Adds HSV Threshold Mask

actually update examples as well

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,