import os
import json
import zipfile
import numpy as np
import pickle
from collections import OrderedDict, Counter
import pandas as pd
def load_pickle(filename):
with open(filename, "rb") as f:
return pickle.load(f)
def save_pickle(data, filename):
with open(filename, "wb") as f:
pickle.dump(data, f, protocol=pickle.HIGHEST_PROTOCOL)
def load_json(filename):
with open(filename, "r") as f:
return json.load(f)
def save_json(data, filename, save_pretty=False, sort_keys=False):
with open(filename, "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 open(filename, "r") as f:
return [json.loads(l.strip("\n")) for l in f.readlines()]
def save_jsonl(data, filename):
"""data is a list"""
with open(filename, "w") as f:
f.write("\n".join([json.dumps(e) for e in data]))
def save_lines(list_of_str, filepath):
with open(filepath, "w") as f:
f.write("\n".join(list_of_str))
def read_lines(filepath):
with open(filepath, "r") as f:
return [e.strip("\n") for e in f.readlines()]
def mkdirp(p):
if not os.path.exists(p):
os.makedirs(p)
def flat_list_of_lists(l):
"""flatten a list of lists [[1,2], [3,4]] to [1,2,3,4]"""
return [item for sublist in l for item in sublist]
def convert_to_seconds(hms_time):
""" convert '00:01:12' to 72 seconds.
:hms_time (str): time in comma separated string, e.g. '00:01:12'
:return (int): time in seconds, e.g. 72
"""
times = [float(t) for t in hms_time.split(":")]
return times[0] * 3600 + times[1] * 60 + times[2]
def get_video_name_from_url(url):
return url.split("/")[-1][:-4]
def merge_dicts(list_dicts):
merged_dict = list_dicts[0].copy()
for i in range(1, len(list_dicts)):
merged_dict.update(list_dicts[i])
return merged_dict
def l2_normalize_np_array(np_array, eps=1e-5):
"""np_array: np.ndarray, (*, D), where the last dim will be normalized"""
return np_array / (np.linalg.norm(np_array, axis=-1, keepdims=True) + eps)
def make_zipfile(src_dir, save_path, enclosing_dir="", exclude_dirs=None, exclude_extensions=None,
exclude_dirs_substring=None):
"""make a zip file of root_dir, save it to save_path.
exclude_paths will be excluded if it is a subdir of root_dir.
An enclosing_dir is added is specified.
"""
abs_src = os.path.abspath(src_dir)
with zipfile.ZipFile(save_path, "w") as zf:
for dirname, subdirs, files in os.walk(src_dir):
if exclude_dirs is not None:
for e_p in exclude_dirs:
if e_p in subdirs:
subdirs.remove(e_p)
if exclude_dirs_substring is not None:
to_rm = []
for d in subdirs:
if exclude_dirs_substring in d:
to_rm.append(d)
for e in to_rm:
subdirs.remove(e)
arcname = os.path.join(enclosing_dir, dirname[len(abs_src) + 1:])
zf.write(dirname, arcname)
for filename in files:
if exclude_extensions is not None:
if os.path.splitext(filename)[1] in exclude_extensions:
continue # do not zip it
absname = os.path.join(dirname, filename)
arcname = os.path.join(enclosing_dir, absname[len(abs_src) + 1:])
zf.write(absname, arcname)
class AverageMeter(object):
"""Computes and stores the average and current/max/min value"""
def __init__(self):
self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
self.max = -1e10
self.min = 1e10
self.reset()
def reset(self):
self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
self.max = -1e10
self.min = 1e10
def update(self, val, n=1):
self.max = max(val, self.max)
self.min = min(val, self.min)
self.val = val
self.sum += val * n
self.count += n
self.avg = self.sum / self.count
def dissect_by_lengths(np_array, lengths, dim=0, assert_equal=True):
"""Dissect an array (N, D) into a list a sub-array,
np_array.shape[0] == sum(lengths), Output is a list of nd arrays, singlton dimention is kept"""
if assert_equal:
assert len(np_array) == sum(lengths)
length_indices = [0, ]
for i in range(len(lengths)):
length_indices.append(length_indices[i] + lengths[i])
if dim == 0:
array_list = [np_array[length_indices[i]:length_indices[i+1]] for i in range(len(lengths))]
elif dim == 1:
array_list = [np_array[:, length_indices[i]:length_indices[i + 1]] for i in range(len(lengths))]
elif dim == 2:
array_list = [np_array[:, :, length_indices[i]:length_indices[i + 1]] for i in range(len(lengths))]
else:
raise NotImplementedError
return array_list
def get_ratio_from_counter(counter_obj, threshold=200):
keys = counter_obj.keys()
values = counter_obj.values()
filtered_values = [counter_obj[k] for k in keys if k > threshold]
return float(sum(filtered_values)) / sum(values)
def get_counter_dist(counter_object, sort_type="none"):
_sum = sum(counter_object.values())
dist = {k: float(f"{100 * v / _sum:.2f}") for k, v in counter_object.items()}
if sort_type == "value":
dist = OrderedDict(sorted(dist.items(), reverse=True))
return dist
def get_show_name(vid_name):
"""
get tvshow name from vid_name
:param vid_name: video clip name
:return: tvshow name
"""
show_list = ["friends", "met", "castle", "house", "grey"]
vid_name_prefix = vid_name.split("_")[0]
show_name = vid_name_prefix if vid_name_prefix in show_list else "bbt"
return show_name
def get_abspaths_by_ext(dir_path, ext=(".jpg",)):
"""Get absolute paths to files in dir_path with extensions specified by ext.
Note this function does work recursively.
"""
if isinstance(ext, list):
ext = tuple(ext)
if isinstance(ext, str):
ext = tuple([ext, ])
filepaths = [os.path.join(root, name)
for root, dirs, files in os.walk(dir_path)
for name in files
if name.endswith(tuple(ext))]
return filepaths
def get_basename_no_ext(path):
""" '/data/movienet/240p_keyframe_feats/tt7672188.npz' --> 'tt7672188' """
return os.path.splitext(os.path.split(path)[1])[0]
def dict_to_markdown(d, max_str_len=120):
# convert list into its str representation
d = {k: v.__repr__() if isinstance(v, list) else v for k, v in d.items()}
# truncate string that is longer than max_str_len
if max_str_len is not None:
d = {k: v[-max_str_len:] if isinstance(v, str) else v for k, v in d.items()}
return pd.DataFrame(d, index=[0]).transpose().to_markdown()