import os
import cv2
import time
import tqdm
import numpy as np
import dearpygui.dearpygui as dpg

import torch
import torch.nn.functional as F

import trimesh
import rembg

from cam_utils import orbit_camera, OrbitCamera
from mesh_renderer import Renderer

# from kiui.lpips import LPIPS

class GUI:
    def __init__(self, opt):
        self.opt = opt  # shared with the trainer's opt to support in-place modification of rendering parameters.
        self.gui = opt.gui # enable gui
        self.W = opt.W
        self.H = opt.H
        self.cam = OrbitCamera(opt.W, opt.H, r=opt.radius, fovy=opt.fovy)

        self.mode = "image"
        self.seed = "random"

        self.buffer_image = np.ones((self.W, self.H, 3), dtype=np.float32)
        self.need_update = True  # update buffer_image

        # models
        self.device = torch.device("cuda")
        self.bg_remover = None

        self.guidance_sd = None
        self.guidance_zero123 = None

        self.enable_sd = False
        self.enable_zero123 = False

        # renderer
        self.renderer = Renderer(opt).to(self.device)

        # input image
        self.input_img = None
        self.input_mask = None
        self.input_img_torch = None
        self.input_mask_torch = None
        self.overlay_input_img = False
        self.overlay_input_img_ratio = 0.5

        # input text
        self.prompt = ""
        self.negative_prompt = ""

        # training stuff
        self.training = False
        self.optimizer = None
        self.step = 0
        self.train_steps = 1  # steps per rendering loop
        # self.lpips_loss = LPIPS(net='vgg').to(self.device)
        
        # load input data from cmdline
        if self.opt.input is not None:
            self.load_input(self.opt.input)
        
        # override prompt from cmdline
        if self.opt.prompt is not None:
            self.prompt = self.opt.prompt
        if self.opt.negative_prompt is not None:
            self.negative_prompt = self.opt.negative_prompt
        
        if self.gui:
            dpg.create_context()
            self.register_dpg()
            self.test_step()

    def __del__(self):
        if self.gui:
            dpg.destroy_context()

    def seed_everything(self):
        try:
            seed = int(self.seed)
        except:
            seed = np.random.randint(0, 1000000)

        os.environ["PYTHONHASHSEED"] = str(seed)
        np.random.seed(seed)
        torch.manual_seed(seed)
        torch.cuda.manual_seed(seed)
        torch.backends.cudnn.deterministic = True
        torch.backends.cudnn.benchmark = True

        self.last_seed = seed

    def prepare_train(self):

        self.step = 0

        # setup training
        self.optimizer = torch.optim.Adam(self.renderer.get_params(), betas=(0.95, 0.999))

        # default camera
        pose = orbit_camera(self.opt.elevation, 0, self.opt.radius)
        self.fixed_cam = (pose, self.cam.perspective)
        

        self.enable_sd = self.opt.lambda_sd > 0 and self.prompt != ""
        self.enable_zero123 = self.opt.lambda_zero123 > 0 and self.input_img is not None

        # lazy load guidance model
        if self.guidance_sd is None and self.enable_sd:
            if self.opt.mvdream:
                print(f"[INFO] loading MVDream...")
                from guidance.mvdream_utils import MVDream
                self.guidance_sd = MVDream(self.device)
                print(f"[INFO] loaded MVDream!")
            else:
                print(f"[INFO] loading SD...")
                from guidance.sd_utils import StableDiffusion
                self.guidance_sd = StableDiffusion(self.device, sd_version="turbo")
                print(f"[INFO] loaded SD!")

        if self.guidance_zero123 is None and self.enable_zero123:
            print(f"[INFO] loading zero123...")
            # from guidance.zero123_utils import Zero123
            from guidance.zero123_cache_utils import Zero123_Cache as Zero123
            self.guidance_zero123 = Zero123(self.device, model_key='ashawkey/stable-zero123-diffusers')
            print(f"[INFO] loaded zero123!")

        # input image
        if self.input_img is not None:
            self.input_img_torch = torch.from_numpy(self.input_img).permute(2, 0, 1).unsqueeze(0).to(self.device)
            self.input_img_torch = F.interpolate(self.input_img_torch, (self.opt.ref_size, self.opt.ref_size), mode="bilinear", align_corners=False)

            self.input_mask_torch = torch.from_numpy(self.input_mask).permute(2, 0, 1).unsqueeze(0).to(self.device)
            self.input_mask_torch = F.interpolate(self.input_mask_torch, (self.opt.ref_size, self.opt.ref_size), mode="bilinear", align_corners=False)
            self.input_img_torch_channel_last = self.input_img_torch[0].permute(1,2,0).contiguous()

        # prepare embeddings
        with torch.no_grad():

            if self.enable_sd:
                self.guidance_sd.get_text_embeds([self.prompt], [self.negative_prompt])

            if self.enable_zero123:
                self.guidance_zero123.get_img_embeds(self.input_img_torch)

    def train_step(self):
        starter = torch.cuda.Event(enable_timing=True)
        ender = torch.cuda.Event(enable_timing=True)
        starter.record()


        for _ in range(self.train_steps):

            self.step += 1
            step_ratio = min(1, self.step / self.opt.iters_refine)

            loss = 0

            ### known view
            if self.input_img_torch is not None:

                ssaa = min(2.0, max(0.125, 2 * np.random.random()))
                out = self.renderer.render(*self.fixed_cam, self.opt.ref_size, self.opt.ref_size, ssaa=ssaa)
                
                # rgb loss
                image = out["image"] # [H, W, 3] in [0, 1]
                valid_mask = ((out["alpha"] > 0) & (out["viewcos"] > 0.5)).detach()
                loss = loss + F.mse_loss(image * valid_mask, self.input_img_torch_channel_last * valid_mask)

            ### novel view (manual batch)
            render_resolution = 512
            images = []
            poses = []
            vers, hors, radii = [], [], []
            # avoid too large elevation (> 80 or < -80), and make sure it always cover [-30, 30]
            min_ver = max(min(-30, -30 - self.opt.elevation), -80 - self.opt.elevation)
            max_ver = min(max(30, 30 - self.opt.elevation), 80 - self.opt.elevation)
            
            depths = []
            for _ in range(self.opt.batch_size):

                # render random view
                ver = np.random.randint(min_ver, max_ver)
                hor = np.random.randint(-180, 180)
                radius = 0

                vers.append(ver)
                hors.append(hor)
                radii.append(radius)

                pose = orbit_camera(self.opt.elevation + ver, hor, self.opt.radius + radius)
                poses.append(pose)

                # random render resolution
                ssaa = min(2.0, max(0.125, 2 * np.random.random()))
                out = self.renderer.render(pose, self.cam.perspective, render_resolution, render_resolution, ssaa=ssaa)

                depth = out["depth"].permute(2,0,1).contiguous().unsqueeze(0) # [1, 1, H, W] in [0, 1]
                # print(depth.shape)
                image = out["image"] # [H, W, 3] in [0, 1]
                image = image.permute(2,0,1).contiguous().unsqueeze(0) # [1, 3, H, W] in [0, 1]
                # print(image.shape)

                images.append(image)
                depths.append(depth)

                # images.append(image)

                # enable mvdream training
                if self.opt.mvdream:
                    for view_i in range(1, 4):
                        pose_i = orbit_camera(self.opt.elevation + ver, hor + 90 * view_i, self.opt.radius + radius)
                        poses.append(pose_i)

                        out_i = self.renderer.render(pose_i, self.cam.perspective, render_resolution, render_resolution, ssaa=ssaa)

                        image = out_i["image"].permute(2,0,1).contiguous().unsqueeze(0) # [1, 3, H, W] in [0, 1]
                        images.append(image)

            # print(len(images))
            images = torch.cat(images, dim=0)
            depths = torch.cat(depths, dim=0)
            poses = torch.from_numpy(np.stack(poses, axis=0)).to(self.device)
            # print(image.shape)

            # guidance loss
            strength = step_ratio * 0.15 + 0.8
            if self.enable_sd:
                if self.opt.mvdream:
                    # loss = loss + self.opt.lambda_sd * self.guidance_sd.train_step(images, poses, step_ratio)
                    refined_images = self.guidance_sd.refine(images, poses, strength=strength).float()
                    refined_images = F.interpolate(refined_images, (render_resolution, render_resolution), mode="bilinear", align_corners=False)
                    loss = loss + self.opt.lambda_sd * F.mse_loss(images, refined_images)
                else:
                    refined_images = self.guidance_sd.refine(images, strength=strength, guidance_scale=3.0, steps=4).float()
                    refined_images = F.interpolate(refined_images, (render_resolution, render_resolution), mode="bilinear", align_corners=False)
                    loss = loss + self.opt.lambda_sd * F.mse_loss(images, refined_images)

            if self.enable_zero123:
                # loss = loss + self.opt.lambda_zero123 * self.guidance_zero123.train_step(images, vers, hors, radii, step_ratio)
                refined_images = self.guidance_zero123.refine(images, vers, hors, radii, strength=strength, guidance_scale=self.opt.get('guidance_scale', 5.0), cache_interval=20).float()
                refined_images = F.interpolate(refined_images, (render_resolution, render_resolution), mode="bilinear", align_corners=False)
                loss = loss + self.opt.lambda_zero123 * F.mse_loss(images, refined_images)
                # loss = loss + self.opt.lambda_zero123 * self.lpips_loss(images, refined_images)
                
            # depth regularization loss, for smooth depth
            if self.opt.lambda_depth > 0:
                def compute_normals(depth_map):
                    # Assuming depth_map is a 4D tensor of shape [1, 1, H, W]

                    # Compute x and y gradients using convolution with sobel operators
                    sobel_x = torch.tensor([[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]], dtype=torch.float32).view(1, 1, 3, 3).to(depth_map.device)
                    sobel_y = torch.tensor([[-1, -2, -1], [0, 0, 0], [1, 2, 1]], dtype=torch.float32).view(1, 1, 3, 3).to(depth_map.device)

                    grad_x = F.conv2d(depth_map, sobel_x, padding=1)
                    grad_y = F.conv2d(depth_map, sobel_y, padding=1)

                    # Normal calculation
                    normals = torch.cat((-grad_x, -grad_y), dim=1)
                    # norm = torch.linalg.norm(normals, dim=1, keepdim=True)
                    # normals /= norm  # Normalize to unit length

                    return normals

                
                def compute_smoothness_loss(depth_map, normal_map, lambda_depth=0.0, lambda_normal=0.1):
                    # Assuming depth_map is a 4D tensor of shape [1, 1, H, W]
                    # and normal_map is a 4D tensor of shape [1, H, W, 3]

                    def gradient_x(img):
                        # Calculate gradient in x direction
                        return img[:, :, :, :-1] - img[:, :, :, 1:]

                    def gradient_y(img):
                        # Calculate gradient in y direction
                        return img[:, :, :-1, :] - img[:, :, 1:, :]

                    total_smoothness_loss = 0.0
                    
                    if lambda_depth > 0:
                        # Calculate gradients for depth map
                        depth_grad_x = gradient_x(depth_map)
                        depth_grad_y = gradient_y(depth_map)
                        
                        # Calculate smoothness loss for depth map
                        depth_loss_x = torch.mean(torch.abs(depth_grad_x))
                        depth_loss_y = torch.mean(torch.abs(depth_grad_y))
                        depth_smoothness_loss = lambda_depth * (depth_loss_x + depth_loss_y)
                        total_smoothness_loss = depth_smoothness_loss
                        
                    if lambda_normal > 0:
                        # Calculate gradients for normal map
                        normal_grad_x = gradient_x(normal_map)
                        normal_grad_y = gradient_y(normal_map)

                        # Calculate smoothness loss for normal map
                        normal_loss_x = torch.mean(torch.abs(normal_grad_x))
                        normal_loss_y = torch.mean(torch.abs(normal_grad_y))
                        normal_smoothness_loss = lambda_normal * (normal_loss_x + normal_loss_y)
                        
                        total_smoothness_loss += normal_smoothness_loss

                    return total_smoothness_loss
                
                normal = compute_normals(depths)

                tv_loss = self.opt.lambda_depth * compute_smoothness_loss(depths, normal)
                loss = loss + tv_loss

            # optimize step
            loss.backward()
            self.optimizer.step()
            self.optimizer.zero_grad()

        ender.record()
        torch.cuda.synchronize()
        t = starter.elapsed_time(ender)

        self.need_update = True

        if self.gui:
            dpg.set_value("_log_train_time", f"{t:.4f}ms")
            dpg.set_value(
                "_log_train_log",
                f"step = {self.step: 5d} (+{self.train_steps: 2d}) loss = {loss.item():.4f}",
            )

        # dynamic train steps (no need for now)
        # max allowed train time per-frame is 500 ms
        # full_t = t / self.train_steps * 16
        # train_steps = min(16, max(4, int(16 * 500 / full_t)))
        # if train_steps > self.train_steps * 1.2 or train_steps < self.train_steps * 0.8:
        #     self.train_steps = train_steps

    @torch.no_grad()
    def test_step(self):
        # ignore if no need to update
        if not self.need_update:
            return

        starter = torch.cuda.Event(enable_timing=True)
        ender = torch.cuda.Event(enable_timing=True)
        starter.record()

        # should update image
        if self.need_update:
            # render image

            out = self.renderer.render(self.cam.pose, self.cam.perspective, self.H, self.W)

            buffer_image = out[self.mode]  # [H, W, 3]

            if self.mode in ['depth', 'alpha']:
                buffer_image = buffer_image.repeat(1, 1, 3)
                if self.mode == 'depth':
                    buffer_image = (buffer_image - buffer_image.min()) / (buffer_image.max() - buffer_image.min() + 1e-20)

            self.buffer_image = buffer_image.contiguous().clamp(0, 1).detach().cpu().numpy()
            
            # display input_image
            if self.overlay_input_img and self.input_img is not None:
                self.buffer_image = (
                    self.buffer_image * (1 - self.overlay_input_img_ratio)
                    + self.input_img * self.overlay_input_img_ratio
                )

            self.need_update = False

        ender.record()
        torch.cuda.synchronize()
        t = starter.elapsed_time(ender)

        if self.gui:
            dpg.set_value("_log_infer_time", f"{t:.4f}ms ({int(1000/t)} FPS)")
            dpg.set_value(
                "_texture", self.buffer_image
            )  # buffer must be contiguous, else seg fault!

    
    def load_input(self, file):
        # load image
        print(f'[INFO] load image from {file}...')
        img = cv2.imread(file, cv2.IMREAD_UNCHANGED)
        if img.shape[-1] == 3:
            if self.bg_remover is None:
                self.bg_remover = rembg.new_session()
            img = rembg.remove(img, session=self.bg_remover)

        img = cv2.resize(
            img, (self.W, self.H), interpolation=cv2.INTER_AREA
        )
        img = img.astype(np.float32) / 255.0

        self.input_mask = img[..., 3:]
        # white bg
        self.input_img = img[..., :3] * self.input_mask + (
            1 - self.input_mask
        )
        # bgr to rgb
        self.input_img = self.input_img[..., ::-1].copy()

        # load prompt
        # file_prompt = file.replace("_rgba.png", "_caption.txt")
        file_prompt = file.replace(".png", "_caption.txt")
        if os.path.exists(file_prompt):
            print(f'[INFO] load prompt from {file_prompt}...')
            with open(file_prompt, "r") as f:
                self.prompt = f.read().strip()
    
    def save_model(self):
        os.makedirs(self.opt.outdir, exist_ok=True)
    
        path = os.path.join(self.opt.outdir, self.opt.save_path + '.' + self.opt.mesh_format)
        self.renderer.export_mesh(path)

        print(f"[INFO] save model to {path}.")

    def register_dpg(self):
        ### register texture

        with dpg.texture_registry(show=False):
            dpg.add_raw_texture(
                self.W,
                self.H,
                self.buffer_image,
                format=dpg.mvFormat_Float_rgb,
                tag="_texture",
            )

        ### register window

        # the rendered image, as the primary window
        with dpg.window(
            tag="_primary_window",
            width=self.W,
            height=self.H,
            pos=[0, 0],
            no_move=True,
            no_title_bar=True,
            no_scrollbar=True,
        ):
            # add the texture
            dpg.add_image("_texture")

        # dpg.set_primary_window("_primary_window", True)

        # control window
        with dpg.window(
            label="Control",
            tag="_control_window",
            width=600,
            height=self.H,
            pos=[self.W, 0],
            no_move=True,
            no_title_bar=True,
        ):
            # button theme
            with dpg.theme() as theme_button:
                with dpg.theme_component(dpg.mvButton):
                    dpg.add_theme_color(dpg.mvThemeCol_Button, (23, 3, 18))
                    dpg.add_theme_color(dpg.mvThemeCol_ButtonHovered, (51, 3, 47))
                    dpg.add_theme_color(dpg.mvThemeCol_ButtonActive, (83, 18, 83))
                    dpg.add_theme_style(dpg.mvStyleVar_FrameRounding, 5)
                    dpg.add_theme_style(dpg.mvStyleVar_FramePadding, 3, 3)

            # timer stuff
            with dpg.group(horizontal=True):
                dpg.add_text("Infer time: ")
                dpg.add_text("no data", tag="_log_infer_time")

            def callback_setattr(sender, app_data, user_data):
                setattr(self, user_data, app_data)

            # init stuff
            with dpg.collapsing_header(label="Initialize", default_open=True):

                # seed stuff
                def callback_set_seed(sender, app_data):
                    self.seed = app_data
                    self.seed_everything()

                dpg.add_input_text(
                    label="seed",
                    default_value=self.seed,
                    on_enter=True,
                    callback=callback_set_seed,
                )

                # input stuff
                def callback_select_input(sender, app_data):
                    # only one item
                    for k, v in app_data["selections"].items():
                        dpg.set_value("_log_input", k)
                        self.load_input(v)

                    self.need_update = True

                with dpg.file_dialog(
                    directory_selector=False,
                    show=False,
                    callback=callback_select_input,
                    file_count=1,
                    tag="file_dialog_tag",
                    width=700,
                    height=400,
                ):
                    dpg.add_file_extension("Images{.jpg,.jpeg,.png}")

                with dpg.group(horizontal=True):
                    dpg.add_button(
                        label="input",
                        callback=lambda: dpg.show_item("file_dialog_tag"),
                    )
                    dpg.add_text("", tag="_log_input")
                
                # overlay stuff
                with dpg.group(horizontal=True):

                    def callback_toggle_overlay_input_img(sender, app_data):
                        self.overlay_input_img = not self.overlay_input_img
                        self.need_update = True

                    dpg.add_checkbox(
                        label="overlay image",
                        default_value=self.overlay_input_img,
                        callback=callback_toggle_overlay_input_img,
                    )

                    def callback_set_overlay_input_img_ratio(sender, app_data):
                        self.overlay_input_img_ratio = app_data
                        self.need_update = True

                    dpg.add_slider_float(
                        label="ratio",
                        min_value=0,
                        max_value=1,
                        format="%.1f",
                        default_value=self.overlay_input_img_ratio,
                        callback=callback_set_overlay_input_img_ratio,
                    )

                # prompt stuff
            
                dpg.add_input_text(
                    label="prompt",
                    default_value=self.prompt,
                    callback=callback_setattr,
                    user_data="prompt",
                )

                dpg.add_input_text(
                    label="negative",
                    default_value=self.negative_prompt,
                    callback=callback_setattr,
                    user_data="negative_prompt",
                )

                # save current model
                with dpg.group(horizontal=True):
                    dpg.add_text("Save: ")

                    dpg.add_button(
                        label="model",
                        tag="_button_save_model",
                        callback=self.save_model,
                    )
                    dpg.bind_item_theme("_button_save_model", theme_button)

                    dpg.add_input_text(
                        label="",
                        default_value=self.opt.save_path,
                        callback=callback_setattr,
                        user_data="save_path",
                    )

            # training stuff
            with dpg.collapsing_header(label="Train", default_open=True):
                # lr and train button
                with dpg.group(horizontal=True):
                    dpg.add_text("Train: ")

                    def callback_train(sender, app_data):
                        if self.training:
                            self.training = False
                            dpg.configure_item("_button_train", label="start")
                        else:
                            self.prepare_train()
                            self.training = True
                            dpg.configure_item("_button_train", label="stop")

                    # dpg.add_button(
                    #     label="init", tag="_button_init", callback=self.prepare_train
                    # )
                    # dpg.bind_item_theme("_button_init", theme_button)

                    dpg.add_button(
                        label="start", tag="_button_train", callback=callback_train
                    )
                    dpg.bind_item_theme("_button_train", theme_button)

                with dpg.group(horizontal=True):
                    dpg.add_text("", tag="_log_train_time")
                    dpg.add_text("", tag="_log_train_log")

            # rendering options
            with dpg.collapsing_header(label="Rendering", default_open=True):
                # mode combo
                def callback_change_mode(sender, app_data):
                    self.mode = app_data
                    self.need_update = True

                dpg.add_combo(
                    ("image", "depth", "alpha", "normal"),
                    label="mode",
                    default_value=self.mode,
                    callback=callback_change_mode,
                )

                # fov slider
                def callback_set_fovy(sender, app_data):
                    self.cam.fovy = np.deg2rad(app_data)
                    self.need_update = True

                dpg.add_slider_int(
                    label="FoV (vertical)",
                    min_value=1,
                    max_value=120,
                    format="%d deg",
                    default_value=np.rad2deg(self.cam.fovy),
                    callback=callback_set_fovy,
                )

        ### register camera handler

        def callback_camera_drag_rotate_or_draw_mask(sender, app_data):
            if not dpg.is_item_focused("_primary_window"):
                return

            dx = app_data[1]
            dy = app_data[2]

            self.cam.orbit(dx, dy)
            self.need_update = True

        def callback_camera_wheel_scale(sender, app_data):
            if not dpg.is_item_focused("_primary_window"):
                return

            delta = app_data

            self.cam.scale(delta)
            self.need_update = True

        def callback_camera_drag_pan(sender, app_data):
            if not dpg.is_item_focused("_primary_window"):
                return

            dx = app_data[1]
            dy = app_data[2]

            self.cam.pan(dx, dy)
            self.need_update = True

        def callback_set_mouse_loc(sender, app_data):
            if not dpg.is_item_focused("_primary_window"):
                return

            # just the pixel coordinate in image
            self.mouse_loc = np.array(app_data)

        with dpg.handler_registry():
            # for camera moving
            dpg.add_mouse_drag_handler(
                button=dpg.mvMouseButton_Left,
                callback=callback_camera_drag_rotate_or_draw_mask,
            )
            dpg.add_mouse_wheel_handler(callback=callback_camera_wheel_scale)
            dpg.add_mouse_drag_handler(
                button=dpg.mvMouseButton_Middle, callback=callback_camera_drag_pan
            )

        dpg.create_viewport(
            title="Gaussian3D",
            width=self.W + 600,
            height=self.H + (45 if os.name == "nt" else 0),
            resizable=False,
        )

        ### global theme
        with dpg.theme() as theme_no_padding:
            with dpg.theme_component(dpg.mvAll):
                # set all padding to 0 to avoid scroll bar
                dpg.add_theme_style(
                    dpg.mvStyleVar_WindowPadding, 0, 0, category=dpg.mvThemeCat_Core
                )
                dpg.add_theme_style(
                    dpg.mvStyleVar_FramePadding, 0, 0, category=dpg.mvThemeCat_Core
                )
                dpg.add_theme_style(
                    dpg.mvStyleVar_CellPadding, 0, 0, category=dpg.mvThemeCat_Core
                )

        dpg.bind_item_theme("_primary_window", theme_no_padding)

        dpg.setup_dearpygui()

        ### register a larger font
        # get it from: https://github.com/lxgw/LxgwWenKai/releases/download/v1.300/LXGWWenKai-Regular.ttf
        if os.path.exists("LXGWWenKai-Regular.ttf"):
            with dpg.font_registry():
                with dpg.font("LXGWWenKai-Regular.ttf", 18) as default_font:
                    dpg.bind_font(default_font)

        # dpg.show_metrics()

        dpg.show_viewport()

    def render(self):
        assert self.gui
        while dpg.is_dearpygui_running():
            # update texture every frame
            if self.training:
                self.train_step()
            self.test_step()
            dpg.render_dearpygui_frame()
    
    # no gui mode
    def train(self, iters=500):
        if iters > 0:
            self.prepare_train()
            for i in tqdm.trange(iters):
                self.train_step()
        # save
        self.save_model()
        

if __name__ == "__main__":
    import argparse
    from omegaconf import OmegaConf

    parser = argparse.ArgumentParser()
    parser.add_argument("--config", required=True, help="path to the yaml config file")
    args, extras = parser.parse_known_args()

    # override default config from cli
    opt = OmegaConf.merge(OmegaConf.load(args.config), OmegaConf.from_cli(extras))

    # auto find mesh from stage 1
    if opt.mesh is None:
        default_path = os.path.join(opt.outdir, opt.save_path + '_mesh.' + opt.mesh_format)
        if os.path.exists(default_path):
            opt.mesh = default_path
        else:
            raise ValueError(f"Cannot find mesh from {default_path}, must specify --mesh explicitly!")

    gui = GUI(opt)

    if opt.gui:
        gui.render()
    else:
        gui.train(opt.iters_refine)
