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bff20b3 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import contextlib
import copy
import os
import sys
from typing import List
import cv2
import numpy as np
from sapiens.engine.datasets import BaseDataset
from sapiens.registry import DATASETS
@contextlib.contextmanager
def suppress_stderr():
devnull_fd = os.open(os.devnull, os.O_WRONLY)
stderr_fd = sys.stderr.fileno()
sys.stderr.flush()
saved_stderr_fd = os.dup(stderr_fd)
os.dup2(devnull_fd, stderr_fd)
os.close(devnull_fd)
try:
yield
finally:
os.dup2(saved_stderr_fd, stderr_fd)
os.close(saved_stderr_fd)
##-----------------------------------------------------------------------
@DATASETS.register_module()
class PointmapBaseDataset(BaseDataset):
def __init__(self, num_samples=None, **kwargs) -> None:
self.num_samples = num_samples
super().__init__(**kwargs)
return
def load_data_list(self) -> List[dict]:
data_list = []
self.rgb_dir = os.path.join(self.data_root, "rgb")
self.mask_dir = os.path.join(self.data_root, "mask")
self.depth_dir = os.path.join(self.data_root, "depth")
self.K_dir = os.path.join(self.data_root, "camera_intrinsics")
self.M_dir = os.path.join(
self.data_root, "camera_extrinsics"
) ## cv camera extrinsics
print(f"\033[92mLoading {self.__class__.__name__}!\033[0m")
# Create a set of common file names from all three directories
rgb_files = {x for x in os.listdir(self.rgb_dir) if x.endswith(".png")}
mask_files = {x for x in os.listdir(self.mask_dir) if x.endswith(".png")}
depth_files = {
x.replace(".npy", ".png")
for x in os.listdir(self.depth_dir)
if x.endswith(".npy")
}
K_files = {
x.replace(".txt", ".png")
for x in os.listdir(self.K_dir)
if x.endswith(".txt")
}
M_files = {
x.replace(".txt", ".png")
for x in os.listdir(self.M_dir)
if x.endswith(".txt")
}
# Find the intersection of file names between images, masks, and normals
common_names = rgb_files & mask_files & depth_files & K_files & M_files
# Create data list using the common file names
data_list = [
{
"rgb_path": os.path.join(self.rgb_dir, name),
"mask_path": os.path.join(self.mask_dir, name),
"depth_path": os.path.join(
self.depth_dir, name.replace(".png", ".npy")
),
"K_path": os.path.join(self.K_dir, name.replace(".png", ".txt")),
"M_path": os.path.join(self.M_dir, name.replace(".png", ".txt")),
}
for name in sorted(common_names)
]
if self.num_samples is not None:
data_list = data_list[: self.num_samples]
print(
"\033[92mDone! {}. Loaded total samples: {}. Test mode: {}\033[0m".format(
self.__class__.__name__, len(data_list), self.test_mode
)
)
return data_list
def get_data_info(self, idx):
data_info = copy.deepcopy(self.data_list[idx])
try:
with suppress_stderr():
image = cv2.imread(data_info["rgb_path"]) ## bgr image is default
mask = cv2.imread(data_info["mask_path"])
depth = np.load(data_info["depth_path"]) ## H x W, ## is not in 0 to 1
K = np.loadtxt(data_info["K_path"]) ## intrinsics, 3 x 3
M = np.loadtxt(data_info["M_path"]) ## extrinsics, 4 x 4
except Exception as e:
return None
mask = mask[:, :, 0] ##
if image is None or mask is None or depth is None:
return None
## remove any nan depth from valid pixels
nan_depth = np.isnan(depth)
if np.any(nan_depth):
mask[nan_depth] = 0
if mask.sum() < 10:
return None
rows = np.any(mask, axis=1)
cols = np.any(mask, axis=0)
# Find the bounding box's bounds
y1, y2 = np.where(rows)[0][[0, -1]]
x1, x2 = np.where(cols)[0][[0, -1]]
bbox = np.array([x1, y1, x2, y2], dtype=np.float32).reshape(1, 4)
data_info = {
"img": image,
"id": idx,
"orig_img_height": image.shape[0],
"orig_img_width": image.shape[1],
"img_id": os.path.basename(data_info["rgb_path"]),
"img_path": data_info["rgb_path"],
"gt_depth": depth,
"K": K,
"M": M,
"mask": mask,
"bbox": bbox,
"bbox_score": np.ones(1, dtype=np.float32),
}
return data_info
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