Grounded-SAM-2/grounded_sam2_image_demo_florence2.py

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 support a set of pipelines built by Florence-2 + SAM 2
"""

"""
Pipeline-1: Object Detection + Segmentation
"""
def object_detection_and_segmentation(
    florence2_model,
    florence2_processor,
    sam2_predictor,
    image_path,
    task_prompt="<OD>",
    text_input=None,
    output_dir=OUTPUT_DIR
):
    # run florence-2 object detection in demo
    image = Image.open(image_path).convert("RGB")
    results = run_florence2(task_prompt, text_input, florence2_model, florence2_processor, image)
    
    """ Florence-2 Object Detection Output Format
    {'<OD>': 
        {
            'bboxes': 
                [
                    [33.599998474121094, 159.59999084472656, 596.7999877929688, 371.7599792480469], 
                    [454.0799865722656, 96.23999786376953, 580.7999877929688, 261.8399963378906], 
                    [224.95999145507812, 86.15999603271484, 333.7599792480469, 164.39999389648438], 
                    [449.5999755859375, 276.239990234375, 554.5599975585938, 370.3199768066406], 
                    [91.19999694824219, 280.0799865722656, 198.0800018310547, 370.3199768066406]
                ], 
            'labels': ['car', 'door', 'door', 'wheel', 'wheel']
        }
    }
    """
    results = results[task_prompt]
    # parse florence-2 detection results
    input_boxes = np.array(results["bboxes"])
    class_names = 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_det_annotated_image.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_det_image_with_mask.jpg"), annotated_frame)

    print(f'Successfully save annotated image to "{output_dir}"')

"""
Pipeline 2: Dense Region Caption + Segmentation
"""
def dense_region_caption_and_segmentation(
    florence2_model,
    florence2_processor,
    sam2_predictor,
    image_path,
    task_prompt="<DENSE_REGION_CAPTION>",
    text_input=None,
    output_dir=OUTPUT_DIR
):
    # run florence-2 object detection in demo
    image = Image.open(image_path).convert("RGB")
    results = run_florence2(task_prompt, text_input, florence2_model, florence2_processor, image)
    
    """ Florence-2 Object Detection Output Format
    {'<DENSE_REGION_CAPTION>': 
        {
            'bboxes': 
                [
                    [33.599998474121094, 159.59999084472656, 596.7999877929688, 371.7599792480469], 
                    [454.0799865722656, 96.23999786376953, 580.7999877929688, 261.8399963378906], 
                    [224.95999145507812, 86.15999603271484, 333.7599792480469, 164.39999389648438], 
                    [449.5999755859375, 276.239990234375, 554.5599975585938, 370.3199768066406], 
                    [91.19999694824219, 280.0799865722656, 198.0800018310547, 370.3199768066406]
                ], 
            'labels': ['turquoise Volkswagen Beetle', 'wooden double doors with metal handles', 'wheel', 'wheel', 'door']
        }
    }
    """
    results = results[task_prompt]
    # parse florence-2 detection results
    input_boxes = np.array(results["bboxes"])
    class_names = 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_dense_region_cap_annotated_image.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_dense_region_cap_image_with_mask.jpg"), annotated_frame)

    print(f'Successfully save annotated image to "{output_dir}"')


"""
Pipeline 3: Region Proposal + Segmentation
"""
def region_proposal_and_segmentation(
    florence2_model,
    florence2_processor,
    sam2_predictor,
    image_path,
    task_prompt="<REGION_PROPOSAL>",
    text_input=None,
    output_dir=OUTPUT_DIR
):
    # run florence-2 object detection in demo
    image = Image.open(image_path).convert("RGB")
    results = run_florence2(task_prompt, text_input, florence2_model, florence2_processor, image)
    
    """ Florence-2 Object Detection Output Format
    {'<REGION_PROPOSAL>': 
        {
            'bboxes': 
                [
                    [33.599998474121094, 159.59999084472656, 596.7999877929688, 371.7599792480469], 
                    [454.0799865722656, 96.23999786376953, 580.7999877929688, 261.8399963378906], 
                    [224.95999145507812, 86.15999603271484, 333.7599792480469, 164.39999389648438], 
                    [449.5999755859375, 276.239990234375, 554.5599975585938, 370.3199768066406], 
                    [91.19999694824219, 280.0799865722656, 198.0800018310547, 370.3199768066406]
                ], 
            'labels': ['', '', '', '', '', '', '']
        }
    }
    """
    results = results[task_prompt]
    # parse florence-2 detection results
    input_boxes = np.array(results["bboxes"])
    class_names = 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"region_{idx}" for idx, class_name in enumerate(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_region_proposal.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_region_proposal_with_mask.jpg"), annotated_frame)

    print(f'Successfully save annotated image to "{output_dir}"')


"""
Pipeline 4: Phrase Grounding + Segmentation
"""
def phrase_grounding_and_segmentation(
    florence2_model,
    florence2_processor,
    sam2_predictor,
    image_path,
    task_prompt="<CAPTION_TO_PHRASE_GROUNDING>",
    text_input=None,
    output_dir=OUTPUT_DIR
):
    # run florence-2 object detection in demo
    image = Image.open(image_path).convert("RGB")
    results = run_florence2(task_prompt, text_input, florence2_model, florence2_processor, image)
    
    """ Florence-2 Object Detection Output Format
    {'<CAPTION_TO_PHRASE_GROUNDING>': 
        {
            'bboxes': 
                [
                    [34.23999786376953, 159.1199951171875, 582.0800170898438, 374.6399841308594], 
                    [1.5999999046325684, 4.079999923706055, 639.0399780273438, 305.03997802734375]
                ], 
            'labels': ['A green car', 'a yellow building']
        }
    }
    """
    assert text_input is not None, "Text input should not be none when calling phrase grounding pipeline."
    results = results[task_prompt]
    # parse florence-2 detection results
    input_boxes = np.array(results["bboxes"])
    class_names = 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_phrase_grounding.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_phrase_grounding_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="object_detection_segmentation", required=True, help="path to image file")
    args = parser.parse_args()

    IMAGE_PATH = args.image_path
    PIPELINE = args.pipeline

    print(f"Running pipeline: {PIPELINE} now.")

    if PIPELINE == "object_detection_segmentation":
        # pipeline-1: detection + segmentation
        object_detection_and_segmentation(
            florence2_model=florence2_model,
            florence2_processor=florence2_processor,
            sam2_predictor=sam2_predictor,
            image_path=IMAGE_PATH
        )
    elif PIPELINE == "dense_region_caption_segmentation":
        # pipeline-2: dense region caption + segmentation
        dense_region_caption_and_segmentation(
            florence2_model=florence2_model,
            florence2_processor=florence2_processor,
            sam2_predictor=sam2_predictor,
            image_path=IMAGE_PATH
        )
    elif PIPELINE == "region_proposal_segmentation":
        # pipeline-3: dense region caption + segmentation
        region_proposal_and_segmentation(
            florence2_model=florence2_model,
            florence2_processor=florence2_processor,
            sam2_predictor=sam2_predictor,
            image_path=IMAGE_PATH
        )
    elif PIPELINE == "phrase_grounding_segmentation":
        # pipeline-4: phrase grounding + segmentation
        phrase_grounding_and_segmentation(
            florence2_model=florence2_model,
            florence2_processor=florence2_processor,
            sam2_predictor=sam2_predictor,
            image_path=IMAGE_PATH,
            text_input="The image shows two vintage Chevrolet cars parked side by side, with one being a red convertible and the other a pink sedan, \
            set against the backdrop of an urban area with a multi-story building and trees. \
            The cars have Cuban license plates, indicating a location likely in Cuba."
        )
    else:
        raise NotImplementedError(f"Pipeline: {args.pipeline} is not implemented at this time")