cleaned up preprocess

preprocess.py

@@ -1,30 +1,28 @@
-import gc
-
-import numpy as np
+# import numpy as np
 import PIL.Image
-import torch
+# import torch
 from controlnet_aux import NormalBaeDetector#, CannyDetector
 
-from controlnet_aux.util import HWC3
-import cv2
+# from controlnet_aux.util import HWC3
+# import cv2
 # from cv_utils import resize_image
 
 class Preprocessor:
     MODEL_ID = "lllyasviel/Annotators"
     
-    def resize_image(input_image, resolution, interpolation=None):
-        H, W, C = input_image.shape
-        H = float(H)
-        W = float(W)
-        k = float(resolution) / max(H, W)
-        H *= k
-        W *= k
-        H = int(np.round(H / 64.0)) * 64
-        W = int(np.round(W / 64.0)) * 64
-        if interpolation is None:
-            interpolation = cv2.INTER_LANCZOS4 if k > 1 else cv2.INTER_AREA
-        img = cv2.resize(input_image, (W, H), interpolation=interpolation)
-        return img
+    # def resize_image(input_image, resolution, interpolation=None):
+    #     H, W, C = input_image.shape
+    #     H = float(H)
+    #     W = float(W)
+    #     k = float(resolution) / max(H, W)
+    #     H *= k
+    #     W *= k
+    #     H = int(np.round(H / 64.0)) * 64
+    #     W = int(np.round(W / 64.0)) * 64
+    #     if interpolation is None:
+    #         interpolation = cv2.INTER_LANCZOS4 if k > 1 else cv2.INTER_AREA
+    #     img = cv2.resize(input_image, (W, H), interpolation=interpolation)
+    #     return img
 
 
     def __init__(self):
@@ -33,7 +31,6 @@ class Preprocessor:
 
     def load(self, name: str) -> None:
         if name == self.name:
-            print("NormalBae already loaded")
             return
         elif name == "NormalBae":
             print("Loading NormalBae")
@@ -48,23 +45,23 @@ class Preprocessor:
         self.name = name
 
     def __call__(self, image: PIL.Image.Image, **kwargs) -> PIL.Image.Image:
-        if self.name == "Canny":
-            if "detect_resolution" in kwargs:
-                detect_resolution = kwargs.pop("detect_resolution")
-                image = np.array(image)
-                image = HWC3(image)
-                image = resize_image(image, resolution=detect_resolution)
-            image = self.model(image, **kwargs)
-            return PIL.Image.fromarray(image)
-        elif self.name == "Midas":
-            detect_resolution = kwargs.pop("detect_resolution", 512)
-            image_resolution = kwargs.pop("image_resolution", 512)
-            image = np.array(image)
-            image = HWC3(image)
-            image = resize_image(image, resolution=detect_resolution)
-            image = self.model(image, **kwargs)
-            image = HWC3(image)
-            image = resize_image(image, resolution=image_resolution)
-            return PIL.Image.fromarray(image)
-        else:
-            return self.model(image, **kwargs)
+        # if self.name == "Canny":
+        #     if "detect_resolution" in kwargs:
+        #         detect_resolution = kwargs.pop("detect_resolution")
+        #         image = np.array(image)
+        #         image = HWC3(image)
+        #         image = resize_image(image, resolution=detect_resolution)
+        #     image = self.model(image, **kwargs)
+        #     return PIL.Image.fromarray(image)
+        # elif self.name == "Midas":
+        #     detect_resolution = kwargs.pop("detect_resolution", 512)
+        #     image_resolution = kwargs.pop("image_resolution", 512)
+        #     image = np.array(image)
+        #     image = HWC3(image)
+        #     image = resize_image(image, resolution=detect_resolution)
+        #     image = self.model(image, **kwargs)
+        #     image = HWC3(image)
+        #     image = resize_image(image, resolution=image_resolution)
+        #     return PIL.Image.fromarray(image)
+        # else:
+        return self.model(image, **kwargs)