src/utils/render.py

# Copyright 2022 Google LLC
#
# 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
#
#     https://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.

import numpy as np
import os
import enum
import types
from typing import List, Mapping, Optional, Text, Tuple, Union
import copy
from PIL import Image
# import mediapy as media
from matplotlib import cm
from tqdm import tqdm

import torch

def normalize(x: np.ndarray) -> np.ndarray:
    """Normalization helper function."""
    return x / np.linalg.norm(x)

def pad_poses(p: np.ndarray) -> np.ndarray:
    """Pad [..., 3, 4] pose matrices with a homogeneous bottom row [0,0,0,1]."""
    bottom = np.broadcast_to([0, 0, 0, 1.], p[..., :1, :4].shape)
    return np.concatenate([p[..., :3, :4], bottom], axis=-2)


def unpad_poses(p: np.ndarray) -> np.ndarray:
    """Remove the homogeneous bottom row from [..., 4, 4] pose matrices."""
    return p[..., :3, :4]


def recenter_poses(poses: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
    """Recenter poses around the origin."""
    cam2world = average_pose(poses)
    transform = np.linalg.inv(pad_poses(cam2world))
    poses = transform @ pad_poses(poses)
    return unpad_poses(poses), transform


def average_pose(poses: np.ndarray) -> np.ndarray:
    """New pose using average position, z-axis, and up vector of input poses."""
    position = poses[:, :3, 3].mean(0)
    z_axis = poses[:, :3, 2].mean(0)
    up = poses[:, :3, 1].mean(0)
    cam2world = viewmatrix(z_axis, up, position)
    return cam2world

def viewmatrix(lookdir: np.ndarray, up: np.ndarray,
               position: np.ndarray) -> np.ndarray:
    """Construct lookat view matrix."""
    vec2 = normalize(lookdir)
    vec0 = normalize(np.cross(up, vec2))
    vec1 = normalize(np.cross(vec2, vec0))
    m = np.stack([vec0, vec1, vec2, position], axis=1)
    return m

def focus_point_fn(poses: np.ndarray) -> np.ndarray:
    """Calculate nearest point to all focal axes in poses."""
    directions, origins = poses[:, :3, 2:3], poses[:, :3, 3:4]
    m = np.eye(3) - directions * np.transpose(directions, [0, 2, 1])
    mt_m = np.transpose(m, [0, 2, 1]) @ m
    focus_pt = np.linalg.inv(mt_m.mean(0)) @ (mt_m @ origins).mean(0)[:, 0]
    return focus_pt

def transform_poses_pca(poses: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
    """Transforms poses so principal components lie on XYZ axes.

    Args:
        poses: a (N, 3, 4) array containing the cameras' camera to world transforms.

    Returns:
        A tuple (poses, transform), with the transformed poses and the applied
        camera_to_world transforms.
    """
    t = poses[:, :3, 3]
    t_mean = t.mean(axis=0)
    t = t - t_mean
    
    eigval, eigvec = np.linalg.eig(t.T @ t)
    # Sort eigenvectors in order of largest to smallest eigenvalue.
    inds = np.argsort(eigval)[::-1]
    eigvec = eigvec[:, inds]
    rot = eigvec.T
    if np.linalg.det(rot) < 0:
        rot = np.diag(np.array([1, 1, -1])) @ rot

    transform = np.concatenate([rot, rot @ -t_mean[:, None]], -1)
    poses_recentered = unpad_poses(transform @ pad_poses(poses))
    transform = np.concatenate([transform, np.eye(4)[3:]], axis=0)

    # Flip coordinate system if z component of y-axis is negative
    if poses_recentered.mean(axis=0)[2, 1] < 0:
        poses_recentered = np.diag(np.array([1, -1, -1])) @ poses_recentered
        transform = np.diag(np.array([1, -1, -1, 1])) @ transform

    return poses_recentered, transform
    # points = np.random.rand(3,100)
    # points_h = np.concatenate((points,np.ones_like(points[:1])), axis=0)
    # (poses_recentered @ points_h)[0]
    # (transform @ pad_poses(poses) @ points_h)[0,:3]
    # import pdb; pdb.set_trace()

    # # Just make sure it's it in the [-1, 1]^3 cube
    # scale_factor = 1. / np.max(np.abs(poses_recentered[:, :3, 3]))
    # poses_recentered[:, :3, 3] *= scale_factor
    # transform = np.diag(np.array([scale_factor] * 3 + [1])) @ transform

    # return poses_recentered, transform

def generate_ellipse_path(poses: np.ndarray,
                          n_frames: int = 120,
                          const_speed: bool = True,
                          z_variation: float = 0.,
                          z_phase: float = 0.) -> np.ndarray:
    """Generate an elliptical render path based on the given poses."""
    # Calculate the focal point for the path (cameras point toward this).
    center = focus_point_fn(poses)
    # Path height sits at z=0 (in middle of zero-mean capture pattern).
    offset = np.array([center[0], center[1], 0])

    # Calculate scaling for ellipse axes based on input camera positions.
    sc = np.percentile(np.abs(poses[:, :3, 3] - offset), 90, axis=0)
    # Use ellipse that is symmetric about the focal point in xy.
    low = -sc + offset
    high = sc + offset
    # Optional height variation need not be symmetric
    z_low = np.percentile((poses[:, :3, 3]), 10, axis=0)
    z_high = np.percentile((poses[:, :3, 3]), 90, axis=0)

    def get_positions(theta):
        # Interpolate between bounds with trig functions to get ellipse in x-y.
        # Optionally also interpolate in z to change camera height along path.
        return np.stack([
            low[0] + (high - low)[0] * (np.cos(theta) * .5 + .5),
            low[1] + (high - low)[1] * (np.sin(theta) * .5 + .5),
            z_variation * (z_low[2] + (z_high - z_low)[2] *
                        (np.cos(theta + 2 * np.pi * z_phase) * .5 + .5)),
        ], -1)

    theta = np.linspace(0, 2. * np.pi, n_frames + 1, endpoint=True)
    positions = get_positions(theta)

    #if const_speed:

    # # Resample theta angles so that the velocity is closer to constant.
    # lengths = np.linalg.norm(positions[1:] - positions[:-1], axis=-1)
    # theta = stepfun.sample(None, theta, np.log(lengths), n_frames + 1)
    # positions = get_positions(theta)

    # Throw away duplicated last position.
    positions = positions[:-1]

    # Set path's up vector to axis closest to average of input pose up vectors.
    avg_up = poses[:, :3, 1].mean(0)
    avg_up = avg_up / np.linalg.norm(avg_up)
    ind_up = np.argmax(np.abs(avg_up))
    up = np.eye(3)[ind_up] * np.sign(avg_up[ind_up])
    
    return np.stack([viewmatrix(p - center, up, p) for p in positions])


def generate_path(viewpoint_cameras, n_frames=480):
    # c2ws = np.array([np.linalg.inv(np.asarray((cam.world_view_transform.T).cpu().numpy())) for cam in viewpoint_cameras])
    c2ws = viewpoint_cameras.cpu().numpy()
    pose = c2ws[:,:3,:] @ np.diag([1, -1, -1, 1])
    pose_recenter, colmap_to_world_transform = transform_poses_pca(pose)

    # generate new poses
    new_poses = generate_ellipse_path(poses=pose_recenter, n_frames=n_frames)
    # warp back to orignal scale
    new_poses = np.linalg.inv(colmap_to_world_transform) @ pad_poses(new_poses)

    return new_poses

    # traj = []
    # for c2w in new_poses:
    #     c2w = c2w @ np.diag([1, -1, -1, 1])
    #     cam = copy.deepcopy(viewpoint_cameras[0])
    #     cam.image_height = int(cam.image_height / 2) * 2
    #     cam.image_width = int(cam.image_width / 2) * 2
    #     cam.world_view_transform = torch.from_numpy(np.linalg.inv(c2w).T).float().cuda()
    #     cam.full_proj_transform = (cam.world_view_transform.unsqueeze(0).bmm(cam.projection_matrix.unsqueeze(0))).squeeze(0)
    #     cam.camera_center = cam.world_view_transform.inverse()[3, :3]
    #     traj.append(cam)

    # return traj

def load_img(pth: str) -> np.ndarray:
    """Load an image and cast to float32."""
    with open(pth, 'rb') as f:
        image = np.array(Image.open(f), dtype=np.float32)
    return image


def create_videos(base_dir, input_dir, out_name, num_frames=480):
  """Creates videos out of the images saved to disk."""
  # Last two parts of checkpoint path are experiment name and scene name.
  video_prefix = f'{out_name}'

  zpad = max(5, len(str(num_frames - 1)))
  idx_to_str = lambda idx: str(idx).zfill(zpad)

  os.makedirs(base_dir, exist_ok=True)
  render_dist_curve_fn = np.log
  
  # Load one example frame to get image shape and depth range.
  depth_file = os.path.join(input_dir, 'vis', f'depth_{idx_to_str(0)}.tiff')
  depth_frame = load_img(depth_file)
  shape = depth_frame.shape
  p = 3
  distance_limits = np.percentile(depth_frame.flatten(), [p, 100 - p])
  lo, hi = [render_dist_curve_fn(x) for x in distance_limits]
  print(f'Video shape is {shape[:2]}')

  video_kwargs = {
      'shape': shape[:2],
      'codec': 'h264',
      'fps': 60,
      'crf': 18,
  }
  
  for k in ['depth', 'normal', 'color']:
    video_file = os.path.join(base_dir, f'{video_prefix}_{k}.mp4')
    input_format = 'gray' if k == 'alpha' else 'rgb'
    

    file_ext = 'png' if k in ['color', 'normal'] else 'tiff'
    idx = 0

    if k == 'color':
      file0 = os.path.join(input_dir, 'renders', f'{idx_to_str(0)}.{file_ext}')
    else:
      file0 = os.path.join(input_dir, 'vis', f'{k}_{idx_to_str(0)}.{file_ext}')

    if not os.path.exists(file0):
      print(f'Images missing for tag {k}')
      continue
    print(f'Making video {video_file}...')
    with media.VideoWriter(
        video_file, **video_kwargs, input_format=input_format) as writer:
        for idx in tqdm(range(num_frames)):
            # img_file = os.path.join(input_dir, f'{k}_{idx_to_str(idx)}.{file_ext}')
            if k == 'color':
                img_file = os.path.join(input_dir, 'renders', f'{idx_to_str(idx)}.{file_ext}')
            else:
                img_file = os.path.join(input_dir, 'vis', f'{k}_{idx_to_str(idx)}.{file_ext}')

            if not os.path.exists(img_file):
                ValueError(f'Image file {img_file} does not exist.')
            img = load_img(img_file)
            if k in ['color', 'normal']:
                img = img / 255.
            elif k.startswith('depth'):
                img = render_dist_curve_fn(img)
                img = np.clip((img - np.minimum(lo, hi)) / np.abs(hi - lo), 0, 1)
                img = cm.get_cmap('turbo')(img)[..., :3]

        frame = (np.clip(np.nan_to_num(img), 0., 1.) * 255.).astype(np.uint8)
        writer.add_image(frame)
        idx += 1

def save_img_u8(img, pth):
    """Save an image (probably RGB) in [0, 1] to disk as a uint8 PNG."""
    with open(pth, 'wb') as f:
        Image.fromarray(
            (np.clip(np.nan_to_num(img), 0., 1.) * 255.).astype(np.uint8)).save(
                f, 'PNG')


def save_img_f32(depthmap, pth):
    """Save an image (probably a depthmap) to disk as a float32 TIFF."""
    with open(pth, 'wb') as f:
        Image.fromarray(np.nan_to_num(depthmap).astype(np.float32)).save(f, 'TIFF')