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103 lines
3.8 KiB
Python
103 lines
3.8 KiB
Python
import os
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import numpy as np
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import imageio
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import logging
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from nerf_sample_ray_split import RaySamplerSingleImage
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import glob
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logger = logging.getLogger(__package__)
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########################################################################################################################
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# camera coordinate system: x-->right, y-->down, z-->scene (opencv/colmap convention)
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# poses is camera-to-world
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########################################################################################################################
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def find_files(dir, exts):
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if os.path.isdir(dir):
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# types should be ['*.png', '*.jpg']
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files_grabbed = []
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for ext in exts:
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files_grabbed.extend(glob.glob(os.path.join(dir, ext)))
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if len(files_grabbed) > 0:
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files_grabbed = sorted(files_grabbed)
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return files_grabbed
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else:
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return []
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def load_data_split(basedir, scene, split, skip=1, try_load_min_depth=True, only_img_files=False):
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def parse_txt(filename):
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assert os.path.isfile(filename)
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nums = open(filename).read().split()
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return np.array([float(x) for x in nums]).reshape([4, 4]).astype(np.float32)
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split_dir = '{}/{}/{}'.format(basedir, scene, split)
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if only_img_files:
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img_files = find_files('{}/rgb'.format(split_dir), exts=['*.png', '*.jpg'])
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return img_files
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# camera parameters files
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intrinsics_files = find_files('{}/intrinsics'.format(split_dir), exts=['*.txt'])
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pose_files = find_files('{}/pose'.format(split_dir), exts=['*.txt'])
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logger.info('raw intrinsics_files: {}'.format(len(intrinsics_files)))
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logger.info('raw pose_files: {}'.format(len(pose_files)))
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intrinsics_files = intrinsics_files[::skip]
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pose_files = pose_files[::skip]
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cam_cnt = len(pose_files)
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# img files
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img_files = find_files('{}/rgb'.format(split_dir), exts=['*.png', '*.jpg'])
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if len(img_files) > 0:
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logger.info('raw img_files: {}'.format(len(img_files)))
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img_files = img_files[::skip]
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assert(len(img_files) == cam_cnt)
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else:
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img_files = [None, ] * cam_cnt
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# mask files
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mask_files = find_files('{}/mask'.format(split_dir), exts=['*.png', '*.jpg'])
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if len(mask_files) > 0:
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logger.info('raw mask_files: {}'.format(len(mask_files)))
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mask_files = mask_files[::skip]
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assert(len(mask_files) == cam_cnt)
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else:
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mask_files = [None, ] * cam_cnt
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# min depth files
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mindepth_files = find_files('{}/min_depth'.format(split_dir), exts=['*.png', '*.jpg'])
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if try_load_min_depth and len(mindepth_files) > 0:
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logger.info('raw mindepth_files: {}'.format(len(mindepth_files)))
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mindepth_files = mindepth_files[::skip]
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assert(len(mindepth_files) == cam_cnt)
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else:
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mindepth_files = [None, ] * cam_cnt
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# assume all images have the same size as training image
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train_imgfile = find_files('{}/{}/train/rgb'.format(basedir, scene), exts=['*.png', '*.jpg'])[0]
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train_im = imageio.imread(train_imgfile)
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H, W = train_im.shape[:2]
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# create ray samplers
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ray_samplers = []
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for i in range(cam_cnt):
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intrinsics = parse_txt(intrinsics_files[i])
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pose = parse_txt(pose_files[i])
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# read max depth
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try:
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max_depth = float(open('{}/max_depth.txt'.format(split_dir)).readline().strip())
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except:
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max_depth = None
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ray_samplers.append(RaySamplerSingleImage(H=H, W=W, intrinsics=intrinsics, c2w=pose,
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img_path=img_files[i],
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mask_path=mask_files[i],
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min_depth_path=mindepth_files[i],
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max_depth=max_depth))
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logger.info('Split {}, # views: {}'.format(split, cam_cnt))
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return ray_samplers
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