update code

app.py

@@ -88,8 +88,8 @@ def calculate_sigmoid_focal_loss(inputs, targets, num_masks = 1, alpha: float =
 
 def inference(ic_image, ic_mask, image1, image2):
     # in context image and mask
-    ic_image = np.array(ic_image.convert("RGB"))
-    ic_mask = np.array(ic_mask.convert("RGB"))
+    ic_image = cv2.cvtColor(ic_image, cv2.COLOR_BGR2RGB)
+    ic_make = cv2.cvtColor(ic_image,cv2.COLOR_BGR2RGB)
 
     sam_type, sam_ckpt = 'vit_h', 'sam_vit_h_4b8939.pth'
     sam = sam_model_registry[sam_type](checkpoint=sam_ckpt).cuda()
@@ -114,7 +114,7 @@ def inference(ic_image, ic_mask, image1, image2):
 
     for test_image in [image1, image2]:
         print("======> Testing Image" )
-        test_image = np.array(test_image.convert("RGB"))
+        test_image = cv2.cvtColor(test_image, cv2.COLOR_BGR2RGB)
 
         # Image feature encoding
         predictor.set_image(test_image)
@@ -188,8 +188,8 @@ def inference_scribble(image, image1, image2):
     # in context image and mask
     ic_image = image["image"]
     ic_mask = image["mask"]
-    ic_image = np.array(ic_image.convert("RGB"))
-    ic_mask = np.array(ic_mask.convert("RGB"))
+    ic_image = cv2.cvtColor(ic_image, cv2.COLOR_BGR2RGB)
+    ic_make = cv2.cvtColor(ic_image,cv2.COLOR_BGR2RGB)
 
     sam_type, sam_ckpt = 'vit_h', 'sam_vit_h_4b8939.pth'
     sam = sam_model_registry[sam_type](checkpoint=sam_ckpt).cuda()
@@ -214,7 +214,7 @@ def inference_scribble(image, image1, image2):
 
     for test_image in [image1, image2]:
         print("======> Testing Image" )
-        test_image = np.array(test_image.convert("RGB"))
+        test_image = cv2.cvtColor(test_image, cv2.COLOR_BGR2RGB)
 
         # Image feature encoding
         predictor.set_image(test_image)
@@ -286,8 +286,8 @@ def inference_scribble(image, image1, image2):
 
 def inference_finetune(ic_image, ic_mask, image1, image2):
     # in context image and mask
-    ic_image = np.array(ic_image.convert("RGB"))
-    ic_mask = np.array(ic_mask.convert("RGB"))
+    ic_image = cv2.cvtColor(ic_image, cv2.COLOR_BGR2RGB)
+    ic_make = cv2.cvtColor(ic_image,cv2.COLOR_BGR2RGB)
 
     gt_mask = torch.tensor(ic_mask)[:, :, 0] > 0
     gt_mask = gt_mask.float().unsqueeze(0).flatten(1).cuda()
@@ -377,7 +377,7 @@ def inference_finetune(ic_image, ic_mask, image1, image2):
     output_image = []
 
     for test_image in [image1, image2]:
-        test_image = np.array(test_image.convert("RGB"))
+        test_image = cv2.cvtColor(test_image, cv2.COLOR_BGR2RGB)
 
         # Image feature encoding
         predictor.set_image(test_image)
@@ -542,3 +542,4 @@ with demo:
     )
 
 demo.launch(enable_queue=False)
+