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import csv
import pickle
import json
import cv2
import yaml
import numpy as np
from pathlib import Path
import torch
import open3d
from plyfile import PlyData
def make_dir(dir_path):
if not Path(dir_path).exists():
Path(dir_path).mkdir(parents=True, exist_ok=True)
def load_imgs(img_paths, option=cv2.IMREAD_COLOR):
imgs = [cv2.imread(img_path, option) for img_path in img_paths]
return imgs
def load_pickle(filename):
with Path(filename).open("rb") as f:
return pickle.load(f)
def save_pickle(data, filename):
with Path(filename).open("wb") as f:
pickle.dump(data, f, protocol=pickle.HIGHEST_PROTOCOL)
def load_json(filename):
with Path(filename).open("rb") as f:
return json.load(f)
def save_json(data, filename, save_pretty=True, sort_keys=False):
with Path(filename).open("w") as f:
if save_pretty:
f.write(json.dumps(data, indent=4, sort_keys=sort_keys))
else:
json.dump(data, f)
def load_jsonl(filename):
with Path(filename).open("r") as f:
return [json.loads(l.strip("\n")) for l in f.readlines()]
def save_jsonl(data, filename):
with Path(filename).open("w") as f:
f.write("\n".join([json.dumps(e) for e in data]))
def load_yaml(filename):
with Path(filename).open("r") as f:
return yaml.load(f, Loader=yaml.SafeLoader)
def save_yaml(data, filename):
with Path(filename).open("w") as f:
yaml.dump(data, f, default_flow_style=False)
def load_csv(filename, delimiter=","):
idx2key = None
contents = {}
with Path(filename).open("r") as f:
reader = csv.reader(f, delimiter=delimiter)
for l_idx, row in reader:
if l_idx == 0:
idx2key = row
for k_idx, key in enumerate(idx2key):
contents[key] = []
else:
for c_idx, col in enumerate(row):
contents[idx2key[c_idx]].append(col)
return contents, idx2key
def save_csv(data, filename, cols=None, delimiter=","):
with Path(filename).open("w") as f:
writer = csv.writer(f, delimiter=delimiter)
num_entries = len(data[list(data.keys())[0]])
assert cols is not None, "Must have column names for dumping csv files."
writer.writerow(cols)
for l_idx in range(num_entries):
row = [data[key][l_idx] for key in cols]
writer.writerow(row)
def load_numpy(filename):
return np.load(filename, allow_pickle=True)
def save_numpy(data, filename):
np.save(filename, data, allow_pickle=True)
def load_tensor(filename):
return torch.load(filename)
def save_tensor(data, filename):
torch.save(data, filename)
def load_ply(filepath):
with open(filepath, "rb") as f:
plydata = PlyData.read(f)
data = plydata.elements[0].data
coords = np.array([data["x"], data["y"], data["z"]], dtype=np.float32).T
feats = None
labels = None
if ({"red", "green", "blue"} - set(data.dtype.names)) == set():
feats = np.array([data["red"], data["green"], data["blue"]], dtype=np.uint8).T
if "label" in data.dtype.names:
labels = np.array(data["label"], dtype=np.uint32)
return coords, feats, labels
def load_ply_with_normals(filepath):
mesh = open3d.io.read_triangle_mesh(str(filepath))
if not mesh.has_vertex_normals():
mesh.compute_vertex_normals()
vertices = np.asarray(mesh.vertices)
normals = np.asarray(mesh.vertex_normals)
coords, feats, labels = load_ply(filepath)
assert np.allclose(coords, vertices), "different coordinates"
feats = np.hstack((feats, normals))
return coords, feats, labels
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