update DINO-X api to V2

grounded_sam2_gd1.5_demo.py

@@ -1,10 +1,7 @@
-# dds cloudapi for Grounding DINO 1.5
+# dds cloudapi for Grounding DINO 1.5 - update to V2Task API
 from dds_cloudapi_sdk import Config
 from dds_cloudapi_sdk import Client
-from dds_cloudapi_sdk import DetectionTask
-from dds_cloudapi_sdk import TextPrompt
-from dds_cloudapi_sdk import DetectionModel
-from dds_cloudapi_sdk import DetectionTarget
+from dds_cloudapi_sdk.tasks.v2_task import V2Task
 
 import os
 import cv2
@@ -27,8 +24,9 @@ TEXT_PROMPT = "car . building ."
 IMG_PATH = "notebooks/images/cars.jpg"
 SAM2_CHECKPOINT = "./checkpoints/sam2.1_hiera_large.pt"
 SAM2_MODEL_CONFIG = "configs/sam2.1/sam2.1_hiera_l.yaml"
-GROUNDING_MODEL = DetectionModel.GDino1_5_Pro # DetectionModel.GDino1_6_Pro
+GROUNDING_MODEL = "GroundingDino-1.5-Pro"  # GroundingDino-1.6-Pro
 BOX_THRESHOLD = 0.2
+IOU_THRESHOLD = 0.8
 WITH_SLICE_INFERENCE = False
 SLICE_WH = (480, 480)
 OVERLAP_RATIO = (0.2, 0.2)
@@ -49,8 +47,7 @@ config = Config(token)
 # Step 2: initialize the client
 client = Client(config)
 
-# Step 3: run the task by DetectionTask class
-# image_url = "https://algosplt.oss-cn-shenzhen.aliyuncs.com/test_files/tasks/detection/iron_man.jpg"
+# Step 3: run the task using V2Task API
 # if you are processing local image file, upload them to DDS server to get the image url
 
 classes = [x.strip().lower() for x in TEXT_PROMPT.split('.') if x]
@@ -65,26 +62,33 @@ if WITH_SLICE_INFERENCE:
             temp_filename = tmpfile.name
         cv2.imwrite(temp_filename, image_slice)
         image_url = client.upload_file(temp_filename)
-        task = DetectionTask(
-            image_url=image_url,
-            prompts=[TextPrompt(text=TEXT_PROMPT)],
-            targets=[DetectionTarget.BBox],  # detect bbox
-            model=GROUNDING_MODEL,  # detect with GroundingDino-1.5-Pro model
-            bbox_threshold=BOX_THRESHOLD, # box confidence threshold
+        task = V2Task(
+            api_path="/v2/task/grounding_dino/detection",
+            api_body={
+                "model": GROUNDING_MODEL,
+                "image": image_url,
+                "prompt": {
+                    "type": "text",
+                    "text": TEXT_PROMPT
+                },
+                "targets": ["bbox"],
+                "bbox_threshold": BOX_THRESHOLD,
+                "iou_threshold": IOU_THRESHOLD,
+            }
         )
         client.run_task(task)
         result = task.result
-        # detele the tempfile
+        # delete the tempfile
         os.remove(temp_filename)
         
         input_boxes = []
         confidences = []
         class_ids = []
-        objects = result.objects
+        objects = result["objects"]
         for idx, obj in enumerate(objects):
-            input_boxes.append(obj.bbox)
-            confidences.append(obj.score)
-            cls_name = obj.category.lower().strip()
+            input_boxes.append(obj["bbox"])
+            confidences.append(obj["score"])
+            cls_name = obj["category"].lower().strip()
             class_ids.append(class_name_to_id[cls_name])
         # ensure input_boxes with shape (_, 4)
         input_boxes = np.array(input_boxes).reshape(-1, 4)
@@ -96,7 +100,7 @@ if WITH_SLICE_INFERENCE:
         callback=callback,
         slice_wh=SLICE_WH,
         overlap_ratio_wh=OVERLAP_RATIO,
-        iou_threshold=0.5,
+        iou_threshold=IOU_THRESHOLD,
         overlap_filter_strategy=sv.OverlapFilter.NON_MAX_SUPPRESSION
         )
     detections = slicer(cv2.imread(IMG_PATH))
@@ -107,18 +111,25 @@ if WITH_SLICE_INFERENCE:
 else:
     image_url = client.upload_file(IMG_PATH)
 
-    task = DetectionTask(
-        image_url=image_url,
-        prompts=[TextPrompt(text=TEXT_PROMPT)],
-        targets=[DetectionTarget.BBox],  # detect bbox
-        model=GROUNDING_MODEL,  # detect with GroundingDINO-1.5-Pro model
-        bbox_threshold=BOX_THRESHOLD, # box confidence threshold
+    task = V2Task(
+        api_path="/v2/task/grounding_dino/detection",
+        api_body={
+            "model": GROUNDING_MODEL,
+            "image": image_url,
+            "prompt": {
+                "type": "text",
+                "text": TEXT_PROMPT
+            },
+            "targets": ["bbox"],
+            "bbox_threshold": BOX_THRESHOLD,
+            "iou_threshold": IOU_THRESHOLD,
+        }
     )
 
     client.run_task(task)
     result = task.result
 
-    objects = result.objects  # the list of detected objects
+    objects = result["objects"]  # the list of detected objects
 
 
     input_boxes = []
@@ -127,9 +138,9 @@ else:
     class_ids = []
 
     for idx, obj in enumerate(objects):
-        input_boxes.append(obj.bbox)
-        confidences.append(obj.score)
-        cls_name = obj.category.lower().strip()
+        input_boxes.append(obj["bbox"])
+        confidences.append(obj["score"])
+        cls_name = obj["category"].lower().strip()
         class_names.append(cls_name)
         class_ids.append(class_name_to_id[cls_name])