add more AQnodes

nodes/filters/AQ_filters.py

@@ -1,5 +1,5 @@
 import torch
-
+import cv2
 
 class AQ_BatchAverageImage:
     @classmethod
@@ -82,6 +82,75 @@ class AQ_ColorMatchImage:
                     t[i, :, :, c] /= blurred[i, :, :, c]
                     t[i, :, :, c] *= blurred_ref[i, :, :, c]
 
+
         t = t - 0.1
         torch.clamp(torch.lerp(images, t, factor), 0, 1)
         return (t,)
+# guided filter a tensor image batch in format [B x H x W x C] on H/W (spatial, per-image, per-channel)
+def guided_filter_tensor(ref, images, d, s):
+    try:
+        from cv2.ximgproc import guidedFilter
+    except ImportError:
+        print("\033[33mUnable to import guidedFilter, make sure you have only opencv-contrib-python or run the import_error_install.bat script\033[m")
+
+    if d > 100:
+        np_img = (
+            torch.nn.functional.interpolate(
+                images.detach().clone().movedim(-1, 1),
+                scale_factor=0.1,
+                mode="bilinear",
+            )
+            .movedim(1, -1)
+            .cpu()
+            .numpy()
+        )
+        np_ref = (
+            torch.nn.functional.interpolate(
+                ref.detach().clone().movedim(-1, 1), scale_factor=0.1, mode="bilinear"
+            )
+            .movedim(1, -1)
+            .cpu()
+            .numpy()
+        )
+        for index, image in enumerate(np_img):
+            np_img[index] = guidedFilter(np_ref[index], image, d // 20 * 2 + 1, s)
+        return torch.nn.functional.interpolate(
+            torch.from_numpy(np_img).movedim(-1, 1),
+            size=(images.shape[1], images.shape[2]),
+            mode="bilinear",
+        ).movedim(1, -1)
+    else:
+        np_img = images.detach().clone().cpu().numpy()
+        np_ref = ref.cpu().numpy()
+        for index, image in enumerate(np_img):
+            np_img[index] = guidedFilter(np_ref[index], image, d, s)
+        return torch.from_numpy(np_img)
+
+
+# gaussian blur a tensor image batch in format [B x H x W x C] on H/W (spatial, per-image, per-channel)
+def cv_blur_tensor(images, dx, dy):
+    if min(dx, dy) > 100:
+        np_img = (
+            torch.nn.functional.interpolate(
+                images.detach().clone().movedim(-1, 1),
+                scale_factor=0.1,
+                mode="bilinear",
+            )
+            .movedim(1, -1)
+            .cpu()
+            .numpy()
+        )
+        for index, image in enumerate(np_img):
+            np_img[index] = cv2.GaussianBlur(
+                image, (dx // 20 * 2 + 1, dy // 20 * 2 + 1), 0
+            )
+        return torch.nn.functional.interpolate(
+            torch.from_numpy(np_img).movedim(-1, 1),
+            size=(images.shape[1], images.shape[2]),
+            mode="bilinear",
+        ).movedim(1, -1)
+    else:
+        np_img = images.detach().clone().cpu().numpy()
+        for index, image in enumerate(np_img):
+            np_img[index] = cv2.GaussianBlur(image, (dx, dy), 0)
+        return torch.from_numpy(np_img)