diff --git a/README.md b/README.md index 3375916..3cb30ff 100644 --- a/README.md +++ b/README.md @@ -21,7 +21,7 @@ Grounded SAM 2 does not introduce significant methodological changes compared to ## Latest updates -- `2024/12/02`: Support **DINO-X SAM 2 Demos**, 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) for more details. +- `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. - `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). - `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). - `2024/08/31`: Support `dump json results` in Grounded SAM 2 Image Demos (with Grounding DINO). @@ -41,6 +41,7 @@ Grounded SAM 2 does not introduce significant methodological changes compared to - [Grounded SAM 2 Video Object Tracking Demo (with Grounding DINO 1.5 & 1.6)](#grounded-sam-2-video-object-tracking-demo-with-grounding-dino-15--16) - [Grounded SAM 2 Video Object Tracking with Custom Video Input (using Grounding DINO)](#grounded-sam-2-video-object-tracking-demo-with-custom-video-input-with-grounding-dino) - [Grounded SAM 2 Video Object Tracking with Custom Video Input (using Grounding DINO 1.5 & 1.6)](#grounded-sam-2-video-object-tracking-demo-with-custom-video-input-with-grounding-dino-15--16) + - [Grounded SAM 2 Video Object Tracking Demo (with DINO-X)](#grounded-sam-2-video-object-tracking-demo-with-custom-video-input-with-dino-x) - [Grounded SAM 2 Video Object Tracking with Continues ID (using Grounding DINO)](#grounded-sam-2-video-object-tracking-with-continuous-id-with-grounding-dino) - [Grounded SAM 2 Florence-2 Demos](#grounded-sam-2-florence-2-demos) - [Grounded SAM 2 Florence-2 Image Demo](#grounded-sam-2-florence-2-image-demo) @@ -280,6 +281,14 @@ And we will automatically save the tracking visualization results in `OUTPUT_VID > [!WARNING] > We initialize the box prompts on the first frame of the input video. If you want to start from different frame, you can refine `ann_frame_idx` by yourself in our code. +### Grounded SAM 2 Video Object Tracking Demo with Custom Video Input (with DINO-X) + +Users can upload their own video file (e.g. `assets/hippopotamus.mp4`) and specify their custom text prompts for grounding and tracking with DINO-X and SAM 2 by using the following scripts: + +```bash +python grounded_sam2_tracking_demo_custom_video_input_dinox.py +``` + ### Grounded-SAM-2 Video Object Tracking with Continuous ID (with Grounding DINO) In above demos, we only prompt Grounded SAM 2 in specific frame, which may not be friendly to find new object during the whole video. In this demo, we try to **find new objects** and assign them with new ID across the whole video, this function is **still under develop**. it's not that stable now. diff --git a/grounded_sam2_tracking_demo_custom_video_input_dinox.py b/grounded_sam2_tracking_demo_custom_video_input_dinox.py new file mode 100644 index 0000000..7f8a971 --- /dev/null +++ b/grounded_sam2_tracking_demo_custom_video_input_dinox.py @@ -0,0 +1,234 @@ +# dds cloudapi for Grounding DINO 1.5 +from dds_cloudapi_sdk import Config +from dds_cloudapi_sdk import Client +from dds_cloudapi_sdk.tasks.dinox import DinoxTask +from dds_cloudapi_sdk import TextPrompt + +import os +import cv2 +import torch +import numpy as np +import supervision as sv + +from pathlib import Path +from tqdm import tqdm +from PIL import Image +from sam2.build_sam import build_sam2_video_predictor, build_sam2 +from sam2.sam2_image_predictor import SAM2ImagePredictor +from utils.track_utils import sample_points_from_masks +from utils.video_utils import create_video_from_images + +""" +Hyperparam for Ground and Tracking +""" +VIDEO_PATH = "./assets/hippopotamus.mp4" +TEXT_PROMPT = "hippopotamus." +OUTPUT_VIDEO_PATH = "./hippopotamus_tracking_demo.mp4" +SOURCE_VIDEO_FRAME_DIR = "./custom_video_frames" +SAVE_TRACKING_RESULTS_DIR = "./tracking_results" +API_TOKEN_FOR_GD1_5 = "Your API token" +PROMPT_TYPE_FOR_VIDEO = "box" # choose from ["point", "box", "mask"] +BOX_THRESHOLD = 0.2 + +""" +Step 1: Environment settings and model initialization for SAM 2 +""" +# use bfloat16 for the entire notebook +torch.autocast(device_type="cuda", dtype=torch.bfloat16).__enter__() + +if torch.cuda.get_device_properties(0).major >= 8: + # turn on tfloat32 for Ampere GPUs (https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices) + torch.backends.cuda.matmul.allow_tf32 = True + torch.backends.cudnn.allow_tf32 = True + +# init sam image predictor and video predictor model +sam2_checkpoint = "./checkpoints/sam2.1_hiera_large.pt" +model_cfg = "configs/sam2.1/sam2.1_hiera_l.yaml" + +video_predictor = build_sam2_video_predictor(model_cfg, sam2_checkpoint) +sam2_image_model = build_sam2(model_cfg, sam2_checkpoint) +image_predictor = SAM2ImagePredictor(sam2_image_model) + + +# # `video_dir` a directory of JPEG frames with filenames like `.jpg` +# video_dir = "notebooks/videos/bedroom" + +""" +Custom video input directly using video files +""" +video_info = sv.VideoInfo.from_video_path(VIDEO_PATH) # get video info +print(video_info) +frame_generator = sv.get_video_frames_generator(VIDEO_PATH, stride=1, start=0, end=None) + +# saving video to frames +source_frames = Path(SOURCE_VIDEO_FRAME_DIR) +source_frames.mkdir(parents=True, exist_ok=True) + +with sv.ImageSink( + target_dir_path=source_frames, + overwrite=True, + image_name_pattern="{:05d}.jpg" +) as sink: + for frame in tqdm(frame_generator, desc="Saving Video Frames"): + sink.save_image(frame) + +# scan all the JPEG frame names in this directory +frame_names = [ + p for p in os.listdir(SOURCE_VIDEO_FRAME_DIR) + if os.path.splitext(p)[-1] in [".jpg", ".jpeg", ".JPG", ".JPEG"] +] +frame_names.sort(key=lambda p: int(os.path.splitext(p)[0])) + +# init video predictor state +inference_state = video_predictor.init_state(video_path=SOURCE_VIDEO_FRAME_DIR) + +ann_frame_idx = 0 # the frame index we interact with +""" +Step 2: Prompt DINO-X with Cloud API for box coordinates +""" + +# prompt grounding dino to get the box coordinates on specific frame +img_path = os.path.join(SOURCE_VIDEO_FRAME_DIR, frame_names[ann_frame_idx]) +image = Image.open(img_path) + +# Step 1: initialize the config +config = Config(API_TOKEN_FOR_GD1_5) + +# 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" +# if you are processing local image file, upload them to DDS server to get the image url +image_url = client.upload_file(img_path) + +task = DinoxTask( + image_url=image_url, + prompts=[TextPrompt(text=TEXT_PROMPT)] +) + +client.run_task(task) +result = task.result + +objects = result.objects # the list of detected objects + + +input_boxes = [] +confidences = [] +class_names = [] + +for idx, obj in enumerate(objects): + input_boxes.append(obj.bbox) + confidences.append(obj.score) + class_names.append(obj.category) + +input_boxes = np.array(input_boxes) + +print(input_boxes) + +# prompt SAM image predictor to get the mask for the object +image_predictor.set_image(np.array(image.convert("RGB"))) + +# process the detection results +OBJECTS = class_names + +print(OBJECTS) + +# prompt SAM 2 image predictor to get the mask for the object +masks, scores, logits = image_predictor.predict( + point_coords=None, + point_labels=None, + box=input_boxes, + multimask_output=False, +) +# convert the mask shape to (n, H, W) +if masks.ndim == 4: + masks = masks.squeeze(1) + +""" +Step 3: Register each object's positive points to video predictor with seperate add_new_points call +""" + +assert PROMPT_TYPE_FOR_VIDEO in ["point", "box", "mask"], "SAM 2 video predictor only support point/box/mask prompt" + +# If you are using point prompts, we uniformly sample positive points based on the mask +if PROMPT_TYPE_FOR_VIDEO == "point": + # sample the positive points from mask for each objects + all_sample_points = sample_points_from_masks(masks=masks, num_points=10) + + for object_id, (label, points) in enumerate(zip(OBJECTS, all_sample_points), start=1): + labels = np.ones((points.shape[0]), dtype=np.int32) + _, out_obj_ids, out_mask_logits = video_predictor.add_new_points_or_box( + inference_state=inference_state, + frame_idx=ann_frame_idx, + obj_id=object_id, + points=points, + labels=labels, + ) +# Using box prompt +elif PROMPT_TYPE_FOR_VIDEO == "box": + for object_id, (label, box) in enumerate(zip(OBJECTS, input_boxes), start=1): + _, out_obj_ids, out_mask_logits = video_predictor.add_new_points_or_box( + inference_state=inference_state, + frame_idx=ann_frame_idx, + obj_id=object_id, + box=box, + ) +# Using mask prompt is a more straightforward way +elif PROMPT_TYPE_FOR_VIDEO == "mask": + for object_id, (label, mask) in enumerate(zip(OBJECTS, masks), start=1): + labels = np.ones((1), dtype=np.int32) + _, out_obj_ids, out_mask_logits = video_predictor.add_new_mask( + inference_state=inference_state, + frame_idx=ann_frame_idx, + obj_id=object_id, + mask=mask + ) +else: + raise NotImplementedError("SAM 2 video predictor only support point/box/mask prompts") + +""" +Step 4: Propagate the video predictor to get the segmentation results for each frame +""" +video_segments = {} # video_segments contains the per-frame segmentation results +for out_frame_idx, out_obj_ids, out_mask_logits in video_predictor.propagate_in_video(inference_state): + video_segments[out_frame_idx] = { + out_obj_id: (out_mask_logits[i] > 0.0).cpu().numpy() + for i, out_obj_id in enumerate(out_obj_ids) + } + +""" +Step 5: Visualize the segment results across the video and save them +""" + +if not os.path.exists(SAVE_TRACKING_RESULTS_DIR): + os.makedirs(SAVE_TRACKING_RESULTS_DIR) + +ID_TO_OBJECTS = {i: obj for i, obj in enumerate(OBJECTS, start=1)} + +for frame_idx, segments in video_segments.items(): + img = cv2.imread(os.path.join(SOURCE_VIDEO_FRAME_DIR, frame_names[frame_idx])) + + object_ids = list(segments.keys()) + masks = list(segments.values()) + masks = np.concatenate(masks, axis=0) + + detections = sv.Detections( + xyxy=sv.mask_to_xyxy(masks), # (n, 4) + mask=masks, # (n, h, w) + class_id=np.array(object_ids, dtype=np.int32), + ) + box_annotator = sv.BoxAnnotator() + annotated_frame = box_annotator.annotate(scene=img.copy(), detections=detections) + label_annotator = sv.LabelAnnotator() + annotated_frame = label_annotator.annotate(annotated_frame, detections=detections, labels=[ID_TO_OBJECTS[i] for i in object_ids]) + mask_annotator = sv.MaskAnnotator() + annotated_frame = mask_annotator.annotate(scene=annotated_frame, detections=detections) + cv2.imwrite(os.path.join(SAVE_TRACKING_RESULTS_DIR, f"annotated_frame_{frame_idx:05d}.jpg"), annotated_frame) + + +""" +Step 6: Convert the annotated frames to video +""" + +create_video_from_images(SAVE_TRACKING_RESULTS_DIR, OUTPUT_VIDEO_PATH) \ No newline at end of file