mirror of
https://github.com/graphdeco-inria/gaussian-splatting
synced 2025-06-26 18:18:11 +00:00
96 lines
3.8 KiB
Python
96 lines
3.8 KiB
Python
#
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# Copyright (C) 2023, Inria
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# GRAPHDECO research group, https://team.inria.fr/graphdeco
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# All rights reserved.
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#
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# This software is free for non-commercial, research and evaluation use
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# under the terms of the LICENSE.md file.
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#
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# For inquiries contact george.drettakis@inria.fr
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#
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from scene.cameras import Camera
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import numpy as np
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from utils.general_utils import PILtoTorch
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from utils.graphics_utils import fov2focal
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WARNED = False
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def loadCam(args, id, cam_info, resolution_scale):
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"""
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调整当前相机对应图像的分辨率，并根据当前相机的info创建相机（包含R、T、FovY、FovX、图像数据image、image_path、image_name、width、height）
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"""
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orig_w, orig_h = cam_info.image.size
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# 1. 计算下采样后的图像尺寸
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if args.resolution in [1, 2, 4, 8]:
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# 计算下采样后的图像尺寸 [1, 1/2, 1/4, 1/8]
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resolution = round(orig_w/(resolution_scale * args.resolution)), round(orig_h/(resolution_scale * args.resolution))
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else:
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if args.resolution == -1:
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# 如果用户没有指定分辨率，即默认为-1，则自动判断图片的宽度是>1.6K：如果大于，则自动进行下采样到1.6K时的采样倍率；如果小于，则采样倍率=1，即使用原图尺寸
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if orig_w > 1600:
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global WARNED
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if not WARNED:
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print("[ INFO ] Encountered quite large input images (>1.6K pixels width), rescaling to 1.6K.\n "
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"If this is not desired, please explicitly specify '--resolution/-r' as 1")
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WARNED = True
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global_down = orig_w / 1600
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else:
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global_down = 1
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else:
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# 如果用户指定了分辨率，则根据用户指定的分辨率计算采样倍率
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global_down = orig_w / args.resolution
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scale = float(global_down) * float(resolution_scale) # 缩放倍率
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resolution = (int(orig_w / scale), int(orig_h / scale)) # 下采样后的图像尺寸
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# 2. 调整图片分辨率，归一化，并转换通道为torch上的 (C, H, W)
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resized_image_rgb = PILtoTorch(cam_info.image, resolution)
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gt_image = resized_image_rgb[:3, ...]
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loaded_mask = None
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if resized_image_rgb.shape[1] == 4:
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# 如果图片有alpha通道，则提取出来
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loaded_mask = resized_image_rgb[3:4, ...]
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# 3. 创建相机
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return Camera(colmap_id=cam_info.uid, R=cam_info.R, T=cam_info.T,
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FoVx=cam_info.FovX, FoVy=cam_info.FovY,
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image=gt_image, gt_alpha_mask=loaded_mask,
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image_name=cam_info.image_name, uid=id, data_device=args.data_device)
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def cameraList_from_camInfos(cam_infos, resolution_scale, args):
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'''
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cam_infos: train或test相机info列表
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resolution_scale: 分辨率倍率
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args: 更新后的ModelParams()中的参数
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'''
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camera_list = []
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# 遍历每个camera_info（包含R、T、FovY、FovX、图像数据image、image_path、image_name、width、height）
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for id, c in enumerate(cam_infos):
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camera_list.append( loadCam(args, id, c, resolution_scale) )
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return camera_list
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def camera_to_JSON(id, camera : Camera):
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Rt = np.zeros((4, 4))
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Rt[:3, :3] = camera.R.transpose()
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Rt[:3, 3] = camera.T
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Rt[3, 3] = 1.0
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W2C = np.linalg.inv(Rt)
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pos = W2C[:3, 3]
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rot = W2C[:3, :3]
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serializable_array_2d = [x.tolist() for x in rot]
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camera_entry = {
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'id' : id,
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'img_name' : camera.image_name,
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'width' : camera.width,
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'height' : camera.height,
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'position': pos.tolist(),
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'rotation': serializable_array_2d,
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'fy' : fov2focal(camera.FovY, camera.height),
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'fx' : fov2focal(camera.FovX, camera.width)
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}
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return camera_entry
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