using Pkg

# 检查并安装必要依赖
function install_dependencies()
    # PyCall 包验证
    pycall_uuid = Base.UUID("438e738f-606a-5dbb-bf0a-cddfbfd45ab0")
    if !haskey(Pkg.dependencies(), pycall_uuid)
        println("正在安装 PyCall 依赖...")
        Pkg.add("PyCall")
    end

    # Images 包验证
    images_uuid = Base.UUID("916415d5-f1e6-5110-898d-aaa5f9f070e0")
    if !haskey(Pkg.dependencies(), images_uuid)
        println("正在安装 Images 依赖...")
        Pkg.add("Images")
    end
end

# 动态加载模块
function load_modules()
    @eval using PyCall
    @eval using Images
    @eval using PyCall: PyReverseDims
end

# OpenCV初始化
function init_opencv()
    try
        global cv2 = pyimport("cv2")
    catch e
        println("正在安装 OpenCV 依赖...")
        pyimport_conda("cv2", "opencv")
        global cv2 = pyimport("cv2")
    end
end

# 视频处理主函数
function video_processor(input_file, output_file; processing_func=identity)
    # 创建视频捕获对象
    cap = cv2.VideoCapture(input_file)
    !cap.isOpened() && error("无法打开视频文件")

    # 获取视频参数
    video_params = Dict(
        :width => Int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)),
        :height => Int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)),
        :fps => cap.get(cv2.CAP_PROP_FPS),
        :total_frames => Int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
    )

    # 创建视频写入对象
    fourcc = cv2.CAP_OPENCV_MJPEG
    output = cv2.VideoWriter(output_file, fourcc, video_params[:fps], 
                            (video_params[:width], video_params[:height]))

    frame_counter = 0
    try
        while true
            ret, frame = cap.read()
            ret || break

            # 处理帧数据
            processed_frame = processing_func(frame)
            output.write(processed_frame)

            # 进度显示
            frame_counter += 1
            if frame_counter % 100 == 0
                println("已处理帧数: $(frame_counter)/$(video_params[:total_frames])")
            end
        end
    finally
        cap.release()
        output.release()
        println("视频处理完成")
    end
    return frame_counter
end

# 维度转换工具函数
function convert_dimension(input_array)
    reversed_dims = permutedims(input_array, ndims(input_array):-1:1)
    return PyReverseDims(reversed_dims)
end

# 自定义帧处理函数
function frame_analyzer(frame)
    frame_height, frame_width = size(frame)[1:2]
    
    # 定义处理参数
    kernel_size = 5  # 可调节的滤波器尺寸
    mask_coords = (162, 485, 360, 699)  # 掩膜坐标范围

    # 创建掩膜矩阵
    mask_matrix = zeros(UInt8, frame_height, frame_width)
    mask_matrix[mask_coords[1]:mask_coords[2], mask_coords[3]:mask_coords[4]] .= 1

    # 灰度转换
    grayscale = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    masked_gray = grayscale .* mask_matrix

    # 中值滤波
    blurred_image = cv2.medianBlur(masked_gray, kernel_size)

    # 边缘检测
    gradient_x = cv2.Sobel(blurred_image, cv2.CV_64F, 1, 0)
    gradient_y = cv2.Sobel(blurred_image, cv2.CV_64F, 0, 1)
    
    absolute_gradients = map(x -> cv2.convertScaleAbs(x), [gradient_x, gradient_y])
    combined_gradient = cv2.addWeighted(absolute_gradients..., 0.5, 0)

    # 归一化处理
    normalized_sobel = round.(255 * combined_gradient / maximum(combined_gradient))
    binary_edges = convert.(UInt8, normalized_sobel) .>= 68
    edge_image = UInt8.(binary_edges) .* 255

    # 轮廓检测
    contours, _ = cv2.findContours(convert_dimension(edge_image), 
                                  cv2.RETR_LIST, cv2.CHAIN_APPROX_NONE)

    # 筛选有效轮廓
    valid_contours = filter(c -> cv2.contourArea(c) > 15, contours)

    # 绘制最终结果
    if !isempty(valid_contours)
        all_points = vcat([reshape(c, :, 2) for c in valid_contours]...)
        combined_hull = cv2.convexHull(convert_dimension(all_points))
        result_frame = cv2.drawContours(convert_dimension(frame), 
                                      [combined_hull], 0, (0, 0, 255), 2)
    else
        result_frame = frame
    end
    
    return result_frame
end

# 执行主程序
function main()
    install_dependencies()
    load_modules()
    init_opencv()
    
    input_video = "D:\\2345Downloads\\Syslab\\Experiment2.mp4"
    output_video = "D:\\2345Downloads\\Syslab\\test.mp4"
    
    processed_frames = video_processor(input_video, output_video, 
                                     processing_func=frame_analyzer)
    println("总处理帧数: $processed_frames")
end

# 启动处理流程
main()