# coding: utf-8

# In[ ]:


# Copyright (c) Meta Platforms, Inc. and affiliates. All rights reserved.


# # Camera position optimization using differentiable rendering
# 
# In this tutorial we will learn the [x, y, z] position of a camera given a reference image using differentiable rendering. 
# 
# We will first initialize a renderer with a starting position for the camera. We will then use this to generate an image, compute a loss with the reference image, and finally backpropagate through the entire pipeline to update the position of the camera. 
# 
# This tutorial shows how to:
# - load a mesh from an `.obj` file
# - initialize a `Camera`, `Shader` and `Renderer`,
# - render a mesh
# - set up an optimization loop with a loss function and optimizer
# 

# ##  0. Install and import modules

# Ensure `torch` and `torchvision` are installed. If `pytorch3d` is not installed, install it using the following cell:

# In[ ]:


import os
import sys
import torch
need_pytorch3d=False
try:
    import pytorch3d
except ModuleNotFoundError:
    need_pytorch3d=True
if need_pytorch3d:
    if torch.__version__.startswith("2.2.") and sys.platform.startswith("linux"):
        # We try to install PyTorch3D via a released wheel.
        pyt_version_str=torch.__version__.split("+")[0].replace(".", "")
        version_str="".join([
            f"py3{sys.version_info.minor}_cu",
            torch.version.cuda.replace(".",""),
            f"_pyt{pyt_version_str}"
        ])
        get_ipython().system('pip install fvcore iopath')
        get_ipython().system('pip install --no-index --no-cache-dir pytorch3d -f https://dl.fbaipublicfiles.com/pytorch3d/packaging/wheels/{version_str}/download.html')
    else:
        # We try to install PyTorch3D from source.
        get_ipython().system("pip install 'git+https://github.com/facebookresearch/pytorch3d.git@stable'")


# In[ ]:


import os
import torch
import numpy as np
from tqdm.notebook import tqdm
import imageio
import torch.nn as nn
import torch.nn.functional as F
import matplotlib.pyplot as plt
from skimage import img_as_ubyte

# io utils
from pytorch3d.io import load_obj

# datastructures
from pytorch3d.structures import Meshes

# 3D transformations functions
from pytorch3d.transforms import Rotate, Translate

# rendering components
from pytorch3d.renderer import (
    FoVPerspectiveCameras, look_at_view_transform, look_at_rotation, 
    RasterizationSettings, MeshRenderer, MeshRasterizer, BlendParams,
    SoftSilhouetteShader, HardPhongShader, PointLights, TexturesVertex,
)


# ## 1. Load the Obj
# 
# We will load an obj file and create a **Meshes** object. **Meshes** is a unique datastructure provided in PyTorch3D for working with **batches of meshes of different sizes**. It has several useful class methods which are used in the rendering pipeline. 

# If you are running this notebook locally after cloning the PyTorch3D repository, the mesh will already be available. **If using Google Colab, fetch the mesh and save it at the path `data/`**:

# In[ ]:


get_ipython().system('mkdir -p data')
get_ipython().system('wget -P data https://dl.fbaipublicfiles.com/pytorch3d/data/teapot/teapot.obj')


# In[ ]:


# Set the cuda device 
if torch.cuda.is_available():
    device = torch.device("cuda:0")
    torch.cuda.set_device(device)
else:
    device = torch.device("cpu")

# Load the obj and ignore the textures and materials.
verts, faces_idx, _ = load_obj("./data/teapot.obj")
faces = faces_idx.verts_idx

# Initialize each vertex to be white in color.
verts_rgb = torch.ones_like(verts)[None]  # (1, V, 3)
textures = TexturesVertex(verts_features=verts_rgb.to(device))

# Create a Meshes object for the teapot. Here we have only one mesh in the batch.
teapot_mesh = Meshes(
    verts=[verts.to(device)],   
    faces=[faces.to(device)], 
    textures=textures
)


# 
# 
# ## 2. Optimization setup

# ### Create a renderer
# 
# A **renderer** in PyTorch3D is composed of a **rasterizer** and a **shader** which each have a number of subcomponents such as a **camera** (orthographic/perspective). Here we initialize some of these components and use default values for the rest. 
# 
# For optimizing the camera position we will use a renderer which produces a **silhouette** of the object only and does not apply any **lighting** or **shading**. We will also initialize another renderer which applies full **Phong shading** and use this for visualizing the outputs. 

# In[ ]:


# Initialize a perspective camera.
cameras = FoVPerspectiveCameras(device=device)

# To blend the 100 faces we set a few parameters which control the opacity and the sharpness of 
# edges. Refer to blending.py for more details. 
blend_params = BlendParams(sigma=1e-4, gamma=1e-4)

# Define the settings for rasterization and shading. Here we set the output image to be of size
# 256x256. To form the blended image we use 100 faces for each pixel. We also set bin_size and max_faces_per_bin to None which ensure that 
# the faster coarse-to-fine rasterization method is used. Refer to rasterize_meshes.py for 
# explanations of these parameters. Refer to docs/notes/renderer.md for an explanation of 
# the difference between naive and coarse-to-fine rasterization. 
raster_settings = RasterizationSettings(
    image_size=256, 
    blur_radius=np.log(1. / 1e-4 - 1.) * blend_params.sigma, 
    faces_per_pixel=100, 
)

# Create a silhouette mesh renderer by composing a rasterizer and a shader. 
silhouette_renderer = MeshRenderer(
    rasterizer=MeshRasterizer(
        cameras=cameras, 
        raster_settings=raster_settings
    ),
    shader=SoftSilhouetteShader(blend_params=blend_params)
)


# We will also create a Phong renderer. This is simpler and only needs to render one face per pixel.
raster_settings = RasterizationSettings(
    image_size=256, 
    blur_radius=0.0, 
    faces_per_pixel=1, 
)
# We can add a point light in front of the object. 
lights = PointLights(device=device, location=((2.0, 2.0, -2.0),))
phong_renderer = MeshRenderer(
    rasterizer=MeshRasterizer(
        cameras=cameras, 
        raster_settings=raster_settings
    ),
    shader=HardPhongShader(device=device, cameras=cameras, lights=lights)
)


# ### Create a reference image
# 
# We will first position the teapot and generate an image. We use helper functions to rotate the teapot to a desired viewpoint. Then we can use the renderers to produce an image. Here we will use both renderers and visualize the silhouette and full shaded image. 
# 
# The world coordinate system is defined as +Y up, +X left and +Z in. The teapot in world coordinates has the spout pointing to the left. 
# 
# We defined a camera which is positioned on the positive z axis hence sees the spout to the right. 

# In[ ]:


# Select the viewpoint using spherical angles  
distance = 3   # distance from camera to the object
elevation = 50.0   # angle of elevation in degrees
azimuth = 0.0  # No rotation so the camera is positioned on the +Z axis. 

# Get the position of the camera based on the spherical angles
R, T = look_at_view_transform(distance, elevation, azimuth, device=device)

# Render the teapot providing the values of R and T. 
silhouette = silhouette_renderer(meshes_world=teapot_mesh, R=R, T=T)
image_ref = phong_renderer(meshes_world=teapot_mesh, R=R, T=T)

silhouette = silhouette.cpu().numpy()
image_ref = image_ref.cpu().numpy()

plt.figure(figsize=(10, 10))
plt.subplot(1, 2, 1)
plt.imshow(silhouette.squeeze()[..., 3])  # only plot the alpha channel of the RGBA image
plt.grid(False)
plt.subplot(1, 2, 2)
plt.imshow(image_ref.squeeze())
plt.grid(False)


# ### Set up a basic model 
# 
# Here we create a simple model class and initialize a parameter for the camera position. 

# In[ ]:


class Model(nn.Module):
    def __init__(self, meshes, renderer, image_ref):
        super().__init__()
        self.meshes = meshes
        self.device = meshes.device
        self.renderer = renderer
        
        # Get the silhouette of the reference RGB image by finding all non-white pixel values. 
        image_ref = torch.from_numpy((image_ref[..., :3].max(-1) != 1).astype(np.float32))
        self.register_buffer('image_ref', image_ref)
        
        # Create an optimizable parameter for the x, y, z position of the camera. 
        self.camera_position = nn.Parameter(
            torch.from_numpy(np.array([3.0,  6.9, +2.5], dtype=np.float32)).to(meshes.device))

    def forward(self):
        
        # Render the image using the updated camera position. Based on the new position of the 
        # camera we calculate the rotation and translation matrices
        R = look_at_rotation(self.camera_position[None, :], device=self.device)  # (1, 3, 3)
        T = -torch.bmm(R.transpose(1, 2), self.camera_position[None, :, None])[:, :, 0]   # (1, 3)
        
        image = self.renderer(meshes_world=self.meshes.clone(), R=R, T=T)
        
        # Calculate the silhouette loss
        loss = torch.sum((image[..., 3] - self.image_ref) ** 2)
        return loss, image
  


# ## 3. Initialize the model and optimizer
# 
# Now we can create an instance of the **model** above and set up an **optimizer** for the camera position parameter. 

# In[ ]:


# We will save images periodically and compose them into a GIF.
filename_output = "./teapot_optimization_demo.gif"
writer = imageio.get_writer(filename_output, mode='I', duration=0.3)

# Initialize a model using the renderer, mesh and reference image
model = Model(meshes=teapot_mesh, renderer=silhouette_renderer, image_ref=image_ref).to(device)

# Create an optimizer. Here we are using Adam and we pass in the parameters of the model
optimizer = torch.optim.Adam(model.parameters(), lr=0.05)


# ### Visualize the starting position and the reference position

# In[ ]:


plt.figure(figsize=(10, 10))

_, image_init = model()
plt.subplot(1, 2, 1)
plt.imshow(image_init.detach().squeeze().cpu().numpy()[..., 3])
plt.grid(False)
plt.title("Starting position")

plt.subplot(1, 2, 2)
plt.imshow(model.image_ref.cpu().numpy().squeeze())
plt.grid(False)
plt.title("Reference silhouette");


# ## 4. Run the optimization 
# 
# We run several iterations of the forward and backward pass and save outputs every 10 iterations. When this has finished take a look at `./teapot_optimization_demo.gif` for a cool gif of the optimization process!

# In[ ]:


loop = tqdm(range(200))
for i in loop:
    optimizer.zero_grad()
    loss, _ = model()
    loss.backward()
    optimizer.step()
    
    loop.set_description('Optimizing (loss %.4f)' % loss.data)
    
    if loss.item() < 200:
        break
    
    # Save outputs to create a GIF. 
    if i % 10 == 0:
        R = look_at_rotation(model.camera_position[None, :], device=model.device)
        T = -torch.bmm(R.transpose(1, 2), model.camera_position[None, :, None])[:, :, 0]   # (1, 3)
        image = phong_renderer(meshes_world=model.meshes.clone(), R=R, T=T)
        image = image[0, ..., :3].detach().squeeze().cpu().numpy()
        image = img_as_ubyte(image)
        writer.append_data(image)
        
        plt.figure()
        plt.imshow(image[..., :3])
        plt.title("iter: %d, loss: %0.2f" % (i, loss.data))
        plt.axis("off")
    
writer.close()


# ## 5. Conclusion 
# 
# In this tutorial we learnt how to **load** a mesh from an obj file, initialize a PyTorch3D datastructure called **Meshes**, set up an **Renderer** consisting of a **Rasterizer** and a **Shader**, set up an optimization loop including a **Model** and a **loss function**, and run  the optimization.