characterize_anything_app.py

import gdown
import cv2
import os
import sys
from PIL import Image

sys.path.append(sys.path[0] + "/tracker")
sys.path.append(sys.path[0] + "/tracker/model")
sys.path.append("MiniGPT-4")
from track_anything import TrackingAnything
from track_anything import parse_augment
import requests
import json
import torchvision
from tools.painter import mask_painter
import psutil
import time
from typing import Union

import numpy as np
import torch
import gradio as gr

from minigpt4.common.config import Config
from minigpt4.common.registry import registry
from minigpt4.conversation.conversation import Chat, CONV_VISION


# ========================================
#             Model Initialization
# ========================================

print("Initializing Chat")
args = parse_augment()
cfg = Config(args)

model_config = cfg.model_cfg
gpu_id = int(args.device.split(":")[-1])
print(gpu_id)
model_config.device_8bit = gpu_id
print(f"args: {args}")
model_cls = registry.get_model_class(model_config.arch)
model = model_cls.from_config(model_config).to(args.device)

vis_processor_cfg = cfg.datasets_cfg.cc_sbu_align.vis_processor.train
vis_processor = registry.get_processor_class(vis_processor_cfg.name).from_config(
    vis_processor_cfg
)
chat = Chat(model, vis_processor, device=args.device)
print("MiniGPT-4 Initialization Finished!")


# download checkpoints
def download_checkpoint(url, folder, filename):
    os.makedirs(folder, exist_ok=True)
    filepath = os.path.join(folder, filename)

    if not os.path.exists(filepath):
        print("download checkpoints ......")
        response = requests.get(url, stream=True)
        with open(filepath, "wb") as f:
            for chunk in response.iter_content(chunk_size=8192):
                if chunk:
                    f.write(chunk)

        print("download successfully!")

    return filepath


def download_checkpoint_from_google_drive(file_id, folder, filename):
    os.makedirs(folder, exist_ok=True)
    filepath = os.path.join(folder, filename)

    if not os.path.exists(filepath):
        print(
            "Downloading checkpoints from Google Drive... tips: If you cannot see the progress bar, please try to download it manuall \
              and put it in the checkpointes directory. E2FGVI-HQ-CVPR22.pth: https://github.com/MCG-NKU/E2FGVI(E2FGVI-HQ model)"
        )
        url = f"https://drive.google.com/uc?id={file_id}"
        gdown.download(url, filepath, quiet=False)
        print("Downloaded successfully!")

    return filepath


# convert points input to prompt state
def get_prompt(click_state, click_input):
    inputs = json.loads(click_input)
    points = click_state[0]
    labels = click_state[1]
    for input in inputs:
        points.append(input[:2])
        labels.append(input[2])
    click_state[0] = points
    click_state[1] = labels
    prompt = {
        "prompt_type": ["click"],
        "input_point": click_state[0],
        "input_label": click_state[1],
        "multimask_output": "True",
    }
    return prompt


# extract frames from upload video
def get_frames_from_video(video_input, video_state):
    """
    Args:
        video_path:str
        timestamp:float64
    Return
        [[0:nearest_frame], [nearest_frame:], nearest_frame]
    """
    video_path = video_input
    frames = []
    user_name = time.time()
    operation_log = [
        ("", ""),
        (
            "Upload video already. Try click the image for adding targets to track and inpaint.",
            "Normal",
        ),
    ]
    first_frame = None
    try:
        cap = cv2.VideoCapture(video_path)
        fps = cap.get(cv2.CAP_PROP_FPS)
        while cap.isOpened():
            ret, frame = cap.read()
            if first_frame is None:
                first_frame = torch.tensor(frame)
                first_frame = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
            if ret == True:
                current_memory_usage = psutil.virtual_memory().percent
                frames.append(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
                if current_memory_usage > 90:
                    operation_log = [
                        (
                            "Memory usage is too high (>90%). Stop the video extraction. Please reduce the video resolution or frame rate.",
                            "Error",
                        )
                    ]
                    print(
                        "Memory usage is too high (>90%). Please reduce the video resolution or frame rate."
                    )
                    break
            else:
                break
    except (OSError, TypeError, ValueError, KeyError, SyntaxError) as e:
        print("read_frame_source:{} error. {}\n".format(video_path, str(e)))
    image_size = (frames[0].shape[0], frames[0].shape[1])
    # initialize video_state
    video_state = {
        "user_name": user_name,
        "video_name": os.path.split(video_path)[-1],
        "origin_images": frames,
        "painted_images": frames.copy(),
        "masks": [np.zeros((frames[0].shape[0], frames[0].shape[1]), np.uint8)]
        * len(frames),
        "logits": [None] * len(frames),
        "select_frame_number": 0,
        "fps": fps,
    }
    video_info = "Video Name: {}, FPS: {}, Total Frames: {}, Image Size:{}".format(
        video_state["video_name"], video_state["fps"], len(frames), image_size
    )

    video_description = ""
    if first_frame is not None:
        chat_state = CONV_VISION.copy()
        img_list = []
        chat.upload_img(first_frame, chat_state, img_list)
        user_message = "Describe this image."
        chat.ask(user_message, chat_state)
        video_description = chat.answer(
            conv=chat_state,
            img_list=img_list,
            num_beams=1,
            temperature=1,
            max_new_tokens=300,
            max_length=2000,
        )[0]

    model.samcontroler.sam_controler.reset_image()
    model.samcontroler.sam_controler.set_image(video_state["origin_images"][0])
    return (
        video_state,
        video_info,
        video_description,
        video_state["origin_images"][0],
        gr.update(visible=True, maximum=len(frames), value=1),
        gr.update(visible=True, maximum=len(frames), value=len(frames)),
        gr.update(visible=True),
        gr.update(visible=True),
        gr.update(visible=True),
        gr.update(visible=True),
        gr.update(visible=True),
        gr.update(visible=True),
        gr.update(visible=True),
        gr.update(visible=True),
        gr.update(visible=True),
        gr.update(visible=True),
        gr.update(visible=True),
        gr.update(visible=True),
        gr.update(visible=True),
        gr.update(visible=True),
        gr.update(visible=True, value=operation_log),
    )


def run_example(example):
    return video_input


# get the select frame from gradio slider
def select_template(image_selection_slider, video_state, interactive_state):
    # images = video_state[1]
    image_selection_slider -= 1
    video_state["select_frame_number"] = image_selection_slider

    # once select a new template frame, set the image in sam

    model.samcontroler.sam_controler.reset_image()
    model.samcontroler.sam_controler.set_image(
        video_state["origin_images"][image_selection_slider]
    )

    # update the masks when select a new template frame
    # if video_state["masks"][image_selection_slider] is not None:
    # video_state["painted_images"][image_selection_slider] = mask_painter(video_state["origin_images"][image_selection_slider], video_state["masks"][image_selection_slider])
    operation_log = [
        ("", ""),
        (
            "Select frame {}. Try click image and add mask for tracking.".format(
                image_selection_slider
            ),
            "Normal",
        ),
    ]

    return (
        video_state["painted_images"][image_selection_slider],
        video_state,
        interactive_state,
        operation_log,
    )


# set the tracking end frame
def get_end_number(track_pause_number_slider, video_state, interactive_state):
    interactive_state["track_end_number"] = track_pause_number_slider
    operation_log = [
        ("", ""),
        (
            "Set the tracking finish at frame {}".format(track_pause_number_slider),
            "Normal",
        ),
    ]

    return (
        video_state["painted_images"][track_pause_number_slider],
        interactive_state,
        operation_log,
    )


def get_resize_ratio(resize_ratio_slider, interactive_state):
    interactive_state["resize_ratio"] = resize_ratio_slider

    return interactive_state


# use sam to get the mask
def sam_refine(
    video_state, point_prompt, click_state, interactive_state, evt: gr.SelectData
):
    """
    Args:
        template_frame: PIL.Image
        point_prompt: flag for positive or negative button click
        click_state: [[points], [labels]]
    """
    if point_prompt == "Positive":
        coordinate = "[[{},{},1]]".format(evt.index[0], evt.index[1])
        interactive_state["positive_click_times"] += 1
    else:
        coordinate = "[[{},{},0]]".format(evt.index[0], evt.index[1])
        interactive_state["negative_click_times"] += 1

    # prompt for sam model
    model.samcontroler.sam_controler.reset_image()
    model.samcontroler.sam_controler.set_image(
        video_state["origin_images"][video_state["select_frame_number"]]
    )
    prompt = get_prompt(click_state=click_state, click_input=coordinate)

    mask, logit, painted_image = model.first_frame_click(
        image=video_state["origin_images"][video_state["select_frame_number"]],
        points=np.array(prompt["input_point"]),
        labels=np.array(prompt["input_label"]),
        multimask=prompt["multimask_output"],
    )
    video_state["masks"][video_state["select_frame_number"]] = mask
    video_state["logits"][video_state["select_frame_number"]] = logit
    video_state["painted_images"][video_state["select_frame_number"]] = painted_image

    operation_log = [
        ("", ""),
        (
            "Use SAM for segment. You can try add positive and negative points by clicking. Or press Clear clicks button to refresh the image. Press Add mask button when you are satisfied with the segment",
            "Normal",
        ),
    ]
    return painted_image, video_state, interactive_state, operation_log


def add_multi_mask(video_state, interactive_state, mask_dropdown):
    try:
        mask = video_state["masks"][video_state["select_frame_number"]]
        interactive_state["multi_mask"]["masks"].append(mask)
        interactive_state["multi_mask"]["mask_names"].append(
            "mask_{:03d}".format(len(interactive_state["multi_mask"]["masks"]))
        )
        mask_dropdown.append(
            "mask_{:03d}".format(len(interactive_state["multi_mask"]["masks"]))
        )
        select_frame, run_status = show_mask(
            video_state, interactive_state, mask_dropdown
        )

        operation_log = [
            ("", ""),
            (
                "Added a mask, use the mask select for target tracking or inpainting.",
                "Normal",
            ),
        ]
    except:
        operation_log = [
            ("Please click the left image to generate mask.", "Error"),
            ("", ""),
        ]
    return (
        interactive_state,
        gr.update(
            choices=interactive_state["multi_mask"]["mask_names"], value=mask_dropdown
        ),
        select_frame,
        [[], []],
        operation_log,
    )


def add_mask_object_detection(video_state, interactive_state):
    seg_mask = video_state["masks"][video_state["select_frame_number"]]
    mask_save_path = f"result/mask_{time.time()}.png"
    if not os.path.exists(os.path.dirname(mask_save_path)):
        os.makedirs(os.path.dirname(mask_save_path))
    seg_mask_img = Image.fromarray(seg_mask.astype("int") * 255.0)
    if seg_mask_img.mode != "RGB":
        seg_mask_img = seg_mask_img.convert("RGB")
    seg_mask_img.save(mask_save_path)
    print("seg_mask path: ", mask_save_path)
    print("seg_mask.shape: ", seg_mask.shape)

    image = video_state["origin_images"][video_state["select_frame_number"]]
    if seg_mask is None:
        seg_mask = np.ones(image.size).astype(bool)

    image = load_image(image, return_type="pil")
    seg_mask = load_image(seg_mask, return_type="pil")

    seg_mask = seg_mask.resize(image.size)
    seg_mask = np.array(seg_mask) > 0

    box = new_seg_to_box(seg_mask)

    if np.array(box).size == 4:
        # [x0, y0, x1, y1], where (x0, y0), (x1, y1) represent top-left and bottom-right corners
        size = max(image.width, image.height)
        x1, y1, x2, y2 = box
        image_crop = np.array(image.crop((x1 * size, y1 * size, x2 * size, y2 * size)))
    elif np.array(box).size == 8:  # four corners of an irregular rectangle
        image_crop = cut_box(np.array(image), box)

    crop_save_path = f"result/crop_{time.time()}.png"
    image_crop_image = Image.fromarray(image_crop)
    image_crop_image.save(crop_save_path)
    print(f"croped image saved in {crop_save_path}")

    chat_state = CONV_VISION.copy()
    img_list = []
    chat.upload_img(image_crop_image, chat_state, img_list)
    user_message = "What’s the object name in this image?"
    chat.ask(user_message, chat_state)
    object_description = chat.answer(
        conv=chat_state,
        img_list=img_list,
        num_beams=1,
        temperature=1,
        max_new_tokens=300,
        max_length=2000,
    )[0]

    interactive_state["masks_object"].append(object_description)
    return video_state, interactive_state


def load_image(image: Union[np.ndarray, Image.Image, str], return_type="numpy"):
    """
    Load image from path or PIL.Image or numpy.ndarray to required format.
    """

    # Check if image is already in return_type
    if (
        isinstance(image, Image.Image)
        and return_type == "pil"
        or isinstance(image, np.ndarray)
        and return_type == "numpy"
    ):
        return image

    # PIL.Image as intermediate format
    if isinstance(image, str):
        image = Image.open(image)
    elif isinstance(image, np.ndarray):
        image = Image.fromarray(image)

    if return_type == "pil":
        return image
    elif return_type == "numpy":
        return np.asarray(image)
    else:
        raise NotImplementedError()


def new_seg_to_box(seg_mask: Union[np.ndarray, Image.Image, str]):
    if type(seg_mask) == str:
        seg_mask = Image.open(seg_mask)
    elif type(seg_mask) == np.ndarray:
        seg_mask = Image.fromarray(seg_mask)
    seg_mask = np.array(seg_mask) > 0
    size = max(seg_mask.shape[0], seg_mask.shape[1])
    top, bottom = boundary(seg_mask)
    left, right = boundary(seg_mask.T)
    return [left / size, top / size, right / size, bottom / size]


def boundary(inputs):
    col = inputs.shape[1]
    inputs = inputs.reshape(-1)
    lens = len(inputs)

    start = np.argmax(inputs)
    end = lens - 1 - np.argmax(np.flip(inputs))

    top = start // col
    bottom = end // col

    return top, bottom


def cut_box(img, rect_points):
    w, h = get_w_h(rect_points)
    dst_pts = np.array(
        [
            [h, 0],
            [h, w],
            [0, w],
            [0, 0],
        ],
        dtype="float32",
    )
    transform = cv2.getPerspectiveTransform(rect_points.astype("float32"), dst_pts)
    cropped_img = cv2.warpPerspective(img, transform, (h, w))
    return cropped_img


def get_w_h(rect_points):
    w = np.linalg.norm(rect_points[0] - rect_points[1], ord=2).astype("int")
    h = np.linalg.norm(rect_points[0] - rect_points[3], ord=2).astype("int")
    return w, h


def clear_click(video_state, click_state):
    click_state = [[], []]
    template_frame = video_state["origin_images"][video_state["select_frame_number"]]
    operation_log = [
        ("", ""),
        ("Clear points history and refresh the image.", "Normal"),
    ]
    return template_frame, click_state, operation_log


def remove_multi_mask(interactive_state, mask_dropdown):
    interactive_state["multi_mask"]["mask_names"] = []
    interactive_state["multi_mask"]["masks"] = []

    operation_log = [("", ""), ("Remove all mask, please add new masks", "Normal")]
    return interactive_state, gr.update(choices=[], value=[]), operation_log


def show_mask(video_state, interactive_state, mask_dropdown):
    mask_dropdown.sort()
    select_frame = video_state["origin_images"][video_state["select_frame_number"]]

    for i in range(len(mask_dropdown)):
        mask_number = int(mask_dropdown[i].split("_")[1]) - 1
        mask = interactive_state["multi_mask"]["masks"][mask_number]
        select_frame = mask_painter(
            select_frame, mask.astype("uint8"), mask_color=mask_number + 2
        )

    operation_log = [
        ("", ""),
        ("Select {} for tracking or inpainting".format(mask_dropdown), "Normal"),
    ]
    return select_frame, operation_log


# tracking vos
def vos_tracking_video(
    video_state,
    interactive_state,
    mask_dropdown,
    video_description,
    language="en",
    font_size=30,
    color1=0,
    color2=0,
    color3=0,
):
    operation_log = [
        ("", ""),
        (
            "Track the selected masks, and then you can select the masks for inpainting.",
            "Normal",
        ),
    ]
    model.xmem.clear_memory()
    if interactive_state["track_end_number"]:
        following_frames = video_state["origin_images"][
            video_state["select_frame_number"] : interactive_state["track_end_number"]
        ]
    else:
        following_frames = video_state["origin_images"][
            video_state["select_frame_number"] :
        ]

    if interactive_state["multi_mask"]["masks"]:
        if len(mask_dropdown) == 0:
            mask_dropdown = ["mask_001"]
        mask_dropdown.sort()
        template_mask = interactive_state["multi_mask"]["masks"][
            int(mask_dropdown[0].split("_")[1]) - 1
        ] * (int(mask_dropdown[0].split("_")[1]))
        for i in range(1, len(mask_dropdown)):
            mask_number = int(mask_dropdown[i].split("_")[1]) - 1
            template_mask = np.clip(
                template_mask
                + interactive_state["multi_mask"]["masks"][mask_number]
                * (mask_number + 1),
                0,
                mask_number + 1,
            )
        video_state["masks"][video_state["select_frame_number"]] = template_mask
    else:
        template_mask = video_state["masks"][video_state["select_frame_number"]]
    fps = video_state["fps"]

    objects_descriptions = []
    for i in range(len(mask_dropdown)):
        mask_number = int(mask_dropdown[i].split("_")[1]) - 1
        objects_descriptions.append(
            f"{mask_number}. {interactive_state['masks_object'][mask_number]}"
        )

    # operation error
    if len(np.unique(template_mask)) == 1:
        template_mask[0][0] = 1
        operation_log = [
            (
                "Error! Please add at least one mask to track by clicking the left image.",
                "Error",
            ),
            ("", ""),
        ]
        # return video_output, video_state, interactive_state, operation_error
    masks, logits, painted_images = model.generator(
        images=following_frames,
        template_mask=template_mask,
        video_description=video_description,
        objects_descriptions=objects_descriptions,
        language=language,
        font_size=int(font_size),
        color1=int(color1),
        color2=int(color2),
        color3=int(color3),
    )
    # clear GPU memory
    model.xmem.clear_memory()

    if interactive_state["track_end_number"]:
        video_state["masks"][
            video_state["select_frame_number"] : interactive_state["track_end_number"]
        ] = masks
        video_state["logits"][
            video_state["select_frame_number"] : interactive_state["track_end_number"]
        ] = logits
        video_state["painted_images"][
            video_state["select_frame_number"] : interactive_state["track_end_number"]
        ] = painted_images
    else:
        video_state["masks"][video_state["select_frame_number"] :] = masks
        video_state["logits"][video_state["select_frame_number"] :] = logits
        video_state["painted_images"][
            video_state["select_frame_number"] :
        ] = painted_images

    video_output = generate_video_from_frames(
        video_state["painted_images"],
        output_path="./result/track/{}".format(video_state["video_name"]),
        fps=fps,
    )  # import video_input to name the output video
    interactive_state["inference_times"] += 1

    print(
        "For generating this tracking result, inference times: {}, click times: {}, positive: {}, negative: {}".format(
            interactive_state["inference_times"],
            interactive_state["positive_click_times"]
            + interactive_state["negative_click_times"],
            interactive_state["positive_click_times"],
            interactive_state["negative_click_times"],
        )
    )

    #### shanggao code for mask save
    if interactive_state["mask_save"]:
        if not os.path.exists(
            "./result/mask/{}".format(video_state["video_name"].split(".")[0])
        ):
            os.makedirs(
                "./result/mask/{}".format(video_state["video_name"].split(".")[0])
            )
        i = 0
        print("save mask")
        for mask in video_state["masks"]:
            np.save(
                os.path.join(
                    "./result/mask/{}".format(video_state["video_name"].split(".")[0]),
                    "{:05d}.npy".format(i),
                ),
                mask,
            )
            i += 1
        # save_mask(video_state["masks"], video_state["video_name"])
    #### shanggao code for mask save
    return video_output, video_state, interactive_state, operation_log


# extracting masks from mask_dropdown
# def extract_sole_mask(video_state, mask_dropdown):
#     combined_masks =
#     unique_masks = np.unique(combined_masks)
#     return 0


# inpaint
def inpaint_video(video_state, interactive_state, mask_dropdown):
    operation_log = [("", ""), ("Removed the selected masks.", "Normal")]

    frames = np.asarray(video_state["origin_images"])
    fps = video_state["fps"]
    inpaint_masks = np.asarray(video_state["masks"])
    if len(mask_dropdown) == 0:
        mask_dropdown = ["mask_001"]
    mask_dropdown.sort()
    # convert mask_dropdown to mask numbers
    inpaint_mask_numbers = [
        int(mask_dropdown[i].split("_")[1]) for i in range(len(mask_dropdown))
    ]
    # interate through all masks and remove the masks that are not in mask_dropdown
    unique_masks = np.unique(inpaint_masks)
    num_masks = len(unique_masks) - 1
    for i in range(1, num_masks + 1):
        if i in inpaint_mask_numbers:
            continue
        inpaint_masks[inpaint_masks == i] = 0
    # inpaint for videos

    try:
        inpainted_frames = model.baseinpainter.inpaint(
            frames, inpaint_masks, ratio=interactive_state["resize_ratio"]
        )  # numpy array, T, H, W, 3
    except:
        operation_log = [
            (
                "Error! You are trying to inpaint without masks input. Please track the selected mask first, and then press inpaint. If VRAM exceeded, please use the resize ratio to scaling down the image size.",
                "Error",
            ),
            ("", ""),
        ]
        inpainted_frames = video_state["origin_images"]
    video_output = generate_video_from_frames(
        inpainted_frames,
        output_path="./result/inpaint/{}".format(video_state["video_name"]),
        fps=fps,
    )  # import video_input to name the output video

    return video_output, operation_log


# generate video after vos inference
def generate_video_from_frames(frames, output_path, fps=30):
    """
    Generates a video from a list of frames.

    Args:
        frames (list of numpy arrays): The frames to include in the video.
        output_path (str): The path to save the generated video.
        fps (int, optional): The frame rate of the output video. Defaults to 30.
    """
    # height, width, layers = frames[0].shape
    # fourcc = cv2.VideoWriter_fourcc(*"mp4v")
    # video = cv2.VideoWriter(output_path, fourcc, fps, (width, height))
    # print(output_path)
    # for frame in frames:
    #     video.write(frame)

    # video.release()
    frames = torch.from_numpy(np.asarray(frames))
    if not os.path.exists(os.path.dirname(output_path)):
        os.makedirs(os.path.dirname(output_path))
    torchvision.io.write_video(output_path, frames, fps=fps, video_codec="libx264")
    return output_path


# args, defined in track_anything.py
# args = parse_augment()

# check and download checkpoints if needed
SAM_checkpoint_dict = {
    "vit_h": "sam_vit_h_4b8939.pth",
    "vit_l": "sam_vit_l_0b3195.pth",
    "vit_b": "sam_vit_b_01ec64.pth",
}
SAM_checkpoint_url_dict = {
    "vit_h": "https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth",
    "vit_l": "https://dl.fbaipublicfiles.com/segment_anything/sam_vit_l_0b3195.pth",
    "vit_b": "https://dl.fbaipublicfiles.com/segment_anything/sam_vit_b_01ec64.pth",
}
sam_checkpoint = SAM_checkpoint_dict[args.sam_model_type]
sam_checkpoint_url = SAM_checkpoint_url_dict[args.sam_model_type]
xmem_checkpoint = "XMem-s012.pth"
xmem_checkpoint_url = (
    "https://github.com/hkchengrex/XMem/releases/download/v1.0/XMem-s012.pth"
)
e2fgvi_checkpoint = "E2FGVI-HQ-CVPR22.pth"
e2fgvi_checkpoint_id = "10wGdKSUOie0XmCr8SQ2A2FeDe-mfn5w3"

SAM_checkpoint = download_checkpoint(
    sam_checkpoint_url, args.checkpoints_dir, sam_checkpoint
)
xmem_checkpoint = download_checkpoint(
    xmem_checkpoint_url, args.checkpoints_dir, xmem_checkpoint
)
e2fgvi_checkpoint = download_checkpoint_from_google_drive(
    e2fgvi_checkpoint_id, args.checkpoints_dir, e2fgvi_checkpoint
)
# args.port = 12212
# args.device = "cuda:1"
# args.mask_save = True

# initialize sam, xmem, e2fgvi models
model = TrackingAnything(SAM_checkpoint, xmem_checkpoint, e2fgvi_checkpoint, args)

title = """<p><h1 align="center">Characterize-Anything</h1></p>"""

with gr.Blocks() as iface:
    """
    state for
    """
    click_state = gr.State([[], []])
    interactive_state = gr.State(
        {
            "inference_times": 0,
            "negative_click_times": 0,
            "positive_click_times": 0,
            "mask_save": args.mask_save,
            "multi_mask": {"mask_names": [], "masks": []},
            "track_end_number": None,
            "resize_ratio": 1,
            "masks_object": [],
        }
    )

    video_state = gr.State(
        {
            "user_name": "",
            "video_name": "",
            "origin_images": None,
            "painted_images": None,
            "masks": None,
            "inpaint_masks": None,
            "logits": None,
            "select_frame_number": 0,
            "fps": 30,
        }
    )
    gr.Markdown(title)
    with gr.Row():
        # for user video input
        with gr.Column():
            with gr.Row(scale=0.4):
                video_input = gr.Video(autosize=True)
                with gr.Column():
                    video_info = gr.Textbox(label="Video Info")
                    video_description = gr.Textbox(label="Video Description")
                    resize_info = gr.Textbox(
                        value="If you want to use the inpaint function, it is best to git clone the repo and use a machine with more VRAM locally. \
                                            Alternatively, you can use the resize ratio slider to scale down the original image to around 360P resolution for faster processing.",
                        label="Tips for running this demo.",
                    )
                    resize_ratio_slider = gr.Slider(
                        minimum=0.02,
                        maximum=1,
                        step=0.02,
                        value=1,
                        label="Resize ratio",
                        visible=True,
                    )

            with gr.Row():
                # put the template frame under the radio button
                with gr.Column():
                    # extract frames
                    with gr.Column():
                        extract_frames_button = gr.Button(
                            value="Get video info", interactive=True, variant="primary"
                        )

                    # click points settins, negative or positive, mode continuous or single
                    with gr.Row():
                        with gr.Row():
                            point_prompt = gr.Radio(
                                choices=["Positive", "Negative"],
                                value="Positive",
                                label="Point Prompt",
                                interactive=True,
                                visible=False,
                            )
                            remove_mask_button = gr.Button(
                                value="Remove mask", interactive=True, visible=False
                            )
                            clear_button_click = gr.Button(
                                value="Clear Clicks", interactive=True, visible=False
                            ).style(height=160)
                            Add_mask_button = gr.Button(
                                value="Add mask", interactive=True, visible=False
                            )
                    template_frame = gr.Image(
                        type="pil",
                        interactive=True,
                        elem_id="template_frame",
                        visible=False,
                    ).style(height=360)
                    image_selection_slider = gr.Slider(
                        minimum=1,
                        maximum=100,
                        step=1,
                        value=1,
                        label="Image Selection",
                        visible=False,
                    )
                    track_pause_number_slider = gr.Slider(
                        minimum=1,
                        maximum=100,
                        step=1,
                        value=1,
                        label="Track end frames",
                        visible=False,
                    )

                with gr.Column():
                    run_status = gr.HighlightedText(
                        value=[
                            ("Text", "Error"),
                            ("to be", "Label 2"),
                            ("highlighted", "Label 3"),
                        ],
                        visible=False,
                    )
                    font_slider = gr.Slider(
                        minimum=10,
                        maximum=100,
                        step=5,
                        value=40,
                        label="Font Size",
                        visible=False,
                    )
                    color1_slider = gr.Slider(
                        minimum=0,
                        maximum=255,
                        step=1,
                        value=0,
                        label="Color-1 Selection",
                        visible=False,
                    )
                    color2_slider = gr.Slider(
                        minimum=0,
                        maximum=255,
                        step=1,
                        value=0,
                        label="Color-2 Selection",
                        visible=False,
                    )
                    color3_slider = gr.Slider(
                        minimum=0,
                        maximum=255,
                        step=1,
                        value=0,
                        label="Color-3 Selection",
                        visible=False,
                    )

                    lang_dropdown = gr.Dropdown(
                        choices=["English", "Chinese"],
                        value="English",
                        label="Language",
                        visible=False,
                    )
                    mask_dropdown = gr.Dropdown(
                        multiselect=True,
                        value=[],
                        label="Mask selection",
                        info=".",
                        visible=False,
                    )
                    video_output = gr.Video(autosize=True, visible=False).style(
                        height=360
                    )
                    with gr.Row():
                        tracking_video_predict_button = gr.Button(
                            value="Tracking", visible=False
                        )
                        inpaint_video_predict_button = gr.Button(
                            value="Inpaint", visible=False
                        )

    # first step: get the video information
    extract_frames_button.click(
        fn=get_frames_from_video,
        inputs=[video_input, video_state],
        outputs=[
            video_state,
            video_info,
            video_description,
            template_frame,
            image_selection_slider,
            track_pause_number_slider,
            point_prompt,
            clear_button_click,
            Add_mask_button,
            template_frame,
            tracking_video_predict_button,
            video_output,
            lang_dropdown,
            font_slider,
            color1_slider,
            color2_slider,
            color3_slider,
            mask_dropdown,
            remove_mask_button,
            inpaint_video_predict_button,
            run_status,
        ],
    )

    # second step: select images from slider
    image_selection_slider.release(
        fn=select_template,
        inputs=[image_selection_slider, video_state, interactive_state],
        outputs=[template_frame, video_state, interactive_state, run_status],
        api_name="select_image",
    )
    track_pause_number_slider.release(
        fn=get_end_number,
        inputs=[track_pause_number_slider, video_state, interactive_state],
        outputs=[template_frame, interactive_state, run_status],
        api_name="end_image",
    )
    resize_ratio_slider.release(
        fn=get_resize_ratio,
        inputs=[resize_ratio_slider, interactive_state],
        outputs=[interactive_state],
        api_name="resize_ratio",
    )

    # click select image to get mask using sam
    template_frame.select(
        fn=sam_refine,
        inputs=[video_state, point_prompt, click_state, interactive_state],
        outputs=[template_frame, video_state, interactive_state, run_status],
    )

    # add different mask
    Add_mask_button.click(
        fn=add_multi_mask,
        inputs=[video_state, interactive_state, mask_dropdown],
        outputs=[
            interactive_state,
            mask_dropdown,
            template_frame,
            click_state,
            run_status,
        ],
    ).then(
        fn=add_mask_object_detection,
        inputs=[video_state, interactive_state],
        outputs=[video_state, interactive_state],
    )

    remove_mask_button.click(
        fn=remove_multi_mask,
        inputs=[interactive_state, mask_dropdown],
        outputs=[interactive_state, mask_dropdown, run_status],
    )

    # tracking video from select image and mask
    tracking_video_predict_button.click(
        fn=vos_tracking_video,
        inputs=[
            video_state,
            interactive_state,
            mask_dropdown,
            video_description,
            lang_dropdown,
            font_slider,
            color1_slider,
            color2_slider,
            color3_slider,
        ],
        outputs=[video_output, video_state, interactive_state, run_status],
    )

    # inpaint video from select image and mask
    inpaint_video_predict_button.click(
        fn=inpaint_video,
        inputs=[video_state, interactive_state, mask_dropdown],
        outputs=[video_output, run_status],
    )

    # click to get mask
    mask_dropdown.change(
        fn=show_mask,
        inputs=[video_state, interactive_state, mask_dropdown],
        outputs=[template_frame, run_status],
    )

    # clear input
    video_input.clear(
        lambda: (
            {
                "user_name": "",
                "video_name": "",
                "origin_images": None,
                "painted_images": None,
                "masks": None,
                "inpaint_masks": None,
                "logits": None,
                "select_frame_number": 0,
                "fps": 30,
            },
            {
                "inference_times": 0,
                "negative_click_times": 0,
                "positive_click_times": 0,
                "mask_save": args.mask_save,
                "multi_mask": {"mask_names": [], "masks": []},
                "track_end_number": 0,
                "resize_ratio": 1,
            },
            [[], []],
            None,
            None,
            gr.update(visible=False),
            gr.update(visible=False),
            gr.update(visible=False),
            gr.update(visible=False),
            gr.update(visible=False),
            gr.update(visible=False),
            gr.update(visible=False),
            gr.update(visible=False),
            gr.update(visible=False),
            gr.update(visible=False, value=[]),
            gr.update(visible=False),
            gr.update(visible=False),
            gr.update(visible=False),
        ),
        [],
        [
            video_state,
            interactive_state,
            click_state,
            video_output,
            template_frame,
            tracking_video_predict_button,
            image_selection_slider,
            track_pause_number_slider,
            point_prompt,
            clear_button_click,
            Add_mask_button,
            template_frame,
            tracking_video_predict_button,
            video_output,
            mask_dropdown,
            remove_mask_button,
            inpaint_video_predict_button,
            run_status,
        ],
        queue=False,
        show_progress=False,
    )

    # points clear
    clear_button_click.click(
        fn=clear_click,
        inputs=[
            video_state,
            click_state,
        ],
        outputs=[template_frame, click_state, run_status],
    )
    # set example
    gr.Markdown("##  Examples")
    gr.Examples(
        examples=[
            os.path.join(os.path.dirname(__file__), "./test_sample/", test_sample)
            for test_sample in [
                "test-sample1.mp4",
            ]
        ],
        fn=run_example,
        inputs=[video_input],
        outputs=[video_input],
        # cache_examples=True,
    )
iface.queue(concurrency_count=1)
iface.launch(
    debug=True, enable_queue=True, server_port=args.port, server_name="0.0.0.0"
)
# iface.launch(debug=True, enable_queue=True)