app.py

import functools
import json
import os
import sys
import tempfile

import cv2
import gradio as gr
import numpy as np
import supervision as sv
import torch
from PIL import Image
from segment_anything import build_sam
from segment_anything import SamAutomaticMaskGenerator
from segment_anything import SamPredictor
from supervision.detection.utils import mask_to_polygons
from supervision.detection.utils import xywh_to_xyxy

if os.environ.get("IS_MY_DEBUG") is None:
    os.system("pip install -e GroundingDINO")

sys.path.append("tag2text")
sys.path.append("GroundingDINO")

from groundingdino.util.inference import Model as DinoModel
from tag2text.models import tag2text
from config import *
from utils import download_file_hf, detect, segment, generate_tags

if not os.path.exists(abs_weight_dir):
    os.makedirs(abs_weight_dir, exist_ok=True)

sam_checkpoint = os.path.join(abs_weight_dir, sam_dict[default_sam]["checkpoint_file"])
if not os.path.exists(sam_checkpoint):
    os.system(f"wget {sam_dict[default_sam]['checkpoint_url']} -O {sam_checkpoint}")

tag2text_checkpoint = os.path.join(
    abs_weight_dir, tag2text_dict[default_tag2text]["checkpoint_file"]
)
if not os.path.exists(tag2text_checkpoint):
    os.system(
        f"wget {tag2text_dict[default_tag2text]['checkpoint_url']} -O {tag2text_checkpoint}"
    )

dino_checkpoint = os.path.join(
    abs_weight_dir, dino_dict[default_dino]["checkpoint_file"]
)
dino_config_file = os.path.join(abs_weight_dir, dino_dict[default_dino]["config_file"])
if not os.path.exists(dino_checkpoint):
    dino_repo_id = dino_dict[default_dino]["repo_id"]
    download_file_hf(
        repo_id=dino_repo_id,
        filename=dino_dict[default_dino]["config_file"],
        cache_dir=weight_dir,
    )
    download_file_hf(
        repo_id=dino_repo_id,
        filename=dino_dict[default_dino]["checkpoint_file"],
        cache_dir=weight_dir,
    )

# load model
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
tag2text_model = tag2text.tag2text_caption(
    pretrained=tag2text_checkpoint,
    image_size=384,
    vit="swin_b",
    delete_tag_index=delete_tag_index,
)
# threshold for tagging
# we reduce the threshold to obtain more tags
tag2text_model.threshold = 0.64
tag2text_model.to(device)
tag2text_model.eval()


sam = build_sam(checkpoint=sam_checkpoint)
sam.to(device=device)
sam_predictor = SamPredictor(sam)
sam_automask_generator = SamAutomaticMaskGenerator(sam)

grounding_dino_model = DinoModel(
    model_config_path=dino_config_file,
    model_checkpoint_path=dino_checkpoint,
    device=device,
)


def process(

    image_path,

    task,

    prompt,

    box_threshold,

    text_threshold,

    iou_threshold,

    kernel_size,

    expand_mask,

):
    global tag2text_model, sam_predictor, sam_automask_generator, grounding_dino_model, device
    output_gallery = []
    detections = None
    metadata = {"image": {}, "annotations": []}

    try:
        # Load image
        image = Image.open(image_path)
        image_pil = image.convert("RGB")
        image = np.array(image_pil)
        orig_image = image.copy()

        # Extract image metadata
        filename = os.path.basename(image_path)
        h, w = image.shape[:2]
        metadata["image"]["file_name"] = filename
        metadata["image"]["width"] = w
        metadata["image"]["height"] = h

        # Generate tags
        if task in ["auto", "detection"] and prompt == "":
            tags, caption = generate_tags(tag2text_model, image_pil, "None", device)
            prompt = " . ".join(tags)
            print(f"Caption: {caption}")
            print(f"Tags: {tags}")

            # ToDo: Extract metadata
            metadata["image"]["caption"] = caption
            metadata["image"]["tags"] = tags

        if prompt:
            metadata["prompt"] = prompt
            print(f"Prompt: {prompt}")

        # Detect boxes
        if prompt != "":
            detections, phrases, classes = detect(
                grounding_dino_model,
                image,
                caption=prompt,
                box_threshold=box_threshold,
                text_threshold=text_threshold,
                iou_threshold=iou_threshold,
                post_process=True,
            )
            print(phrases)

            # Draw boxes
            box_annotator = sv.BoxAnnotator()
            labels = [
                f"{phrases[i]} {detections.confidence[i]:0.2f}"
                for i in range(len(phrases))
            ]
            image = box_annotator.annotate(
                scene=image, detections=detections, labels=labels
            )
            output_gallery.append(image)

        # Segmentation
        if task in ["auto", "segment"]:
            kernel = cv2.getStructuringElement(
                cv2.MORPH_ELLIPSE, (2 * kernel_size + 1, 2 * kernel_size + 1)
            )
            if detections:
                masks, scores = segment(
                    sam_predictor, image=orig_image, boxes=detections.xyxy
                )
                if expand_mask:
                    masks = [
                        cv2.dilate(mask.astype(np.uint8), kernel) for mask in masks
                    ]
                else:
                    masks = [
                        cv2.morphologyEx(mask.astype(np.uint8), cv2.MORPH_CLOSE, kernel)
                        for mask in masks
                    ]
                detections.mask = masks
                binary_mask = functools.reduce(
                    lambda x, y: x + y, detections.mask
                ).astype(bool)
            else:
                masks = sam_automask_generator.generate(orig_image)
                sorted_generated_masks = sorted(
                    masks, key=lambda x: x["area"], reverse=True
                )

                xywh = np.array([mask["bbox"] for mask in sorted_generated_masks])
                scores = np.array(
                    [mask["predicted_iou"] for mask in sorted_generated_masks]
                )
                if expand_mask:
                    mask = np.array(
                        [
                            cv2.dilate(mask["segmentation"].astype(np.uint8), kernel)
                            for mask in sorted_generated_masks
                        ]
                    )
                else:
                    mask = np.array(
                        [mask["segmentation"] for mask in sorted_generated_masks]
                    )
                detections = sv.Detections(
                    xyxy=xywh_to_xyxy(boxes_xywh=xywh), mask=mask
                )
                binary_mask = None

            mask_annotator = sv.MaskAnnotator()
            mask_image = np.zeros_like(image, dtype=np.uint8)
            mask_image = mask_annotator.annotate(
                mask_image, detections=detections, opacity=1
            )
            annotated_image = mask_annotator.annotate(image, detections=detections)

            output_gallery.append(mask_image)
            if binary_mask is not None:
                binary_mask_image = binary_mask * 255
                cutout_image = np.expand_dims(binary_mask, axis=-1) * orig_image
                output_gallery.append(binary_mask_image)
                output_gallery.append(cutout_image)
            output_gallery.append(annotated_image)

        # ToDo: Extract metadata
        if detections:
            i = 0
            for (xyxy, mask, confidence, _, _), area, box_area in zip(
                detections, detections.area, detections.box_area
            ):
                annotation = {
                    "id": i + 1,
                    "bbox": [int(x) for x in xyxy],
                    "box_area": float(box_area),
                }
                if confidence:
                    annotation["confidence"] = float(confidence)
                    annotation["label"] = phrases[i]
                if mask is not None:
                    # annotation["segmentation"] = mask_to_polygons(mask)
                    annotation["area"] = int(area)
                    annotation["predicted_iou"] = float(scores[i])
                metadata["annotations"].append(annotation)
                i += 1

        meta_file = tempfile.NamedTemporaryFile(delete=False, suffix=".json")
        meta_file_path = meta_file.name
        with open(meta_file_path, "w", encoding="utf-8") as fp:
            json.dump(metadata, fp)

        return output_gallery, meta_file_path
    except Exception as error:
        raise gr.Error(f"global exception: {error}")


title = "Annotate Anything"

with gr.Blocks(css="style.css", title=title) as demo:
    with gr.Row(elem_classes=["container"]):
        with gr.Column(scale=1):
            input_image = gr.Image(type="filepath", label="Input")
            task = gr.Dropdown(
                ["detect", "segment", "auto"], value="auto", label="task_type"
            )
            text_prompt = gr.Textbox(
                label="Detection Prompt",
                info="To detect multiple objects, seperating each name with '.', like this: cat . dog . chair ",
            )
            with gr.Accordion("Advanced parameters", open=False):
                box_threshold = gr.Slider(
                    minimum=0,
                    maximum=1,
                    value=0.3,
                    step=0.05,
                    label="Box threshold",
                )
                text_threshold = gr.Slider(
                    minimum=0,
                    maximum=1,
                    value=0.25,
                    step=0.05,
                    label="Text threshold",
                )
                iou_threshold = gr.Slider(
                    minimum=0,
                    maximum=1,
                    value=0.5,
                    step=0.05,
                    label="IOU threshold",
                    info="Intersection over Union threshold",
                )
                kernel_size = gr.Slider(
                    minimum=1,
                    maximum=5,
                    value=2,
                    step=1,
                    label="Kernel size",
                    info="Use to smooth segment masks",
                )
                expand_mask = gr.Checkbox(
                    label="Expand mask",
                )
            run_button = gr.Button(label="Run")

        with gr.Column(scale=2):
            gallery = gr.Gallery(
                label="Generated images", show_label=False, elem_id="gallery"
            ).style(preview=True, grid=2, object_fit="scale-down")
            meta_file = gr.File(label="Metadata file")
    with gr.Column(elem_classes=["container"]):
        gr.Examples(
            [
                ["examples/dog.png", "auto", ""],
                ["examples/eiffel.jpg", "auto", "tower . lake . grass . sky"],
                ["examples/eiffel.png", "segment", ""],
                ["examples/girl.png", "auto", "girl . face"],
                ["examples/horse.png", "detect", "horse"],
                ["examples/traffic.jpg", "auto", ""],
            ],
            [input_image, task, text_prompt],
        )
        gr.HTML(
            """<br><br><br><center>You can duplicate this Space to skip the queue:<a href="https://huggingface.co/spaces/dragonSwing/annotate-anything?duplicate=true"><img src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a><br>

                <p><img src="https://visitor-badge.glitch.me/badge?page_id=dragonswing.annotate-anything" alt="visitors"></p></center>"""
        )

    run_button.click(
        fn=process,
        inputs=[
            input_image,
            task,
            text_prompt,
            box_threshold,
            text_threshold,
            iou_threshold,
            kernel_size,
            expand_mask,
        ],
        outputs=[gallery, meta_file],
    )

demo.queue(concurrency_count=2).launch()