app.py

import os
from typing import Tuple, Optional

import cv2
import gradio as gr
import numpy as np
import spaces
import supervision as sv
import torch
from PIL import Image
from tqdm import tqdm
from utils.video import generate_unique_name, create_directory, delete_directory

from utils.florence import load_florence_model, run_florence_inference, \
    FLORENCE_DETAILED_CAPTION_TASK, \
    FLORENCE_CAPTION_TO_PHRASE_GROUNDING_TASK, FLORENCE_OPEN_VOCABULARY_DETECTION_TASK
from utils.modes import IMAGE_INFERENCE_MODES, IMAGE_OPEN_VOCABULARY_DETECTION_MODE, \
    IMAGE_CAPTION_GROUNDING_MASKS_MODE, VIDEO_INFERENCE_MODES
from utils.sam import load_sam_image_model, run_sam_inference, load_sam_video_model

MARKDOWN = """
# Florence2 + SAM2 🔥

<div>
    <a href="https://github.com/facebookresearch/segment-anything-2">
        <img src="https://badges.aleen42.com/src/github.svg" alt="GitHub" style="display:inline-block;">
    </a>
    <a href="https://colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/how-to-segment-images-with-sam-2.ipynb">
        <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Colab" style="display:inline-block;">
    </a>
    <a href="https://blog.roboflow.com/what-is-segment-anything-2/">
        <img src="https://raw.githubusercontent.com/roboflow-ai/notebooks/main/assets/badges/roboflow-blogpost.svg" alt="Roboflow" style="display:inline-block;">
    </a>
    <a href="https://www.youtube.com/watch?v=Dv003fTyO-Y">
        <img src="https://badges.aleen42.com/src/youtube.svg" alt="YouTube" style="display:inline-block;">
    </a>
</div>

This demo integrates Florence2 and SAM2 by creating a two-stage inference pipeline. In 
the first stage, Florence2 performs tasks such as object detection, open-vocabulary 
object detection, image captioning, or phrase grounding. In the second stage, SAM2 
performs object segmentation on the image.
"""

IMAGE_PROCESSING_EXAMPLES = [
    [IMAGE_OPEN_VOCABULARY_DETECTION_MODE, "https://media.roboflow.com/notebooks/examples/dog-2.jpeg", 'straw, white napkin, black napkin, dog, hair, man'],
    [IMAGE_OPEN_VOCABULARY_DETECTION_MODE, "https://media.roboflow.com/notebooks/examples/dog-3.jpeg", 'tail'],
    [IMAGE_CAPTION_GROUNDING_MASKS_MODE, "https://media.roboflow.com/notebooks/examples/dog-2.jpeg", None],
    [IMAGE_CAPTION_GROUNDING_MASKS_MODE, "https://media.roboflow.com/notebooks/examples/dog-3.jpeg", None],
]

VIDEO_SCALE_FACTOR = 0.5
VIDEO_TARGET_DIRECTORY = "tmp"
create_directory(directory_path=VIDEO_TARGET_DIRECTORY)

DEVICE = torch.device("cuda")
# DEVICE = torch.device("cpu")

torch.autocast(device_type="cuda", dtype=torch.bfloat16).__enter__()
if torch.cuda.get_device_properties(0).major >= 8:
    torch.backends.cuda.matmul.allow_tf32 = True
    torch.backends.cudnn.allow_tf32 = True


FLORENCE_MODEL, FLORENCE_PROCESSOR = load_florence_model(device=DEVICE)
SAM_IMAGE_MODEL = load_sam_image_model(device=DEVICE)
SAM_VIDEO_MODEL = load_sam_video_model(device=DEVICE)
COLORS = ['#FF1493', '#00BFFF', '#FF6347', '#FFD700', '#32CD32', '#8A2BE2']
COLOR_PALETTE = sv.ColorPalette.from_hex(COLORS)
BOX_ANNOTATOR = sv.BoxAnnotator(color=COLOR_PALETTE, color_lookup=sv.ColorLookup.INDEX)
LABEL_ANNOTATOR = sv.LabelAnnotator(
    color=COLOR_PALETTE,
    color_lookup=sv.ColorLookup.INDEX,
    text_position=sv.Position.CENTER_OF_MASS,
    text_color=sv.Color.from_hex("#000000"),
    border_radius=5
)
MASK_ANNOTATOR = sv.MaskAnnotator(
    color=COLOR_PALETTE,
    color_lookup=sv.ColorLookup.INDEX
)


def annotate_image(image, detections):
    output_image = image.copy()
    output_image = MASK_ANNOTATOR.annotate(output_image, detections)
    output_image = BOX_ANNOTATOR.annotate(output_image, detections)
    output_image = LABEL_ANNOTATOR.annotate(output_image, detections)
    return output_image


def on_mode_dropdown_change(text):
    return [
        gr.Textbox(visible=text == IMAGE_OPEN_VOCABULARY_DETECTION_MODE),
        gr.Textbox(visible=text == IMAGE_CAPTION_GROUNDING_MASKS_MODE),
    ]


@spaces.GPU
@torch.inference_mode()
@torch.autocast(device_type="cuda", dtype=torch.bfloat16)
def process_image(
    mode_dropdown, image_input, text_input
) -> Tuple[Optional[Image.Image], Optional[str]]:
    if not image_input:
        gr.Info("Please upload an image.")
        return None, None

    if mode_dropdown == IMAGE_OPEN_VOCABULARY_DETECTION_MODE:
        if not text_input:
            gr.Info("Please enter a text prompt.")
            return None, None

        texts = [prompt.strip() for prompt in text_input.split(",")]
        detections_list = []
        for text in texts:
            _, result = run_florence_inference(
                model=FLORENCE_MODEL,
                processor=FLORENCE_PROCESSOR,
                device=DEVICE,
                image=image_input,
                task=FLORENCE_OPEN_VOCABULARY_DETECTION_TASK,
                text=text
            )
            detections = sv.Detections.from_lmm(
                lmm=sv.LMM.FLORENCE_2,
                result=result,
                resolution_wh=image_input.size
            )
            detections = run_sam_inference(SAM_IMAGE_MODEL, image_input, detections)
            detections_list.append(detections)

        detections = sv.Detections.merge(detections_list)
        detections = run_sam_inference(SAM_IMAGE_MODEL, image_input, detections)
        return annotate_image(image_input, detections), None

    if mode_dropdown == IMAGE_CAPTION_GROUNDING_MASKS_MODE:
        _, result = run_florence_inference(
            model=FLORENCE_MODEL,
            processor=FLORENCE_PROCESSOR,
            device=DEVICE,
            image=image_input,
            task=FLORENCE_DETAILED_CAPTION_TASK
        )
        caption = result[FLORENCE_DETAILED_CAPTION_TASK]
        _, result = run_florence_inference(
            model=FLORENCE_MODEL,
            processor=FLORENCE_PROCESSOR,
            device=DEVICE,
            image=image_input,
            task=FLORENCE_CAPTION_TO_PHRASE_GROUNDING_TASK,
            text=caption
        )
        detections = sv.Detections.from_lmm(
            lmm=sv.LMM.FLORENCE_2,
            result=result,
            resolution_wh=image_input.size
        )
        detections = run_sam_inference(SAM_IMAGE_MODEL, image_input, detections)
        return annotate_image(image_input, detections), caption


@spaces.GPU(duration=300)
@torch.inference_mode()
@torch.autocast(device_type="cuda", dtype=torch.bfloat16)
def process_video(
    mode_dropdown, video_input, text_input, progress=gr.Progress(track_tqdm=True)
) -> Optional[str]:
    if not video_input:
        gr.Info("Please upload a video.")
        return None

    if not text_input:
        gr.Info("Please enter a text prompt.")
        return None

    frame_generator = sv.get_video_frames_generator(video_input)
    frame = next(frame_generator)
    frame = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))

    texts = [prompt.strip() for prompt in text_input.split(",")]
    detections_list = []
    for text in texts:
        _, result = run_florence_inference(
            model=FLORENCE_MODEL,
            processor=FLORENCE_PROCESSOR,
            device=DEVICE,
            image=frame,
            task=FLORENCE_OPEN_VOCABULARY_DETECTION_TASK,
            text=text
        )
        detections = sv.Detections.from_lmm(
            lmm=sv.LMM.FLORENCE_2,
            result=result,
            resolution_wh=frame.size
        )
        detections = run_sam_inference(SAM_IMAGE_MODEL, frame, detections)
        detections_list.append(detections)

    detections = sv.Detections.merge(detections_list)
    detections = run_sam_inference(SAM_IMAGE_MODEL, frame, detections)

    if len(detections.mask) == 0:
        gr.Info(
            "No objects of class {text_input} found in the first frame of the video. "
            "Trim the video to make the object appear in the first frame or try a "
            "different text prompt."
        )
        return None

    name = generate_unique_name()
    frame_directory_path = os.path.join(VIDEO_TARGET_DIRECTORY, name)
    frames_sink = sv.ImageSink(
        target_dir_path=frame_directory_path,
        image_name_pattern="{:05d}.jpeg"
    )

    video_info = sv.VideoInfo.from_video_path(video_input)
    video_info.width = int(video_info.width * VIDEO_SCALE_FACTOR)
    video_info.height = int(video_info.height * VIDEO_SCALE_FACTOR)

    frames_generator = sv.get_video_frames_generator(video_input)
    with frames_sink:
        for frame in tqdm(
                frames_generator,
                total=video_info.total_frames,
                desc="splitting video into frames"
        ):
            frame = sv.scale_image(frame, VIDEO_SCALE_FACTOR)
            frames_sink.save_image(frame)

    inference_state = SAM_VIDEO_MODEL.init_state(
        video_path=frame_directory_path,
        device=DEVICE
    )

    for mask_index, mask in enumerate(detections.mask):
        _, object_ids, mask_logits = SAM_VIDEO_MODEL.add_new_mask(
            inference_state=inference_state,
            frame_idx=0,
            obj_id=mask_index,
            mask=mask
        )

    video_path = os.path.join(VIDEO_TARGET_DIRECTORY, f"{name}.mp4")
    frames_generator = sv.get_video_frames_generator(video_input)
    masks_generator = SAM_VIDEO_MODEL.propagate_in_video(inference_state)
    with sv.VideoSink(video_path, video_info=video_info) as sink:
        for frame, (_, tracker_ids, mask_logits) in zip(frames_generator, masks_generator):
            frame = sv.scale_image(frame, VIDEO_SCALE_FACTOR)
            masks = (mask_logits > 0.0).cpu().numpy().astype(bool)
            if len(masks.shape) == 4:
                masks = np.squeeze(masks, axis=1)

            detections = sv.Detections(
                xyxy=sv.mask_to_xyxy(masks=masks),
                mask=masks,
                class_id=np.array(tracker_ids)
            )
            annotated_frame = frame.copy()
            annotated_frame = MASK_ANNOTATOR.annotate(
                scene=annotated_frame, detections=detections)
            annotated_frame = BOX_ANNOTATOR.annotate(
                scene=annotated_frame, detections=detections)
            sink.write_frame(annotated_frame)

    delete_directory(frame_directory_path)
    return video_path


with gr.Blocks() as demo:
    gr.Markdown(MARKDOWN)
    with gr.Tab("Image"):
        image_processing_mode_dropdown_component = gr.Dropdown(
            choices=IMAGE_INFERENCE_MODES,
            value=IMAGE_INFERENCE_MODES[0],
            label="Mode",
            info="Select a mode to use.",
            interactive=True
        )
        with gr.Row():
            with gr.Column():
                image_processing_image_input_component = gr.Image(
                    type='pil', label='Upload image')
                image_processing_text_input_component = gr.Textbox(
                    label='Text prompt',
                    placeholder='Enter comma separated text prompts')
                image_processing_submit_button_component = gr.Button(
                    value='Submit', variant='primary')
            with gr.Column():
                image_processing_image_output_component = gr.Image(
                    type='pil', label='Image output')
                image_processing_text_output_component = gr.Textbox(
                    label='Caption output', visible=False)

        with gr.Row():
            gr.Examples(
                fn=process_image,
                examples=IMAGE_PROCESSING_EXAMPLES,
                inputs=[
                    image_processing_mode_dropdown_component,
                    image_processing_image_input_component,
                    image_processing_text_input_component
                ],
                outputs=[
                    image_processing_image_output_component,
                    image_processing_text_output_component
                ],
                run_on_click=True
            )
    with gr.Tab("Video"):
        video_processing_mode_dropdown_component = gr.Dropdown(
            choices=VIDEO_INFERENCE_MODES,
            value=VIDEO_INFERENCE_MODES[0],
            label="Mode",
            info="Select a mode to use.",
            interactive=True
        )
        with gr.Row():
            with gr.Column():
                video_processing_video_input_component = gr.Video(
                    label='Upload video')
                video_processing_text_input_component = gr.Textbox(
                    label='Text prompt',
                    placeholder='Enter comma separated text prompts')
                video_processing_submit_button_component = gr.Button(
                    value='Submit', variant='primary')
            with gr.Column():
                video_processing_video_output_component = gr.Video(
                    label='Video output')

    image_processing_submit_button_component.click(
        fn=process_image,
        inputs=[
            image_processing_mode_dropdown_component,
            image_processing_image_input_component,
            image_processing_text_input_component
        ],
        outputs=[
            image_processing_image_output_component,
            image_processing_text_output_component
        ]
    )
    image_processing_text_input_component.submit(
        fn=process_image,
        inputs=[
            image_processing_mode_dropdown_component,
            image_processing_image_input_component,
            image_processing_text_input_component
        ],
        outputs=[
            image_processing_image_output_component,
            image_processing_text_output_component
        ]
    )
    image_processing_mode_dropdown_component.change(
        on_mode_dropdown_change,
        inputs=[image_processing_mode_dropdown_component],
        outputs=[
            image_processing_text_input_component,
            image_processing_text_output_component
        ]
    )
    video_processing_submit_button_component.click(
        fn=process_video,
        inputs=[
            video_processing_mode_dropdown_component,
            video_processing_video_input_component,
            video_processing_text_input_component
        ],
        outputs=video_processing_video_output_component
    )
    video_processing_text_input_component.submit(
        fn=process_video,
        inputs=[
            video_processing_mode_dropdown_component,
            video_processing_video_input_component,
            video_processing_text_input_component
        ],
        outputs=video_processing_video_output_component
    )

demo.launch(debug=False, show_error=True)