commit HSVValue, CheckMask nodes, and CropByMaskV2 node add round_to_multiple option

2024-06-24 19:54:14 +08:00
parent 6ceb41f92e
commit 318c2e26d4
10 changed files with 171 additions and 14 deletions
@@ -80,6 +80,8 @@ When this error has occurred, please check the network environment.
 ## Update
 <font size="4">**If the dependency package error after updating, please reinstall the relevant dependency packages. </font><br />    

+* [CropByMaskV2](#CropByMaskV2) add option to round the cutting size by multiples.
+* Commit [CheckMask](#CheckMask) node, it detect whether the mask contains sufficient effective areas. Commit [HSVValue](#HSVValue) node, it convert color values to HSV values.
 * [BooleanOperatorV2](#BooleanOperatorV2), [NumberCalculatorV2](#NumberCalculatorV2), [Integer](#Integer), [Float](#Float), [Boolean](#Boolean) nodes add string output to output the value as a string for use with [SwitchCase](#SwitchCase).
 * Commit [SwitchCase](#SwitchCase) node, Switches the output based on the matching string. Can be used for any type of data switching.
 * Commit [String](#String) node, Used to output a string. It is the TextBox simplified node.
@@ -564,6 +566,7 @@ The following changes have been made based on CropByMask:
 ![image](image/corp_by_mask_v2_node.jpg)    
 * The input ```mask_for_crop``` reanme to ```mask```。
 * Add optional inputs to the ```crop_box```. If there are inputs here, mask detection will be ignored and this data will be directly used for cropping.
+* Add the option ```round_to_multiple``` to round the trimming edge length multiple. For example, setting it to 8 will force the width and height to be multiples of 8.

 ### <a id="table1">RestoreCropBox</a>
 Restore the cropped image to the original image by [CropByMask](#CropByMask).
@@ -768,6 +771,13 @@ Output the color value as a single R, G, B three decimal values. Supports HEX an
 Node Options:
 * color_value: Supports hexadecimal (HEX) or decimal (DEC) color values and should be of string or tuple type. Forcing in other types will result in an error.

+### <a id="table1">HSVValue</a>
+Output color values as individual decimal values of H, S, and V (maximum value of 255). Supports HEX and DEC formats for ColorPicker node output.
+![image](image/hsv_value_node.jpg)    
+
+Node Options:
+* color_value: Supports hexadecimal (HEX) or decimal (DEC) color values and should be of string or tuple type. Forcing in other types will result in an error.
+
 ### <a id="table1">GetColorTone</a>
 Obtain the main color or average color from the image and output RGB values.
 ![image](image/get_color_tone_example.jpg)    
@@ -1169,6 +1179,14 @@ Node Options:
 * condition: Judgment conditions. ```include``` determines whether it contains a substring, and ```exclude``` determines whether it does not.
 * sub_string: Substring.

+### <a id="table1">CheckMask</a>
+Check if the mask contains enough valid areas and output a Boolean value.
+
+Node Options:    
+![image](image/check_mask_node.jpg)    
+* white_point: The white point threshold used to determine whether the mask is valid is considered valid if it exceeds this value.
+* area_percent: The percentage of effective areas. If the proportion of effective areas exceeds this value, output True.
+

 ### <a id="table1">If</a>
 ![image](image/if_example.jpg)    
@@ -80,6 +80,9 @@ git clone https://github.com/chflame163/ComfyUI_LayerStyle.git
 ## 更新说明
 <font size="4">**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。

+* [CropByMaskV2](#CropByMaskV2) 增加裁切尺寸按倍数取整选项。
+* 添加 [CheckMask](#CheckMask) 节点, 用于检测遮罩是否包含足够的有效区域。
+* 添加 [HSVValue](#HSVValue) 节点, 用于转换色值为HSV值。
 * [BooleanOperatorV2](#BooleanOperatorV2), [NumberCalculatorV2](#NumberCalculatorV2), [Integer](#Integer), [Float](#Float), [Boolean](#Boolean)节点增加string输出，将值输出为字符串，以配合[SwitchCase](#SwitchCase)使用。
 * 添加 [SwitchCase](#SwitchCase) 节点, 根据匹配字符串切换输出。可用于任意类型的数据切换。
 * 添加 [String](#String) 节点, 用于输出字符串。这是TextBox简化版节点。
@@ -556,6 +559,7 @@ CropByMask的V2升级版。支持crop_box输入，方便裁切相同尺寸的图
 ![image](image/corp_by_mask_v2_node.jpg)    
 * ```mask_for_crop```更名为```mask```。
 * 增加```crop_box```可选输入，如果这里有输入将忽略遮罩探测，直接使用此数据裁切。
+* 增加```round_to_multiple```选项，使裁切边长倍数取整。例如设置为8，宽和高将强制设置为8的倍数。


 ### <a id="table1">RestoreCropBox</a>
@@ -758,6 +762,12 @@ ImageScaleByAspectRatio的V2升级版
 节点选项说明:
 * color_value： 支持十六进制(HEX)或十进制(DEC)色值，应是string或tuple类型，强行接入其他类型将导致错误。

+### <a id="table1">HSVValue</a>
+将色值输出为单独的H, S, V三个10进制数值(最大值255)。支持ColorPicker节点输出的HEX和DEC格式。
+![image](image/hsv_value_node.jpg)    
+
+节点选项说明:
+* color_value： 支持十六进制(HEX)或十进制(DEC)色值，应是string或tuple类型，强行接入其他类型将导致错误。

 ### <a id="table1">GetColorTone</a>
 从图片中获取主颜色或平均色。
@@ -1160,6 +1170,14 @@ BooleanOperator的升级版，增加了节点内数值输入，增加了大于
 * sub_string: 子字符串文本。


+### <a id="table1">CheckMask</a>
+检测遮罩是否包含足够的有效区域, 输出布尔值。
+
+节点选项说明:    
+![image](image/check_mask_node.jpg)    
+* white_point: 判断遮罩是否有效的白点值，高于此值被计入有效。
+* area_percent: 有效区域所占百分比。检测有效区域占比超过此值则输出True。
+
 ### <a id="table1">If</a>
 ![image](image/if_example.jpg)    
 根据布尔值条件输入切换输出。可用于任意类型的数据切换，包括且不限于数值、字符串、图片、遮罩、模型、latent、pipe管线等。
@@ -0,0 +1,46 @@
+from .imagefunc import *
+
+NODE_NAME = 'CheckMask'
+
+# 检查mask是否有效，如果mask面积少于指定比例则判为无效mask
+class CheckMask:
+
+    def __init__(self):
+        pass
+
+    @classmethod
+    def INPUT_TYPES(self):
+        blank_mask_list = ['white', 'black']
+        return {
+            "required": {
+                "mask": ("MASK",),  #
+                "white_point": ("INT", {"default": 1, "min": 1, "max": 254, "step": 1}), # 用于判断mask是否有效的白点值，高于此值被计入有效
+                "area_percent": ("INT", {"default": 1, "min": 1, "max": 99, "step": 1}), # 区域百分比，低于此则mask判定无效
+            },
+            "optional": { #
+            }
+        }
+
+    RETURN_TYPES = ("BOOLEAN",)
+    RETURN_NAMES = ('bool',)
+    FUNCTION = 'check_mask'
+    CATEGORY = '😺dzNodes/LayerUtility'
+
+    def check_mask(self, mask, white_point, area_percent,):
+
+        if mask.dim() == 2:
+            mask = torch.unsqueeze(mask, 0)
+        mask = tensor2pil(mask[0])
+        if mask.width * mask.height > 262144:
+            target_width = 512
+            target_height = int(target_width * mask.height / mask.width)
+            mask = mask.resize((target_width, target_height), Image.LANCZOS)
+        return (mask_white_area(mask, white_point) * 100 > area_percent,)
+
+NODE_CLASS_MAPPINGS = {
+    "LayerUtility: CheckMask": CheckMask
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "LayerUtility: CheckMask": "LayerUtility: Check Mask"
+}
@@ -0,0 +1,44 @@
+from .imagefunc import *
+
+NODE_NAME = 'HSV Value'
+any = AnyType("*")
+
+class ColorValuetoHSVValue:
+
+    def __init__(self):
+        pass
+
+    @classmethod
+    def INPUT_TYPES(self):
+
+        return {
+            "required": {
+                "color_value": (any, {}),
+            },
+            "optional": {
+            }
+        }
+
+    RETURN_TYPES = ("INT", "INT", "INT")
+    RETURN_NAMES = ("H", "S", "V")
+    FUNCTION = 'color_value_to_hsv_value'
+    CATEGORY = '😺dzNodes/LayerUtility/Data'
+
+    def color_value_to_hsv_value(self, color_value,):
+        H, S, V = 0, 0, 0
+        if isinstance(color_value, str):
+            H, S, V = Hex_to_HSV_255level(color_value)
+        elif isinstance(color_value, tuple):
+            H, S, V = Hex_to_HSV_255level(RGB_to_Hex(color_value))
+        else:
+            log(f"{NODE_NAME}: color_value input type must be tuple or string.", message_type="error")
+
+        return (H, S, V,)
+
+NODE_CLASS_MAPPINGS = {
+    "LayerUtility: HSV Value": ColorValuetoHSVValue
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "LayerUtility: HSV Value": "LayerUtility: HSV Value"
+}
@@ -9,7 +9,8 @@ class CropByMaskV2:

    @classmethod
    def INPUT_TYPES(self):
-        detect_mode = ['min_bounding_rect', 'max_inscribed_rect', 'mask_area']
+        detect_mode = ['mask_area', 'min_bounding_rect', 'max_inscribed_rect']
+        multiple_list = ['8', '16', '32', '64', 'None']
        return {
            "required": {
                "image": ("IMAGE", ),  #
@@ -20,6 +21,7 @@ class CropByMaskV2:
                "bottom_reserve": ("INT", {"default": 20, "min": -9999, "max": 9999, "step": 1}),
                "left_reserve": ("INT", {"default": 20, "min": -9999, "max": 9999, "step": 1}),
                "right_reserve": ("INT", {"default": 20, "min": -9999, "max": 9999, "step": 1}),
+                "round_to_multiple": (multiple_list,),
            },
            "optional": {
                "crop_box": ("BOX",),
@@ -33,7 +35,7 @@ class CropByMaskV2:

    def crop_by_mask_v2(self, image, mask, invert_mask, detect,
                     top_reserve, bottom_reserve,
-                     left_reserve, right_reserve,
+                     left_reserve, right_reserve, round_to_multiple,
                     crop_box=None
                     ):

@@ -63,24 +65,31 @@ class CropByMaskV2:
            width = 0
            height = 0
            if detect == "min_bounding_rect":
-                (x, y, width, height) = min_bounding_rect(bluredmask)
+                (x, y, w, h) = min_bounding_rect(bluredmask)
            elif detect == "max_inscribed_rect":
-                (x, y, width, height) = max_inscribed_rect(bluredmask)
+                (x, y, w, h) = max_inscribed_rect(bluredmask)
            else:
-                (x, y, width, height) = mask_area(_mask)
+                (x, y, w, h) = mask_area(_mask)

-            width = num_round_to_multiple(width, 8)
-            height = num_round_to_multiple(height, 8)
-
-            log(f"{NODE_NAME}: Box detected. x={x},y={y},width={width},height={height}")
            canvas_width, canvas_height = tensor2pil(torch.unsqueeze(image[0], 0)).convert('RGB').size
            x1 = x - left_reserve if x - left_reserve > 0 else 0
            y1 = y - top_reserve if y - top_reserve > 0 else 0
-            x2 = x + width + right_reserve if x + width + right_reserve < canvas_width else canvas_width
-            y2 = y + height + bottom_reserve if y + height + bottom_reserve < canvas_height else canvas_height
+            x2 = x + w + right_reserve if x + w + right_reserve < canvas_width else canvas_width
+            y2 = y + h + bottom_reserve if y + h + bottom_reserve < canvas_height else canvas_height
+
+            if round_to_multiple != 'None':
+                multiple = int(round_to_multiple)
+                width = num_round_up_to_multiple(x2 - x1, multiple)
+                height = num_round_up_to_multiple(y2 - y1, multiple)
+                x1 = x1 - (width - (x2 - x1)) // 2
+                y1 = y1 - (height - (y2 - y1)) // 2
+                x2 = x1 + width
+                y2 = y1 + height
+
+            log(f"{NODE_NAME}: Box detected. x={x1},y={y1},width={width},height={height}")
            crop_box = (x1, y1, x2, y2)
-            preview_image = draw_rect(preview_image, x, y, width, height, line_color="#F00000",
-                                      line_width=(width + height) // 100)
+            preview_image = draw_rect(preview_image, x, y, w, h, line_color="#F00000",
+                                      line_width=(w + h) // 100)
        preview_image = draw_rect(preview_image, crop_box[0], crop_box[1],
                                  crop_box[2] - crop_box[0], crop_box[3] - crop_box[1],
                                  line_color="#00F000",
@@ -1631,6 +1631,18 @@ def gray_threshold(image:Image, thresh:int=127, otsu:bool=False) -> Image:
 def image_to_colormap(image:Image, index:int) -> Image:
    return cv22pil(cv2.applyColorMap(pil2cv2(image), index))

+# 检查mask有效区域面积比例
+def mask_white_area(mask:Image, white_point:int) -> float:
+    if mask.mode != 'L':
+        mask.convert('L')
+    white_pixels = 0
+    for y in range(mask.height):
+        for x in range(mask.width):
+            mask.getpixel((x, y)) > 16
+            if mask.getpixel((x, y)) > white_point:
+                white_pixels += 1
+    return white_pixels / (mask.width * mask.height)
+
 '''Color Functions'''


@@ -1766,6 +1778,7 @@ 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

+# 四舍五入取整数倍
 def num_round_to_multiple(number:int, multiple:int) -> int:
    remainder = number % multiple
    if remainder == 0 :
@@ -1776,6 +1789,15 @@ def num_round_to_multiple(number:int, multiple:int) -> int:
            factor += 1
        return factor * multiple

+# 向上取整数倍
+def num_round_up_to_multiple(number: int, multiple: int) -> int:
+    remainder = number % multiple
+    if remainder == 0:
+        return number
+    else:
+        factor = (number + multiple - 1) // multiple  # 向上取整的计算方式
+        return factor * multiple
+
 def calculate_side_by_ratio(orig_width:int, orig_height:int, ratio:float, longest_side:int=0) -> int:

    if orig_width > orig_height:
@@ -1,7 +1,7 @@
 [project]
 name = "comfyui_layerstyle"
 description = "A set of nodes for ComfyUI it generate image like Adobe Photoshop's Layer Style. the Drop Shadow is first completed node, and follow-up work is in progress."
-version = "1.0.8"
+version = "1.0.9"
 license = "MIT"
 dependencies = ["numpy", "pillow", "torch", "matplotlib", "Scipy", "scikit_image", "opencv-contrib-python", "pymatting", "segment_anything", "timm", "addict", "yapf", "colour-science", "wget", "mediapipe", "loguru", "typer_config", "fastapi", "rich", "google-generativeai", "diffusers", "omegaconf", "tqdm", "transformers", "kornia", "image-reward", "ultralytics", "blend_modes", "blind-watermark", "qrcode", "pyzbar", "psd-tools"]