update dds-cloudapi-sdk to 0.3.3
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@@ -21,7 +21,7 @@ Grounded SAM 2 does not introduce significant methodological changes compared to
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## Latest updates
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- `2024/12/02`: Support **DINO-X SAM 2 Demos** (including object segmentation and tracking), please install the latest version of `dds-cloudapi-sdk` and refer to [Grounded SAM 2 (with DINO-X)](#grounded-sam-2-image-demo-with-dino-x) and [Grounded SAM 2 Video (with DINO-X)](#grounded-sam-2-video-object-tracking-demo-with-custom-video-input-with-dino-x) for more details.
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- `2024/12/02`: Support **DINO-X with SAM 2** demos (including object segmentation and tracking), please install the latest version of `dds-cloudapi-sdk==0.3.3` and refer to [Grounded SAM 2 (with DINO-X)](#grounded-sam-2-image-demo-with-dino-x) and [Grounded SAM 2 Video (with DINO-X)](#grounded-sam-2-video-object-tracking-demo-with-custom-video-input-with-dino-x) for more details.
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- `2024/10/24`: Support [SAHI (Slicing Aided Hyper Inference)](https://docs.ultralytics.com/guides/sahi-tiled-inference/) on Grounded SAM 2 (with Grounding DINO 1.5) which may be helpful for inferencing high resolution image with dense small objects (e.g. **4K** images).
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- `2024/10/10`: Support `SAM-2.1` models, if you want to use `SAM 2.1` model, you need to update to the latest code and reinstall SAM 2 follow [SAM 2.1 Installation](https://github.com/facebookresearch/sam2?tab=readme-ov-file#latest-updates).
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- `2024/08/31`: Support `dump json results` in Grounded SAM 2 Image Demos (with Grounding DINO).
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@@ -2,6 +2,7 @@
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from dds_cloudapi_sdk import Config
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from dds_cloudapi_sdk import Client
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from dds_cloudapi_sdk.tasks.dinox import DinoxTask
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from dds_cloudapi_sdk.tasks.types import DetectionTarget
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from dds_cloudapi_sdk import TextPrompt
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import os
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@@ -64,7 +65,9 @@ if WITH_SLICE_INFERENCE:
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image_url = client.upload_file(temp_filename)
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task = DinoxTask(
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image_url=image_url,
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prompts=[TextPrompt(text=TEXT_PROMPT)]
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prompts=[TextPrompt(text=TEXT_PROMPT)],
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bbox_threshold=0.25,
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targets=[DetectionTarget.BBox],
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)
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client.run_task(task)
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result = task.result
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@@ -103,7 +106,9 @@ else:
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task = DinoxTask(
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image_url=image_url,
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prompts=[TextPrompt(text=TEXT_PROMPT)]
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prompts=[TextPrompt(text=TEXT_PROMPT)],
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bbox_threshold=0.25,
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targets=[DetectionTarget.BBox],
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)
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client.run_task(task)
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@@ -2,6 +2,7 @@
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from dds_cloudapi_sdk import Config
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from dds_cloudapi_sdk import Client
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from dds_cloudapi_sdk.tasks.dinox import DinoxTask
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from dds_cloudapi_sdk.tasks.types import DetectionTarget
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from dds_cloudapi_sdk import TextPrompt
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import os
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@@ -26,7 +27,7 @@ TEXT_PROMPT = "hippopotamus."
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OUTPUT_VIDEO_PATH = "./hippopotamus_tracking_demo.mp4"
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SOURCE_VIDEO_FRAME_DIR = "./custom_video_frames"
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SAVE_TRACKING_RESULTS_DIR = "./tracking_results"
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API_TOKEN_FOR_GD1_5 = "Your API token"
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API_TOKEN_FOR_DINOX = "Your API token"
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PROMPT_TYPE_FOR_VIDEO = "box" # choose from ["point", "box", "mask"]
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BOX_THRESHOLD = 0.2
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@@ -92,7 +93,7 @@ img_path = os.path.join(SOURCE_VIDEO_FRAME_DIR, frame_names[ann_frame_idx])
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image = Image.open(img_path)
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# Step 1: initialize the config
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config = Config(API_TOKEN_FOR_GD1_5)
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config = Config(API_TOKEN_FOR_DINOX)
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# Step 2: initialize the client
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client = Client(config)
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@@ -104,7 +105,9 @@ image_url = client.upload_file(img_path)
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task = DinoxTask(
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image_url=image_url,
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prompts=[TextPrompt(text=TEXT_PROMPT)]
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prompts=[TextPrompt(text=TEXT_PROMPT)],
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bbox_threshold=0.25,
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targets=[DetectionTarget.BBox],
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)
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client.run_task(task)
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