import open3d_zerogpu_fix
import numpy as np
from PIL import Image
import gradio as gr
import open3d as o3d
import trimesh
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, EulerAncestralDiscreteScheduler
import torch
from collections import Counter
import random

import spaces

pipe = None
device = None
torch_dtype = None

def load_model():
  global pipe, device, torch_dtype
  device = "cuda" if torch.cuda.is_available() else "cpu"
  torch_dtype = torch.float16 if device == "cuda" else torch.float32

  pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
      "yeq6x/animagine_position_map",
      controlnet=ControlNetModel.from_pretrained("yeq6x/Image2PositionColor_v3"),
  ).to(device)
  pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
  
  return pipe

def convert_pil_to_opencv(pil_image):
  return np.array(pil_image)

def inv_func(y,
  c = -712.380100,
  a = 137.375240,
  b = 192.435866):
  return (np.exp((y - c) / a) - np.exp(-c/a)) / 964.8468371292845

def create_point_cloud(img1, img2):
  if img1.shape != img2.shape:
    raise ValueError("Both images must have the same dimensions.")

  h, w, _ = img1.shape
  points = []
  colors = []
  for y in range(h):
    for x in range(w):
      # ピクセル位置 (x, y) のRGBをXYZとして取得
      r, g, b = img1[y, x]
      r = inv_func(r) * 0.9
      g = inv_func(g) / 1.7 * 0.6
      b = inv_func(b)
      r *= 150
      g *= 150
      b *= 150
      points.append([g, b, r])  # X, Y, Z
      # 対応するピクセル位置の画像2の色を取得
      colors.append(img2[y, x] / 255.0)  # 色は0〜1にスケール

  return np.array(points), np.array(colors)

def point_cloud_to_glb(points, colors):
  # Open3Dでポイントクラウドを作成
  pc = o3d.geometry.PointCloud()
  pc.points = o3d.utility.Vector3dVector(points)
  pc.colors = o3d.utility.Vector3dVector(colors)
  
  # 一時的にPLY形式で保存
  temp_ply_file = "temp_output.ply"
  o3d.io.write_point_cloud(temp_ply_file, pc)
  
  # PLYをGLBに変換
  mesh = trimesh.load(temp_ply_file)
  glb_file = "output.glb"
  mesh.export(glb_file)

  return glb_file

def visualize_3d(image1, image2):
  print("Processing...")
  # PIL画像をOpenCV形式に変換
  img1 = convert_pil_to_opencv(image1)
  img2 = convert_pil_to_opencv(image2)

  # ポイントクラウド生成
  points, colors = create_point_cloud(img1, img2)

  # GLB形式に変換
  glb_file = point_cloud_to_glb(points, colors)

  return glb_file

def scale_image(original_image):
  aspect_ratio = original_image.width / original_image.height

  if original_image.width > original_image.height:
    new_width = 1024
    new_height = round(new_width / aspect_ratio)
  else:
    new_height = 1024
    new_width = round(new_height * aspect_ratio)

  resized_original = original_image.resize((new_width, new_height), Image.LANCZOS)

  return resized_original

def get_edge_mode_color(img, edge_width=10):
  # 外周の10ピクセル領域を取得
  left = img.crop((0, 0, edge_width, img.height))  # 左端
  right = img.crop((img.width - edge_width, 0, img.width, img.height))  # 右端
  top = img.crop((0, 0, img.width, edge_width))  # 上端
  bottom = img.crop((0, img.height - edge_width, img.width, img.height))  # 下端

  # 各領域のピクセルデータを取得して結合
  colors = list(left.getdata()) + list(right.getdata()) + list(top.getdata()) + list(bottom.getdata())

  # 最頻値（mode）を計算
  mode_color = Counter(colors).most_common(1)[0][0]  # 最も頻繁に出現する色を取得

  return mode_color

def paste_image(resized_img):
  # 外周10pxの最頻値を背景色に設定
  mode_color = get_edge_mode_color(resized_img, edge_width=10)
  mode_background = Image.new("RGBA", (1024, 1024), mode_color)
  mode_background = mode_background.convert('RGB')

  x = (1024 - resized_img.width) // 2
  y = (1024 - resized_img.height) // 2
  mode_background.paste(resized_img, (x, y))

  return mode_background

def outpaint_image(image):
  if type(image) == type(None):
    return None
  resized_img = scale_image(image)
  image = paste_image(resized_img)
  
  return image

@spaces.GPU
def predict_image(cond_image, prompt, negative_prompt, controlnet_conditioning_scale):
  print("predict position map")
  global pipe
  generator = torch.Generator()
  generator.manual_seed(random.randint(0, 2147483647))
  image = pipe(
      prompt,
      prompt,
      cond_image,
      negative_prompt=negative_prompt,
      width=1024,
      height=1024,
      guidance_scale=8,
      num_inference_steps=20,
      generator=generator,
      guess_mode = True,
      controlnet_conditioning_scale = controlnet_conditioning_scale,
  ).images[0]
  
  return image

load_model()

# Gradioアプリケーション
with gr.Blocks() as demo:
  gr.Markdown("## Position Map Visualizer")
  
  with gr.Row():
    with gr.Column():
      with gr.Row():
        img1 = gr.Image(type="pil", label="color Image", height=300)
        img2 = gr.Image(type="pil", label="map Image", height=300)
      prompt = gr.Textbox("position map, 1girl, solo, white background, simple background", label="Prompt")
      negative_prompt = gr.Textbox("lowres, bad anatomy, bad hands, bad feet, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry", label="Negative Prompt")
      controlnet_conditioning_scale = gr.Slider(label="ControlNet conditioning scale", minimum=0.1, maximum=2.0, value=0.6, step=0.05)
      predict_map_btn = gr.Button("Predict Position Map")
      visualize_3d_btn = gr.Button("Generate 3D Point Cloud")
    with gr.Column():
      reconstruction_output = gr.Model3D(label="3D Viewer", height=600)
      gr.Examples(
          examples=[
          ["resources/source/000006.png", "resources/target/000006.png"],
          ["resources/source/006420.png", "resources/target/006420.png"],
      ],
          inputs=[img1, img2]
      )

  img1.input(outpaint_image, inputs=img1, outputs=img1)
  predict_map_btn.click(predict_image, inputs=[img1, prompt, negative_prompt, controlnet_conditioning_scale], outputs=img2)
  visualize_3d_btn.click(visualize_3d, inputs=[img2, img1], outputs=reconstruction_output)

demo.launch()