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import numpy as np |
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import argparse |
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import cv2 |
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from joblib import delayed, Parallel |
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import json |
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from read_write_model import * |
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def get_scales(key, cameras, images, points3d_ordered, args): |
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image_meta = images[key] |
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cam_intrinsic = cameras[image_meta.camera_id] |
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pts_idx = images_metas[key].point3D_ids |
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mask = pts_idx >= 0 |
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mask *= pts_idx < len(points3d_ordered) |
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pts_idx = pts_idx[mask] |
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valid_xys = image_meta.xys[mask] |
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if len(pts_idx) > 0: |
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pts = points3d_ordered[pts_idx] |
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else: |
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pts = np.array([0, 0, 0]) |
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R = qvec2rotmat(image_meta.qvec) |
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pts = np.dot(pts, R.T) + image_meta.tvec |
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invcolmapdepth = 1. / pts[..., 2] |
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n_remove = len(image_meta.name.split('.')[-1]) + 1 |
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invmonodepthmap = cv2.imread(f"{args.depths_dir}/{image_meta.name[:-n_remove]}.png", cv2.IMREAD_UNCHANGED) |
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if invmonodepthmap is None: |
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return None |
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if invmonodepthmap.ndim != 2: |
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invmonodepthmap = invmonodepthmap[..., 0] |
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invmonodepthmap = invmonodepthmap.astype(np.float32) / (2**16) |
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s = invmonodepthmap.shape[0] / cam_intrinsic.height |
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maps = (valid_xys * s).astype(np.float32) |
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valid = ( |
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(maps[..., 0] >= 0) * |
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(maps[..., 1] >= 0) * |
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(maps[..., 0] < cam_intrinsic.width * s) * |
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(maps[..., 1] < cam_intrinsic.height * s) * (invcolmapdepth > 0)) |
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if valid.sum() > 10 and (invcolmapdepth.max() - invcolmapdepth.min()) > 1e-3: |
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maps = maps[valid, :] |
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invcolmapdepth = invcolmapdepth[valid] |
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invmonodepth = cv2.remap(invmonodepthmap, maps[..., 0], maps[..., 1], interpolation=cv2.INTER_LINEAR, borderMode=cv2.BORDER_REPLICATE)[..., 0] |
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t_colmap = np.median(invcolmapdepth) |
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s_colmap = np.mean(np.abs(invcolmapdepth - t_colmap)) |
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t_mono = np.median(invmonodepth) |
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s_mono = np.mean(np.abs(invmonodepth - t_mono)) |
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scale = s_colmap / s_mono |
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offset = t_colmap - t_mono * scale |
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else: |
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scale = 0 |
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offset = 0 |
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return {"image_name": image_meta.name[:-n_remove], "scale": scale, "offset": offset} |
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if __name__ == '__main__': |
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parser = argparse.ArgumentParser() |
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parser.add_argument('--base_dir', default="../data/big_gaussians/standalone_chunks/campus") |
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parser.add_argument('--depths_dir', default="../data/big_gaussians/standalone_chunks/campus/depths_any") |
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parser.add_argument('--model_type', default="bin") |
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args = parser.parse_args() |
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cam_intrinsics, images_metas, points3d = read_model(os.path.join(args.base_dir, "sparse", "0"), ext=f".{args.model_type}") |
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pts_indices = np.array([points3d[key].id for key in points3d]) |
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pts_xyzs = np.array([points3d[key].xyz for key in points3d]) |
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points3d_ordered = np.zeros([pts_indices.max()+1, 3]) |
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points3d_ordered[pts_indices] = pts_xyzs |
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depth_param_list = Parallel(n_jobs=-1, backend="threading")( |
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delayed(get_scales)(key, cam_intrinsics, images_metas, points3d_ordered, args) for key in images_metas |
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) |
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depth_params = { |
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depth_param["image_name"]: {"scale": depth_param["scale"], "offset": depth_param["offset"]} |
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for depth_param in depth_param_list if depth_param != None |
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} |
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with open(f"{args.base_dir}/sparse/0/depth_params.json", "w") as f: |
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json.dump(depth_params, f, indent=2) |
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print(0) |
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