import csv import gc import io import json import math import os import random from random import shuffle import albumentations import cv2 import numpy as np import torch import torch.nn.functional as F import torchvision.transforms as transforms from decord import VideoReader from einops import rearrange from packaging import version as pver from PIL import Image from torch.utils.data import BatchSampler, Sampler from torch.utils.data.dataset import Dataset class Camera(object): """Copied from https://github.com/hehao13/CameraCtrl/blob/main/inference.py """ def __init__(self, entry): fx, fy, cx, cy = entry[1:5] self.fx = fx self.fy = fy self.cx = cx self.cy = cy w2c_mat = np.array(entry[7:]).reshape(3, 4) w2c_mat_4x4 = np.eye(4) w2c_mat_4x4[:3, :] = w2c_mat self.w2c_mat = w2c_mat_4x4 self.c2w_mat = np.linalg.inv(w2c_mat_4x4) def get_relative_pose(cam_params): """Copied from https://github.com/hehao13/CameraCtrl/blob/main/inference.py """ abs_w2cs = [cam_param.w2c_mat for cam_param in cam_params] abs_c2ws = [cam_param.c2w_mat for cam_param in cam_params] cam_to_origin = 0 target_cam_c2w = np.array([ [1, 0, 0, 0], [0, 1, 0, -cam_to_origin], [0, 0, 1, 0], [0, 0, 0, 1] ]) abs2rel = target_cam_c2w @ abs_w2cs[0] ret_poses = [target_cam_c2w, ] + [abs2rel @ abs_c2w for abs_c2w in abs_c2ws[1:]] ret_poses = np.array(ret_poses, dtype=np.float32) return ret_poses def ray_condition(K, c2w, H, W, device): """Copied from https://github.com/hehao13/CameraCtrl/blob/main/inference.py """ # c2w: B, V, 4, 4 # K: B, V, 4 B = K.shape[0] j, i = custom_meshgrid( torch.linspace(0, H - 1, H, device=device, dtype=c2w.dtype), torch.linspace(0, W - 1, W, device=device, dtype=c2w.dtype), ) i = i.reshape([1, 1, H * W]).expand([B, 1, H * W]) + 0.5 # [B, HxW] j = j.reshape([1, 1, H * W]).expand([B, 1, H * W]) + 0.5 # [B, HxW] fx, fy, cx, cy = K.chunk(4, dim=-1) # B,V, 1 zs = torch.ones_like(i) # [B, HxW] xs = (i - cx) / fx * zs ys = (j - cy) / fy * zs zs = zs.expand_as(ys) directions = torch.stack((xs, ys, zs), dim=-1) # B, V, HW, 3 directions = directions / directions.norm(dim=-1, keepdim=True) # B, V, HW, 3 rays_d = directions @ c2w[..., :3, :3].transpose(-1, -2) # B, V, 3, HW rays_o = c2w[..., :3, 3] # B, V, 3 rays_o = rays_o[:, :, None].expand_as(rays_d) # B, V, 3, HW # c2w @ dirctions rays_dxo = torch.cross(rays_o, rays_d) plucker = torch.cat([rays_dxo, rays_d], dim=-1) plucker = plucker.reshape(B, c2w.shape[1], H, W, 6) # B, V, H, W, 6 # plucker = plucker.permute(0, 1, 4, 2, 3) return plucker def custom_meshgrid(*args): """Copied from https://github.com/hehao13/CameraCtrl/blob/main/inference.py """ # ref: https://pytorch.org/docs/stable/generated/torch.meshgrid.html?highlight=meshgrid#torch.meshgrid if pver.parse(torch.__version__) < pver.parse('1.10'): return torch.meshgrid(*args) else: return torch.meshgrid(*args, indexing='ij') def process_pose_file(pose_file_path, width=672, height=384, original_pose_width=1280, original_pose_height=720, device='cpu', return_poses=False): """Modified from https://github.com/hehao13/CameraCtrl/blob/main/inference.py """ with open(pose_file_path, 'r') as f: poses = f.readlines() poses = [pose.strip().split(' ') for pose in poses[1:]] cam_params = [[float(x) for x in pose] for pose in poses] if return_poses: return cam_params else: cam_params = [Camera(cam_param) for cam_param in cam_params] sample_wh_ratio = width / height pose_wh_ratio = original_pose_width / original_pose_height # Assuming placeholder ratios, change as needed if pose_wh_ratio > sample_wh_ratio: resized_ori_w = height * pose_wh_ratio for cam_param in cam_params: cam_param.fx = resized_ori_w * cam_param.fx / width else: resized_ori_h = width / pose_wh_ratio for cam_param in cam_params: cam_param.fy = resized_ori_h * cam_param.fy / height intrinsic = np.asarray([[cam_param.fx * width, cam_param.fy * height, cam_param.cx * width, cam_param.cy * height] for cam_param in cam_params], dtype=np.float32) K = torch.as_tensor(intrinsic)[None] # [1, 1, 4] c2ws = get_relative_pose(cam_params) # Assuming this function is defined elsewhere c2ws = torch.as_tensor(c2ws)[None] # [1, n_frame, 4, 4] plucker_embedding = ray_condition(K, c2ws, height, width, device=device)[0].permute(0, 3, 1, 2).contiguous() # V, 6, H, W plucker_embedding = plucker_embedding[None] plucker_embedding = rearrange(plucker_embedding, "b f c h w -> b f h w c")[0] return plucker_embedding def process_pose_params(cam_params, width=672, height=384, original_pose_width=1280, original_pose_height=720, device='cpu'): """Modified from https://github.com/hehao13/CameraCtrl/blob/main/inference.py """ cam_params = [Camera(cam_param) for cam_param in cam_params] sample_wh_ratio = width / height pose_wh_ratio = original_pose_width / original_pose_height # Assuming placeholder ratios, change as needed if pose_wh_ratio > sample_wh_ratio: resized_ori_w = height * pose_wh_ratio for cam_param in cam_params: cam_param.fx = resized_ori_w * cam_param.fx / width else: resized_ori_h = width / pose_wh_ratio for cam_param in cam_params: cam_param.fy = resized_ori_h * cam_param.fy / height intrinsic = np.asarray([[cam_param.fx * width, cam_param.fy * height, cam_param.cx * width, cam_param.cy * height] for cam_param in cam_params], dtype=np.float32) K = torch.as_tensor(intrinsic)[None] # [1, 1, 4] c2ws = get_relative_pose(cam_params) # Assuming this function is defined elsewhere c2ws = torch.as_tensor(c2ws)[None] # [1, n_frame, 4, 4] plucker_embedding = ray_condition(K, c2ws, height, width, device=device)[0].permute(0, 3, 1, 2).contiguous() # V, 6, H, W plucker_embedding = plucker_embedding[None] plucker_embedding = rearrange(plucker_embedding, "b f c h w -> b f h w c")[0] return plucker_embedding