SAM2Long本地部署,视频分割处理,绿幕抠像,超长视频支持

分享一个视频分割神器 SAM2Long。该项目可以一键抠出视频中的主体，快速输出绿幕视频或者是透明通道视频。SAM2Long是SAM2的优化版本，使得 SAM2Long 在处理遮挡、目标重现等长视频常见问题时表现得更加稳健。

本地部署

部署有两种方式，一种是源码部署，一种是使用我已经制作好的一键包。

源码部署

安装PyTorch环境

python 复制代码

pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu128

安装本地SAM2代码库

python 复制代码

pip install -e .

下载模型检查点

Linux/macOS使用脚本下载

创建脚本文件：

plain 复制代码

mkdir -p SAM2Long/checkpoints
nano SAM2Long/checkpoints/download_ckpts.sh

download_ckpts.sh内容

python 复制代码

#!/bin/bash

# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.

# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.

# Use either wget or curl to download the checkpoints
if command -v wget &> /dev/null; then
    CMD="wget"
elif command -v curl &> /dev/null; then
    CMD="curl -L -O"
else
    echo "Please install wget or curl to download the checkpoints."
    exit 1
fi

# Define the URLs for SAM 2 checkpoints
# SAM2_BASE_URL="https://dl.fbaipublicfiles.com/segment_anything_2/072824"
# sam2_hiera_t_url="${SAM2_BASE_URL}/sam2_hiera_tiny.pt"
# sam2_hiera_s_url="${SAM2_BASE_URL}/sam2_hiera_small.pt"
# sam2_hiera_b_plus_url="${SAM2_BASE_URL}/sam2_hiera_base_plus.pt"
# sam2_hiera_l_url="${SAM2_BASE_URL}/sam2_hiera_large.pt"

# Download each of the four checkpoints using wget
# echo "Downloading sam2_hiera_tiny.pt checkpoint..."
# $CMD $sam2_hiera_t_url || { echo "Failed to download checkpoint from $sam2_hiera_t_url"; exit 1; }

# echo "Downloading sam2_hiera_small.pt checkpoint..."
# $CMD $sam2_hiera_s_url || { echo "Failed to download checkpoint from $sam2_hiera_s_url"; exit 1; }

# echo "Downloading sam2_hiera_base_plus.pt checkpoint..."
# $CMD $sam2_hiera_b_plus_url || { echo "Failed to download checkpoint from $sam2_hiera_b_plus_url"; exit 1; }

# echo "Downloading sam2_hiera_large.pt checkpoint..."
# $CMD $sam2_hiera_l_url || { echo "Failed to download checkpoint from $sam2_hiera_l_url"; exit 1; }

# Define the URLs for SAM 2.1 checkpoints
SAM2p1_BASE_URL="https://dl.fbaipublicfiles.com/segment_anything_2/092824"
sam2p1_hiera_t_url="${SAM2p1_BASE_URL}/sam2.1_hiera_tiny.pt"
sam2p1_hiera_s_url="${SAM2p1_BASE_URL}/sam2.1_hiera_small.pt"
sam2p1_hiera_b_plus_url="${SAM2p1_BASE_URL}/sam2.1_hiera_base_plus.pt"
sam2p1_hiera_l_url="${SAM2p1_BASE_URL}/sam2.1_hiera_large.pt"

# SAM 2.1 checkpoints
echo "Downloading sam2.1_hiera_tiny.pt checkpoint..."
$CMD $sam2p1_hiera_t_url || { echo "Failed to download checkpoint from $sam2p1_hiera_t_url"; exit 1; }

echo "Downloading sam2.1_hiera_small.pt checkpoint..."
$CMD $sam2p1_hiera_s_url || { echo "Failed to download checkpoint from $sam2p1_hiera_s_url"; exit 1; }

echo "Downloading sam2.1_hiera_base_plus.pt checkpoint..."
$CMD $sam2p1_hiera_b_plus_url || { echo "Failed to download checkpoint from $sam2p1_hiera_b_plus_url"; exit 1; }

echo "Downloading sam2.1_hiera_large.pt checkpoint..."
$CMD $sam2p1_hiera_l_url || { echo "Failed to download checkpoint from $sam2p1_hiera_l_url"; exit 1; }

echo "All checkpoints are downloaded successfully."

添加执行权限：

plain 复制代码

chmod +x SAM2Long/checkpoints/download_ckpts.sh

运行脚本：

plain 复制代码

cd SAM2Long/checkpoints && ./download_ckpts.sh

Windows脚本下载

创建批处理文件：

SAM2Long\checkpoints\download_ckpts.bat

bat脚本内容

python 复制代码

@echo off
rem Copyright (c) Meta Platforms, Inc. and affiliates.
rem All rights reserved.

rem This source code is licensed under the license found in the
rem LICENSE file in the root directory of this source tree.

rem Check for wget or curl
set CMD=""
where wget >nul 2>&1
if %ERRORLEVEL% equ 0 (
    set CMD=wget -O
) else (
    where curl >nul 2>&1
    if %ERRORLEVEL% equ 0 (
        set CMD=curl -L -o
    ) else (
        rem 如果既没有wget也没有curl，尝试使用PowerShell的Invoke-WebRequest
        echo Neither wget nor curl found. Trying PowerShell Invoke-WebRequest...
        set CMD=powershell
    )
)

rem Define the URLs for SAM 2.1 checkpoints
set SAM2p1_BASE_URL=https://dl.fbaipublicfiles.com/segment_anything_2/092824
set sam2p1_hiera_t_url=%SAM2p1_BASE_URL%/sam2.1_hiera_tiny.pt
set sam2p1_hiera_s_url=%SAM2p1_BASE_URL%/sam2.1_hiera_small.pt
set sam2p1_hiera_b_plus_url=%SAM2p1_BASE_URL%/sam2.1_hiera_base_plus.pt
set sam2p1_hiera_l_url=%SAM2p1_BASE_URL%/sam2.1_hiera_large.pt

rem SAM 2.1 checkpoints
echo Downloading sam2.1_hiera_tiny.pt checkpoint...
if "%CMD%"=="wget -O" (
    wget -O sam2.1_hiera_tiny.pt %sam2p1_hiera_t_url%
) else if "%CMD%"=="curl -L -o" (
    curl -L -o sam2.1_hiera_tiny.pt %sam2p1_hiera_t_url%
) else (
    powershell -Command "Invoke-WebRequest -Uri '%sam2p1_hiera_t_url%' -OutFile 'sam2.1_hiera_tiny.pt'"
)
if %ERRORLEVEL% neq 0 (
    echo Failed to download checkpoint from %sam2p1_hiera_t_url%
    exit /b 1
)

echo Downloading sam2.1_hiera_small.pt checkpoint...
if "%CMD%"=="wget -O" (
    wget -O sam2.1_hiera_small.pt %sam2p1_hiera_s_url%
) else if "%CMD%"=="curl -L -o" (
    curl -L -o sam2.1_hiera_small.pt %sam2p1_hiera_s_url%
) else (
    powershell -Command "Invoke-WebRequest -Uri '%sam2p1_hiera_s_url%' -OutFile 'sam2.1_hiera_small.pt'"
)
if %ERRORLEVEL% neq 0 (
    echo Failed to download checkpoint from %sam2p1_hiera_s_url%
    exit /b 1
)

echo Downloading sam2.1_hiera_base_plus.pt checkpoint...
if "%CMD%"=="wget -O" (
    wget -O sam2.1_hiera_base_plus.pt %sam2p1_hiera_b_plus_url%
) else if "%CMD%"=="curl -L -o" (
    curl -L -o sam2.1_hiera_base_plus.pt %sam2p1_hiera_b_plus_url%
) else (
    powershell -Command "Invoke-WebRequest -Uri '%sam2p1_hiera_b_plus_url%' -OutFile 'sam2.1_hiera_base_plus.pt'"
)
if %ERRORLEVEL% neq 0 (
    echo Failed to download checkpoint from %sam2p1_hiera_b_plus_url%
    exit /b 1
)

echo Downloading sam2.1_hiera_large.pt checkpoint...
if "%CMD%"=="wget -O" (
    wget -O sam2.1_hiera_large.pt %sam2p1_hiera_l_url%
) else if "%CMD%"=="curl -L -o" (
    curl -L -o sam2.1_hiera_large.pt %sam2p1_hiera_l_url%
) else (
    powershell -Command "Invoke-WebRequest -Uri '%sam2p1_hiera_l_url%' -OutFile 'sam2.1_hiera_large.pt'"
)
if %ERRORLEVEL% neq 0 (
    echo Failed to download checkpoint from %sam2p1_hiera_l_url%
    exit /b 1
)

echo All checkpoints are downloaded successfully.

创建完成后，双击运行批处理文件。

手动下载

也可以直接访问以下四个链接下载对应模型

Tiny模型

plain 复制代码

https://dl.fbaipublicfiles.com/segment_anything_2/092824/sam2.1_hiera_tiny.pt

Small模型

plain 复制代码

https://dl.fbaipublicfiles.com/segment_anything_2/092824/sam2.1_hiera_small.pt

Base+模型

plain 复制代码

https://dl.fbaipublicfiles.com/segment_anything_2/092824/sam2.1_hiera_base_plus.pt

Large模型

plain 复制代码

https://dl.fbaipublicfiles.com/segment_anything_2/092824/sam2.1_hiera_large.pt

模型说明：包含四种大小的预训练模型（Tiny<Small<Base<Large），根据机器配置自行选择。

安装Gradio

python 复制代码

pip install gradio

到这里安装就完成了，关于推理部分的实现，可以参考下方

基础调用（以DAVIS数据集为例）

python 复制代码

python ./tools/vos_inference.py \
  --sam2_cfg configs/sam2.1/sam2.1_hiera_b+.yaml \   # 配置文件
  --sam2_checkpoint ./checkpoints/sam2.1_hiera_base_plus.pt \  # 模型文件
  --base_video_dir /你的视频文件夹路径 \  # 存放视频的文件夹
  --input_mask_dir /你的初始标记路径 \   # 第一帧的物体标记
  --output_mask_dir ./预测结果输出 \    # 结果保存位置
  --num_pathway 3 \      # 使用3条处理通道（平衡精度和速度）
  --iou_thre 0.1 \       # 过滤掉低质量识别（值越小要求越严）
  --uncertainty 2        # 识别稳定性设置（1-3之间调整）

初次尝试建议num_pathway=1快速测试，正式使用时设为3效果最佳

gradio界面源码:

python 复制代码

import subprocess
import re
from typing import List, Tuple, Optional
import spaces

# Define the command to be executed
command = ["python", "setup.py", "build_ext", "--inplace"]

# Execute the command
result = subprocess.run(command, capture_output=True, text=True)

css="""
div#component-18, div#component-25, div#component-35, div#component-41{
    align-items: stretch!important;
}
"""

# Print the output and error (if any)
print("Output:\n", result.stdout)
print("Errors:\n", result.stderr)

# Check if the command was successful
if result.returncode == 0:
    print("Command executed successfully.")
else:
    print("Command failed with return code:", result.returncode)

import gradio as gr
from datetime import datetime
import os
os.environ["TORCH_CUDNN_SDPA_ENABLED"] = "1"
import torch
import numpy as np
import cv2
import matplotlib.pyplot as plt
from PIL import Image, ImageFilter
from sam2.build_sam import build_sam2_video_predictor

from moviepy.editor import ImageSequenceClip

def sparse_sampling(jpeg_images, original_fps, target_fps=6):
    # Calculate the frame interval for sampling based on the target fps
    frame_interval = int(original_fps // target_fps)
    
    # Sparse sample the jpeg_images by selecting every 'frame_interval' frame
    sampled_images = [jpeg_images[i] for i in range(0, len(jpeg_images), frame_interval)]
    
    return sampled_images

def get_video_fps(video_path):
    # Open the video file
    cap = cv2.VideoCapture(video_path)
    
    if not cap.isOpened():
        print("Error: Could not open video.")
        return None
    
    # Get the FPS of the video
    fps = cap.get(cv2.CAP_PROP_FPS)

    return fps

def clear_points(image):
    # we clean all
    return [
        image,   # first_frame_path
        [],      # tracking_points
        [],      # trackings_input_label
        image,   # points_map
        #gr.State()     # stored_inference_state
    ]

def preprocess_video_in(video_path):

    # Generate a unique ID based on the current date and time
    unique_id = datetime.now().strftime('%Y%m%d%H%M%S')
    
    # Set directory with this ID to store video frames 
    extracted_frames_output_dir = f'frames_{unique_id}'
    
    # Create the output directory
    os.makedirs(extracted_frames_output_dir, exist_ok=True)

    ### Process video frames ###
    # Open the video file
    cap = cv2.VideoCapture(video_path)
    
    if not cap.isOpened():
        print("Error: Could not open video.")
        return None

    # Get the frames per second (FPS) of the video
    fps = cap.get(cv2.CAP_PROP_FPS)
    
    # Calculate the number of frames to process (60 seconds of video)
    max_frames = int(fps * 60)
    
    frame_number = 0
    first_frame = None
    
    while True:
        ret, frame = cap.read()
        if not ret or frame_number >= max_frames:
            break
        if frame_number % 6 == 0:
            # Format the frame filename as '00000.jpg'
            frame_filename = os.path.join(extracted_frames_output_dir, f'{frame_number:05d}.jpg')
            
            # Save the frame as a JPEG file
            cv2.imwrite(frame_filename, frame)
        
        # Store the first frame
        if frame_number == 0:
            first_frame = frame_filename
        
        frame_number += 1
    
    # Release the video capture object
    cap.release()
    
    # scan all the JPEG frame names in this directory
    scanned_frames = [
        p for p in os.listdir(extracted_frames_output_dir)
        if os.path.splitext(p)[-1] in [".jpg", ".jpeg", ".JPG", ".JPEG"]
    ]
    scanned_frames.sort(key=lambda p: int(os.path.splitext(p)[0]))
    # print(f"SCANNED_FRAMES: {scanned_frames}")
    
    return [
        first_frame,           # first_frame_path
        [],          # tracking_points
        [],          # trackings_input_label
        first_frame,           # input_first_frame_image
        first_frame,           # points_map
        extracted_frames_output_dir,            # video_frames_dir
        scanned_frames,        # scanned_frames
        None,                  # stored_inference_state
        None,                  # stored_frame_names
        gr.update(open=False)  # video_in_drawer
    ]

def get_point(point_type, tracking_points, trackings_input_label, input_first_frame_image, evt: gr.SelectData):
    print(f"You selected {evt.value} at {evt.index} from {evt.target}")

    tracking_points.append(evt.index)
    # tracking_points.value.append(evt.index)
    print(f"TRACKING POINT: {tracking_points}")

    if point_type == "include":
        trackings_input_label.append(1)
        # trackings_input_label.value.append(1)
    elif point_type == "exclude":
        trackings_input_label.append(0)
        # trackings_input_label.value.append(0)
    print(f"TRACKING INPUT LABEL: {trackings_input_label}")
    
    # Open the image and get its dimensions
    transparent_background = Image.open(input_first_frame_image).convert('RGBA')
    w, h = transparent_background.size
    
    # Define the circle radius as a fraction of the smaller dimension
    fraction = 0.02  # You can adjust this value as needed
    radius = int(fraction * min(w, h))
    
    # Create a transparent layer to draw on
    transparent_layer = np.zeros((h, w, 4), dtype=np.uint8)
    
    for index, track in enumerate(tracking_points):
        if trackings_input_label[index] == 1:
            cv2.circle(transparent_layer, track, radius, (0, 255, 0, 255), -1)
        else:
            cv2.circle(transparent_layer, track, radius, (255, 0, 0, 255), -1)

    # Convert the transparent layer back to an image
    transparent_layer = Image.fromarray(transparent_layer, 'RGBA')
    selected_point_map = Image.alpha_composite(transparent_background, transparent_layer)
    
    return tracking_points, trackings_input_label, selected_point_map
    

    
def show_mask(mask, ax, obj_id=None, random_color=False):
    if random_color:
        color = np.concatenate([np.random.random(3), np.array([0.6])], axis=0)
    else:
        cmap = plt.get_cmap("tab10")
        cmap_idx = 0 if obj_id is None else obj_id
        color = np.array([*cmap(cmap_idx)[:3], 0.6])
    h, w = mask.shape[-2:]
    mask_image = mask.reshape(h, w, 1) * color.reshape(1, 1, -1)
    ax.imshow(mask_image)


def show_points(coords, labels, ax, marker_size=200):
    pos_points = coords[labels==1]
    neg_points = coords[labels==0]
    ax.scatter(pos_points[:, 0], pos_points[:, 1], color='green', marker='*', s=marker_size, edgecolor='white', linewidth=1.25)
    ax.scatter(neg_points[:, 0], neg_points[:, 1], color='red', marker='*', s=marker_size, edgecolor='white', linewidth=1.25)

def show_box(box, ax):
    x0, y0 = box[0], box[1]
    w, h = box[2] - box[0], box[3] - box[1]
    ax.add_patch(plt.Rectangle((x0, y0), w, h, edgecolor='green', facecolor=(0, 0, 0, 0), lw=2))    


def load_model(checkpoint):
    # Load model accordingly to user's choice
    if checkpoint == "tiny":
        sam2_checkpoint = "./checkpoints/sam2.1_hiera_tiny.pt"
        model_cfg = "configs/sam2.1/sam2.1_hiera_t.yaml"
        return [sam2_checkpoint, model_cfg]
    elif checkpoint == "samll":
        sam2_checkpoint = "./checkpoints/sam2.1_hiera_small.pt"
        model_cfg = "configs/sam2.1/sam2.1_hiera_s.yaml"
        return [sam2_checkpoint, model_cfg]
    elif checkpoint == "base-plus":
        sam2_checkpoint = "./checkpoints/sam2.1_hiera_base_plus.pt"
        model_cfg = "configs/sam2.1/sam2.1_hiera_b+.yaml"
        return [sam2_checkpoint, model_cfg]
    # elif checkpoint == "large":
    #     sam2_checkpoint = "./checkpoints/sam2.1_hiera_large.pt"
    #     model_cfg = "configs/sam2.1/sam2.1_hiera_l.yaml"
    #     return [sam2_checkpoint, model_cfg]

    

def get_mask_sam_process(
    stored_inference_state,
    input_first_frame_image, 
    checkpoint, 
    tracking_points, 
    trackings_input_label, 
    video_frames_dir, # extracted_frames_output_dir defined in 'preprocess_video_in' function
    scanned_frames, 
    working_frame: str = None, # current frame being added points
    available_frames_to_check: List[str] = [],
    # progress=gr.Progress(track_tqdm=True)
):
    
    # get model and model config paths
    print(f"USER CHOSEN CHECKPOINT: {checkpoint}")
    sam2_checkpoint, model_cfg = load_model(checkpoint)
    print("MODEL LOADED")

    # set predictor 
    
    predictor = build_sam2_video_predictor(model_cfg, sam2_checkpoint, device='cpu')
        
    print("PREDICTOR READY")

    # `video_dir` a directory of JPEG frames with filenames like `<frame_index>.jpg`
    # print(f"STATE FRAME OUTPUT DIRECTORY: {video_frames_dir}")
    video_dir = video_frames_dir
    
    # scan all the JPEG frame names in this directory
    frame_names = scanned_frames

    # print(f"STORED INFERENCE STEP: {stored_inference_state}")
    if stored_inference_state is None:
        # Init SAM2 inference_state
        inference_state = predictor.init_state(video_path=video_dir)
        inference_state['num_pathway'] = 3
        inference_state['iou_thre'] = 0.3
        inference_state['uncertainty'] = 2
        print("NEW INFERENCE_STATE INITIATED")
    else:
        inference_state = stored_inference_state



    inference_state["device"] = 'cpu'
        
    # segment and track one object
    # predictor.reset_state(inference_state) # if any previous tracking, reset
    

    ### HANDLING WORKING FRAME
    # new_working_frame = None
    # Add new point
    if working_frame is None:
        ann_frame_idx = 0  # the frame index we interact with, 0 if it is the first frame
        working_frame = "00000.jpg"
    else:
        # Use a regular expression to find the integer
        match = re.search(r'frame_(\d+)', working_frame)
        if match:
            # Extract the integer from the match
            frame_number = int(match.group(1))
            ann_frame_idx = frame_number
            
    print(f"NEW_WORKING_FRAME PATH: {working_frame}")
    
    ann_obj_id = 1  # give a unique id to each object we interact with (it can be any integers)
    
    # Let's add a positive click at (x, y) = (210, 350) to get started
    points = np.array(tracking_points, dtype=np.float32)
    # for labels, `1` means positive click and `0` means negative click
    labels = np.array(trackings_input_label, np.int32)


    _, out_obj_ids, out_mask_logits = predictor.add_new_points(
        inference_state=inference_state,
        frame_idx=ann_frame_idx,
        obj_id=ann_obj_id,
        points=points,
        labels=labels,
    )

    # Create the plot
    plt.figure(figsize=(12, 8))
    plt.title(f"frame {ann_frame_idx}")
    plt.imshow(Image.open(os.path.join(video_dir, frame_names[ann_frame_idx])))
    show_points(points, labels, plt.gca())
    show_mask((out_mask_logits[0] > 0.0).cpu().numpy(), plt.gca(), obj_id=out_obj_ids[0])
    
    # Save the plot as a JPG file
    first_frame_output_filename = "output_first_frame.jpg"
    plt.savefig(first_frame_output_filename, format='jpg')
    plt.close()
    # torch.cuda.empty_cache()

    # Assuming available_frames_to_check.value is a list
    if working_frame not in available_frames_to_check:
        available_frames_to_check.append(working_frame)
        print(available_frames_to_check)
    
    # return gr.update(visible=True), "output_first_frame.jpg", frame_names, predictor, inference_state, gr.update(choices=available_frames_to_check, value=working_frame, visible=True)
    return "output_first_frame.jpg", frame_names, predictor, inference_state, gr.update(choices=available_frames_to_check, value=working_frame, visible=False)

@spaces.GPU
def propagate_to_all(video_in, checkpoint, stored_inference_state, stored_frame_names, video_frames_dir, vis_frame_type, available_frames_to_check, working_frame, progress=gr.Progress(track_tqdm=True)):   
    # use bfloat16 for the entire notebook
    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
    
    #### PROPAGATION ####
    sam2_checkpoint, model_cfg = load_model(checkpoint)
    # set predictor 
   
    inference_state = stored_inference_state

    if torch.cuda.is_available():
        inference_state["device"] = 'cuda'
        predictor = build_sam2_video_predictor(model_cfg, sam2_checkpoint)
    else:
        inference_state["device"] = 'cpu'
        predictor = build_sam2_video_predictor(model_cfg, sam2_checkpoint, device='cpu')
    

    
    frame_names = stored_frame_names
    video_dir = video_frames_dir
    
    # Define a directory to save the JPEG images
    frames_output_dir = "frames_output_images"
    os.makedirs(frames_output_dir, exist_ok=True)
    
    # Initialize a list to store file paths of saved images
    jpeg_images = []

    # run propagation throughout the video and collect the results in a dict
    video_segments = {}  # video_segments contains the per-frame segmentation results
    # for out_frame_idx, out_obj_ids, out_mask_logits in predictor.propagate_in_video(inference_state):
    #     video_segments[out_frame_idx] = {
    #         out_obj_id: (out_mask_logits[i] > 0.0).cpu().numpy()
    #         for i, out_obj_id in enumerate(out_obj_ids)
    #     }
    
    out_obj_ids, out_mask_logits = predictor.propagate_in_video(inference_state, start_frame_idx=0, reverse=False,)
    print(out_obj_ids)
    for frame_idx in range(0, inference_state['num_frames']):
        
        video_segments[frame_idx] = {out_obj_ids[0]: (out_mask_logits[frame_idx]> 0.0).cpu().numpy()}
        # output_scores_per_object[object_id][frame_idx] = out_mask_logits[frame_idx].cpu().numpy()

    # render the segmentation results every few frames
    if vis_frame_type == "check":
        vis_frame_stride = 15
    elif vis_frame_type == "render":
        vis_frame_stride = 1
    
    plt.close("all")
    for out_frame_idx in range(0, len(frame_names), vis_frame_stride):
        plt.figure(figsize=(6, 4))
        plt.title(f"frame {out_frame_idx}")
        plt.imshow(Image.open(os.path.join(video_dir, frame_names[out_frame_idx])))
        for out_obj_id, out_mask in video_segments[out_frame_idx].items():
            show_mask(out_mask, plt.gca(), obj_id=out_obj_id)

        # Define the output filename and save the figure as a JPEG file
        output_filename = os.path.join(frames_output_dir, f"frame_{out_frame_idx}.jpg")
        plt.savefig(output_filename, format='jpg')
    
        # Close the plot
        plt.close()

        # Append the file path to the list
        jpeg_images.append(output_filename)

        if f"frame_{out_frame_idx}.jpg" not in available_frames_to_check:
            available_frames_to_check.append(f"frame_{out_frame_idx}.jpg")

    torch.cuda.empty_cache()
    print(f"JPEG_IMAGES: {jpeg_images}")

    if vis_frame_type == "check":
        return gr.update(value=jpeg_images), gr.update(value=None), gr.update(choices=available_frames_to_check, value=working_frame, visible=True), available_frames_to_check, gr.update(visible=True)
    elif vis_frame_type == "render":
        # Create a video clip from the image sequence
        original_fps = get_video_fps(video_in)
        # sampled_images = sparse_sampling(jpeg_images, original_fps, target_fps=6)
        clip = ImageSequenceClip(jpeg_images, fps=original_fps//6)
        # clip = ImageSequenceClip(jpeg_images, fps=fps)
        # Write the result to a file
        final_vid_output_path = "output_video.mp4"
        
        # Write the result to a file
        clip.write_videofile(
            final_vid_output_path,
            codec='libx264'
        )
        
        return gr.update(value=None), gr.update(value=final_vid_output_path), working_frame, available_frames_to_check, gr.update(visible=True)

def update_ui(vis_frame_type):
    if vis_frame_type == "check":
        return gr.update(visible=True), gr.update(visible=False)
    elif vis_frame_type == "render":
        return gr.update(visible=False), gr.update(visible=True)

def switch_working_frame(working_frame, scanned_frames, video_frames_dir):
    new_working_frame = None
    if working_frame == None:
        new_working_frame = os.path.join(video_frames_dir, scanned_frames[0])
        
    else:
        # Use a regular expression to find the integer
        match = re.search(r'frame_(\d+)', working_frame)
        if match:
            # Extract the integer from the match
            frame_number = int(match.group(1))
            ann_frame_idx = frame_number
            new_working_frame = os.path.join(video_frames_dir, scanned_frames[ann_frame_idx])
    return gr.State([]), gr.State([]), new_working_frame, new_working_frame

def reset_propagation(first_frame_path, predictor, stored_inference_state):
    
    predictor.reset_state(stored_inference_state)
    # print(f"RESET State: {stored_inference_state} ")
    return first_frame_path, [], [], gr.update(value=None, visible=False), stored_inference_state, None, ["frame_0.jpg"], first_frame_path, "frame_0.jpg", gr.update(visible=False)


with gr.Blocks(css=css) as demo:
    first_frame_path = gr.State()
    tracking_points = gr.State([])
    trackings_input_label = gr.State([])
    video_frames_dir = gr.State()
    scanned_frames = gr.State()
    loaded_predictor = gr.State()
    stored_inference_state = gr.State()
    stored_frame_names = gr.State()
    available_frames_to_check = gr.State([])
    with gr.Column():
        gr.Markdown(
            """
            <h1 style="text-align: center;">🔥 SAM2Long Demo 🔥</h1>
            """
        )
        gr.Markdown(
            """
            This is a simple demo for video segmentation with [SAM2Long](https://github.com/Mark12Ding/SAM2Long).
            """
        )
        gr.Markdown(
            """
            ### 📋 Instructions:
            It is largely built on the [SAM2-Video-Predictor](https://huggingface.co/spaces/fffiloni/SAM2-Video-Predictor).
            1. **Upload your video** [MP4-24fps]
            2. With **'include' point type** selected, click on the object to mask on the first frame
            3. Switch to **'exclude' point type** if you want to specify an area to avoid
            4. **Get Mask!**
            5. **Check Propagation** every 15 frames
            6. **Propagate with "render"** to render the final masked video
            7. **Hit Reset** button if you want to refresh and start again
            
            *Note: Input video will be processed for up to 60 seconds only for demo purposes.*
            """
        )
        with gr.Row():
            
            with gr.Column():
                with gr.Group():
                    with gr.Group():
                        with gr.Row():
                            point_type = gr.Radio(label="point type", choices=["include", "exclude"], value="include", scale=2)
                            clear_points_btn = gr.Button("Clear Points", scale=1)
                    
                    input_first_frame_image = gr.Image(label="input image", interactive=False, type="filepath", visible=False)                 
                    
                    points_map = gr.Image(
                        label="Point n Click map", 
                        type="filepath",
                        interactive=False
                    )
    
                    with gr.Group():
                        with gr.Row():
                            checkpoint = gr.Dropdown(label="Checkpoint", choices=["tiny", "small", "base-plus"], value="tiny")
                            submit_btn = gr.Button("Get Mask", size="lg")

                with gr.Accordion("Your video IN", open=True) as video_in_drawer:
                    video_in = gr.Video(label="Video IN", format="mp4")

                gr.HTML("""
                
                <a href="https://huggingface.co/spaces/{os.environ['SPACE_ID']}?duplicate=true">
                    <img src="https://huggingface.co/datasets/huggingface/badges/resolve/main/duplicate-this-space-lg-dark.svg" alt="Duplicate this Space" />
                </a> to skip queue and avoid OOM errors from heavy public load
                """)
            
            with gr.Column():
                with gr.Group():
                    # with gr.Group():
                        # with gr.Row():
                    working_frame = gr.Dropdown(label="working frame ID", choices=["frame_0.jpg"], value="frame_0.jpg", visible=False, allow_custom_value=False, interactive=True)
                        #     change_current = gr.Button("change current", visible=False)
                    # working_frame = []
                    output_result = gr.Image(label="current working mask ref")
                with gr.Group():
                    with gr.Row():
                        vis_frame_type = gr.Radio(label="Propagation level", choices=["check", "render"], value="check", scale=2)
                        propagate_btn = gr.Button("Propagate", scale=2)

                reset_prpgt_brn = gr.Button("Reset", visible=False)
                output_propagated = gr.Gallery(label="Propagated Mask samples gallery", columns=4, visible=False)
                output_video = gr.Video(visible=False)
                # output_result_mask = gr.Image()
    
    

    # When new video is uploaded
    video_in.upload(
        fn = preprocess_video_in, 
        inputs = [video_in], 
        outputs = [
            first_frame_path, 
            tracking_points, # update Tracking Points in the gr.State([]) object
            trackings_input_label, # update Tracking Labels in the gr.State([]) object
            input_first_frame_image, # hidden component used as ref when clearing points
            points_map, # Image component where we add new tracking points
            video_frames_dir, # Array where frames from video_in are deep stored
            scanned_frames, # Scanned frames by SAM2
            stored_inference_state, # Sam2 inference state
            stored_frame_names, # 
            video_in_drawer, # Accordion to hide uploaded video player
        ],
        queue = False
    )

    
    # triggered when we click on image to add new points
    points_map.select(
        fn = get_point, 
        inputs = [
            point_type, # "include" or "exclude"
            tracking_points, # get tracking_points values
            trackings_input_label, # get tracking label values
            input_first_frame_image, # gr.State() first frame path
        ], 
        outputs = [
            tracking_points, # updated with new points
            trackings_input_label, # updated with corresponding labels
            points_map, # updated image with points
        ], 
        queue = False
    )

    # Clear every points clicked and added to the map
    clear_points_btn.click(
        fn = clear_points,
        inputs = input_first_frame_image, # we get the untouched hidden image
        outputs = [
            first_frame_path, 
            tracking_points, 
            trackings_input_label, 
            points_map, 
            #stored_inference_state,
        ],
        queue=False
    )

    
    # change_current.click(
    #     fn = switch_working_frame,
    #     inputs = [working_frame, scanned_frames, video_frames_dir],
    #     outputs = [tracking_points, trackings_input_label, input_first_frame_image, points_map],
    #     queue=False
    # )
    

    submit_btn.click(
        fn = get_mask_sam_process,
        inputs = [
            stored_inference_state,
            input_first_frame_image, 
            checkpoint, 
            tracking_points, 
            trackings_input_label, 
            video_frames_dir, 
            scanned_frames, 
            working_frame,
            available_frames_to_check,
        ],
        outputs = [
            output_result, 
            stored_frame_names, 
            loaded_predictor,
            stored_inference_state,
            working_frame,
        ],
        queue=False
    )

    reset_prpgt_brn.click(
        fn = reset_propagation,
        inputs = [first_frame_path, loaded_predictor, stored_inference_state],
        outputs = [points_map, tracking_points, trackings_input_label, output_propagated, stored_inference_state, output_result, available_frames_to_check, input_first_frame_image, working_frame, reset_prpgt_brn],
        queue=False
    )


    propagate_btn.click(
        fn = update_ui,
        inputs = [vis_frame_type],
        outputs = [output_propagated, output_video],
        queue=False
    ).then(
        fn = propagate_to_all,
        inputs = [video_in, checkpoint, stored_inference_state, stored_frame_names, video_frames_dir, vis_frame_type, available_frames_to_check, working_frame],
        outputs = [output_propagated, output_video, working_frame, available_frames_to_check, reset_prpgt_brn]
    )

demo.launch()