support auto label pipeline with florence-2

README.md

@@ -28,10 +28,12 @@ Grounded SAM 2 does not introduce significant methodological changes compared to
   - [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 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 (Updating)](#grounded-sam-2-florence-2-image-demo-updating)
+  - [Grounded SAM 2 Florence-2 Image Demo](#grounded-sam-2-florence-2-image-demo-updating)
+  - [Grounded SAM 2 Florence-2 Image Auto-Labeling Demo](#grounded-sam-2-florence-2-image-auto-labeling-demo)
 - [Citation](#citation)
 
 
+
 ## Installation
 
 Download the pretrained `SAM 2` checkpoints:
@@ -231,7 +233,7 @@ python grounded_sam2_tracking_demo_with_continuous_id_plus.py
 ```
 
 ## Grounded SAM 2 Florence-2 Demos
-### Grounded SAM 2 Florence-2 Image Demo (Updating)
+### Grounded SAM 2 Florence-2 Image Demo
 
 In this section, we will explore how to integrate the feature-rich and robust open-source models [Florence-2](https://arxiv.org/abs/2311.06242) and SAM 2 to develop practical applications.
 
@@ -244,6 +246,7 @@ In this section, we will explore how to integrate the feature-rich and robust op
 | Region Proposal | `<REGION_PROPOSAL>` | &#10008; | Generate proposals without category name |
 | Phrase Grounding | `<CAPTION_TO_PHRASE_GROUNDING>` | &#10004; | Ground main objects in image mentioned in caption |
 | Referring Expression Segmentation | `<REFERRING_EXPRESSION_SEGMENTATION>` | &#10004; | Ground the object which is most related to the text input |
+| Open Vocabulary Detection and Segmentation | `<OPEN_VOCABULARY_DETECTION>` | &#10004; | Ground any object with text input |
 
 
 Integrate `Florence-2` with `SAM-2`, we can build a strong vision pipeline to solve complex vision tasks, you can try the following scripts to run the demo:
@@ -298,6 +301,27 @@ python grounded_sam2_florence2_image_demo.py \
     --text_input "two cars"
 ```
 
+### Grounded SAM 2 Florence-2 Image Auto-Labeling Demo
+`Florence-2` can be used as a auto image annotator by cascading its caption capability with its grounding capability. 
+
+| Task | Task Prompt | Text Input |
+|:---:|:---:|:---:|
+| Caption + Phrase Grounding | `<CAPTION>` + `<CAPTION_TO_PHRASE_GROUNDING>` | &#10008; |
+| Detailed Caption + Phrase Grounding | `<DETAILED_CAPTION>` + `<CAPTION_TO_PHRASE_GROUNDING>` | &#10008; |
+| More Detailed Caption + Phrase Grounding | `<MORE_DETAILED_CAPTION>` + `<CAPTION_TO_PHRASE_GROUNDING>` | &#10008; |
+
+You can try the following scripts to run these demo:
+
+**Caption to Phrase Grounding**
+```bash
+python grounded_sam2_florence2_autolabel_pipeline.py \
+    --image_path ./notebooks/images/groceries.jpg \
+    --pipeline caption_to_phrase_grounding \
+    --caption_type caption
+```
+
+- You can specify `caption_type` to control the granularity of the caption, if you want a more detailed caption, you can try `--caption_type detailed_caption` or `--caption_type more_detailed_caption`.
+
 ### Citation
 
 If you find this project helpful for your research, please consider citing the following BibTeX entry.

grounded_sam2_florence2_autolabel_pipeline.py

@@ -0,0 +1,198 @@
+import os
+import cv2
+import torch
+import argparse
+import numpy as np
+import supervision as sv
+from PIL import Image
+from sam2.build_sam import build_sam2
+from sam2.sam2_image_predictor import SAM2ImagePredictor
+from transformers import AutoProcessor, AutoModelForCausalLM
+from utils.supervision_utils import CUSTOM_COLOR_MAP
+
+"""
+Define Some Hyperparam
+"""
+
+TASK_PROMPT = {
+    "caption": "<CAPTION>",
+    "detailed_caption": "<DETAILED_CAPTION>",
+    "more_detailed_caption": "<MORE_DETAILED_CAPTION",
+    "object_detection": "<OD>",
+    "dense_region_caption": "<DENSE_REGION_CAPTION>",
+    "region_proposal": "<REGION_PROPOSAL>",
+    "phrase_grounding": "<CAPTION_TO_PHRASE_GROUNDING>",
+    "referring_expression_segmentation": "<REFERRING_EXPRESSION_SEGMENTATION>",
+    "region_to_segmentation": "<REGION_TO_SEGMENTATION>",
+    "open_vocabulary_detection": "<OPEN_VOCABULARY_DETECTION>",
+    "region_to_category": "<REGION_TO_CATEGORY>",
+    "region_to_description": "<REGION_TO_DESCRIPTION>",
+    "ocr": "<OCR>",
+    "ocr_with_region": "<OCR_WITH_REGION>",
+}
+
+OUTPUT_DIR = "./outputs"
+
+if not os.path.exists(OUTPUT_DIR):
+    os.makedirs(OUTPUT_DIR, exist_ok=True)
+
+"""
+Init Florence-2 and SAM 2 Model
+"""
+
+FLORENCE2_MODEL_ID = "microsoft/Florence-2-large"
+SAM2_CHECKPOINT = "./checkpoints/sam2_hiera_large.pt"
+SAM2_CONFIG = "sam2_hiera_l.yaml"
+
+# environment settings
+# use bfloat16
+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
+
+device = "cuda:0" if torch.cuda.is_available() else "cpu"
+torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
+
+# build florence-2
+florence2_model = AutoModelForCausalLM.from_pretrained(FLORENCE2_MODEL_ID, trust_remote_code=True, torch_dtype='auto').eval().to(device)
+florence2_processor = AutoProcessor.from_pretrained(FLORENCE2_MODEL_ID, trust_remote_code=True)
+
+# build sam 2
+sam2_model = build_sam2(SAM2_CONFIG, SAM2_CHECKPOINT, device=device)
+sam2_predictor = SAM2ImagePredictor(sam2_model)
+
+def run_florence2(task_prompt, text_input, model, processor, image):
+    assert model is not None, "You should pass the init florence-2 model here"
+    assert processor is not None, "You should set florence-2 processor here"
+
+    device = model.device
+
+    if text_input is None:
+        prompt = task_prompt
+    else:
+        prompt = task_prompt + text_input
+    
+    inputs = processor(text=prompt, images=image, return_tensors="pt").to(device, torch.float16)
+    generated_ids = model.generate(
+      input_ids=inputs["input_ids"].to(device),
+      pixel_values=inputs["pixel_values"].to(device),
+      max_new_tokens=1024,
+      early_stopping=False,
+      do_sample=False,
+      num_beams=3,
+    )
+    generated_text = processor.batch_decode(generated_ids, skip_special_tokens=False)[0]
+    parsed_answer = processor.post_process_generation(
+        generated_text, 
+        task=task_prompt, 
+        image_size=(image.width, image.height)
+    )
+    return parsed_answer
+
+
+"""
+We try to support a series of cascaded auto-labelling pipelines with Florence-2 and SAM 2
+"""
+
+"""
+Auto-Labelling Pipeline: Caption/Detailed Caption/More Detailed Caption + Phrase Grounding + Segmentation
+"""
+def caption_phrase_grounding_and_segmentation(
+    florence2_model,
+    florence2_processor,
+    sam2_predictor,
+    image_path,
+    caption_task_prompt='<CAPTION>',
+    output_dir=OUTPUT_DIR
+):
+    assert caption_task_prompt in ["<CAPTION>", "<DETAILED_CAPTION>", "<MORE_DETAILED_CAPTION>"]
+    image = Image.open(image_path).convert("RGB")
+    
+    # image caption
+    caption_results = run_florence2(caption_task_prompt, None, florence2_model, florence2_processor, image)
+    text_input = caption_results[caption_task_prompt]
+    print(f'Image caption for "{image_path}": ', text_input)
+    
+    # phrase grounding
+    grounding_results = run_florence2('<CAPTION_TO_PHRASE_GROUNDING>', text_input, florence2_model, florence2_processor, image)
+    grounding_results = grounding_results['<CAPTION_TO_PHRASE_GROUNDING>']
+    
+    # parse florence-2 detection results
+    input_boxes = np.array(grounding_results["bboxes"])
+    class_names = grounding_results["labels"]
+    class_ids = np.array(list(range(len(class_names))))
+    
+    # predict mask with SAM 2
+    sam2_predictor.set_image(np.array(image))
+    masks, scores, logits = sam2_predictor.predict(
+        point_coords=None,
+        point_labels=None,
+        box=input_boxes,
+        multimask_output=False,
+    )
+    
+    if masks.ndim == 4:
+        masks = masks.squeeze(1)
+    
+    # specify labels
+    labels = [
+        f"{class_name}" for class_name in class_names
+    ]
+    
+    # visualization results
+    img = cv2.imread(image_path)
+    detections = sv.Detections(
+        xyxy=input_boxes,
+        mask=masks.astype(bool),
+        class_id=class_ids
+    )
+    
+    box_annotator = sv.BoxAnnotator()
+    annotated_frame = box_annotator.annotate(scene=img.copy(), detections=detections)
+    
+    label_annotator = sv.LabelAnnotator()
+    annotated_frame = label_annotator.annotate(scene=annotated_frame, detections=detections, labels=labels)
+    cv2.imwrite(os.path.join(output_dir, "grounded_sam2_florence2_auto_labelling.jpg"), annotated_frame)
+    
+    mask_annotator = sv.MaskAnnotator()
+    annotated_frame = mask_annotator.annotate(scene=annotated_frame, detections=detections)
+    cv2.imwrite(os.path.join(output_dir, "grounded_sam2_florence2_auto_labelling_with_mask.jpg"), annotated_frame)
+
+    print(f'Successfully save annotated image to "{output_dir}"')
+
+
+if __name__ == "__main__":
+
+    parser = argparse.ArgumentParser("Grounded SAM 2 Florence-2 Demos", add_help=True)
+    parser.add_argument("--image_path", type=str, default="./notebooks/images/cars.jpg", required=True, help="path to image file")
+    parser.add_argument("--pipeline", type=str, default="caption_to_phrase_grounding", required=True, help="pipeline to use")
+    parser.add_argument("--caption_type", type=str, default="caption", required=False, help="granularity of caption")
+    args = parser.parse_args()
+
+    CAPTION_TO_TASK_PROMPT = {
+        "caption": "<CAPTION>",
+        "detailed_caption": "<DETAILED_CAPTION>",
+        "more_detailed_caption": "<MORE_DETAILED_CAPTION>"
+    }
+
+    IMAGE_PATH = args.image_path
+    PIPELINE = args.pipeline
+    CAPTION_TYPE = args.caption_type
+    assert CAPTION_TYPE in ["caption", "detailed_caption", "more_detailed_caption"]
+    
+    print(f"Running pipeline: {PIPELINE} now.")
+
+    if PIPELINE == "caption_to_phrase_grounding":
+        # pipeline-1: caption + phrase grounding + segmentation
+        caption_phrase_grounding_and_segmentation(
+            florence2_model=florence2_model,
+            florence2_processor=florence2_processor,
+            sam2_predictor=sam2_predictor,
+            caption_task_prompt=CAPTION_TO_TASK_PROMPT[CAPTION_TYPE],
+            image_path=IMAGE_PATH
+        )
+    else:
+        raise NotImplementedError(f"Pipeline: {args.pipeline} is not implemented at this time")