sort function

handler.py

@@ -180,6 +180,37 @@ class EndpointHandler():
         def convert_from_image_to_cv2(img: Image) -> np.ndarray:
             return cv2.cvtColor(np.array(img), cv2.COLOR_RGB2BGR)
 
+        def resize_img(
+            input_image,
+            max_side=1280,
+            min_side=1024,
+            size=None,
+            pad_to_max_side=False,
+            mode=PIL.Image.BILINEAR,
+            base_pixel_number=64,
+        ):
+            w, h = input_image.size
+            if size is not None:
+                w_resize_new, h_resize_new = size
+            else:
+                ratio = min_side / min(h, w)
+                w, h = round(ratio * w), round(ratio * h)
+                ratio = max_side / max(h, w)
+                input_image = input_image.resize([round(ratio * w), round(ratio * h)], mode)
+                w_resize_new = (round(ratio * w) // base_pixel_number) * base_pixel_number
+                h_resize_new = (round(ratio * h) // base_pixel_number) * base_pixel_number
+            input_image = input_image.resize([w_resize_new, h_resize_new], mode)
+
+            if pad_to_max_side:
+                res = np.ones([max_side, max_side, 3], dtype=np.uint8) * 255
+                offset_x = (max_side - w_resize_new) // 2
+                offset_y = (max_side - h_resize_new) // 2
+                res[
+                    offset_y : offset_y + h_resize_new, offset_x : offset_x + w_resize_new
+                ] = np.array(input_image)
+                input_image = Image.fromarray(res)
+            return input_image        
+
         # check if the input is valid
         # if face_image_path is None:
         #     raise gr.Error(
@@ -215,37 +246,6 @@ class EndpointHandler():
         ]  # only use the maximum face
         
         
-        def resize_img(
-            input_image,
-            max_side=1280,
-            min_side=1024,
-            size=None,
-            pad_to_max_side=False,
-            mode=PIL.Image.BILINEAR,
-            base_pixel_number=64,
-        ):
-            w, h = input_image.size
-            if size is not None:
-                w_resize_new, h_resize_new = size
-            else:
-                ratio = min_side / min(h, w)
-                w, h = round(ratio * w), round(ratio * h)
-                ratio = max_side / max(h, w)
-                input_image = input_image.resize([round(ratio * w), round(ratio * h)], mode)
-                w_resize_new = (round(ratio * w) // base_pixel_number) * base_pixel_number
-                h_resize_new = (round(ratio * h) // base_pixel_number) * base_pixel_number
-            input_image = input_image.resize([w_resize_new, h_resize_new], mode)
-
-            if pad_to_max_side:
-                res = np.ones([max_side, max_side, 3], dtype=np.uint8) * 255
-                offset_x = (max_side - w_resize_new) // 2
-                offset_y = (max_side - h_resize_new) // 2
-                res[
-                    offset_y : offset_y + h_resize_new, offset_x : offset_x + w_resize_new
-                ] = np.array(input_image)
-                input_image = Image.fromarray(res)
-            return input_image        
-        
         face_emb = face_info["embedding"]
         face_kps = draw_kps(convert_from_cv2_to_image(face_image_cv2), face_info["kps"])
         img_controlnet = face_image