Adds HSV Threshold Mask

actually update examples as well

README.md

@@ -21,14 +21,15 @@ Both images have the workflow attached, and are included with the repo. Feel fre
  - ArithmeticBlend: Blends two images using arithmetic operations like addition, subtraction, and difference.
  - Blend: Blends two images together with a variety of different modes
  - Blur: Applies a Gaussian blur to the input image, softening the details
- - CannyEdgeDetection: Applies Canny edge detection to the input image
+ - CannyEdgeMask: Creates a mask using canny edge detection
  - Chromatic Aberration: Shifts the color channels in an image, creating a glitch aesthetic
  - $\color{#00A7B5}\textbf{ColorCorrect:}$ Adjusts the color balance, temperature, hue, brightness, contrast, saturation, and gamma of an image
  - $\color{#00A7B5}\textbf{ColorTint:}$ Applies a customizable tint to the input image, with various color modes such as sepia, RGB, CMY and several composite colors
  - Dissolve: Creates a grainy blend of two images using random pixels based on a dissolve factor.
  - DodgeAndBurn: Adjusts image brightness using dodge and burn effects based on a mask and intensity.
- - FilmGrain: Adds a film grain effect to the image, along with options to control the temperature, and vignetting
+ - FilmGrain: Adds a film grain effect to the image, along with options to control the temperature, and vignetting.
  - Glow: Applies a blur with a specified radius and then blends it with the original image. Creates a nice glowing effect.
+ - HSVThresholdMask: Creates a mask by thresholding HSV (hue, saturation, and value) channels
  - $\color{#00A7B5}\textbf{KuwaharaBlur:}$ Applies an edge preserving blur, creating a more realistic blur than Gaussian.
  - Parabolize: Applies a color transformation effect using a parabolic formula
  - PencilSketch: Converts an image into a hand-drawn pencil sketch style.

combine_files.py

@@ -3,12 +3,13 @@ from pathlib import Path
 import argparse
 
 ignore_dirs = ["old"]
+ignore_files = ["__init__.py", "combine_files.py", "test.py"]
 
 def get_python_files(path, recursive=False, args=None):
     search_pattern = "**/*.py" if recursive else "*.py"
 
     def should_include(file):
-        if file.is_file() and not file.name.startswith("combine") and not args.output in str(file) and not file.name.startswith("__init__"):
+        if file.is_file() and not args.output in str(file) and not file.name in ignore_files:
             for ignore_dir in ignore_dirs:
                 if ignore_dir in str(file.parent):
                     return False

post_processing/canny_edge_mask.py

@@ -3,7 +3,7 @@ import numpy as np
 import torch
 
 
-class CannyEdgeDetection:
+class CannyEdgeMask:
     def __init__(self):
         pass
 
@@ -46,5 +46,5 @@ class CannyEdgeDetection:
         return (result,)
 
 NODE_CLASS_MAPPINGS = {
-    "CannyEdgeDetection": CannyEdgeDetection
+    "CannyEdgeMask": CannyEdgeMask
 }

post_processing/hsv_threshold_mask.py

@@ -0,0 +1,61 @@
+import cv2
+import torch
+import numpy as np
+
+class HSVThresholdMask:
+    def __init__(self):
+        pass
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "image": ("IMAGE",),
+                "low_threshold": ("FLOAT", {
+                    "default": 0.2,
+                    "min": 0,
+                    "max": 1,
+                    "step": 0.1
+                }),
+                "high_threshold": ("FLOAT", {
+                    "default": 0.7,
+                    "min": 0,
+                    "max": 1,
+                    "step": 0.1
+                }),
+                "hsv_channel": (["hue", "saturation", "value"],),
+            },
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "hsv_threshold"
+
+    CATEGORY = "postprocessing/Masks"
+
+    def hsv_threshold(self, image: torch.Tensor, low_threshold: float, high_threshold: float, hsv_channel: str):
+        batch_size, height, width, _ = image.shape
+        result = torch.zeros(batch_size, height, width)
+
+        if hsv_channel == "hue":
+            channel = 0
+            low_threshold, high_threshold = int(low_threshold * 180), int(high_threshold * 180)
+        elif hsv_channel == "saturation":
+            channel = 1
+            low_threshold, high_threshold = int(low_threshold * 255), int(high_threshold * 255)
+        elif hsv_channel == "value":
+            channel = 2
+            low_threshold, high_threshold = int(low_threshold * 255), int(high_threshold * 255)
+
+        for b in range(batch_size):
+            tensor_image = (image[b].numpy().copy() * 255).astype(np.uint8)
+            hsv_image = cv2.cvtColor(tensor_image, cv2.COLOR_RGB2HSV)
+
+            mask = cv2.inRange(hsv_image[:, :, channel], low_threshold, high_threshold)
+            tensor = torch.from_numpy(mask).float() / 255.
+            result[b] = tensor
+
+        return (result,)
+
+NODE_CLASS_MAPPINGS = {
+    "HSVThresholdMask": HSVThresholdMask,
+}

post_processing_nodes.py

@@ -166,7 +166,7 @@ class Blur:
 
         return (blurred,)
 
-class CannyEdgeDetection:
+class CannyEdgeMask:
     def __init__(self):
         pass
 
@@ -717,6 +717,60 @@ class Glow:
     def add_glow(self, img, blurred_img, intensity):
         return img + blurred_img * intensity
 
+class HSVThresholdMask:
+    def __init__(self):
+        pass
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "image": ("IMAGE",),
+                "low_threshold": ("FLOAT", {
+                    "default": 0.2,
+                    "min": 0,
+                    "max": 1,
+                    "step": 0.1
+                }),
+                "high_threshold": ("FLOAT", {
+                    "default": 0.7,
+                    "min": 0,
+                    "max": 1,
+                    "step": 0.1
+                }),
+                "hsv_channel": (["hue", "saturation", "value"],),
+            },
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "hsv_threshold"
+
+    CATEGORY = "postprocessing/Masks"
+
+    def hsv_threshold(self, image: torch.Tensor, low_threshold: float, high_threshold: float, hsv_channel: str):
+        batch_size, height, width, _ = image.shape
+        result = torch.zeros(batch_size, height, width)
+
+        if hsv_channel == "hue":
+            channel = 0
+            low_threshold, high_threshold = int(low_threshold * 180), int(high_threshold * 180)
+        elif hsv_channel == "saturation":
+            channel = 1
+            low_threshold, high_threshold = int(low_threshold * 255), int(high_threshold * 255)
+        elif hsv_channel == "value":
+            channel = 2
+            low_threshold, high_threshold = int(low_threshold * 255), int(high_threshold * 255)
+
+        for b in range(batch_size):
+            tensor_image = (image[b].numpy().copy() * 255).astype(np.uint8)
+            hsv_image = cv2.cvtColor(tensor_image, cv2.COLOR_RGB2HSV)
+
+            mask = cv2.inRange(hsv_image[:, :, channel], low_threshold, high_threshold)
+            tensor = torch.from_numpy(mask).float() / 255.
+            result[b] = tensor
+
+        return (result,)
+
 class KuwaharaBlur:
     def __init__(self):
         pass
@@ -1265,7 +1319,7 @@ NODE_CLASS_MAPPINGS = {
     "ArithmeticBlend": ArithmeticBlend,
     "Blend": Blend,
     "Blur": Blur,
-    "CannyEdgeDetection": CannyEdgeDetection,
+    "CannyEdgeMask": CannyEdgeMask,
     "ChromaticAberration": ChromaticAberration,
     "ColorCorrect": ColorCorrect,
     "ColorTint": ColorTint,
@@ -1273,6 +1327,7 @@ NODE_CLASS_MAPPINGS = {
     "DodgeAndBurn": DodgeAndBurn,
     "FilmGrain": FilmGrain,
     "Glow": Glow,
+    "HSVThresholdMask": HSVThresholdMask,
     "KuwaharaBlur": KuwaharaBlur,
     "Parabolize": Parabolize,
     "PencilSketch": PencilSketch,