradation in [LUT Apply](#LUT) node rendering, and this node now support for log color space. *Please load the dedicated log lut file for the log color space image.
 * Commit [CreateGradientMask](#CreateGradientMask) node. Commit [LayerImageTransform](#LayerImageTransform) and [LayerMaskTransform](#LayerMaskTransform) nodes.
 * Commit [MaskEdgeUltraDetail](#MaskEdgeUltraDetail) node, it process rough masks to ultra fine edges.Commit [Exposure](#Exposure) node.
@@ -394,6 +395,25 @@ Outputs:
 * mask: If there is a mask input, the scaled mask will be output.
 * original_size: The original size data of the image is used for subsequent node recovery.

+### <a id="table1">ImageScaleByAspectRatio</a>
+Scale the image or mask by aspect ratio. the scaled size can be rounded to a multiple of 8 or 16, and can be scaled to the long side size.
+![image](image/image_scale_by_aspect_ratio_example.png)
+
+Node options:   
+![image](image/image_scale_by_aspect_ratio_node.png)
+* aspect_ratio: Here are several common frame ratios provided. alternatively, you can customize the scale using "custom".
+* proportional_width: Proportional width. if the aspect ratio option is not "custom", this setting will be ignored.
+* proportional_height: Proportional height. if the aspect ratio option is not "custom", this setting will be ignored.
+* fit: Scale aspect ratio mode. when the width to height ratio of the original image does not match the scaled size, there are three modes to choose from, 
+The _letterbox_ mode retains the complete frame and fills in the blank spaces with black; 
+The _crop_ mode retains the complete short edge, and any excess of the long edge will be cut off;
+The _fill_ mode does not maintain frame ratio and fills the screen with width and height.
+* method: Scaling sampling methods, including lanczos, bicubic, hamming, bilinear, box, and nearest.
+* round_to_multiple: Round multiples. for example, setting it to 8 will force the width and height to be multiples of 8.
+* scale_by_longest_side: Allow scaling by long edge size.
+* longest_side: When the scale_by_longest_side is set to True, this will be used this value to the long edge of the image. when the original_size have input, this setting will be ignored.
+
+
 ### <a id="table1">ImageShift</a>
 Shift the image. this node supports the output of displacement seam masks, making it convenient to create continuous textures.
 ![image](image/image_shift_example.png)
@@ -13,6 +13,7 @@

 ## 更新说明
 **如果本插件更新后出现依赖包错误，请重新安装相关依赖包。详情见[这里](https://github.com/chflame163/ComfyUI_LayerStyle/issues/5)。
+* 添加 [ImageScaleByAspectRatio](#ImageScaleByAspectRatio) 节点， 可按画幅比例缩放图像。
 * 改正 [LUT Apply](#LUT) 节点渲染出现色阶的bug, 并增加log色彩空间支持。*log色彩空间图片请加载专门的log lut。
 * 添加[CreateGradientMask](#CreateGradientMask) 节点。添加 [LayerImageTransform](#LayerImageTransform) 和 [LayerMaskTransform](#LayerMaskTransform) 节点。
 * 添加[MaskEdgeUltraDetail](#MaskEdgeUltraDetail) 节点，给粗糙的遮罩进行处理获得超精细的边缘。添加 [Exposure](#Exposure) 节点，调整图像曝光。
@@ -375,6 +376,28 @@

 <sup>*</sup>仅限输入image和mask, 如果强制接入其他类型输入，将导致节点错误。

+输出:
+* image: 如果有image输入，将输出缩放后的图像。
+* mask: 如果有mask输入，将输出缩放后的遮罩。
+* original_size: 图像的原始大小数据，用于后续节点进行恢复。
+
+
+### <a id="table1">ImageScaleByAspectRatio</a>
+将图像或遮罩按宽高比缩放。可设置将缩放后的尺寸按8或者16的倍数取整，可按长边尺寸缩放。
+![image](image/image_scale_by_aspect_ratio_example.png)
+
+节点选项说明:   
+![image](image/image_scale_by_aspect_ratio_node.png)
+* aspect_ratio: 宽高比。此处提供了几个常见画幅比例。也可选"custom"自定义比例。
+* proportional_width: 比例宽。如果aspect_ratio选项不是"custom"，此处设置将被忽略。
+* proportional_height: 比例高。如果aspect_ratio选项不是"custom"，此处设置将被忽略。
+* fit: 缩放画幅宽高比模式。有3种模式可以选择, letterbox模式保留完整的画幅，空白处用黑色补足；crop模式保留完整的短边，长边超出部分将被切除；fill模式不保持画幅比例，宽高各自填满画面。
+* method: 缩放的采样方法，包括lanczos、bicubic、hamming、bilinear、box和nearest。
+* round_to_multiple: 倍数取整。例如设置为8，宽和高将强制设置为8的倍数。
+* scale_by_longest_side: 允许按长边尺寸缩放。
+* longest_side: scale_by_longest_side被设置为True时，此项将作为是图像长边的长度。
+
+
 输出:
 * image: 如果有image输入，将输出缩放后的图像。
 * mask: 如果有mask输入，将输出缩放后的遮罩。
@@ -0,0 +1,157 @@
+import torch
+
+from .imagefunc import *
+
+NODE_NAME = 'ImageScaleByAspectRatio'
+
+class ImageScaleByAspectRatio:
+
+    def __init__(self):
+        pass
+
+    @classmethod
+    def INPUT_TYPES(self):
+        ratio_list = ['custom', '1:1', '3:2', '4:3', '16:9', '2:3', '3:4', '9:16']
+        fit_mode = ['letterbox', 'crop', 'fill']
+        method_mode = ['lanczos', 'bicubic', 'hamming', 'bilinear', 'box', 'nearest']
+        multiple_list = ['8', '16', 'None']
+
+        return {
+            "required": {
+                "aspect_ratio": (ratio_list,),
+                "proportional_width": ("INT", {"default": 2, "min": 1, "max": 999, "step": 1}),
+                "proportional_height": ("INT", {"default": 1, "min": 1, "max": 999, "step": 1}),
+                "fit": (fit_mode,),
+                "method": (method_mode,),
+                "round_to_multiple": (multiple_list,),
+                "scale_to_longest_side": ("BOOLEAN", {"default": False}),  # 是否按长边缩放
+                "longest_side": ("INT", {"default": 1024, "min": 4, "max": 999999, "step": 1}),
+            },
+            "optional": {
+                "image": ("IMAGE",),  #
+                "mask": ("MASK",),  #
+            }
+        }
+
+    RETURN_TYPES = ("IMAGE", "MASK", "BOX",)
+    RETURN_NAMES = ("image", "mask", "original_size")
+    FUNCTION = 'image_scale_by_aspect_ratio'
+    CATEGORY = '😺dzNodes/LayerUtility'
+    OUTPUT_NODE = True
+
+    def image_scale_by_aspect_ratio(self, aspect_ratio, proportional_width, proportional_height,
+                                    fit, method, round_to_multiple, scale_to_longest_side, longest_side,
+                                    image=None, mask = None,
+                                    ):
+        orig_images = []
+        orig_masks = []
+        orig_width = 0
+        orig_height = 0
+        target_width = 0
+        target_height = 0
+        ratio = 1.0
+        ret_images = []
+        ret_masks = []
+        if image is not None:
+            for i in image:
+                i = torch.unsqueeze(i, 0)
+                orig_images.append(i)
+            orig_width, orig_height = tensor2pil(orig_images[0]).size
+        if mask is not None:
+            if mask.dim() == 2:
+                mask = torch.unsqueeze(mask, 0)
+            for m in mask:
+                m = torch.unsqueeze(m, 0)
+                orig_masks.append(m)
+            _width, _height = tensor2pil(orig_masks[0]).size
+            if (orig_width > 0 and orig_width != _width) or (orig_height > 0 and orig_height != _height):
+                log(f"Error: {NODE_NAME} skipped, because the mask is does'nt match image.", message_type='error')
+                return (None, None,)
+            elif orig_width + orig_height == 0:
+                orig_width = _width
+                orig_height = _height
+
+        if orig_width + orig_height == 0:
+            log(f"Error: {NODE_NAME} skipped, because the image or mask at least one must be input.", message_type='error')
+            return (None, None,)
+
+        if aspect_ratio == 'custom':
+            ratio = proportional_width / proportional_height
+        else:
+            s = aspect_ratio.split(":")
+            ratio = int(s[0]) / int(s[1])
+
+        # calculate target width and height
+        if orig_width > orig_height:
+            if scale_to_longest_side:
+                target_width = longest_side
+            else:
+                target_width = orig_width
+            target_height = int(target_width  / ratio)
+        else:
+            if scale_to_longest_side:
+                target_height = longest_side
+            else:
+                target_height = orig_height
+            target_width = int(target_height * ratio)
+
+        if ratio < 1:
+            if scale_to_longest_side:
+                _r = longest_side / target_height
+                target_height = longest_side
+            else:
+                _r = orig_height / target_height
+                target_height = orig_height
+            target_width = int(target_width * _r)
+
+
+        if round_to_multiple != 'None':
+            multiple = int(round_to_multiple)
+            target_width = num_round_to_multiple(target_width, multiple)
+            target_height = num_round_to_multiple(target_height, multiple)
+
+        _mask = Image.new('L', size=(target_width, target_height), color='black')
+        _image = Image.new('RGB', size=(target_width, target_height), color='black')
+
+        resize_sampler = Image.LANCZOS
+        if method == "bicubic":
+            resize_sampler = Image.BICUBIC
+        elif method == "hamming":
+            resize_sampler = Image.HAMMING
+        elif method == "bilinear":
+            resize_sampler = Image.BILINEAR
+        elif method == "box":
+            resize_sampler = Image.BOX
+        elif method == "nearest":
+            resize_sampler = Image.NEAREST
+
+        if len(orig_images) > 0:
+            for i in orig_images:
+                _image = tensor2pil(i).convert('RGB')
+                _image = fit_resize_image(_image, target_width, target_height, fit, resize_sampler)
+                ret_images.append(pil2tensor(_image))
+        if len(orig_masks) > 0:
+            for m in orig_masks:
+                _mask = tensor2pil(m).convert('L')
+                _mask = fit_resize_image(_mask, target_width, target_height, fit, resize_sampler).convert('L')
+                ret_masks.append(image2mask(_mask))
+        if len(ret_images) > 0 and len(ret_masks) >0:
+            log(f"{NODE_NAME} Processed {len(ret_images)} image(s).", message_type='finish')
+            return (torch.cat(ret_images, dim=0), torch.cat(ret_masks, dim=0),)
+        elif len(ret_images) > 0 and len(ret_masks) == 0:
+            log(f"{NODE_NAME} Processed {len(ret_images)} image(s).", message_type='finish')
+            return (torch.cat(ret_images, dim=0), None,)
+        elif len(ret_images) == 0 and len(ret_masks) > 0:
+            log(f"{NODE_NAME} Processed {len(ret_masks)} image(s).", message_type='finish')
+            return (None, torch.cat(ret_masks, dim=0),)
+        else:
+            log(f"Error: {NODE_NAME} skipped, because the available image or mask is not found.", message_type='error')
+            return (None, None,)
+
+NODE_CLASS_MAPPINGS = {
+    "LayerUtility: ImageScaleByAspectRatio": ImageScaleByAspectRatio
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "LayerUtility: ImageScaleByAspectRatio": "LayerUtility: ImageScaleByAspectRatio"
+}
@@ -925,6 +925,16 @@ def random_numbers(total:int, random_range:int, seed:int=0, sum_of_numbers:int=0
    ret_list.append((sum_of_numbers - sum(ret_list)) // 2)
    return ret_list

+def num_round_to_multiple(number:int, multiple:int) -> int:
+    remainder = number % multiple
+    if remainder == 0 :
+        return number
+    else:
+        factor = int(number / multiple)
+        if number - factor * multiple > multiple / 2:
+            factor += 1
+        return factor * multiple
+
 '''CLASS'''

 class AnyType(str):