fix bug of Ultra nodes, ExtendCanvas

py/extand_canvas.py

@@ -56,7 +56,8 @@ class ExtendCanvas:
                     m = 1 - m
                 l_masks.append(tensor2pil(torch.unsqueeze(m, 0)).convert('L'))
         else:
-            l_masks.append(Image.new('L', size=tensor2pil(l_images[0]).size, color='white'))
+            if len(l_masks) == 0:
+                l_masks.append(Image.new('L', size=tensor2pil(l_images[0]).size, color='white'))
 
         max_batch = max(len(l_images), len(l_masks))
         for i in range(max_batch):

py/mask_edge_ultrl_detail.py

@@ -49,6 +49,7 @@ class MaskEdgeUltraDetail:
         for i in range(len(l_images)):
             _image = l_images[i]
             orig_image = tensor2pil(_image).convert('RGB')
+            _image = pil2tensor(orig_image)
             _mask = l_masks[i]
             if mask_grow != 0:
                 _mask = expand_mask(_mask, mask_grow, mask_grow//2)

py/mask_edge_ultrl_detail_v2.py

@@ -50,6 +50,7 @@ class MaskEdgeUltraDetailV2:
         for i in range(len(l_images)):
             _image = l_images[i]
             orig_image = tensor2pil(_image).convert('RGB')
+            _image = pil2tensor(orig_image)
             _mask = l_masks[i]
             if mask_grow != 0:
                 _mask = expand_mask(_mask, mask_grow, mask_grow//2)

py/person_mask_Ultra.py

@@ -76,8 +76,9 @@ class PersonMaskUltra:
                 # image = torch.unsqueeze(image, 0)
                 orig_image = tensor2pil(image.unsqueeze(0)).convert('RGB')
                 # Convert the Tensor to a PIL image
-                i = 255. * image.cpu().numpy()
-                image_pil = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8))
+                # i = 255. * image.cpu().numpy()
+                # image_pil = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8))
+                image_pil = tensor2pil(image.unsqueeze(0)).convert('RGB')
                 # create our foreground and background arrays for storing the mask results
                 mask_background_array = np.zeros((image_pil.size[0], image_pil.size[1], 4), dtype=np.uint8)
                 mask_background_array[:] = (0, 0, 0, 255)
@@ -127,7 +128,7 @@ class PersonMaskUltra:
                 tensor_mask = tensor_mask.squeeze(3)[..., 0]
                 _mask = tensor2pil(tensor_mask).convert('L')
                 if process_detail:
-                    _mask = tensor2pil(mask_edge_detail(image.unsqueeze(0), pil2tensor(_mask), detail_range, black_point, white_point))
+                    _mask = tensor2pil(mask_edge_detail(pil2tensor(orig_image), pil2tensor(_mask), detail_range, black_point, white_point))
                 ret_image = RGB2RGBA(orig_image, _mask)
                 ret_images.append(pil2tensor(ret_image))
                 ret_masks.append(image2mask(_mask))

py/person_mask_ultra_v2.py

@@ -134,11 +134,11 @@ class PersonMaskUltraV2:
                 detail_range = detail_erode + detail_dilate
                 if process_detail:
                     if detail_method == 'GuidedFilter':
-                        _mask = guided_filter_alpha(_image, _mask, detail_range // 6 + 1)
+                        _mask = guided_filter_alpha(pil2tensor(orig_image), _mask, detail_range // 6 + 1)
                         _mask = tensor2pil(histogram_remap(_mask, black_point, white_point))
                     elif detail_method == 'PyMatting':
                         _mask = tensor2pil(
-                            mask_edge_detail(_image, _mask,
+                            mask_edge_detail(pil2tensor(orig_image), _mask,
                                              detail_range // 8 + 1, black_point, white_point))
                     else:
                         _trimap = generate_VITMatte_trimap(_mask, detail_erode, detail_dilate)

py/rembg_ultra.py

@@ -32,6 +32,7 @@ class RemBgUltra:
 
         for i in image:
             i = torch.unsqueeze(i, 0)
+            i = pil2tensor(tensor2pil(i).convert('RGB'))
             orig_image = tensor2pil(i).convert('RGB')
             _mask = RMBG(orig_image)
             if process_detail:

py/rembg_ultra_v2.py

@@ -37,6 +37,7 @@ class RmBgUltraV2:
 
         for i in image:
             i = torch.unsqueeze(i, 0)
+            i = pil2tensor(tensor2pil(i).convert('RGB'))
             orig_image = tensor2pil(i).convert('RGB')
             _mask = RMBG(orig_image)
             _mask = pil2tensor(_mask)

py/segment_anything_ultra.py

@@ -47,6 +47,7 @@ class SegmentAnythingUltra:
 
         for i in image:
             i = torch.unsqueeze(i, 0)
+            i = pil2tensor(tensor2pil(i).convert('RGB'))
             item = tensor2pil(i).convert('RGBA')
             boxes = groundingdino_predict(DINO_MODEL, item, prompt, threshold)
             if boxes.shape[0] == 0:

py/segment_anything_ultra_v2.py

@@ -61,6 +61,7 @@ class SegmentAnythingUltraV2:
 
         for i in image:
             i = torch.unsqueeze(i, 0)
+            i = pil2tensor(tensor2pil(i).convert('RGB'))
             _image = tensor2pil(i).convert('RGBA')
             boxes = groundingdino_predict(DINO_MODEL, _image, prompt, threshold)
             if boxes.shape[0] == 0: