add combined_nodes file and generator

2023-03-30 20:30:01 -04:00
parent 228c85c450
commit 4e5c8b106f
3 changed files with 210 additions and 1 deletions
@@ -0,0 +1,92 @@
+import cv2
+import numpy as np
+import torch
+
+
+class CannyEdgeDetection:
+    def __init__(self):
+        pass
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "image": ("IMAGE",),
+                "lower_threshold": ("INT", {
+                    "default": 100,
+                    "min": 0,
+                    "max": 500,
+                    "step": 10
+                }),
+                "upper_threshold": ("INT", {
+                    "default": 200,
+                    "min": 0,
+                    "max": 500,
+                    "step": 10
+                }),
+            },
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "canny"
+
+    CATEGORY = "postprocessing"
+
+    def canny(self, image: torch.Tensor, lower_threshold: int, upper_threshold: int):
+        batch_size, height, width, _ = image.shape
+        result = torch.zeros(batch_size, height, width)
+
+        for b in range(batch_size):
+            tensor_image = image[b].numpy().copy()
+            gray_image = (cv2.cvtColor(tensor_image, cv2.COLOR_RGB2GRAY) * 255).astype(np.uint8)
+            canny = cv2.Canny(gray_image, lower_threshold, upper_threshold)
+            tensor = torch.from_numpy(canny)
+            result[b] = tensor
+
+        return (result,)
+
+class GaussianBlur:
+    def __init__(self):
+        pass
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "image": ("IMAGE",),
+                "kernel_size": ("INT", {
+                    "default": 5,
+                    "min": 1,
+                    "max": 31,
+                    "step": 1
+                }),
+                "sigma": ("FLOAT", {
+                    "default": 1.0,
+                    "min": 0.1,
+                    "max": 10.0,
+                    "step": 0.1
+                }),
+            },
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "blur"
+
+    CATEGORY = "postprocessing"
+
+    def blur(self, image: torch.Tensor, kernel_size: int, sigma: float):
+        batch_size, height, width, _ = image.shape
+        result = torch.zeros_like(image)
+
+        for b in range(batch_size):
+            tensor_image = image[b].numpy()
+            blurred = cv2.GaussianBlur(tensor_image, (kernel_size, kernel_size), sigma)
+            tensor = torch.from_numpy(blurred).unsqueeze(0)
+            result[b] = tensor
+
+        return (result,)
+
+NODE_CLASS_MAPPINGS = {
+    "CannyEdgeDetection": CannyEdgeDetection,
+    "GaussianBlur": GaussianBlur,
+}