# Copyright 2024 Anton Obukhov, ETH Zurich. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# --------------------------------------------------------------------------
# If you find this code useful, we kindly ask you to cite our paper in your work.
# Please find bibtex at: https://github.com/prs-eth/Marigold#-citation
# More information about the method can be found at https://marigoldmonodepth.github.io
# --------------------------------------------------------------------------
from __future__ import annotations

import functools
import os
import tempfile

import diffusers
import gradio as gr
import imageio as imageio
import numpy as np
import spaces
import torch as torch
from PIL import Image
from gradio_imageslider import ImageSlider
from tqdm import tqdm

from pathlib import Path
import gradio
from gradio.utils import get_cache_folder
from stablenormal.pipeline_yoso_normal import YOSONormalsPipeline
from stablenormal.pipeline_stablenormal import StableNormalPipeline
from stablenormal.scheduler.heuristics_ddimsampler import HEURI_DDIMScheduler

class Examples(gradio.helpers.Examples):
    def __init__(self, *args, directory_name=None, **kwargs):
        super().__init__(*args, **kwargs, _initiated_directly=False)
        if directory_name is not None:
            self.cached_folder = get_cache_folder() / directory_name
            self.cached_file = Path(self.cached_folder) / "log.csv"
        self.create()


default_seed = 2024
default_batch_size = 1

default_image_processing_resolution = 768

default_video_num_inference_steps = 10
default_video_processing_resolution = 768
default_video_out_max_frames = 450

def process_image_check(path_input):
    if path_input is None:
        raise gr.Error(
            "Missing image in the first pane: upload a file or use one from the gallery below."
        )

def resize_image(input_image, resolution):
    # Ensure input_image is a PIL Image object
    if not isinstance(input_image, Image.Image):
        raise ValueError("input_image should be a PIL Image object")

    # Convert image to numpy array
    input_image_np = np.asarray(input_image)

    # Get image dimensions
    H, W, C = input_image_np.shape
    H = float(H)
    W = float(W)
    
    # Calculate the scaling factor
    k = float(resolution) / min(H, W)
    
    # Determine new dimensions
    H *= k
    W *= k
    H = int(np.round(H / 64.0)) * 64
    W = int(np.round(W / 64.0)) * 64
    
    # Resize the image using PIL's resize method
    img = input_image.resize((W, H), Image.LANCZOS if k > 1 else Image.ANTIALIAS)
    
    return img

def process_image(
    pipe,
    path_input,
):
    name_base, name_ext = os.path.splitext(os.path.basename(path_input))
    print(f"Processing image {name_base}{name_ext}")

    path_output_dir = tempfile.mkdtemp()
    path_out_png = os.path.join(path_output_dir, f"{name_base}_normal_colored.png")

    input_image = Image.open(path_input)
    input_image = resize_image(input_image, default_image_processing_resolution)

    pipe_out = pipe(
        input_image,
        match_input_resolution=False,
        processing_resolution=max(input_image.size)
    )

    normal_pred = pipe_out.prediction[0, :, :]
    normal_colored = pipe.image_processor.visualize_normals(pipe_out.prediction)
    normal_colored[-1].save(path_out_png)
    return [input_image, path_out_png]

def center_crop(img):
    # Open the image file
    img_width, img_height = img.size
    crop_width =min(img_width, img_height)
    # Calculate the cropping box
    left = (img_width - crop_width) / 2
    top = (img_height - crop_width) / 2
    right = (img_width + crop_width) / 2
    bottom = (img_height + crop_width) / 2
    
    # Crop the image
    img_cropped = img.crop((left, top, right, bottom))
    return img_cropped

def process_video(
    pipe,
    path_input,
    out_max_frames=default_video_out_max_frames,
    target_fps=3,
    progress=gr.Progress(),
):
    if path_input is None:
        raise gr.Error(
            "Missing video in the first pane: upload a file or use one from the gallery below."
        )

    name_base, name_ext = os.path.splitext(os.path.basename(path_input))
    print(f"Processing video {name_base}{name_ext}")

    path_output_dir = tempfile.mkdtemp()
    path_out_vis = os.path.join(path_output_dir, f"{name_base}_normal_colored.mp4")

    reader, writer = None, None
    try:
        reader = imageio.get_reader(path_input)

        meta_data = reader.get_meta_data()
        fps = meta_data["fps"]
        size = meta_data["size"]
        duration_sec = meta_data["duration"]

        writer = imageio.get_writer(path_out_vis, fps=target_fps)

        out_frame_id = 0
        pbar = tqdm(desc="Processing Video", total=duration_sec)

        for frame_id, frame in enumerate(reader):
            if frame_id % (fps // target_fps) != 0:
                continue
            else:
                out_frame_id += 1
                pbar.update(1)
            if out_frame_id > out_max_frames:
                break

            frame_pil = Image.fromarray(frame)
            frame_pil = center_crop(frame_pil)
            pipe_out = pipe(
                frame_pil,
                match_input_resolution=False,
            )

            processed_frame = pipe.image_processor.visualize_normals(  # noqa
                pipe_out.prediction
            )[0]
            processed_frame = np.array(processed_frame)

            _processed_frame = imageio.core.util.Array(processed_frame)
            writer.append_data(_processed_frame)
            
            yield (
                [frame_pil, processed_frame],
                None,
            )
    finally:

        if writer is not None:
            writer.close()

        if reader is not None:
            reader.close()

    yield (
        [frame_pil, processed_frame],
        [path_out_vis,]
    )


def run_demo_server(pipe):
    process_pipe_image = spaces.GPU(functools.partial(process_image, pipe))
    process_pipe_video = spaces.GPU(
        functools.partial(process_video, pipe), duration=120
    )

    gradio_theme = gr.themes.Default()

    with gr.Blocks(
        theme=gradio_theme,
        title="Stable Normal Estimation",
        css="""
            #download {
                height: 118px;
            }
            .slider .inner {
                width: 5px;
                background: #FFF;
            }
            .viewport {
                aspect-ratio: 4/3;
            }
            .tabs button.selected {
                font-size: 20px !important;
                color: crimson !important;
            }
            h1 {
                text-align: center;
                display: block;
            }
            h2 {
                text-align: center;
                display: block;
            }
            h3 {
                text-align: center;
                display: block;
            }
            .md_feedback li {
                margin-bottom: 0px !important;
            }
        """,
        head="""
            <script async src="https://www.googletagmanager.com/gtag/js?id=G-1FWSVCGZTG"></script>
            <script>
                window.dataLayer = window.dataLayer || [];
                function gtag() {dataLayer.push(arguments);}
                gtag('js', new Date());
                gtag('config', 'G-1FWSVCGZTG');
            </script>
        """,
    ) as demo:
        gr.Markdown(
            """
            # StableNormal: Reducing Diffusion Variance for Stable and Sharp Normal
            <p align="center">
        """
        )

        with gr.Tabs(elem_classes=["tabs"]):
            with gr.Tab("Image"):
                with gr.Row():
                    with gr.Column():
                        image_input = gr.Image(
                            label="Input Image",
                            type="filepath",
                        )
                        with gr.Row():
                            image_submit_btn = gr.Button(
                                value="Compute Normal", variant="primary"
                            )
                            image_reset_btn = gr.Button(value="Reset")
                    with gr.Column():
                        image_output_slider = ImageSlider(
                            label="Normal outputs",
                            type="filepath",
                            show_download_button=True,
                            show_share_button=True,
                            interactive=False,
                            elem_classes="slider",
                            position=0.25,
                        )

                Examples(
                    fn=process_pipe_image,
                    examples=sorted([
                        os.path.join("files", "image", name)
                        for name in os.listdir(os.path.join("files", "image"))
                    ]),
                    inputs=[image_input],
                    outputs=[image_output_slider],
                    cache_examples=True,
                    directory_name="examples_image",
                )

            with gr.Tab("Video"):
                with gr.Row():
                    with gr.Column():
                        video_input = gr.Video(
                            label="Input Video",
                            sources=["upload", "webcam"],
                        )
                        with gr.Row():
                            video_submit_btn = gr.Button(
                                value="Compute Normal", variant="primary"
                            )
                            video_reset_btn = gr.Button(value="Reset")
                    with gr.Column():
                        processed_frames = ImageSlider(
                            label="Realtime Visualization",
                            type="filepath",
                            show_download_button=True,
                            show_share_button=True,
                            interactive=False,
                            elem_classes="slider",
                            position=0.25,
                        )
                        video_output_files = gr.Files(
                            label="Normal outputs",
                            elem_id="download",
                            interactive=False,
                        )
                Examples(
                    fn=process_pipe_video,
                    examples=sorted([
                        os.path.join("files", "video", name)
                        for name in os.listdir(os.path.join("files", "video"))
                    ]),
                    inputs=[video_input],
                    outputs=[processed_frames, video_output_files],
                    directory_name="examples_video",
                    cache_examples=True,
                )
                
            with gr.Tab("Panorama"):
                with gr.Column():
                    gr.Markdown("Functionality coming soon on June.10th")

            with gr.Tab("4K Image"):
                with gr.Column():
                    gr.Markdown("Functionality coming soon on June.17th")

            with gr.Tab("Normal Mapping"):
                with gr.Column():
                    gr.Markdown("Functionality coming soon on June.24th")
            
            with gr.Tab("Normal SuperResolution"):
                with gr.Column():
                    gr.Markdown("Functionality coming soon on June.30th")

        ### Image tab
        image_submit_btn.click(
            fn=process_image_check,
            inputs=image_input,
            outputs=None,
            preprocess=False,
            queue=False,
        ).success(
            fn=process_pipe_image,
            inputs=[
                image_input,
            ],
            outputs=[image_output_slider],
            concurrency_limit=1,
        )

        image_reset_btn.click(
            fn=lambda: (
                None,
                None,
                None,
            ),
            inputs=[],
            outputs=[
                image_input,
                image_output_slider,
            ],
            queue=False,
        )

        ### Video tab

        video_submit_btn.click(
            fn=process_pipe_video,
            inputs=[video_input],
            outputs=[processed_frames, video_output_files],
            concurrency_limit=1,
        )

        video_reset_btn.click(
            fn=lambda: (None, None, None),
            inputs=[],
            outputs=[video_input, processed_frames, video_output_files],
            concurrency_limit=1,
        )

        ### Server launch

        demo.queue(
            api_open=False,
        ).launch(
            server_name="0.0.0.0",
            server_port=7860,
        )

from einops import rearrange  
class DINOv2_Encoder:
    IMAGENET_DEFAULT_MEAN = [0.485, 0.456, 0.406]
    IMAGENET_DEFAULT_STD = [0.229, 0.224, 0.225]

    def __init__(
        self,
        model_name = 'dinov2_vitl14',
        freeze = True,
        antialias=True,
        device="cuda",
        size = 448,
    ):

        super(DINOv2_Encoder).__init__()

        self.model = torch.hub.load('facebookresearch/dinov2', model_name)
        self.model.eval()
        self.device = device
        self.antialias = antialias
        self.dtype = torch.float32

        self.mean = torch.Tensor(self.IMAGENET_DEFAULT_MEAN)
        self.std = torch.Tensor(self.IMAGENET_DEFAULT_STD)
        self.size = size
        if freeze:
            self.freeze()


    def freeze(self):
        for param in self.model.parameters():
            param.requires_grad = False

    @torch.no_grad()
    def encoder(self, x):
        '''
        x: [b h w c], range from (-1, 1), rbg
        '''

        x = self.preprocess(x).to(self.device, self.dtype)

        b, c, h, w = x.shape
        patch_h, patch_w = h // 14, w // 14

        embeddings = self.model.forward_features(x)['x_norm_patchtokens']
        embeddings = rearrange(embeddings, 'b (h w) c -> b h w c', h = patch_h, w = patch_w)

        return  rearrange(embeddings, 'b h w c -> b c h w')

    def preprocess(self, x):
        ''' x
        '''
        # normalize to [0,1],
        x = torch.nn.functional.interpolate(
            x,
            size=(self.size, self.size),
            mode='bicubic',
            align_corners=True,
            antialias=self.antialias,
        )

        x = (x + 1.0) / 2.0
        # renormalize according to dino
        mean = self.mean.view(1, 3, 1, 1).to(x.device)
        std = self.std.view(1, 3, 1, 1).to(x.device)
        x = (x - mean) / std

        return x
    
    def to(self, device, dtype=None):
        if dtype is not None:
            self.dtype = dtype
            self.model.to(device, dtype)
            self.mean.to(device, dtype)
            self.std.to(device, dtype)
        else:
            self.model.to(device)
            self.mean.to(device)
            self.std.to(device)
        return self

    def __call__(self, x, **kwargs):
        return self.encoder(x, **kwargs)
    
def main():
    os.system("pip freeze")

    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

    x_start_pipeline = YOSONormalsPipeline.from_pretrained(
        'Stable-X/yoso-normal-v0-1', trust_remote_code=True,
        t_start=300).to(device)
    dinov2_prior = DINOv2_Encoder(size=672)
    dinov2_prior.to(device)
    
    pipe = StableNormalPipeline.from_pretrained('Stable-X/stable-normal-v0-1', t_start=300, trust_remote_code=True,
                                                scheduler=HEURI_DDIMScheduler(prediction_type='sample', 
                                                                              beta_start=0.00085, beta_end=0.0120, 
                                                                              beta_schedule = "scaled_linear"))
    pipe.x_start_pipeline = x_start_pipeline
    pipe.prior = dinov2_prior
    pipe.to(device)
    
    try:
        import xformers
        pipe.enable_xformers_memory_efficient_attention()
    except:
        pass  # run without xformers

    run_demo_server(pipe)


if __name__ == "__main__":
    main()