15090929758

Committed 19 Oct 2024 05:10AM UTC coverage: 54.553%. First build

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Merge pull request #228 from cosanlab/huggingface

WIP: Huggingface Integration

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75.0

/feat/utils/io.py

"""
Feat utility and helper functions for inputting and outputting data.
"""

import os
import contextlib
import pandas as pd
import feat
from feat.utils import (
    FEAT_EMOTION_COLUMNS,
    FEAT_FACEBOX_COLUMNS,
    FEAT_TIME_COLUMNS,
    OPENFACE_ORIG_COLUMNS,
    openface_AU_columns,
    openface_2d_landmark_columns,
    openface_facepose_columns,
    openface_gaze_columns,
    openface_time_columns,
    FEAT_FACEPOSE_COLUMNS_6D,
    FEAT_IDENTITY_COLUMNS,
)


from torchvision.datasets.utils import download_url as tv_download_url
from torchvision.io import read_image, read_video
from torchvision.transforms.functional import to_pil_image
import warnings
import av
import torch
from torch import swapaxes

__all__ = [
    "get_resource_path",
    "get_test_data_path",
    "validate_input",
    "download_url",
    "read_openface",
    "load_pil_img",
]


def get_resource_path():
    """Get path to feat resource directory."""
    return os.path.join(feat.__path__[0], "resources")


def get_test_data_path():
    """Get path to feat test data directory."""
    return os.path.join(feat.__path__[0], "tests", "data")


def validate_input(inputFname):
    """
    Given a string filename or list containing string files names, ensures that the
    file(s) exist. Always returns a non-nested list, potentionally containing a single element.

    Args:
        inputFname (str or list): file name(s)

    Raises:
        FileNotFoundError: if any file name(s) don't exist

    Returns:
        list: list of file names (even if input was a str)
    """

    assert isinstance(
        inputFname, (str, list)
    ), "inputFname must be a string path to image or list of image paths"

    if isinstance(inputFname, str):
        inputFname = [inputFname]

    for inputF in inputFname:
        if not os.path.exists(inputF):
            raise FileNotFoundError(f"File {inputF} not found.")
    return inputFname


def download_url(*args, **kwargs):
    """By default just call download_url from torch vision, but we pass a verbose =
    False keyword argument, then call download_url with a special context manager than
    supresses the print messages"""
    verbose = kwargs.pop("verbose", True)

    if verbose:
        return tv_download_url(*args, **kwargs)

    with open(os.devnull, "w") as f, contextlib.redirect_stdout(f):
        return tv_download_url(*args, **kwargs)


def read_feat(fexfile):
    """This function reads files extracted using the Detector from the Feat package.

    Args:
        fexfile: Path to facial expression file.

    Returns:
        Fex of processed facial expressions
    """
    d = pd.read_csv(fexfile)
    au_columns = [col for col in d.columns if "AU" in col]
    fex = feat.Fex(
        d,
        filename=fexfile,
        au_columns=au_columns,
        emotion_columns=FEAT_EMOTION_COLUMNS,
        facebox_columns=FEAT_FACEBOX_COLUMNS,
        landmark_columns=openface_2d_landmark_columns,
        facepose_columns=FEAT_FACEPOSE_COLUMNS_6D,
        identity_columns=FEAT_IDENTITY_COLUMNS[1:],
        detector="Feat",
        time_columns=FEAT_TIME_COLUMNS,
    )
    return fex


def read_openface(openfacefile, features=None):
    """
    This function reads in an OpenFace exported facial expression file.
    Args:
        features: If a list of column names are passed, those are returned. Otherwise, default returns the following features:
        ['frame', 'timestamp', 'confidence', 'success', 'gaze_0_x',
       'gaze_0_y', 'gaze_0_z', 'gaze_1_x', 'gaze_1_y', 'gaze_1_z',
       'pose_Tx', 'pose_Ty', 'pose_Tz', 'pose_Rx', 'pose_Ry', 'pose_Rz',
       'x_0', 'x_1', 'x_2', 'x_3', 'x_4', 'x_5', 'x_6', 'x_7', 'x_8',
       'x_9', 'x_10', 'x_11', 'x_12', 'x_13', 'x_14', 'x_15', 'x_16',
       'x_17', 'x_18', 'x_19', 'x_20', 'x_21', 'x_22', 'x_23', 'x_24',
       'x_25', 'x_26', 'x_27', 'x_28', 'x_29', 'x_30', 'x_31', 'x_32',
       'x_33', 'x_34', 'x_35', 'x_36', 'x_37', 'x_38', 'x_39', 'x_40',
       'x_41', 'x_42', 'x_43', 'x_44', 'x_45', 'x_46', 'x_47', 'x_48',
       'x_49', 'x_50', 'x_51', 'x_52', 'x_53', 'x_54', 'x_55', 'x_56',
       'x_57', 'x_58', 'x_59', 'x_60', 'x_61', 'x_62', 'x_63', 'x_64',
       'x_65', 'x_66', 'x_67', 'y_0', 'y_1', 'y_2', 'y_3', 'y_4', 'y_5',
       'y_6', 'y_7', 'y_8', 'y_9', 'y_10', 'y_11', 'y_12', 'y_13', 'y_14',
       'y_15', 'y_16', 'y_17', 'y_18', 'y_19', 'y_20', 'y_21', 'y_22',
       'y_23', 'y_24', 'y_25', 'y_26', 'y_27', 'y_28', 'y_29', 'y_30',
       'y_31', 'y_32', 'y_33', 'y_34', 'y_35', 'y_36', 'y_37', 'y_38',
       'y_39', 'y_40', 'y_41', 'y_42', 'y_43', 'y_44', 'y_45', 'y_46',
       'y_47', 'y_48', 'y_49', 'y_50', 'y_51', 'y_52', 'y_53', 'y_54',
       'y_55', 'y_56', 'y_57', 'y_58', 'y_59', 'y_60', 'y_61', 'y_62',
       'y_63', 'y_64', 'y_65', 'y_66', 'y_67', 'X_0', 'X_1', 'X_2', 'X_3',
       'X_4', 'X_5', 'X_6', 'X_7', 'X_8', 'X_9', 'X_10', 'X_11', 'X_12',
       'X_13', 'X_14', 'X_15', 'X_16', 'X_17', 'X_18', 'X_19', 'X_20',
       'X_21', 'X_22', 'X_23', 'X_24', 'X_25', 'X_26', 'X_27', 'X_28',
       'X_29', 'X_30', 'X_31', 'X_32', 'X_33', 'X_34', 'X_35', 'X_36',
       'X_37', 'X_38', 'X_39', 'X_40', 'X_41', 'X_42', 'X_43', 'X_44',
       'X_45', 'X_46', 'X_47', 'X_48', 'X_49', 'X_50', 'X_51', 'X_52',
       'X_53', 'X_54', 'X_55', 'X_56', 'X_57', 'X_58', 'X_59', 'X_60',
       'X_61', 'X_62', 'X_63', 'X_64', 'X_65', 'X_66', 'X_67', 'Y_0',
       'Y_1', 'Y_2', 'Y_3', 'Y_4', 'Y_5', 'Y_6', 'Y_7', 'Y_8', 'Y_9',
       'Y_10', 'Y_11', 'Y_12', 'Y_13', 'Y_14', 'Y_15', 'Y_16', 'Y_17',
       'Y_18', 'Y_19', 'Y_20', 'Y_21', 'Y_22', 'Y_23', 'Y_24', 'Y_25',
       'Y_26', 'Y_27', 'Y_28', 'Y_29', 'Y_30', 'Y_31', 'Y_32', 'Y_33',
       'Y_34', 'Y_35', 'Y_36', 'Y_37', 'Y_38', 'Y_39', 'Y_40', 'Y_41',
       'Y_42', 'Y_43', 'Y_44', 'Y_45', 'Y_46', 'Y_47', 'Y_48', 'Y_49',
       'Y_50', 'Y_51', 'Y_52', 'Y_53', 'Y_54', 'Y_55', 'Y_56', 'Y_57',
       'Y_58', 'Y_59', 'Y_60', 'Y_61', 'Y_62', 'Y_63', 'Y_64', 'Y_65',
       'Y_66', 'Y_67', 'Z_0', 'Z_1', 'Z_2', 'Z_3', 'Z_4', 'Z_5', 'Z_6',
       'Z_7', 'Z_8', 'Z_9', 'Z_10', 'Z_11', 'Z_12', 'Z_13', 'Z_14', 'Z_15',
       'Z_16', 'Z_17', 'Z_18', 'Z_19', 'Z_20', 'Z_21', 'Z_22', 'Z_23',
       'Z_24', 'Z_25', 'Z_26', 'Z_27', 'Z_28', 'Z_29', 'Z_30', 'Z_31',
       'Z_32', 'Z_33', 'Z_34', 'Z_35', 'Z_36', 'Z_37', 'Z_38', 'Z_39',
       'Z_40', 'Z_41', 'Z_42', 'Z_43', 'Z_44', 'Z_45', 'Z_46', 'Z_47',
       'Z_48', 'Z_49', 'Z_50', 'Z_51', 'Z_52', 'Z_53', 'Z_54', 'Z_55',
       'Z_56', 'Z_57', 'Z_58', 'Z_59', 'Z_60', 'Z_61', 'Z_62', 'Z_63',
       'Z_64', 'Z_65', 'Z_66', 'Z_67', 'p_scale', 'p_rx', 'p_ry', 'p_rz',
       'p_tx', 'p_ty', 'p_0', 'p_1', 'p_2', 'p_3', 'p_4', 'p_5', 'p_6',
       'p_7', 'p_8', 'p_9', 'p_10', 'p_11', 'p_12', 'p_13', 'p_14', 'p_15',
       'p_16', 'p_17', 'p_18', 'p_19', 'p_20', 'p_21', 'p_22', 'p_23',
       'p_24', 'p_25', 'p_26', 'p_27', 'p_28', 'p_29', 'p_30', 'p_31',
       'p_32', 'p_33', 'AU01_r', 'AU02_r', 'AU04_r', 'AU05_r', 'AU06_r',
       'AU07_r', 'AU09_r', 'AU10_r', 'AU12_r', 'AU14_r', 'AU15_r',
       'AU17_r', 'AU20_r', 'AU23_r', 'AU25_r', 'AU26_r', 'AU45_r',
       'AU01_c', 'AU02_c', 'AU04_c', 'AU05_c', 'AU06_c', 'AU07_c',
       'AU09_c', 'AU10_c', 'AU12_c', 'AU14_c', 'AU15_c', 'AU17_c',
       'AU20_c', 'AU23_c', 'AU25_c', 'AU26_c', 'AU28_c', 'AU45_c']

    Returns:
        dataframe of processed facial expressions

    """
    d = pd.read_csv(openfacefile, sep=",")
    d.columns = d.columns.str.strip(" ")
    # Check if features argument is passed and return only those features, else return basic emotion/AU features
    if isinstance(features, list):
        try:
            d = d[features]
        except Exception:
            raise KeyError([features, "not in openfacefile"])
    elif isinstance(features, type(None)):
        features = OPENFACE_ORIG_COLUMNS
        try:
            d = d[features]
        except Exception:
            pass
    fex = feat.Fex(
        d,
        filename=openfacefile,
        au_columns=openface_AU_columns,
        emotion_columns=None,
        facebox_columns=None,
        landmark_columns=openface_2d_landmark_columns,
        facepose_columns=openface_facepose_columns,
        gaze_columns=openface_gaze_columns,
        time_columns=openface_time_columns,
        detector="OpenFace",
    )
    fex["input"] = openfacefile
    return fex


def load_pil_img(file_name, frame_id):
    """Helper function to load a PIL image from a picture or video

    Args:
        file_name (str): path to file. Can be image or video
        frame_id (int): if video, load frame

    Returns:
        image: pil image instance
    """

    file_extension = os.path.basename(file_name).split(".")[-1]
    if file_extension.lower() in ["jpg", "jpeg", "png", "bmp", "tiff", "pdf"]:
        frame_img = read_image(file_name)  # image path
    else:
        # Ignore UserWarning: The pts_unit 'pts' gives wrong results. Please use
        # pts_unit 'sec'. See why it's ok in this issue:
        # https://github.com/pytorch/vision/issues/1931
        with warnings.catch_warnings():
            warnings.simplefilter("ignore", UserWarning)
            video, audio, info = read_video(file_name, output_format="TCHW")
        frame_img = video[frame_id, :, :]
    return to_pil_image(frame_img)


def video_to_tensor(file_name):
    container = av.open(file_name)
    stream = container.streams.video[0]
    tensor = []
    for frame in container.decode(stream):
        frame_data = torch.from_numpy(frame.to_ndarray(format="rgb24"))
        frame_data = swapaxes(swapaxes(frame_data, 0, -1), 1, 2)
        tensor.append(frame_data)
    container.close()
    return torch.stack(tensor, dim=0)

1	"""
2	Feat utility and helper functions for inputting and outputting data.
3	"""
4
5	import os	6✔
6	import contextlib	6✔
7	import pandas as pd	6✔
8	import feat	6✔
9	from feat.utils import (	6✔
10	FEAT_EMOTION_COLUMNS,
11	FEAT_FACEBOX_COLUMNS,
12	FEAT_TIME_COLUMNS,
13	OPENFACE_ORIG_COLUMNS,
14	openface_AU_columns,
15	openface_2d_landmark_columns,
16	openface_facepose_columns,
17	openface_gaze_columns,
18	openface_time_columns,
19	FEAT_FACEPOSE_COLUMNS_6D,
20	FEAT_IDENTITY_COLUMNS,
21	)
22
23
24	from torchvision.datasets.utils import download_url as tv_download_url	6✔
25	from torchvision.io import read_image, read_video	6✔
26	from torchvision.transforms.functional import to_pil_image	6✔
27	import warnings	6✔
28	import av	6✔
29	import torch	6✔
30	from torch import swapaxes	6✔
31
32	__all__ = [	6✔
33	"get_resource_path",
34	"get_test_data_path",
35	"validate_input",
36	"download_url",
37	"read_openface",
38	"load_pil_img",
39	]
40
41
42	def get_resource_path():	6✔
43	"""Get path to feat resource directory."""
44	return os.path.join(feat.__path__[0], "resources")	6✔
45
46
47	def get_test_data_path():	6✔
48	"""Get path to feat test data directory."""
49	return os.path.join(feat.__path__[0], "tests", "data")	6✔
50
51
52	def validate_input(inputFname):	6✔
53	"""
54	Given a string filename or list containing string files names, ensures that the
55	file(s) exist. Always returns a non-nested list, potentionally containing a single element.
56
57	Args:
58	inputFname (str or list): file name(s)
59
60	Raises:
61	FileNotFoundError: if any file name(s) don't exist
62
63	Returns:
64	list: list of file names (even if input was a str)
65	"""
66
67	assert isinstance(	×
68	inputFname, (str, list)
69	), "inputFname must be a string path to image or list of image paths"
70
71	if isinstance(inputFname, str):	×
72	inputFname = [inputFname]	×
73
74	for inputF in inputFname:	×
75	if not os.path.exists(inputF):	×
76	raise FileNotFoundError(f"File {inputF} not found.")	×
77	return inputFname	×
78
79
80	def download_url(args, *kwargs):	6✔
81	"""By default just call download_url from torch vision, but we pass a verbose =
82	False keyword argument, then call download_url with a special context manager than
83	supresses the print messages"""
84	verbose = kwargs.pop("verbose", True)	6✔
85
86	if verbose:	6✔
87	return tv_download_url(args, *kwargs)	×
88
89	with open(os.devnull, "w") as f, contextlib.redirect_stdout(f):	6✔
90	return tv_download_url(args, *kwargs)	6✔
91
92
93	def read_feat(fexfile):	6✔
94	"""This function reads files extracted using the Detector from the Feat package.
95
96	Args:
97	fexfile: Path to facial expression file.
98
99	Returns:
100	Fex of processed facial expressions
101	"""
102	d = pd.read_csv(fexfile)	6✔
103	au_columns = [col for col in d.columns if "AU" in col]	6✔
104	fex = feat.Fex(	6✔
105	d,
106	filename=fexfile,
107	au_columns=au_columns,
108	emotion_columns=FEAT_EMOTION_COLUMNS,
109	facebox_columns=FEAT_FACEBOX_COLUMNS,
110	landmark_columns=openface_2d_landmark_columns,
111	facepose_columns=FEAT_FACEPOSE_COLUMNS_6D,
112	identity_columns=FEAT_IDENTITY_COLUMNS[1:],
113	detector="Feat",
114	time_columns=FEAT_TIME_COLUMNS,
115	)
116	return fex	6✔
117
118
119	def read_openface(openfacefile, features=None):	6✔
120	"""
121	This function reads in an OpenFace exported facial expression file.
122	Args:
123	features: If a list of column names are passed, those are returned. Otherwise, default returns the following features:
124	['frame', 'timestamp', 'confidence', 'success', 'gaze_0_x',
125	'gaze_0_y', 'gaze_0_z', 'gaze_1_x', 'gaze_1_y', 'gaze_1_z',
126	'pose_Tx', 'pose_Ty', 'pose_Tz', 'pose_Rx', 'pose_Ry', 'pose_Rz',
127	'x_0', 'x_1', 'x_2', 'x_3', 'x_4', 'x_5', 'x_6', 'x_7', 'x_8',
128	'x_9', 'x_10', 'x_11', 'x_12', 'x_13', 'x_14', 'x_15', 'x_16',
129	'x_17', 'x_18', 'x_19', 'x_20', 'x_21', 'x_22', 'x_23', 'x_24',
130	'x_25', 'x_26', 'x_27', 'x_28', 'x_29', 'x_30', 'x_31', 'x_32',
131	'x_33', 'x_34', 'x_35', 'x_36', 'x_37', 'x_38', 'x_39', 'x_40',
132	'x_41', 'x_42', 'x_43', 'x_44', 'x_45', 'x_46', 'x_47', 'x_48',
133	'x_49', 'x_50', 'x_51', 'x_52', 'x_53', 'x_54', 'x_55', 'x_56',
134	'x_57', 'x_58', 'x_59', 'x_60', 'x_61', 'x_62', 'x_63', 'x_64',
135	'x_65', 'x_66', 'x_67', 'y_0', 'y_1', 'y_2', 'y_3', 'y_4', 'y_5',
136	'y_6', 'y_7', 'y_8', 'y_9', 'y_10', 'y_11', 'y_12', 'y_13', 'y_14',
137	'y_15', 'y_16', 'y_17', 'y_18', 'y_19', 'y_20', 'y_21', 'y_22',
138	'y_23', 'y_24', 'y_25', 'y_26', 'y_27', 'y_28', 'y_29', 'y_30',
139	'y_31', 'y_32', 'y_33', 'y_34', 'y_35', 'y_36', 'y_37', 'y_38',
140	'y_39', 'y_40', 'y_41', 'y_42', 'y_43', 'y_44', 'y_45', 'y_46',
141	'y_47', 'y_48', 'y_49', 'y_50', 'y_51', 'y_52', 'y_53', 'y_54',
142	'y_55', 'y_56', 'y_57', 'y_58', 'y_59', 'y_60', 'y_61', 'y_62',
143	'y_63', 'y_64', 'y_65', 'y_66', 'y_67', 'X_0', 'X_1', 'X_2', 'X_3',
144	'X_4', 'X_5', 'X_6', 'X_7', 'X_8', 'X_9', 'X_10', 'X_11', 'X_12',
145	'X_13', 'X_14', 'X_15', 'X_16', 'X_17', 'X_18', 'X_19', 'X_20',
146	'X_21', 'X_22', 'X_23', 'X_24', 'X_25', 'X_26', 'X_27', 'X_28',
147	'X_29', 'X_30', 'X_31', 'X_32', 'X_33', 'X_34', 'X_35', 'X_36',
148	'X_37', 'X_38', 'X_39', 'X_40', 'X_41', 'X_42', 'X_43', 'X_44',
149	'X_45', 'X_46', 'X_47', 'X_48', 'X_49', 'X_50', 'X_51', 'X_52',
150	'X_53', 'X_54', 'X_55', 'X_56', 'X_57', 'X_58', 'X_59', 'X_60',
151	'X_61', 'X_62', 'X_63', 'X_64', 'X_65', 'X_66', 'X_67', 'Y_0',
152	'Y_1', 'Y_2', 'Y_3', 'Y_4', 'Y_5', 'Y_6', 'Y_7', 'Y_8', 'Y_9',
153	'Y_10', 'Y_11', 'Y_12', 'Y_13', 'Y_14', 'Y_15', 'Y_16', 'Y_17',
154	'Y_18', 'Y_19', 'Y_20', 'Y_21', 'Y_22', 'Y_23', 'Y_24', 'Y_25',
155	'Y_26', 'Y_27', 'Y_28', 'Y_29', 'Y_30', 'Y_31', 'Y_32', 'Y_33',
156	'Y_34', 'Y_35', 'Y_36', 'Y_37', 'Y_38', 'Y_39', 'Y_40', 'Y_41',
157	'Y_42', 'Y_43', 'Y_44', 'Y_45', 'Y_46', 'Y_47', 'Y_48', 'Y_49',
158	'Y_50', 'Y_51', 'Y_52', 'Y_53', 'Y_54', 'Y_55', 'Y_56', 'Y_57',
159	'Y_58', 'Y_59', 'Y_60', 'Y_61', 'Y_62', 'Y_63', 'Y_64', 'Y_65',
160	'Y_66', 'Y_67', 'Z_0', 'Z_1', 'Z_2', 'Z_3', 'Z_4', 'Z_5', 'Z_6',
161	'Z_7', 'Z_8', 'Z_9', 'Z_10', 'Z_11', 'Z_12', 'Z_13', 'Z_14', 'Z_15',
162	'Z_16', 'Z_17', 'Z_18', 'Z_19', 'Z_20', 'Z_21', 'Z_22', 'Z_23',
163	'Z_24', 'Z_25', 'Z_26', 'Z_27', 'Z_28', 'Z_29', 'Z_30', 'Z_31',
164	'Z_32', 'Z_33', 'Z_34', 'Z_35', 'Z_36', 'Z_37', 'Z_38', 'Z_39',
165	'Z_40', 'Z_41', 'Z_42', 'Z_43', 'Z_44', 'Z_45', 'Z_46', 'Z_47',
166	'Z_48', 'Z_49', 'Z_50', 'Z_51', 'Z_52', 'Z_53', 'Z_54', 'Z_55',
167	'Z_56', 'Z_57', 'Z_58', 'Z_59', 'Z_60', 'Z_61', 'Z_62', 'Z_63',
168	'Z_64', 'Z_65', 'Z_66', 'Z_67', 'p_scale', 'p_rx', 'p_ry', 'p_rz',
169	'p_tx', 'p_ty', 'p_0', 'p_1', 'p_2', 'p_3', 'p_4', 'p_5', 'p_6',
170	'p_7', 'p_8', 'p_9', 'p_10', 'p_11', 'p_12', 'p_13', 'p_14', 'p_15',
171	'p_16', 'p_17', 'p_18', 'p_19', 'p_20', 'p_21', 'p_22', 'p_23',
172	'p_24', 'p_25', 'p_26', 'p_27', 'p_28', 'p_29', 'p_30', 'p_31',
173	'p_32', 'p_33', 'AU01_r', 'AU02_r', 'AU04_r', 'AU05_r', 'AU06_r',
174	'AU07_r', 'AU09_r', 'AU10_r', 'AU12_r', 'AU14_r', 'AU15_r',
175	'AU17_r', 'AU20_r', 'AU23_r', 'AU25_r', 'AU26_r', 'AU45_r',
176	'AU01_c', 'AU02_c', 'AU04_c', 'AU05_c', 'AU06_c', 'AU07_c',
177	'AU09_c', 'AU10_c', 'AU12_c', 'AU14_c', 'AU15_c', 'AU17_c',
178	'AU20_c', 'AU23_c', 'AU25_c', 'AU26_c', 'AU28_c', 'AU45_c']
179
180	Returns:
181	dataframe of processed facial expressions
182
183	"""
184	d = pd.read_csv(openfacefile, sep=",")	6✔
185	d.columns = d.columns.str.strip(" ")	6✔
186	# Check if features argument is passed and return only those features, else return basic emotion/AU features
187	if isinstance(features, list):	6✔
188	try:	6✔
189	d = d[features]	6✔
190	except Exception:	6✔
191	raise KeyError([features, "not in openfacefile"])	6✔
192	elif isinstance(features, type(None)):	6✔
193	features = OPENFACE_ORIG_COLUMNS	6✔
194	try:	6✔
195	d = d[features]	6✔
196	except Exception:	×
197	pass	×
198	fex = feat.Fex(	6✔
199	d,
200	filename=openfacefile,
201	au_columns=openface_AU_columns,
202	emotion_columns=None,
203	facebox_columns=None,
204	landmark_columns=openface_2d_landmark_columns,
205	facepose_columns=openface_facepose_columns,
206	gaze_columns=openface_gaze_columns,
207	time_columns=openface_time_columns,
208	detector="OpenFace",
209	)
210	fex["input"] = openfacefile	6✔
211	return fex	6✔
212
213
214	def load_pil_img(file_name, frame_id):	6✔
215	"""Helper function to load a PIL image from a picture or video
216
217	Args:
218	file_name (str): path to file. Can be image or video
219	frame_id (int): if video, load frame
220
221	Returns:
222	image: pil image instance
223	"""
224
NEW 225	file_extension = os.path.basename(file_name).split(".")[-1]	×
NEW 226	if file_extension.lower() in ["jpg", "jpeg", "png", "bmp", "tiff", "pdf"]:	×
NEW 227	frame_img = read_image(file_name) # image path	×
228	else:
229	# Ignore UserWarning: The pts_unit 'pts' gives wrong results. Please use
230	# pts_unit 'sec'. See why it's ok in this issue:
231	# https://github.com/pytorch/vision/issues/1931
NEW 232	with warnings.catch_warnings():	×
NEW 233	warnings.simplefilter("ignore", UserWarning)	×
NEW 234	video, audio, info = read_video(file_name, output_format="TCHW")	×
NEW 235	frame_img = video[frame_id, :, :]	×
NEW 236	return to_pil_image(frame_img)	×
237
238
239	def video_to_tensor(file_name):	6✔
240	container = av.open(file_name)	6✔
241	stream = container.streams.video[0]	6✔
242	tensor = []	6✔
243	for frame in container.decode(stream):	6✔
244	frame_data = torch.from_numpy(frame.to_ndarray(format="rgb24"))	6✔
245	frame_data = swapaxes(swapaxes(frame_data, 0, -1), 1, 2)	6✔
246	tensor.append(frame_data)	6✔
247	container.close()	6✔
248	return torch.stack(tensor, dim=0)	6✔

cosanlab / py-feat / 15090929758

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