From 002f47dd1596432b4ee9c20bdfdb769a6c66ae9f Mon Sep 17 00:00:00 2001 From: Solal Nathan Date: Tue, 23 Feb 2021 09:16:05 +0100 Subject: [PATCH] :tada: initial commit --- data/bundle_adjustment_final.png | Bin 0 -> 43651 bytes data/bundle_adjustment_initialization.png | Bin 0 -> 49151 bytes data/camera_graph.pth | Bin 0 -> 16896 bytes data/cow_mesh/README.md | 6 + data/cow_mesh/cow.mtl | 9 + data/cow_mesh/cow.obj | 12015 ++++++++++++++++ data/cow_mesh/cow_texture.png | Bin 0 -> 78699 bytes data/teapot.obj | 5046 +++++++ main.py | 646 + utils/__init__.py | 4 + utils/__pycache__/__init__.cpython-38.pyc | Bin 0 -> 305 bytes .../camera_visualization.cpython-38.pyc | Bin 0 -> 1866 bytes .../generate_cow_renders.cpython-38.pyc | Bin 0 -> 4012 bytes .../plot_image_grid.cpython-38.pyc | Bin 0 -> 1404 bytes utils/camera_visualization.py | 55 + utils/generate_cow_renders.py | 165 + utils/plot_image_grid.py | 49 + 17 files changed, 17995 insertions(+) create mode 100644 data/bundle_adjustment_final.png create mode 100644 data/bundle_adjustment_initialization.png create mode 100644 data/camera_graph.pth create mode 100644 data/cow_mesh/README.md create mode 100755 data/cow_mesh/cow.mtl create mode 100755 data/cow_mesh/cow.obj create mode 100755 data/cow_mesh/cow_texture.png create mode 100644 data/teapot.obj create mode 100644 main.py create mode 100644 utils/__init__.py create mode 100644 utils/__pycache__/__init__.cpython-38.pyc create mode 100644 utils/__pycache__/camera_visualization.cpython-38.pyc create mode 100644 utils/__pycache__/generate_cow_renders.cpython-38.pyc create mode 100644 utils/__pycache__/plot_image_grid.cpython-38.pyc create mode 100644 utils/camera_visualization.py create mode 100644 utils/generate_cow_renders.py create mode 100644 utils/plot_image_grid.py diff --git a/data/bundle_adjustment_final.png b/data/bundle_adjustment_final.png new file mode 100644 index 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file mode 100644 index 0000000..27c1a6e --- /dev/null +++ b/data/cow_mesh/README.md @@ -0,0 +1,6 @@ + +# Acknowledgements + +Thank you to Keenen Crane for allowing the cow mesh model to be used freely in the public domain. + +###### Source: http://www.cs.cmu.edu/~kmcrane/Projects/ModelRepository/ diff --git a/data/cow_mesh/cow.mtl b/data/cow_mesh/cow.mtl new file mode 100755 index 0000000..c3bc054 --- /dev/null +++ b/data/cow_mesh/cow.mtl @@ -0,0 +1,9 @@ +newmtl material_1 +map_Kd cow_texture.png + +# Test colors + +Ka 1.000 1.000 1.000 # white +Kd 1.000 1.000 1.000 # white +Ks 0.000 0.000 0.000 # black +Ns 10.0 diff --git a/data/cow_mesh/cow.obj b/data/cow_mesh/cow.obj new file mode 100755 index 0000000..6fb8282 --- /dev/null +++ b/data/cow_mesh/cow.obj @@ -0,0 +1,12015 @@ + +mtllib cow.mtl + +v 0.348799 -0.334989 -0.0832331 +v 0.313132 -0.399051 0.881192 +v 0.266758 0.181628 0.122726 +v 0.229555 0.0663178 0.828702 +v 0.353623 -0.0486456 0.443969 +v 0.335878 -0.384495 0.425693 +v 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-0,0 +1,646 @@ +# Inspired from Pytorch3D NeRF Tutorial +# https://github.com/facebookresearch/pytorch3d/blob/master/docs/tutorials/fit_simple_neural_radiance_field.ipynb +# Created on 2021-02-22 + +import os +import sys +import time +import json +import glob +import torch +import math +import matplotlib.pyplot as plt +import numpy as np +from PIL import Image +from tqdm import tqdm + +# Data structures and functions for rendering +from pytorch3d.structures import Volumes +from pytorch3d.transforms import so3_exponential_map +from pytorch3d.renderer import ( + FoVPerspectiveCameras, + NDCGridRaysampler, + MonteCarloRaysampler, + EmissionAbsorptionRaymarcher, + ImplicitRenderer, + RayBundle, + ray_bundle_to_ray_points, +) + +# add path for demo utils functions +sys.path.append(os.path.abspath('')) +from utils.plot_image_grid import image_grid +from utils.generate_cow_renders import generate_cow_renders + + +# Intialize CUDA gpu +device = torch.device("cuda:0") +torch.cuda.set_device(device) + +# Generate dataset +target_cameras, target_images, target_silhouettes = \ + generate_cow_renders(num_views=40, azimuth_range=180) +print(f'Generated {len(target_images)} images/silhouettes/cameras.') + +############################################################################### +# Intitialize the implicit rendered +############################################################################### + +# render_size describes the size of both sides of the +# rendered images in pixels. Since an advantage of +# Neural Radiance Fields are high quality renders +# with a significant amount of details, we render +# the implicit function at double the size of +# target images. +render_size = target_images.shape[1] * 2 + +# Our rendered scene is centered around (0,0,0) +# and is enclosed inside a bounding box +# whose side is roughly equal to 3.0 (world units). +volume_extent_world = 3.0 + +# 1) Instantiate the raysamplers. + +# Here, NDCGridRaysampler generates a rectangular image +# grid of rays whose coordinates follow the PyTorch3d +# coordinate conventions. +raysampler_grid = NDCGridRaysampler( + image_height=render_size, + image_width=render_size, + n_pts_per_ray=128, + min_depth=0.1, + max_depth=volume_extent_world, +) + +# MonteCarloRaysampler generates a random subset +# of `n_rays_per_image` rays emitted from the image plane. +raysampler_mc = MonteCarloRaysampler( + min_x = -1.0, + max_x = 1.0, + min_y = -1.0, + max_y = 1.0, + n_rays_per_image=750, + n_pts_per_ray=128, + min_depth=0.1, + max_depth=volume_extent_world, +) + +# 2) Instantiate the raymarcher. +# Here, we use the standard EmissionAbsorptionRaymarcher +# which marches along each ray in order to render +# the ray into a single 3D color vector +# and an opacity scalar. +raymarcher = EmissionAbsorptionRaymarcher() + +# Finally, instantiate the implicit renders +# for both raysamplers. +renderer_grid = ImplicitRenderer( + raysampler=raysampler_grid, raymarcher=raymarcher, +) +renderer_mc = ImplicitRenderer( + raysampler=raysampler_mc, raymarcher=raymarcher, +) + +############################################################################### +# Define the NeRF model +############################################################################### + +class HarmonicEmbedding(torch.nn.Module): + def __init__(self, n_harmonic_functions=60, omega0=0.1): + """ + Given an input tensor `x` of shape [minibatch, ... , dim], + the harmonic embedding layer converts each feature + in `x` into a series of harmonic features `embedding` + as follows: + embedding[..., i*dim:(i+1)*dim] = [ + sin(x[..., i]), + sin(2*x[..., i]), + sin(4*x[..., i]), + ... + sin(2**self.n_harmonic_functions * x[..., i]), + cos(x[..., i]), + cos(2*x[..., i]), + cos(4*x[..., i]), + ... + cos(2**self.n_harmonic_functions * x[..., i]) + ] + + Note that `x` is also premultiplied by `omega0` before + evaluting the harmonic functions. + """ + super().__init__() + self.register_buffer( + 'frequencies', + omega0 * (2.0 ** torch.arange(n_harmonic_functions)), + ) + def forward(self, x): + """ + Args: + x: tensor of shape [..., dim] + Returns: + embedding: a harmonic embedding of `x` + of shape [..., n_harmonic_functions * dim * 2] + """ + embed = (x[..., None] * self.frequencies).view(*x.shape[:-1], -1) + return torch.cat((embed.sin(), embed.cos()), dim=-1) + + +class NeuralRadianceField(torch.nn.Module): + def __init__(self, n_harmonic_functions=60, n_hidden_neurons=256): + super().__init__() + """ + Args: + n_harmonic_functions: The number of harmonic functions + used to form the harmonic embedding of each point. + n_hidden_neurons: The number of hidden units in the + fully connected layers of the MLPs of the model. + """ + + # The harmonic embedding layer converts input 3D coordinates + # to a representation that is more suitable for + # processing with a deep neural network. + self.harmonic_embedding = HarmonicEmbedding(n_harmonic_functions) + + # The dimension of the harmonic embedding. + embedding_dim = n_harmonic_functions * 2 * 3 + + # self.mlp is a simple 2-layer multi-layer perceptron + # which converts the input per-point harmonic embeddings + # to a latent representation. + # Not that we use Softplus activations instead of ReLU. + self.mlp = torch.nn.Sequential( + torch.nn.Linear(embedding_dim, n_hidden_neurons), + torch.nn.Softplus(beta=10.0), + torch.nn.Linear(n_hidden_neurons, n_hidden_neurons), + torch.nn.Softplus(beta=10.0), + ) + + # Given features predicted by self.mlp, self.color_layer + # is responsible for predicting a 3-D per-point vector + # that represents the RGB color of the point. + self.color_layer = torch.nn.Sequential( + torch.nn.Linear(n_hidden_neurons + embedding_dim, n_hidden_neurons), + torch.nn.Softplus(beta=10.0), + torch.nn.Linear(n_hidden_neurons, 3), + torch.nn.Sigmoid(), + # To ensure that the colors correctly range between [0-1], + # the layer is terminated with a sigmoid layer. + ) + + # The density layer converts the features of self.mlp + # to a 1D density value representing the raw opacity + # of each point. + self.density_layer = torch.nn.Sequential( + torch.nn.Linear(n_hidden_neurons, 1), + torch.nn.Softplus(beta=10.0), + # Sofplus activation ensures that the raw opacity + # is a non-negative number. + ) + + # We set the bias of the density layer to -1.5 + # in order to initialize the opacities of the + # ray points to values close to 0. + # This is a crucial detail for ensuring convergence + # of the model. + self.density_layer[0].bias.data[0] = -1.5 + + def _get_densities(self, features): + """ + This function takes `features` predicted by `self.mlp` + and converts them to `raw_densities` with `self.density_layer`. + `raw_densities` are later mapped to [0-1] range with + 1 - inverse exponential of `raw_densities`. + """ + raw_densities = self.density_layer(features) + return 1 - (-raw_densities).exp() + + def _get_colors(self, features, rays_directions): + """ + This function takes per-point `features` predicted by `self.mlp` + and evaluates the color model in order to attach to each + point a 3D vector of its RGB color. + + In order to represent viewpoint dependent effects, + before evaluating `self.color_layer`, `NeuralRadianceField` + concatenates to the `features` a harmonic embedding + of `ray_directions`, which are per-point directions + of point rays expressed as 3D l2-normalized vectors + in world coordinates. + """ + spatial_size = features.shape[:-1] + + # Normalize the ray_directions to unit l2 norm. + rays_directions_normed = torch.nn.functional.normalize( + rays_directions, dim=-1 + ) + + # Obtain the harmonic embedding of the normalized ray directions. + rays_embedding = self.harmonic_embedding( + rays_directions_normed + ) + + # Expand the ray directions tensor so that its spatial size + # is equal to the size of features. + rays_embedding_expand = rays_embedding[..., None, :].expand( + *spatial_size, rays_embedding.shape[-1] + ) + + # Concatenate ray direction embeddings with + # features and evaluate the color model. + color_layer_input = torch.cat( + (features, rays_embedding_expand), + dim=-1 + ) + return self.color_layer(color_layer_input) + + + def forward( + self, + ray_bundle: RayBundle, + **kwargs, + ): + """ + The forward function accepts the parametrizations of + 3D points sampled along projection rays. The forward + pass is responsible for attaching a 3D vector + and a 1D scalar representing the point's + RGB color and opacity respectively. + + Args: + ray_bundle: A RayBundle object containing the following variables: + origins: A tensor of shape `(minibatch, ..., 3)` denoting the + origins of the sampling rays in world coords. + directions: A tensor of shape `(minibatch, ..., 3)` + containing the direction vectors of sampling rays in world coords. + lengths: A tensor of shape `(minibatch, ..., num_points_per_ray)` + containing the lengths at which the rays are sampled. + + Returns: + rays_densities: A tensor of shape `(minibatch, ..., num_points_per_ray, 1)` + denoting the opacitiy of each ray point. + rays_colors: A tensor of shape `(minibatch, ..., num_points_per_ray, 3)` + denoting the color of each ray point. + """ + # We first convert the ray parametrizations to world + # coordinates with `ray_bundle_to_ray_points`. + rays_points_world = ray_bundle_to_ray_points(ray_bundle) + # rays_points_world.shape = [minibatch x ... x 3] + + # For each 3D world coordinate, we obtain its harmonic embedding. + embeds = self.harmonic_embedding( + rays_points_world + ) + # embeds.shape = [minibatch x ... x self.n_harmonic_functions*6] + + # self.mlp maps each harmonic embedding to a latent feature space. + features = self.mlp(embeds) + # features.shape = [minibatch x ... x n_hidden_neurons] + + # Finally, given the per-point features, + # execute the density and color branches. + + rays_densities = self._get_densities(features) + # rays_densities.shape = [minibatch x ... x 1] + + rays_colors = self._get_colors(features, ray_bundle.directions) + # rays_colors.shape = [minibatch x ... x 3] + + return rays_densities, rays_colors + + def batched_forward( + self, + ray_bundle: RayBundle, + n_batches: int = 16, + **kwargs, + ): + """ + This function is used to allow for memory efficient processing + of input rays. The input rays are first split to `n_batches` + chunks and passed through the `self.forward` function one at a time + in a for loop. Combined with disabling Pytorch gradient caching + (`torch.no_grad()`), this allows for rendering large batches + of rays that do not all fit into GPU memory in a single forward pass. + In our case, batched_forward is used to export a fully-sized render + of the radiance field for visualisation purposes. + + Args: + ray_bundle: A RayBundle object containing the following variables: + origins: A tensor of shape `(minibatch, ..., 3)` denoting the + origins of the sampling rays in world coords. + directions: A tensor of shape `(minibatch, ..., 3)` + containing the direction vectors of sampling rays in world coords. + lengths: A tensor of shape `(minibatch, ..., num_points_per_ray)` + containing the lengths at which the rays are sampled. + n_batches: Specifies the number of batches the input rays are split into. + The larger the number of batches, the smaller the memory footprint + and the lower the processing speed. + + Returns: + rays_densities: A tensor of shape `(minibatch, ..., num_points_per_ray, 1)` + denoting the opacitiy of each ray point. + rays_colors: A tensor of shape `(minibatch, ..., num_points_per_ray, 3)` + denoting the color of each ray point. + + """ + + # Parse out shapes needed for tensor reshaping in this function. + n_pts_per_ray = ray_bundle.lengths.shape[-1] + spatial_size = [*ray_bundle.origins.shape[:-1], n_pts_per_ray] + + # Split the rays to `n_batches` batches. + tot_samples = ray_bundle.origins.shape[:-1].numel() + batches = torch.chunk(torch.arange(tot_samples), n_batches) + + # For each batch, execute the standard forward pass. + batch_outputs = [ + self.forward( + RayBundle( + origins=ray_bundle.origins.view(-1, 3)[batch_idx], + directions=ray_bundle.directions.view(-1, 3)[batch_idx], + lengths=ray_bundle.lengths.view(-1, n_pts_per_ray)[batch_idx], + xys=None, + ) + ) for batch_idx in batches + ] + + # Concatenate the per-batch rays_densities and rays_colors + # and reshape according to the sizes of the inputs. + rays_densities, rays_colors = [ + torch.cat( + [batch_output[output_i] for batch_output in batch_outputs], dim=0 + ).view(*spatial_size, -1) for output_i in (0, 1) + ] + return rays_densities, rays_colors + +############################################################################### +# Helper functions +############################################################################### + +def huber(x, y, scaling=0.1): + """ + A helper function for evaluating the smooth L1 (huber) loss + between the rendered silhouettes and colors. + """ + diff_sq = (x - y) ** 2 + loss = ((1 + diff_sq / (scaling**2)).clamp(1e-4).sqrt() - 1) * float(scaling) + return loss + +def sample_images_at_mc_locs(target_images, sampled_rays_xy): + """ + Given a set of Monte Carlo pixel locations `sampled_rays_xy`, + this method samples the tensor `target_images` at the + respective 2D locations. + + This function is used in order to extract the colors from + ground truth images that correspond to the colors + rendered using `MonteCarloRaysampler`. + """ + ba = target_images.shape[0] + dim = target_images.shape[-1] + spatial_size = sampled_rays_xy.shape[1:-1] + # In order to sample target_images, we utilize + # the grid_sample function which implements a + # bilinear image sampler. + # Note that we have to invert the sign of the + # sampled ray positions to convert the NDC xy locations + # of the MonteCarloRaysampler to the coordinate + # convention of grid_sample. + images_sampled = torch.nn.functional.grid_sample( + target_images.permute(0, 3, 1, 2), + -sampled_rays_xy.view(ba, -1, 1, 2), # note the sign inversion + align_corners=True + ) + return images_sampled.permute(0, 2, 3, 1).view( + ba, *spatial_size, dim + ) + +def show_full_render( + neural_radiance_field, camera, + target_image, target_silhouette, + loss_history_color, loss_history_sil, +): + """ + This is a helper function for visualizing the + intermediate results of the learning. + + Since the `NeuralRadianceField` suffers from + a large memory footprint, which does not allow to + render the full image grid in a single forward pass, + we utilize the `NeuralRadianceField.batched_forward` + function in combination with disabling the gradient caching. + This chunks the set of emitted rays to batches and + evaluates the implicit function on one-batch at a time + to prevent GPU memory overflow. + """ + + # Prevent gradient caching. + with torch.no_grad(): + # Render using the grid renderer and the + # batched_forward function of neural_radiance_field. + rendered_image_silhouette, _ = renderer_grid( + cameras=camera, + volumetric_function=neural_radiance_field.batched_forward + ) + # Split the rendering result to a silhouette render + # and the image render. + rendered_image, rendered_silhouette = ( + rendered_image_silhouette[0].split([3, 1], dim=-1) + ) + + # Generate plots. + fig, ax = plt.subplots(2, 3, figsize=(15, 10)) + ax = ax.ravel() + clamp_and_detach = lambda x: x.clamp(0.0, 1.0).cpu().detach().numpy() + ax[0].plot(list(range(len(loss_history_color))), loss_history_color, linewidth=1) + ax[1].imshow(clamp_and_detach(rendered_image)) + ax[2].imshow(clamp_and_detach(rendered_silhouette[..., 0])) + ax[3].plot(list(range(len(loss_history_sil))), loss_history_sil, linewidth=1) + ax[4].imshow(clamp_and_detach(target_image)) + ax[5].imshow(clamp_and_detach(target_silhouette)) + for ax_, title_ in zip( + ax, + ( + "loss color", "rendered image", "rendered silhouette", + "loss silhouette", "target image", "target silhouette", + ) + ): + if not title_.startswith('loss'): + ax_.grid("off") + ax_.axis("off") + ax_.set_title(title_) + fig.canvas.draw(); fig.show() + display.clear_output(wait=True) + display.display(fig) + return fig + + +############################################################################### +# Fit the radiance field +############################################################################### + +# First move all relevant variables to the correct device. +renderer_grid = renderer_grid.to(device) +renderer_mc = renderer_mc.to(device) +target_cameras = target_cameras.to(device) +target_images = target_images.to(device) +target_silhouettes = target_silhouettes.to(device) + +# Set the seed for reproducibility +torch.manual_seed(1) + +# Instantiate the radiance field model. +neural_radiance_field = NeuralRadianceField().to(device) + +# Instantiate the Adam optimizer. We set its master learning rate to 1e-3. +lr = 1e-3 +optimizer = torch.optim.Adam(neural_radiance_field.parameters(), lr=lr) + +# We sample 6 random cameras in a minibatch. Each camera +# emits raysampler_mc.n_pts_per_image rays. +batch_size = 6 + +# 3000 iterations take ~20 min on a Tesla M40 and lead to +# reasonably sharp results. However, for the best possible +# results, we recommend setting n_iter=20000. +n_iter = 3000 + +# Init the loss history buffers. +loss_history_color, loss_history_sil = [], [] + +# The main optimization loop. +for iteration in range(n_iter): + # In case we reached the last 75% of iterations, + # decrease the learning rate of the optimizer 10-fold. + if iteration == round(n_iter * 0.75): + print('Decreasing LR 10-fold ...') + optimizer = torch.optim.Adam( + neural_radiance_field.parameters(), lr=lr * 0.1 + ) + + # Zero the optimizer gradient. + optimizer.zero_grad() + + # Sample random batch indices. + batch_idx = torch.randperm(len(target_cameras))[:batch_size] + + # Sample the minibatch of cameras. + batch_cameras = FoVPerspectiveCameras( + R = target_cameras.R[batch_idx], + T = target_cameras.T[batch_idx], + znear = target_cameras.znear[batch_idx], + zfar = target_cameras.zfar[batch_idx], + aspect_ratio = target_cameras.aspect_ratio[batch_idx], + fov = target_cameras.fov[batch_idx], + device = device, + ) + + # Evaluate the nerf model. + rendered_images_silhouettes, sampled_rays = renderer_mc( + cameras=batch_cameras, + volumetric_function=neural_radiance_field + ) + rendered_images, rendered_silhouettes = ( + rendered_images_silhouettes.split([3, 1], dim=-1) + ) + + # Compute the silhoutte error as the mean huber + # loss between the predicted masks and the + # sampled target silhouettes. + silhouettes_at_rays = sample_images_at_mc_locs( + target_silhouettes[batch_idx, ..., None], + sampled_rays.xys + ) + sil_err = huber( + rendered_silhouettes, + silhouettes_at_rays, + ).abs().mean() + + # Compute the color error as the mean huber + # loss between the rendered colors and the + # sampled target images. + colors_at_rays = sample_images_at_mc_locs( + target_images[batch_idx], + sampled_rays.xys + ) + color_err = huber( + rendered_images, + colors_at_rays, + ).abs().mean() + + # The optimization loss is a simple + # sum of the color and silhouette errors. + loss = color_err + sil_err + + # Log the loss history. + loss_history_color.append(float(color_err)) + loss_history_sil.append(float(sil_err)) + + # Every 10 iterations, print the current values of the losses. + if iteration % 10 == 0: + print( + f'Iteration {iteration:05d}:' + + f' loss color = {float(color_err):1.2e}' + + f' loss silhouette = {float(sil_err):1.2e}' + ) + + # Take the optimization step. + loss.backward() + optimizer.step() + + # Visualize the full renders every 100 iterations. + if iteration % 100 == 0: + show_idx = torch.randperm(len(target_cameras))[:1] + show_full_render( + neural_radiance_field, + FoVPerspectiveCameras( + R = target_cameras.R[show_idx], + T = target_cameras.T[show_idx], + znear = target_cameras.znear[show_idx], + zfar = target_cameras.zfar[show_idx], + aspect_ratio = target_cameras.aspect_ratio[show_idx], + fov = target_cameras.fov[show_idx], + device = device, + ), + target_images[show_idx][0], + target_silhouettes[show_idx][0], + loss_history_color, + loss_history_sil, + ) + +############################################################################### +# Visualizing the optimized neural radiance field +############################################################################### + +def generate_rotating_nerf(neural_radiance_field, n_frames = 50): + logRs = torch.zeros(n_frames, 3, device=device) + logRs[:, 1] = torch.linspace(-3.14, 3.14, n_frames, device=device) + Rs = so3_exponential_map(logRs) + Ts = torch.zeros(n_frames, 3, device=device) + Ts[:, 2] = 2.7 + frames = [] + print('Rendering rotating NeRF ...') + for R, T in zip(tqdm(Rs), Ts): + camera = FoVPerspectiveCameras( + R=R[None], + T=T[None], + znear=target_cameras.znear[0], + zfar=target_cameras.zfar[0], + aspect_ratio=target_cameras.aspect_ratio[0], + fov=target_cameras.fov[0], + device=device, + ) + # Note that we again render with `NDCGridSampler` + # and the batched_forward function of neural_radiance_field. + frames.append( + renderer_grid( + cameras=camera, + volumetric_function=neural_radiance_field.batched_forward, + )[0][..., :3] + ) + return torch.cat(frames) + +with torch.no_grad(): + rotating_nerf_frames = generate_rotating_nerf(neural_radiance_field, n_frames=3*5) + +image_grid(rotating_nerf_frames.clamp(0., 1.).cpu().numpy(), rows=3, cols=5, rgb=True, fill=True) +plt.show() diff --git a/utils/__init__.py b/utils/__init__.py new file mode 100644 index 0000000..8e3bf5e --- /dev/null +++ b/utils/__init__.py @@ -0,0 +1,4 @@ +# Copyright (c) Facebook, Inc. and its affiliates. 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All rights reserved. + +import matplotlib.pyplot as plt +from mpl_toolkits.mplot3d import Axes3D # noqa: F401 unused import +from pytorch3d.vis.plotly_vis import get_camera_wireframe + + +def plot_cameras(ax, cameras, color: str = "blue"): + """ + Plots a set of `cameras` objects into the maplotlib axis `ax` with + color `color`. + """ + cam_wires_canonical = get_camera_wireframe().cuda()[None] + cam_trans = cameras.get_world_to_view_transform().inverse() + cam_wires_trans = cam_trans.transform_points(cam_wires_canonical) + plot_handles = [] + for wire in cam_wires_trans: + # the Z and Y axes are flipped intentionally here! + x_, z_, y_ = wire.detach().cpu().numpy().T.astype(float) + (h,) = ax.plot(x_, y_, z_, color=color, linewidth=0.3) + plot_handles.append(h) + return plot_handles + + +def plot_camera_scene(cameras, cameras_gt, status: str): + """ + Plots a set of predicted cameras `cameras` and their corresponding + ground truth locations `cameras_gt`. The plot is named with + a string passed inside the `status` argument. + """ + fig = plt.figure() + ax = fig.gca(projection="3d") + ax.clear() + ax.set_title(status) + handle_cam = plot_cameras(ax, cameras, color="#FF7D1E") + handle_cam_gt = plot_cameras(ax, cameras_gt, color="#812CE5") + plot_radius = 3 + ax.set_xlim3d([-plot_radius, plot_radius]) + ax.set_ylim3d([3 - plot_radius, 3 + plot_radius]) + ax.set_zlim3d([-plot_radius, plot_radius]) + ax.set_xlabel("x") + ax.set_ylabel("z") + ax.set_zlabel("y") + labels_handles = { + "Estimated cameras": handle_cam[0], + "GT cameras": handle_cam_gt[0], + } + ax.legend( + labels_handles.values(), + labels_handles.keys(), + loc="upper center", + bbox_to_anchor=(0.5, 0), + ) + plt.show() + return fig diff --git a/utils/generate_cow_renders.py b/utils/generate_cow_renders.py new file mode 100644 index 0000000..b24e9ef --- /dev/null +++ b/utils/generate_cow_renders.py @@ -0,0 +1,165 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved. + +import os + +import numpy as np +import torch + +# Util function for loading meshes +from pytorch3d.io import load_objs_as_meshes +from pytorch3d.renderer import ( + BlendParams, + FoVPerspectiveCameras, + MeshRasterizer, + MeshRenderer, + PointLights, + RasterizationSettings, + SoftPhongShader, + SoftSilhouetteShader, + look_at_view_transform, +) + + +# create the default data directory +current_dir = os.path.dirname(os.path.realpath(__file__)) +DATA_DIR = os.path.join(current_dir, "..", "data", "cow_mesh") + + +def generate_cow_renders( + num_views: int = 40, data_dir: str = DATA_DIR, azimuth_range: float = 180 +): + """ + This function generates `num_views` renders of a cow mesh. + The renders are generated from viewpoints sampled at uniformly distributed + azimuth intervals. The elevation is kept constant so that the camera's + vertical position coincides with the equator. + + For a more detailed explanation of this code, please refer to the + docs/tutorials/fit_textured_mesh.ipynb notebook. + + Args: + num_views: The number of generated renders. + data_dir: The folder that contains the cow mesh files. If the cow mesh + files do not exist in the folder, this function will automatically + download them. + + Returns: + cameras: A batch of `num_views` `FoVPerspectiveCameras` from which the + images are rendered. + images: A tensor of shape `(num_views, height, width, 3)` containing + the rendered images. + silhouettes: A tensor of shape `(num_views, height, width)` containing + the rendered silhouettes. + """ + + # set the paths + + # download the cow mesh if not done before + cow_mesh_files = [ + os.path.join(data_dir, fl) for fl in ("cow.obj", "cow.mtl", "cow_texture.png") + ] + if any(not os.path.isfile(f) for f in cow_mesh_files): + os.makedirs(data_dir, exist_ok=True) + os.system( + f"wget -P {data_dir} " + + "https://dl.fbaipublicfiles.com/pytorch3d/data/cow_mesh/cow.obj" + ) + os.system( + f"wget -P {data_dir} " + + "https://dl.fbaipublicfiles.com/pytorch3d/data/cow_mesh/cow.mtl" + ) + os.system( + f"wget -P {data_dir} " + + "https://dl.fbaipublicfiles.com/pytorch3d/data/cow_mesh/cow_texture.png" + ) + + # Setup + if torch.cuda.is_available(): + device = torch.device("cuda:0") + torch.cuda.set_device(device) + else: + device = torch.device("cpu") + + # Load obj file + obj_filename = os.path.join(data_dir, "cow.obj") + mesh = load_objs_as_meshes([obj_filename], device=device) + + # We scale normalize and center the target mesh to fit in a sphere of radius 1 + # centered at (0,0,0). (scale, center) will be used to bring the predicted mesh + # to its original center and scale. Note that normalizing the target mesh, + # speeds up the optimization but is not necessary! + verts = mesh.verts_packed() + N = verts.shape[0] + center = verts.mean(0) + scale = max((verts - center).abs().max(0)[0]) + mesh.offset_verts_(-(center.expand(N, 3))) + mesh.scale_verts_((1.0 / float(scale))) + + # Get a batch of viewing angles. + elev = torch.linspace(0, 0, num_views) # keep constant + azim = torch.linspace(-azimuth_range, azimuth_range, num_views) + 180.0 + + # Place a point light in front of the object. As mentioned above, the front of + # the cow is facing the -z direction. + lights = PointLights(device=device, location=[[0.0, 0.0, -3.0]]) + + # Initialize an OpenGL perspective camera that represents a batch of different + # viewing angles. All the cameras helper methods support mixed type inputs and + # broadcasting. So we can view the camera from the a distance of dist=2.7, and + # then specify elevation and azimuth angles for each viewpoint as tensors. + R, T = look_at_view_transform(dist=2.7, elev=elev, azim=azim) + cameras = FoVPerspectiveCameras(device=device, R=R, T=T) + + # Define the settings for rasterization and shading. Here we set the output + # image to be of size 128X128. As we are rendering images for visualization + # purposes only we will set faces_per_pixel=1 and blur_radius=0.0. Refer to + # rasterize_meshes.py for explanations of these parameters. We also leave + # bin_size and max_faces_per_bin to their default values of None, which sets + # their values using huristics and ensures that the faster coarse-to-fine + # rasterization method is used. Refer to docs/notes/renderer.md for an + # explanation of the difference between naive and coarse-to-fine rasterization. + raster_settings = RasterizationSettings( + image_size=128, blur_radius=0.0, faces_per_pixel=1 + ) + + # Create a phong renderer by composing a rasterizer and a shader. The textured + # phong shader will interpolate the texture uv coordinates for each vertex, + # sample from a texture image and apply the Phong lighting model + blend_params = BlendParams(sigma=1e-4, gamma=1e-4, background_color=(0.0, 0.0, 0.0)) + renderer = MeshRenderer( + rasterizer=MeshRasterizer(cameras=cameras, raster_settings=raster_settings), + shader=SoftPhongShader( + device=device, cameras=cameras, lights=lights, blend_params=blend_params + ), + ) + + # Create a batch of meshes by repeating the cow mesh and associated textures. + # Meshes has a useful `extend` method which allows us do this very easily. + # This also extends the textures. + meshes = mesh.extend(num_views) + + # Render the cow mesh from each viewing angle + target_images = renderer(meshes, cameras=cameras, lights=lights) + + # Rasterization settings for silhouette rendering + sigma = 1e-4 + raster_settings_silhouette = RasterizationSettings( + image_size=128, blur_radius=np.log(1.0 / 1e-4 - 1.0) * sigma, faces_per_pixel=50 + ) + + # Silhouette renderer + renderer_silhouette = MeshRenderer( + rasterizer=MeshRasterizer( + cameras=cameras, raster_settings=raster_settings_silhouette + ), + shader=SoftSilhouetteShader(), + ) + + # Render silhouette images. The 3rd channel of the rendering output is + # the alpha/silhouette channel + silhouette_images = renderer_silhouette(meshes, cameras=cameras, lights=lights) + + # binary silhouettes + silhouette_binary = (silhouette_images[..., 3] > 1e-4).float() + + return cameras, target_images[..., :3], silhouette_binary diff --git a/utils/plot_image_grid.py b/utils/plot_image_grid.py new file mode 100644 index 0000000..7af8e16 --- /dev/null +++ b/utils/plot_image_grid.py @@ -0,0 +1,49 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved. + +import matplotlib.pyplot as plt + + +def image_grid( + images, + rows=None, + cols=None, + fill: bool = True, + show_axes: bool = False, + rgb: bool = True, +): + """ + A util function for plotting a grid of images. + + Args: + images: (N, H, W, 4) array of RGBA images + rows: number of rows in the grid + cols: number of columns in the grid + fill: boolean indicating if the space between images should be filled + show_axes: boolean indicating if the axes of the plots should be visible + rgb: boolean, If True, only RGB channels are plotted. + If False, only the alpha channel is plotted. + + Returns: + None + """ + if (rows is None) != (cols is None): + raise ValueError("Specify either both rows and cols or neither.") + + if rows is None: + rows = len(images) + cols = 1 + + gridspec_kw = {"wspace": 0.0, "hspace": 0.0} if fill else {} + fig, axarr = plt.subplots(rows, cols, gridspec_kw=gridspec_kw, figsize=(15, 9)) + bleed = 0 + fig.subplots_adjust(left=bleed, bottom=bleed, right=(1 - bleed), top=(1 - bleed)) + + for ax, im in zip(axarr.ravel(), images): + if rgb: + # only render RGB channels + ax.imshow(im[..., :3]) + else: + # only render Alpha channel + ax.imshow(im[..., 3]) + if not show_axes: + ax.set_axis_off()