Update utils.py

utils.py

@@ -568,7 +568,7 @@ def display_images(images, titles=None, cols=4, cmap=None, norm=None,
     norm: Optional. A Normalize instance to map values to colors.
     interpolation: Optional. Image interpolation to use for display.
     """
-    image = np.array(images)
+
     titles = titles if titles is not None else [""] * len(images) 
     rows = len(images) // cols + 1
     plt.figure(figsize=(14, 14 * rows // cols))
@@ -698,6 +698,25 @@ def display_instances(image, boxes, masks, class_ids, class_names,
     if auto_show:
         plt.show()
 
+def pred_images_from_folder(directory):
+  for filename in os.listdir(directory):
+    if filename.endswith(".png"): 
+          print(os.path.join(directory, filename))
+          path = os.path.join(directory, filename)
+          augs = torchvision.transforms.Compose([torchvision.transforms.ToTensor()])
+          img = np.array(Image.open(path).convert("RGB"))
+          img, window, scale, padding, crop = resize_image(
+        img, [img.shape[1], img.shape[0]],
+        min_dim=None,
+        min_scale=None,
+        max_dim=384)
+          img = augs(img)
+          pred_mask, pred_boxes, class_ids, pred_score = get_prediction(model, img, threshold)
+          masked_image = display_instances(np.array(img.mul(255)).transpose(1,2,0), pred_boxes, pred_mask, class_ids, dataset_train.class_names, auto_show=False)     
+          continue
+    else:
+        continue
+        
 def extract_bboxes(mask):
     """Compute bounding boxes from masks.
     mask: [height, width, num_instances]. Mask pixels are either 1 or 0.