update visualization func

grounded_sam2_tracking_demo_with_continuous_id.py

@@ -29,7 +29,7 @@ if torch.cuda.get_device_properties(0).major >= 8:
 sam2_checkpoint = "./checkpoints/sam2_hiera_large.pt"
 model_cfg = "sam2_hiera_l.yaml"
 device = "cuda" if torch.cuda.is_available() else "cpu"
-print("device",device)
+print("device", device)
 
 video_predictor = build_sam2_video_predictor(model_cfg, sam2_checkpoint)
 sam2_image_model = build_sam2(model_cfg, sam2_checkpoint, device=device)
@@ -189,10 +189,9 @@ for start_frame_idx in range(0, len(frame_names), step):
             json.dump(json_data, f)
 
 
-
 """
 Step 6: Draw the results and save the video
 """
-CommonUtils.draw_masks_and_box(video_dir, mask_data_dir, json_data_dir, result_dir)
+CommonUtils.draw_masks_and_box_with_supervision(video_dir, mask_data_dir, json_data_dir, result_dir)
 
 create_video_from_images(result_dir, output_video_path, frame_rate=30)

utils/common_utils.py

@@ -3,6 +3,7 @@ import json
 import cv2
 import numpy as np
 from dataclasses import dataclass
+import supervision as sv
 import random
 
 class CommonUtils:
@@ -21,7 +22,90 @@ class CommonUtils:
                 print(f"Path '{path}' already exists.")
         except Exception as e:
             print(f"An error occurred while creating the path: {e}")
-    
+
+    @staticmethod
+    def draw_masks_and_box_with_supervision(raw_image_path, mask_path, json_path, output_path):
+        CommonUtils.creat_dirs(output_path)
+        raw_image_name_list = os.listdir(raw_image_path)
+        raw_image_name_list.sort()
+        for raw_image_name in raw_image_name_list:
+            image_path = os.path.join(raw_image_path, raw_image_name)
+            image = cv2.imread(image_path)
+            if image is None:
+                raise FileNotFoundError("Image file not found.")
+            # load mask
+            mask_npy_path = os.path.join(mask_path, "mask_"+raw_image_name.split(".")[0]+".npy")
+            mask = np.load(mask_npy_path)
+            # color map
+            unique_ids = np.unique(mask)
+            
+            # get each mask from unique mask file
+            all_object_masks = []
+            for uid in unique_ids:
+                if uid == 0: # skip background id
+                    continue
+                else:
+                    object_mask = (mask == uid)
+                    all_object_masks.append(object_mask[None])
+            
+            # get n masks: (n, h, w)
+            all_object_masks = np.concatenate(all_object_masks, axis=0)
+            
+            # load box information
+            file_path = os.path.join(json_path, "mask_"+raw_image_name.split(".")[0]+".json")
+            
+            all_object_boxes = []
+            all_object_ids = []
+            all_class_names = []
+            object_id_to_name = {}
+            with open(file_path, "r") as file:
+                json_data = json.load(file)
+                for obj_id, obj_item in json_data["labels"].items():
+                    # box id
+                    instance_id = obj_item["instance_id"]
+                    if instance_id not in unique_ids: # not a valid box
+                        continue
+                    # box coordinates
+                    x1, y1, x2, y2 = obj_item["x1"], obj_item["y1"], obj_item["x2"], obj_item["y2"]
+                    all_object_boxes.append([x1, y1, x2, y2])
+                    # box name
+                    class_name = obj_item["class_name"]
+                    
+                    # build id list and id2name mapping
+                    all_object_ids.append(instance_id)
+                    all_class_names.append(class_name)
+                    object_id_to_name[instance_id] = class_name
+            
+            # Adjust object id and boxes to ascending order
+            paired_id_and_box = zip(all_object_ids, all_object_boxes)
+            sorted_pair = sorted(paired_id_and_box, key=lambda pair: pair[0])
+            
+            # Because we get the mask data as ascending order, so we also need to ascend box and ids
+            all_object_ids = [pair[0] for pair in sorted_pair]
+            all_object_boxes = [pair[1] for pair in sorted_pair]
+            
+            detections = sv.Detections(
+                xyxy=np.array(all_object_boxes),
+                mask=all_object_masks,
+                class_id=np.array(all_object_ids, dtype=np.int32),
+            )
+            
+            # custom label to show both id and class name
+            labels = [
+                f"{instance_id}: {class_name}" for instance_id, class_name in zip(all_object_ids, all_class_names)
+            ]
+            
+            box_annotator = sv.BoxAnnotator()
+            annotated_frame = box_annotator.annotate(scene=image.copy(), detections=detections)
+            label_annotator = sv.LabelAnnotator()
+            annotated_frame = label_annotator.annotate(annotated_frame, detections=detections, labels=labels)
+            mask_annotator = sv.MaskAnnotator()
+            annotated_frame = mask_annotator.annotate(scene=annotated_frame, detections=detections)
+            
+            output_image_path = os.path.join(output_path, raw_image_name)
+            cv2.imwrite(output_image_path, annotated_frame)
+            print(f"Annotated image saved as {output_image_path}")
+
     @staticmethod
     def draw_masks_and_box(raw_image_path, mask_path, json_path, output_path):
         CommonUtils.creat_dirs(output_path)
@@ -40,7 +124,7 @@ class CommonUtils:
             colors = {uid: CommonUtils.random_color() for uid in unique_ids}
             colors[0] = (0, 0, 0)  # background color
 
-            # apply mask to image
+            # apply mask to image in RBG channels
             colored_mask = np.zeros_like(image)
             for uid in unique_ids:
                 colored_mask[mask == uid] = colors[uid]