import argparse
import glob
import multiprocessing as mp
import os
import time
import cv2
import tqdm
import numpy as np
import gradio as gr
from tools.util import *

from detectron2.config import get_cfg

from detectron2.projects.deeplab import add_deeplab_config
from detectron2.data.detection_utils import read_image
from detectron2.utils.logger import setup_logger
from open_vocab_seg import add_ovseg_config

from open_vocab_seg.utils import VisualizationDemo, VisualizationDemoIndoor

# constants
WINDOW_NAME = "Open vocabulary segmentation"


def setup_cfg(args):
    # load config from file and command-line arguments
    cfg = get_cfg()
    # for poly lr schedule
    add_deeplab_config(cfg)
    add_ovseg_config(cfg)
    cfg.merge_from_file(args.config_file)
    cfg.merge_from_list(args.opts)
    cfg.freeze()
    return cfg


def get_parser():
    parser = argparse.ArgumentParser(description="Detectron2 demo for open vocabulary segmentation")
    parser.add_argument(
        "--config-file",
        default="configs/ovseg_swinB_vitL_demo.yaml",
        metavar="FILE",
        help="path to config file",
    )
    parser.add_argument(
        "--input",
        default=["/mnt/lustre/jkyang/PSG4D/sailvos3d/downloads/sailvos3d/trevor_1_int/images/000160.bmp"],
        nargs="+",
        help="A list of space separated input images; "
        "or a single glob pattern such as 'directory/*.jpg'",
    )
    parser.add_argument(
        "--class-names",
        default=["person", "car", "motorcycle", "truck", "bird", "dog", "handbag", "suitcase", "bottle", "cup", "bowl", "chair", "potted plant", "bed", "dining table", "tv", "laptop", "cell phone", "bag", "bin", "box", "door", "road barrier", "stick", "lamp", "floor", "wall"],
        nargs="+",
        help="A list of user-defined class_names"
    )
    parser.add_argument(
        "--output", 
        default = "./pred",
        help="A file or directory to save output visualizations. "
        "If not given, will show output in an OpenCV window.",
    )
    parser.add_argument(
        "--opts",
        help="Modify config options using the command-line 'KEY VALUE' pairs",
        default=["MODEL.WEIGHTS", "ovseg_swinbase_vitL14_ft_mpt.pth"],
        nargs=argparse.REMAINDER,
    )
    return parser

args = get_parser().parse_args()

def greet_sailvos3d(rgb_input, depth_map_input, rage_matrices_input, class_candidates):
    print(args.class_names)
    print(class_candidates[0], class_candidates[1], class_candidates[2], class_candidates[3],)
    print(class_candidates.split(', '))
    args.input = [rgb_input]
    args.class_names = class_candidates.split(', ')
    depth_map_path = depth_map_input.name
    rage_matrices_path = rage_matrices_input.name
    print(args.input, args.class_names, depth_map_path, rage_matrices_path)
    mp.set_start_method("spawn", force=True)
    setup_logger(name="fvcore")
    logger = setup_logger()
    logger.info("Arguments: " + str(args))

    cfg = setup_cfg(args)

    demo = VisualizationDemo(cfg)
    class_names = args.class_names
    print(args.input)
    if args.input:
        if len(args.input) == 1:
            args.input = glob.glob(os.path.expanduser(args.input[0]))
            assert args.input, "The input path(s) was not found"
        for path in tqdm.tqdm(args.input, disable=not args.output):
            # use PIL, to be consistent with evaluation
            start_time = time.time()
            predictions, visualized_output_rgb, visualized_output_depth, visualized_output_rgb_sam, visualized_output_depth_sam = demo.run_on_image_sam(path, class_names, depth_map_path, rage_matrices_path)
            logger.info(
                "{}: {} in {:.2f}s".format(
                    path,
                    "detected {} instances".format(len(predictions["instances"]))
                    if "instances" in predictions
                    else "finished",
                    time.time() - start_time,
                )
            )

            if args.output:
                if os.path.isdir(args.output):
                    assert os.path.isdir(args.output), args.output
                    out_filename = os.path.join(args.output, os.path.basename(path))
                else:
                    assert len(args.input) == 1, "Please specify a directory with args.output"
                    out_filename = args.output
                visualized_output_rgb.save('outputs/RGB_Semantic_SAM.png')
                visualized_output_depth.save('outputs/Depth_Semantic_SAM.png')
                visualized_output_rgb_sam.save('outputs/RGB_Semantic_SAM_Mask.png')
                visualized_output_depth_sam.save('outputs/Depth_Semantic_SAM_Mask.png')
                rgb_3d_sam = demo.get_xyzrgb('outputs/RGB_Semantic_SAM.png', depth_map_path, rage_matrices_path)
                depth_3d_sam = demo.get_xyzrgb('outputs/Depth_Semantic_SAM.png', depth_map_path, rage_matrices_path)
                rgb_3d_sam_mask = demo.get_xyzrgb('outputs/RGB_Semantic_SAM_Mask.png', depth_map_path, rage_matrices_path)
                depth_3d_sam_mask = demo.get_xyzrgb('outputs/Depth_Semantic_SAM_Mask.png', depth_map_path, rage_matrices_path)
                np.savez('outputs/xyzrgb.npz', rgb_3d_sam = rgb_3d_sam, depth_3d_sam = depth_3d_sam, rgb_3d_sam_mask = rgb_3d_sam_mask, depth_3d_sam_mask = depth_3d_sam_mask)
                demo.render_3d_video('outputs/xyzrgb.npz', depth_map_path)
            else:
                cv2.namedWindow(WINDOW_NAME, cv2.WINDOW_NORMAL)
                cv2.imwrite('./1.png',visualized_output_rgb.get_image()[:, :, ::-1])
                # cv2.imshow(WINDOW_NAME, visualized_output_rgb.get_image()[:, :, ::-1])
                if cv2.waitKey(0) == 27:
                    break  # esc to quit
    else:
        raise NotImplementedError
    
    Depth_Semantic_SAM_Mask = read_image('outputs/Depth_Semantic_SAM_Mask.png')
    RGB_Semantic_SAM_Mask = read_image('outputs/RGB_Semantic_SAM_Mask.png')
    Depth_Semantic_SAM = read_image('outputs/Depth_Semantic_SAM.png')
    RGB_Semantic_SAM = read_image('outputs/RGB_Semantic_SAM.png')
    two_image_to_gif(Depth_Semantic_SAM_Mask, Depth_Semantic_SAM, 'Depth_Semantic_SAM_2D')
    two_image_to_gif(RGB_Semantic_SAM_Mask, RGB_Semantic_SAM, 'RGB_Semantic_SAM_2D')
    Depth_Semantic_SAM_2D = 'outputs/Depth_Semantic_SAM_2D.gif'
    RGB_Semantic_SAM_2D = 'outputs/RGB_Semantic_SAM_2D.gif'
    Depth_map = read_image('outputs/Depth_rendered.png')
    Depth_Semantic_SAM_Mask_gif = 'outputs/Depth_3D_All.gif'
    RGB_Semantic_SAM_Mask_gif = 'outputs/RGB_3D_All.gif'
    return RGB_Semantic_SAM_2D, RGB_Semantic_SAM_Mask_gif, Depth_map, Depth_Semantic_SAM_2D, Depth_Semantic_SAM_Mask_gif

def greet_scannet(rgb_input, depth_map_input, class_candidates):
    rgb_input = rgb_input
    depth_map_input = depth_map_input.name
    class_candidates = class_candidates.split(', ')
    print(rgb_input, depth_map_input, class_candidates)
    mp.set_start_method("spawn", force=True)
    args = get_parser().parse_args()
    setup_logger(name="fvcore")
    logger = setup_logger()
    logger.info("Arguments: " + str(args))

    cfg = setup_cfg(args)

    demo = VisualizationDemoIndoor(cfg)
    from datasets.scannet_preprocess.meta_data.scannet200_constants import CLASS_LABELS_200
    class_names = list(CLASS_LABELS_200)
    """ args.input = glob.glob(os.path.expanduser(args.input[0]))
    assert args.input, "The input path(s) was not found" """
    start_time = time.time()
    predictions, output2D, output3D = demo.run_on_pcd_ui(rgb_input, depth_map_input, class_candidates)

    output2D['sem_seg_on_rgb'].save('outputs/RGB_Semantic_SAM.png')
    output2D['sem_seg_on_depth'].save('outputs/Depth_Semantic_SAM.png')
    output2D['sam_seg_on_rgb'].save('outputs/RGB_Semantic_SAM_Mask.png')
    output2D['sam_seg_on_depth'].save('outputs/Depth_Semantic_SAM_Mask.png')
    """ rgb_3d_sam = demo.get_xyzrgb('outputs/RGB_Semantic_SAM.png', path)
    depth_3d_sam = demo.get_xyzrgb('outputs/Depth_Semantic_SAM.png', path)
    rgb_3d_sam_mask = demo.get_xyzrgb('outputs/RGB_Semantic_SAM_Mask.png', path)
    depth_3d_sam_mask = demo.get_xyzrgb(outputs/'Depth_Semantic_SAM_Mask.png', path) """
    rgb_3d_sem = output3D['rgb_3d_sem']
    depth_3d_sem = output3D['depth_3d_sem']
    rgb_3d_sam = output3D['rgb_3d_sam']
    depth_3d_sam = output3D['depth_3d_sam']
    
    np.savez('outputs/xyzrgb.npz', rgb_3d_sam = rgb_3d_sem, depth_3d_sam = depth_3d_sem, rgb_3d_sam_mask = rgb_3d_sam, depth_3d_sam_mask = depth_3d_sam)
    demo.render_3d_video('outputs/xyzrgb.npz')

    Depth_Semantic_SAM_Mask = read_image('outputs/Depth_Semantic_SAM_Mask.png')
    RGB_Semantic_SAM_Mask = read_image('outputs/RGB_Semantic_SAM_Mask.png')
    Depth_Semantic_SAM = read_image('outputs/Depth_Semantic_SAM.png')
    RGB_Semantic_SAM = read_image('outputs/RGB_Semantic_SAM.png')
    two_image_to_gif(Depth_Semantic_SAM_Mask, Depth_Semantic_SAM, 'Depth_Semantic_SAM_2D')
    two_image_to_gif(RGB_Semantic_SAM_Mask, RGB_Semantic_SAM, 'RGB_Semantic_SAM_2D')
    Depth_Semantic_SAM_2D = 'outputs/Depth_Semantic_SAM_2D.gif'
    RGB_Semantic_SAM_2D = 'outputs/RGB_Semantic_SAM_2D.gif'
    Depth_map = read_image('outputs/Depth_rendered.png')
    Depth_Semantic_SAM_Mask_gif = 'outputs/Depth_3D_All.gif'
    RGB_Semantic_SAM_Mask_gif = 'outputs/RGB_3D_All.gif'
    return RGB_Semantic_SAM_2D, RGB_Semantic_SAM_Mask_gif, Depth_map, Depth_Semantic_SAM_2D, Depth_Semantic_SAM_Mask_gif


with gr.Blocks(analytics_enabled=False) as segrgbd_iface:
        gr.Markdown("<div align='center'> <h2> Semantic Segment AnyRGBD </span> </h2> \
                     <a style='font-size:18px;color: #000000' href='https://github.com/Jun-CEN/SegmentAnyRGBD'> Github </div>")
        
        #######t2v#######
        with gr.Tab(label="Dataset: Sailvos3D"):
            with gr.Column():
                with gr.Row():
                    # with gr.Tab(label='input'):
                    with gr.Column():
                        with gr.Row():
                            Input_RGB_Component = gr.Image(label = 'RGB_Input', type = 'filepath').style(width=320, height=200)
                            Depth_Map_Output_Component = gr.Image(label = "Vis_Depth_Map").style(width=320, height=200)
                        with gr.Row():
                            Depth_Map_Input_Component = gr.File(label = 'Input_Depth_map')
                            Component_2D_to_3D_Projection_Parameters = gr.File(label = '2D_to_3D_Projection_Parameters')
                        with gr.Row():
                            Class_Candidates_Component = gr.Text(label = 'Class_Candidates')
                        vc_end_btn = gr.Button("Send")
                    with gr.Tab(label='Result'):
                        with gr.Row():
                            RGB_Semantic_SAM_Mask_Component = gr.Image(label = "RGB_Semantic_SAM_Mask").style(width=320, height=200)
                            RGB_Semantic_SAM_Mask_3D_Component = gr.Image(label = "3D_RGB_Semantic_SAM_Mask").style(width=320, height=200)
                        with gr.Row():
                            Depth_Semantic_SAM_Mask_Component = gr.Image(label = "Depth_Semantic_SAM_Mask").style(width=320, height=200)
                            Depth_Semantic_SAM_Mask_3D_Component = gr.Image(label = "3D_Depth_Semantic_SAM_Mask").style(width=320, height=200)
                gr.Examples(examples=[
                        [
                            'UI/sailvos3d/ex1/inputs/rgb_000160.bmp',
                            'UI/sailvos3d/ex1/inputs/depth_000160.npy',
                            'UI/sailvos3d/ex1/inputs/rage_matrices_000160.npz',
                            'person, car, motorcycle, truck, bird, dog, handbag, suitcase, bottle, cup, bowl, chair, potted plant, bed, dining table, tv, laptop, cell phone, bag, bin, box, door, road barrier, stick, lamp, floor, wall',
                        ],
                        [
                            'UI/sailvos3d/ex2/inputs/rgb_000540.bmp',
                            'UI/sailvos3d/ex2/inputs/depth_000540.npy',
                            'UI/sailvos3d/ex2/inputs/rage_matrices_000540.npz',
                            'person, car, motorcycle, truck, bird, dog, handbag, suitcase, bottle, cup, bowl, chair, potted plant, bed, dining table, tv, laptop, cell phone, bag, bin, box, door, road barrier, stick, lamp, floor, wall',
                        ],
                        [
                            'UI/sailvos3d/ex3/inputs/rgb_000100.bmp',
                            'UI/sailvos3d/ex3/inputs/depth_000100.npy',
                            'UI/sailvos3d/ex3/inputs/rage_matrices_000100.npz',
                            'person, car, motorcycle, truck, bird, dog, handbag, suitcase, bottle, cup, bowl, chair, potted plant, bed, dining table, tv, laptop, cell phone, bag, bin, box, door, road barrier, stick, lamp, floor, wall',
                        ],
                        ],
                            inputs=[Input_RGB_Component, Depth_Map_Input_Component, Component_2D_to_3D_Projection_Parameters, Class_Candidates_Component],
                            outputs=[RGB_Semantic_SAM_Mask_Component, RGB_Semantic_SAM_Mask_3D_Component, Depth_Map_Output_Component, Depth_Semantic_SAM_Mask_Component, Depth_Semantic_SAM_Mask_3D_Component],
                            fn=greet_sailvos3d)
            vc_end_btn.click(inputs=[Input_RGB_Component, Depth_Map_Input_Component, Component_2D_to_3D_Projection_Parameters, Class_Candidates_Component],
                            outputs=[RGB_Semantic_SAM_Mask_Component, RGB_Semantic_SAM_Mask_3D_Component, Depth_Map_Output_Component, Depth_Semantic_SAM_Mask_Component, Depth_Semantic_SAM_Mask_3D_Component],
                            fn=greet_sailvos3d)
        
        with gr.Tab(label="Dataset: Scannet"):
            with gr.Column():
                with gr.Row():
                    # with gr.Tab(label='input'):
                    with gr.Column():
                        with gr.Row():
                            Input_RGB_Component = gr.Image(label = 'RGB_Input', type = 'filepath').style(width=320, height=200)
                            Depth_Map_Output_Component = gr.Image(label = "Vis_Depth_Map").style(width=320, height=200)
                        with gr.Row():
                            Depth_Map_Input_Component = gr.File(label = "Input_Depth_Map")
                            Class_Candidates_Component = gr.Text(label = 'Class_Candidates')
                        vc_end_btn = gr.Button("Send")
                    with gr.Tab(label='Result'):
                        with gr.Row():
                            RGB_Semantic_SAM_Mask_Component = gr.Image(label = "RGB_Semantic_SAM_Mask").style(width=320, height=200)
                            RGB_Semantic_SAM_Mask_3D_Component = gr.Image(label = "3D_RGB_Semantic_SAM_Mask").style(width=320, height=200)
                        with gr.Row():
                            Depth_Semantic_SAM_Mask_Component = gr.Image(label = "Depth_Semantic_SAM_Mask").style(width=320, height=200)
                            Depth_Semantic_SAM_Mask_3D_Component = gr.Image(label = "3D_Depth_Semantic_SAM_Mask").style(width=320, height=200)
                gr.Examples(examples=[
                        [
                            'UI/scannetv2/examples/scene0000_00/color/1660.jpg',
                            'UI/scannetv2/examples/scene0000_00/depth/1660.png',
                            'wall, floor, cabinet, bed, chair, sofa, table, door, window, bookshelf, picture, counter, desk, curtain, refrigerator, shower curtain, toilet, sink, bathtub, other furniture',
                        ],
                        [
                            'UI/scannetv2/examples/scene0000_00/color/5560.jpg',
                            'UI/scannetv2/examples/scene0000_00/depth/5560.png',
                            'wall, floor, cabinet, bed, chair, sofa, table, door, window, bookshelf, picture, counter, desk, curtain, refrigerator, shower curtain, toilet, sink, bathtub, other furniture',
                        ],
                        [
                            'UI/scannetv2/examples/scene0000_00/color/2760.jpg',
                            'UI/scannetv2/examples/scene0000_00/depth/2760.png',
                            'wall, floor, cabinet, bed, chair, sofa, table, door, window, bookshelf, picture, counter, desk, curtain, refrigerator, shower curtain, toilet, sink, bathtub, other furniture',
                        ],
                        [
                            'UI/scannetv2/examples/scene0000_00/color/3700.jpg',
                            'UI/scannetv2/examples/scene0000_00/depth/3700.png',
                            'wall, floor, cabinet, bed, chair, sofa, table, door, window, bookshelf, picture, counter, desk, curtain, refrigerator, shower curtain, toilet, sink, bathtub, guitar, other furniture',
                        ],
                        ],
                            inputs=[Input_RGB_Component, Depth_Map_Input_Component, Class_Candidates_Component],
                            outputs=[RGB_Semantic_SAM_Mask_Component, RGB_Semantic_SAM_Mask_3D_Component, Depth_Map_Output_Component, Depth_Semantic_SAM_Mask_Component, Depth_Semantic_SAM_Mask_3D_Component],
                            fn=greet_scannet)
            vc_end_btn.click(inputs=[Input_RGB_Component, Depth_Map_Input_Component, Class_Candidates_Component],
                            outputs=[RGB_Semantic_SAM_Mask_Component, RGB_Semantic_SAM_Mask_3D_Component, Depth_Map_Output_Component, Depth_Semantic_SAM_Mask_Component, Depth_Semantic_SAM_Mask_3D_Component],
                            fn=greet_scannet)


demo = segrgbd_iface
demo.launch()

基于以上代码，写一个test代码，使得不使用ui，而是直接保存输出的图像