import os import torch import cv2 import numpy as np import PIL.Image from PIL.ImageOps import exif_transpose from plyfile import PlyData, PlyElement import torchvision.transforms as tvf import roma import dust3r.cloud_opt.init_im_poses as init_fun from dust3r.cloud_opt.base_opt import global_alignment_loop from dust3r.utils.geometry import geotrf, inv, depthmap_to_absolute_camera_coordinates from dust3r.cloud_opt.commons import edge_str from dust3r.utils.image import _resize_pil_image, imread_cv2 import dust3r.datasets.utils.cropping as cropping import torch.nn.functional as F def get_known_poses(scene): if scene.has_im_poses: known_poses_msk = torch.tensor([not (p.requires_grad) for p in scene.im_poses]) known_poses = scene.get_im_poses() return known_poses_msk.sum(), known_poses_msk, known_poses else: return 0, None, None def init_from_pts3d(scene, pts3d, im_focals, im_poses): # init poses nkp, known_poses_msk, known_poses = get_known_poses(scene) if nkp == 1: raise NotImplementedError("Would be simpler to just align everything afterwards on the single known pose") elif nkp > 1: # global rigid SE3 alignment s, R, T = init_fun.align_multiple_poses(im_poses[known_poses_msk], known_poses[known_poses_msk]) trf = init_fun.sRT_to_4x4(s, R, T, device=known_poses.device) # rotate everything im_poses = trf @ im_poses im_poses[:, :3, :3] /= s # undo scaling on the rotation part for img_pts3d in pts3d: img_pts3d[:] = geotrf(trf, img_pts3d) # set all pairwise poses for e, (i, j) in enumerate(scene.edges): i_j = edge_str(i, j) # compute transform that goes from cam to world s, R, T = init_fun.rigid_points_registration(scene.pred_i[i_j], pts3d[i], conf=scene.conf_i[i_j]) scene._set_pose(scene.pw_poses, e, R, T, scale=s) # take into account the scale normalization s_factor = scene.get_pw_norm_scale_factor() im_poses[:, :3, 3] *= s_factor # apply downscaling factor for img_pts3d in pts3d: img_pts3d *= s_factor # init all image poses if scene.has_im_poses: for i in range(scene.n_imgs): cam2world = im_poses[i] depth = geotrf(inv(cam2world), pts3d[i])[..., 2] scene._set_depthmap(i, depth) scene._set_pose(scene.im_poses, i, cam2world) if im_focals[i] is not None: scene._set_focal(i, im_focals[i]) if scene.verbose: print(' init loss =', float(scene())) @torch.no_grad() def init_minimum_spanning_tree(scene, focal_avg=False, known_focal=None, **kw): """ Init all camera poses (image-wise and pairwise poses) given an initial set of pairwise estimations. """ device = scene.device pts3d, _, im_focals, im_poses = init_fun.minimum_spanning_tree(scene.imshapes, scene.edges, scene.pred_i, scene.pred_j, scene.conf_i, scene.conf_j, scene.im_conf, scene.min_conf_thr, device, has_im_poses=scene.has_im_poses, verbose=scene.verbose, **kw) if known_focal is not None: repeat_focal = np.repeat(known_focal, len(im_focals)) for i in range(len(im_focals)): im_focals[i] = known_focal scene.preset_focal(known_focals=repeat_focal) elif focal_avg: im_focals_avg = np.array(im_focals).mean() for i in range(len(im_focals)): im_focals[i] = im_focals_avg repeat_focal = np.array(im_focals)#.cpu().numpy() scene.preset_focal(known_focals=repeat_focal) return init_from_pts3d(scene, pts3d, im_focals, im_poses) @torch.cuda.amp.autocast(enabled=False) def compute_global_alignment(scene, init=None, niter_PnP=10, focal_avg=False, known_focal=None, **kw): if init is None: pass elif init == 'msp' or init == 'mst': init_minimum_spanning_tree(scene, niter_PnP=niter_PnP, focal_avg=focal_avg, known_focal=known_focal) elif init == 'known_poses': init_fun.init_from_known_poses(scene, min_conf_thr=scene.min_conf_thr, niter_PnP=niter_PnP) else: raise ValueError(f'bad value for {init=}') return global_alignment_loop(scene, **kw) def load_images(folder_or_list, size, square_ok=False): """ open and convert all images in a list or folder to proper input format for DUSt3R """ if isinstance(folder_or_list, str): print(f'>> Loading images from {folder_or_list}') root, folder_content = folder_or_list, sorted(os.listdir(folder_or_list)) elif isinstance(folder_or_list, list): print(f'>> Loading a list of {len(folder_or_list)} images') root, folder_content = '', folder_or_list else: raise ValueError(f'bad {folder_or_list=} ({type(folder_or_list)})') imgs = [] for path in folder_content: if not path.endswith(('.jpg', '.jpeg', '.png', '.JPG')): continue img = exif_transpose(PIL.Image.open(os.path.join(root, path))).convert('RGB') W1, H1 = img.size if size == 224: # resize short side to 224 (then crop) img = _resize_pil_image(img, round(size * max(W1/H1, H1/W1))) else: # resize long side to 512 img = _resize_pil_image(img, size) W, H = img.size W2 = W//16*16 H2 = H//16*16 img = np.array(img) img = cv2.resize(img, (W2,H2), interpolation=cv2.INTER_LINEAR) img = PIL.Image.fromarray(img) print(f' - adding {path} with resolution {W1}x{H1} --> {W2}x{H2}') ImgNorm = tvf.Compose([tvf.ToTensor(), tvf.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]) imgs.append(dict(img=ImgNorm(img)[None], true_shape=np.int32( [img.size[::-1]]), idx=len(imgs), instance=str(len(imgs)))) assert imgs, 'no images foud at '+root print(f' (Found {len(imgs)} images)') return imgs, (W1,H1) def load_cam_mvsnet(file, interval_scale=1): """ read camera txt file """ cam = np.zeros((2, 4, 4)) words = file.read().split() # read extrinsic for i in range(0, 4): for j in range(0, 4): extrinsic_index = 4 * i + j + 1 cam[0][i][j] = words[extrinsic_index] # read intrinsic for i in range(0, 3): for j in range(0, 3): intrinsic_index = 3 * i + j + 18 cam[1][i][j] = words[intrinsic_index] if len(words) == 29: cam[1][3][0] = words[27] cam[1][3][1] = float(words[28]) * interval_scale cam[1][3][2] = 192 cam[1][3][3] = cam[1][3][0] + cam[1][3][1] * cam[1][3][2] elif len(words) == 30: cam[1][3][0] = words[27] cam[1][3][1] = float(words[28]) * interval_scale cam[1][3][2] = words[29] cam[1][3][3] = cam[1][3][0] + cam[1][3][1] * cam[1][3][2] elif len(words) == 31: cam[1][3][0] = words[27] cam[1][3][1] = float(words[28]) * interval_scale cam[1][3][2] = words[29] cam[1][3][3] = words[30] else: cam[1][3][0] = 0 cam[1][3][1] = 0 cam[1][3][2] = 0 cam[1][3][3] = 0 extrinsic = cam[0].astype(np.float32) intrinsic = cam[1].astype(np.float32) return intrinsic, extrinsic def _crop_resize_if_necessary(image, depthmap, intrinsics, resolution, rng=None, info=None): """ This function: - first downsizes the image with LANCZOS inteprolation, which is better than bilinear interpolation in """ if not isinstance(image, PIL.Image.Image): image = PIL.Image.fromarray(image) # downscale with lanczos interpolation so that image.size == resolution # cropping centered on the principal point W, H = image.size cx, cy = intrinsics[:2, 2].round().astype(int) # calculate min distance to margin min_margin_x = min(cx, W-cx) min_margin_y = min(cy, H-cy) assert min_margin_x > W/5, f'Bad principal point in view={info}' assert min_margin_y > H/5, f'Bad principal point in view={info}' ## Center crop # Crop on the principal point, make it always centered # the new window will be a rectangle of size (2*min_margin_x, 2*min_margin_y) centered on (cx,cy) l, t = cx - min_margin_x, cy - min_margin_y r, b = cx + min_margin_x, cy + min_margin_y crop_bbox = (l, t, r, b) image, depthmap, intrinsics = cropping.crop_image_depthmap(image, depthmap, intrinsics, crop_bbox) # transpose the resolution if necessary W, H = image.size # new size assert resolution[0] >= resolution[1] if H > 1.1*W: # image is portrait mode resolution = resolution[::-1] elif 0.9 < H/W < 1.1 and resolution[0] != resolution[1]: # image is square, so we chose (portrait, landscape) randomly if rng.integers(2): resolution = resolution[::-1] # high-quality Lanczos down-scaling target_resolution = np.array(resolution) ## Recale with max factor, so one of width or height might be larger than target_resolution image, depthmap, intrinsics = cropping.rescale_image_depthmap(image, depthmap, intrinsics, target_resolution) # actual cropping (if necessary) with bilinear interpolation intrinsics2 = cropping.camera_matrix_of_crop(intrinsics, image.size, resolution, offset_factor=0.5) crop_bbox = cropping.bbox_from_intrinsics_in_out(intrinsics, intrinsics2, resolution) image, depthmap, intrinsics2 = cropping.crop_image_depthmap(image, depthmap, intrinsics, crop_bbox) return image, depthmap, intrinsics2 def load_images_dtu(folder_or_list, size, scene_folder): """ Preprocessing DTU requires depth, camera param and mask. We follow Splatt3R to compute valid_mask. """ if isinstance(folder_or_list, str): print(f'>> Loading images from {folder_or_list}') root, folder_content = folder_or_list, sorted(os.listdir(folder_or_list)) elif isinstance(folder_or_list, list): print(f'>> Loading a list of {len(folder_or_list)} images') root = os.path.dirname(folder_or_list[0]) if folder_or_list else '' folder_content = [os.path.basename(p) for p in folder_or_list] else: raise ValueError(f'bad {folder_or_list=} ({type(folder_or_list)})') depth_root = os.path.join(scene_folder, 'depths') mask_root = os.path.join(scene_folder, 'binary_masks') cam_root = os.path.join(scene_folder, 'cams') imgs = [] for path in folder_content: if not path.endswith(('.jpg', '.jpeg', '.png', '.JPG')): continue impath = os.path.join(root, path) depthpath = os.path.join(depth_root, path.replace('.jpg', '.npy')) campath = os.path.join(cam_root, path.replace('.jpg', '_cam.txt')) maskpath = os.path.join(mask_root, path.replace('.jpg', '.png')) rgb_image = imread_cv2(impath) H1, W1 = rgb_image.shape[:2] depthmap = np.load(depthpath) depthmap = np.nan_to_num(depthmap.astype(np.float32), 0.0) mask = imread_cv2(maskpath, cv2.IMREAD_UNCHANGED)/255.0 mask = mask.astype(np.float32) mask[mask>0.5] = 1.0 mask[mask<0.5] = 0.0 mask = cv2.resize(mask, (depthmap.shape[1], depthmap.shape[0]), interpolation=cv2.INTER_NEAREST) kernel = np.ones((10, 10), np.uint8) # Define the erosion kernel mask = cv2.erode(mask, kernel, iterations=1) depthmap = depthmap * mask cur_intrinsics, camera_pose = load_cam_mvsnet(open(campath, 'r')) intrinsics = cur_intrinsics[:3, :3] camera_pose = np.linalg.inv(camera_pose) new_size = tuple(int(round(x*size/max(W1, H1))) for x in (W1, H1)) W, H = new_size W2 = W//16*16 H2 = H//16*16 rgb_image, depthmap, intrinsics = _crop_resize_if_necessary( rgb_image, depthmap, intrinsics, (W2, H2), info=impath) print(f' - adding {path} with resolution {W1}x{H1} --> {W2}x{H2}') ImgNorm = tvf.Compose([tvf.ToTensor(), tvf.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]) img = dict( img=ImgNorm(rgb_image)[None], true_shape=np.int32([rgb_image.size[::-1]]), idx=len(imgs), instance=str(len(imgs)), depthmap=depthmap, camera_pose=camera_pose, camera_intrinsics=intrinsics ) pts3d, valid_mask = depthmap_to_absolute_camera_coordinates(**img) img['pts3d'] = pts3d img['valid_mask'] = valid_mask & np.isfinite(pts3d).all(axis=-1) imgs.append(img) assert imgs, 'no images foud at '+root print(f' (Found {len(imgs)} images)') return imgs, (W1,H1) def storePly(path, xyz, rgb, feat=None): # Define the dtype for the structured array dtype = [('x', 'f4'), ('y', 'f4'), ('z', 'f4'), ('nx', 'f4'), ('ny', 'f4'), ('nz', 'f4'), ('red', 'u1'), ('green', 'u1'), ('blue', 'u1')] if feat is not None: for i in range(feat.shape[1]): dtype.append((f'feat_{i}', 'f4')) normals = np.zeros_like(xyz) elements = np.empty(xyz.shape[0], dtype=dtype) attributes = np.concatenate((xyz, normals, rgb), axis=1) if feat is not None: attributes = np.concatenate((attributes, feat), axis=1) elements[:] = list(map(tuple, attributes)) # Create the PlyData object and write to file vertex_element = PlyElement.describe(elements, 'vertex') ply_data = PlyData([vertex_element]) ply_data.write(path) def R_to_quaternion(R): """ Convert a rotation matrix to a quaternion. Parameters: - R: A 3x3 numpy array representing a rotation matrix. Returns: - A numpy array representing the quaternion [w, x, y, z]. """ m00, m01, m02 = R[0, 0], R[0, 1], R[0, 2] m10, m11, m12 = R[1, 0], R[1, 1], R[1, 2] m20, m21, m22 = R[2, 0], R[2, 1], R[2, 2] trace = m00 + m11 + m22 if trace > 0: s = 0.5 / np.sqrt(trace + 1.0) w = 0.25 / s x = (m21 - m12) * s y = (m02 - m20) * s z = (m10 - m01) * s elif (m00 > m11) and (m00 > m22): s = np.sqrt(1.0 + m00 - m11 - m22) * 2 w = (m21 - m12) / s x = 0.25 * s y = (m01 + m10) / s z = (m02 + m20) / s elif m11 > m22: s = np.sqrt(1.0 + m11 - m00 - m22) * 2 w = (m02 - m20) / s x = (m01 + m10) / s y = 0.25 * s z = (m12 + m21) / s else: s = np.sqrt(1.0 + m22 - m00 - m11) * 2 w = (m10 - m01) / s x = (m02 + m20) / s y = (m12 + m21) / s z = 0.25 * s return np.array([w, x, y, z]) def save_colmap_cameras(ori_size, intrinsics, camera_file): with open(camera_file, 'w') as f: for i, K in enumerate(intrinsics, 1): # Starting index at 1 width, height = ori_size scale_factor_x = width/2 / K[0, 2] scale_factor_y = height/2 / K[1, 2] # assert scale_factor_x==scale_factor_y, "scale factor is not same for x and y" # print(f'scale factor is not same for x {scale_factor_x} and y {scale_factor_y}') f.write(f"{i} PINHOLE {width} {height} {K[0, 0]*scale_factor_x} {K[1, 1]*scale_factor_x} {width/2} {height/2}\n") # scale focal # f.write(f"{i} PINHOLE {width} {height} {K[0, 0]} {K[1, 1]} {K[0, 2]} {K[1, 2]}\n") def save_colmap_images(poses, images_file, train_img_list): with open(images_file, 'w') as f: for i, pose in enumerate(poses, 1): # Starting index at 1 # breakpoint() pose = np.linalg.inv(pose) R = pose[:3, :3] t = pose[:3, 3] q = R_to_quaternion(R) # Convert rotation matrix to quaternion f.write(f"{i} {q[0]} {q[1]} {q[2]} {q[3]} {t[0]} {t[1]} {t[2]} {i} {os.path.basename(train_img_list[i-1])}\n") f.write(f"\n") def round_python3(number): rounded = round(number) if abs(number - rounded) == 0.5: return 2.0 * round(number / 2.0) return rounded def rigid_points_registration(pts1, pts2, conf=None): R, T, s = roma.rigid_points_registration( pts1.reshape(-1, 3), pts2.reshape(-1, 3), weights=conf, compute_scaling=True) return s, R, T # return un-scaled (R, T)