face01lib package

Subpackages

face01lib.models package
- Module contents
  - Models

Submodules

face01lib.Calc module

License for the Code.

Copyright Owner: Yoshitsugu Kesamaru Please refer to the separate license file for the license of the code.

class face01lib.Calc.Cal(log_level: str = 'info')

Bases: object

Cal class include various calculation methods.

HANDLING_FRAME_TIME: float

HANDLING_FRAME_TIME_FRONT: float

HANDLING_FRAME_TIME_REAR: float

Measure_func(func): Used as a decorator to time a function.

static Measure_processing_time(HANDLING_FRAME_TIME_FRONT, HANDLING_FRAME_TIME_REAR) → float

Measurement of processing time (calculation) and output to log.

Parameters

HANDLING_FRAME_TIME_FRONT (float) – First half point
HANDLING_FRAME_TIME_REAR (float) – Second half point

static Measure_processing_time_backward() → float

Measurement of processing time (second half).

Returns: Second half point
Return type: float

static Measure_processing_time_forward() → float

Measurement of processing time (first half).

Returns: First half point
Return type: float

cal_resized_logo_image(resized_logo_image: ndarray[Any, dtype[float64]], set_height: int, set_width: int) → Tuple[int, int, int, int, ndarray[Any, dtype[float64]], ndarray[Any, dtype[float64]]]

Calculate logo image data.

Parameters

resized_logo_image (npt.NDArray[np.float64]) – Resized logo image data
set_height (int) – Height
set_width (int) – Width

Returns

Return tuple

Return type

Tuple[int,int,int,int,npt.NDArray[np.float64],npt.NDArray[np.float64]]

Example

>>> cal_resized_logo_nums = Cal().cal_resized_logo_image(
        resized_logo_image,
        set_height,
        set_width
    )

cal_resized_telop_image(resized_telop_image: ndarray[Any, dtype[float64]]) → Tuple[int, int, int, int, ndarray[Any, dtype[float64]], ndarray[Any, dtype[float64]]]

Calculate telop image data.

Parameters: resized_telop_image (npt.NDArray[np.float64]) – Resized telop image data
Returns: Tuple
Return type: Tuple[int,int,int,int,npt.NDArray[np.float64],npt.NDArray[np.float64]]

Example

>>> cal_resized_telop_nums = Cal().cal_resized_telop_image(resized_telop_image)

cal_specify_date(logger) → None: Run evaluation version only.

decide_text_position(error_messg_rectangle_bottom, error_messg_rectangle_left, error_messg_rectangle_right, error_messg_rectangle_fontsize, error_messg_rectangle_messg): Not use.

make_draw_rgb_object(pil_img_obj_rgb)

Generate object.

Parameters: pil_img_obj_rgb (object) – object
Returns: object
Return type: object

make_error_messg_rectangle_font(fontpath: str, error_messg_rectangle_fontsize: str, encoding='utf-8'): Not use.

pil_img_instance(frame: ndarray[Any, dtype[uint8]])

Generate pil_img object.

Parameters: frame (npt.NDArray[np.uint8]) – Image data
Returns: PIL object
Return type: object

return_percentage(distance: float, deep_learning_model: int) → float

Receive ‘distance’ and return percentage.

Parameters

distance (float) – distance
deep_learning_model (int) – deep_learning_model

Returns

percentage

Return type

float

Note

deep_learning_model:: 0:dlib_face_recognition_resnet_model_v1.dat, 1:efficientnetv2_arcface.onnx, 2:mobilefacenet.onnx

Example

>>> percentage = Cal().return_percentage(distance, deep_learning_model)

to_percentage(tolerance: float, deep_learning_model: int) → float

Receive ‘tolerance’ and return ‘percentage’.

Parameters: tolerance (float) – tolerance
Returns: percentage
Return type: float

to_tolerance(similar_percentage: float, deep_learning_model: int) → float

Receive similar_percentage and return tolerance.

Parameters

similar_percentage (float) – ‘Probability of similarity’ described in config.ini
deep_learning_model (int) – Deep learning model.

Returns

tolerance

Return type

float

Note

deep_learning_model:: 0:dlib_face_recognition_resnet_model_v1.dat, 1:efficientnetv2_arcface.onnx, 2:mobilefacenet.onnx

Example

>>> tolerance: float = Cal().to_tolerance(
        self.CONFIG["similar_percentage"],
        self.CONFIG["deep_learning_model"]
    )

x1: int

x2: int

y1: int

y2: int

face01lib.Core module

License for the Code.

Copyright Owner: Yoshitsugu Kesamaru Please refer to the separate license file for the license of the code.

class face01lib.Core.Core(log_level: str = 'info')

Bases: object

Core class.

This class include many useful methods.

common_process(CONFIG: Dict) → Generator

Generator of frame_datas_array.

common_process function consists 3 part of Core() methods.

Core().frame_pre_processing
Core().face_encoding_process
Core().frame_post_processing

Yields

Generator –

frame_datas_array

frame_datas_array: List[Dict]

Example

Make generator object

>>> obj = Core().common_process(CONFIG)

Call ‘__next__()’ method

>>> while True:
    frame_datas_array = obj.__next__()

face_encoding_process(logger, CONFIG: Dict, frame_datas_array: List[Dict]) → Tuple[List[ndarray[Any, dtype[float64]]], List[Dict]]

Encode face data and Return bellow.

list of encoded data
frame_datas_array

Parameters

logger (_type_) – logger
CONFIG (Dict) – Dict including initial setting
frame_datas_array (List[Dict]) – frame datas

Returns

list of encoded data
- Tuple[List[npt.NDArray[np.float64]]
frame_datas_array
- List[Dict]]

Definition of person_data_list and frame_datas_array:

>>> person_data = {
        'name': name,
        'pict':filename,
        'date':date,
        'location':(top,right,bottom,left),
        'percentage_and_symbol': percentage_and_symbol
    }
>>> person_data_list.append(person_data)

>>> frame_datas = {
        'img': resized_frame,
        'face_location_list': face_location_list,
        'overlay': overlay,
        'person_data_list': person_data_list
    }
>>> frame_datas_array.append(frame_datas)

frame_post_processing(logger, CONFIG: Dict, face_encodings: List[ndarray[Any, dtype[float64]]], frame_datas_array: List[Dict]) → List[Dict]

Modify each frame’s datas.

Parameters

logger (_type_) – logger
CONFIG (Dict) – Dict of initial settings
face_encodings (List[npt.NDArray[np.float64]]) – List of encoded face datas
frame_datas_array (List[Dict]) – List of datas

Returns

List of modified datas

Return type

List[Dict]

Modify:

Composite images
- Default face image
- Rectangle
- Percentage
- Name
Percentage calculation
Save cropped face images
Make person_data_list
Make frame_datas_list

frame_pre_processing(logger, CONFIG: Dict, resized_frame: ndarray[Any, dtype[uint8]]) → List[Dict]

Return frame_datas_array.

Parameters

logger (_type_) – logger
CONFIG (Dict) – Dict of Initial settings
resized_frame (npt.NDArray[np.uint8]) – Resized frame

Returns

frame_datas_array

Return type

List[Dict]

Processing:

If CONFIG[“headless”] == False
- copy frame data for overlay
- composite telop and logo
Compute face coordinates
Make frame_datas
- img
- face_location_list
- overlay
  
  If CONFIG[“headless”] == True, insert dummy data
  
  np.empty((2,2,3), dtype=np.uint8)
- person_data_list
Make frame_datas_array
- frame_datas_array.append(frame_datas)

Data structure of frame_datas_array:

overlay (npt.NDArray[np.uint8])
face_location_list (List[Tuple[int,int,int,int]])
name (str)
filename (str)
percentage_and_symbol (str)
person_data_list (List)
frame_datas_array (List[Dict])
resized_frame (npt.NDArray[np.uint8])

See Core.make_frame_datas_array()

Example

>>> exec_times = 2
# Initialize
CONFIG: Dict =  Initialize('FACE-COORDINATE', 'info').initialize()
# Make generator
frame_generator_obj = VidCap().frame_generator(CONFIG)
# Make logger
import os.path
dir: str = os.path.dirname(__file__)
log = Logger().logger(__file__, dir)
# Make generator
core = Core()
# Repeat 'exec_times' times
for i in range(0, exec_times):
    # Call __next__() from the generator object
    resized_frame = frame_generator_obj.__next__()
    frame_datas_array = core.frame_pre_processing(log, CONFIG,resized_frame)

make_frame_datas_array(overlay: ndarray[Any, dtype[uint8]], face_location_list: List[Tuple[int, int, int, int]], name: str, filename: str, percentage_and_symbol: str, person_data_list: List, frame_datas_array: List[Dict], resized_frame: ndarray[Any, dtype[uint8]]) → List[Dict]

Method to make frame_datas_array.

Return the data structure of frame_datas_list.

Parameters

overlay (npt.NDArray[np.uint8]) – Copy of frame
face_location_list (List[Tuple[int,int,int,int]]) – List of face_location
name (str) – Each of person’s name
filename (str) – File name
percentage_and_symbol (str) – Concatenate of digit of percentage and ‘%’ symbol
person_data_list (List) – List of person_data
frame_datas_array (List[Dict]) – Array of frame_datas
resized_frame (npt.NDArray[np.uint8]) – Numpy array of frame

Returns

List of frame_datas_array

Return type

List[Dict]

Example

person_data

>>> {
    'name': name,
    'pict': filename,
    'date': date,
    'location': (top,right,bottom,left),
    'percentage_and_symbol': percentage_and_symbol
}

person_data_list

>>> person_data_list.append(person_data)

frame_datas

>>> {
    'img': resized_frame,
    'face_location_list': face_location_list,
    'overlay': overlay,
    'person_data_list': person_data_list
}

frame_datas_list

>>> frame_datas_array.append(frame_datas)

mp_face_detection_func(resized_frame: ndarray[Any, dtype[uint8]], model_selection: int = 0, min_detection_confidence: float = 0.4)

Processes an RGB image and returns a list of the detected face location data.

Parameters

resized_frame (npt.NDArray[np.uint8]) – Resized image frame
model_selection (int, optional) – Value set in ‘config.ini’. Defaults to 0.
min_detection_confidence (float, optional) – Value set in ‘config.ini’. Defaults to 0.4.

Returns

A NamedTuple object with a “detections” field that contains a list of the detected face location data.’

Refer:: https://solutions.mediapipe.dev/face_detection#python-solution-api

Note

number_of_people and same_time_recognize in config.ini are disabled when using mp_face_detection_func.

override_args_dict(CONFIG: Dict, override_list: List[Tuple]) → Dict

Override CONFIG for example.

Parameters

Dict – CONFIG
List[Tuple] – override_list

Returns

CONFIG

Return type

Dict

Example

>>> CONFIG = Core.override_args_dict(
    CONFIG,
    [
        ('crop_face_image', False),
        ('output_debug_log', True)
    ]
)

Note

THIS METHOD IS EXPERIMENTAL
- Unexpected side effects may occur.
If you specified key is not exist, application will fail down
with print out log ‘warning’.
You cannot change key ‘headless’. If override it, application will fall down.

r_face_image(frame: ndarray[Any, dtype[uint8]], face_location: Tuple[int, int, int, int]) → ndarray[Any, dtype[uint8]]

Return face image which expressed for ndarray.

Parameters

frame (npt.NDArray[np.uint8]) – Frame image
face_location (Tuple[int,int,int,int]) – Face location (coordinate)

Returns

Face image which expressed for ndarray

Return type

npt.NDArray[np.uint8]

return_anti_spoof(frame: ndarray[Any, dtype[uint8]], face_location: Tuple[int, int, int, int]) → Tuple[str, float, bool]

Return result of anti spoof.

Note

This function is EXPERIMENTAL! It might occur side effects.

Parameters

frame (npt.NDArray[np.uint8]) – Each of frame
face_location (Tuple[int,int,int,int]) – Face location

Returns

spoof_or_real
score
ELE
- Equally Likely Events

Return type

Tuple[str, float, bool]

return_concatenate_location_and_frame(resized_frame: ndarray[Any, dtype[uint8]], face_location_list: List[Tuple[int, int, int, int]]) → Tuple[List[Tuple[int, int, int, int]], ndarray[Any, dtype[uint8]]]

Return tuple.

concatenate_face_location_list
concatenate_person_frame

Parameters

resized_frame (npt.NDArray[np.uint8]) – Resized frame
face_location_list (List[Tuple[int,int,int,int]]) – Face location list

Returns

concatenate_face_location_list (Tuple[List[Tuple[int,int,int,int]]): List of concatenated coordinates
concatenate_person_frame (npt.NDArray[np.uint8]]): Image data of concatenated person image data

face01lib.Initialize module

License for the Code.

Copyright Owner: Yoshitsugu Kesamaru Please refer to the separate license file for the license of the code.

class face01lib.Initialize.Initialize(section: str = 'DEFAULT', log_level: str = 'info')

Bases: object

Initialize class.

Load config.ini, return Dict style.

initialize() → Dict

Initialize values.

Returns: CONFIG Dictionary of initialized preferences
Return type: Dict

Example

CONFIG: Dict =  Initialize("SECTION").initialize()

face01lib.LoadImage module

License for the Code.

Copyright Owner: Yoshitsugu Kesamaru Please refer to the separate license file for the license of the code.

class face01lib.LoadImage.LoadImage(headless: bool, conf_dict: Dict)

Bases: object

This class include method to load images.

LI(set_height: int, set_width: int) → Tuple[Mat, ...]

Return values.

Summary:: Load images, and return all together in a tuple.

Parameters

self – self
set_height (int) – Height described in config.ini
set_width (int) – Width described in config.ini

Returns

Tuple.

rect01_png (cv2.Mat): Loaded image data as ndarray
rect01_NG_png (cv2.Mat): Loaded image data as ndarray
rect01_REAL_png (cv2.Mat): Loaded image data as ndarray
rect01_SPOOF_png (cv2.Mat): Loaded image data as ndarray
rect01_CANNOT_DISTINCTION_png (cv2.Mat): Loaded image data as ndarray
resized_telop_image (Union[cv2.Mat, None]): Loaded image data as ndarray
cal_resized_telop_nums : Return Tuple or None
resized_logo_image (Union[cv2.Mat, None]): Loaded image data as ndarray or None
cal_resized_logo_nums (Union[Tuple[int,int,int,int,npt.NDArray[np.float64],npt.NDArray[np.float64]], None]):
load_unregistered_face_image (bool): Bool
telop_image (Union[cv2.Mat, None]): Loaded image data as ndarray or None
logo_image (Union[cv2.Mat, None]): Loaded image data as ndarray or None
unregistered_face_image (Union[cv2.Mat, None]): Loaded image data as ndarray or None

face01lib.api module

License for the Code.

Copyright Owner: Yoshitsugu Kesamaru Please refer to the separate license file for the license of the code.

class face01lib.api.Dlib_api(log_level: str = 'info')

Bases: object

Dlib api.

Author: Original code written by Adam Geitgey, modified by YOSHITSUGU KESAMARU

Email: y.kesamaru@tokai-kaoninsho.com

compare_faces(deep_learning_model: int, known_face_encodings: List[ndarray[Any, dtype[float64]]], face_encoding_to_check: ndarray[Any, dtype[float64]], tolerance: float = 0.6, threshold: float = 0.4) → Tuple[ndarray, float]

Compare a list of face encodings against a candidate encoding to see if they match.

Parameters

deep_learning_model (int) – 0: dlib cnn model, 1: efficientnetv2_arcface.onnx
known_face_encodings (List[npt.NDArray[np.float64]]) – A list of known face encodings
face_encoding_to_check (npt.NDArray[np.float64]) – A single face encoding to compare against the list
tolerance (float) – How much distance between faces to consider it a match. Lower is more strict. 0.6 is typical best performance.

Returns

A tuple of True/False values indicating which known_face_encodings match the face encoding to check, and the min distance between them.

cosine_similarity(embedding1, embedding2, threshold=0.4)

cosine_similarity 特徴量ベクトルを受け取り、類似度を計算し、閾値を超えているかどうかを返す

Parameters

embedding1 (npt.NDArray) – feature vector
embedding2 (npt.NDArray) – feature vector
threshold (float, optional) – threshold. Defaults to 0.4.

Returns

Returns a tuple of a numpy array of booleans and the minimum cos_sim

Return type

Tuple[np.array, float]

efficientnetv2_arcface_model_compute_face_descriptor(resized_frame: ndarray[Any, dtype[uint8]], raw_face_landmark, size: int = 224, _PADDING: float = 0.1) → ndarray[Any, dtype[float32]]

EfficientNetV2とArcFaceモデルを使用して顔の特徴量を計算します。

この関数は、与えられた顔の画像データから、EfficientNetV2とArcFaceモデルを使用して顔の特徴量（embedding）を計算します。

Parameters

resized_frame (npt.NDArray[np.uint8]) – リサイズされたフレームの画像データ。
raw_face_landmark (dlib.rectangle) – 顔のランドマーク情報。
size (int, optional) – 顔のチップのサイズ。デフォルトは224。
_PADDING (float, optional) – 顔のチップを取得する際のパディング。デフォルトは0.1。

Returns

顔の特徴量（embedding）。

Return type

npt.NDArray[np.float32]

face_distance(face_encodings: List[ndarray[Any, dtype[float64]]], face_to_compare: ndarray[Any, dtype[float64]]) → ndarray[Any, dtype[float64]]

Given a list of face encodings, compare them to a known face encoding and get a euclidean distance for each comparison face.

The distance tells you how similar the faces are.

Parameters

face_encodings (List[npt.NDArray[np.float64]]) – List of face encodings to compare (=small_frame)
face_to_compare (npt.NDArray[np.float64]) – A face encoding to compare against (=face_location_list)

Returns

A numpy ndarray with the distance for each face in the same order as the ‘faces’ array

Return type

npt.NDArray[np.float64]

face_encodings(deep_learning_model: int, resized_frame: ndarray[Any, dtype[uint8]], face_location_list: List = [], num_jitters: int = 0, model: str = 'small') → List[ndarray]

Given an image, return the 128-dimension face encoding for each face in the image.

Parameters

resized_frame (npt.NDArray[np.uint8]) – The image that contains one or more faces (=small_frame)
face_location_list (List) – Optional - the bounding boxes of each face if you already know them. (=face_location_list)
num_jitters (int) – How many times to re-sample the face when calculating encoding. Higher is more accurate, but slower (i.e. 100 is 100x slower)
model (str) – Do not modify.

Returns

A list of 128-dimensional face encodings (one for each face in the image). If deep_learning_model == 1, the size of the list is 512-dimensional face encodings, and the type is List[npt.NDArray[np.float32]]. See Issue # 19.

Image size, it should be of size 150x150. Also cropping must be done as ‘dlib.get_face_chip’ would do it. That is, centered and scaled essentially the same way.

Return type

List[npt.NDArray[np.float64]]

face01lib.combine module

License for the Code.

Copyright Owner: Yoshitsugu Kesamaru Please refer to the separate license file for the license of the code.

class face01lib.combine.Comb(log_level: str = 'info')

Bases: object

static comb(a: List[T], b: List[T]) → List[T]

face01lib.compile module

face01lib.draft module

face01lib.load_preset_image module

License for the Code.

Copyright Owner: Yoshitsugu Kesamaru Please refer to the separate license file for the license of the code.

class face01lib.load_preset_image.LoadPresetImage(log_level: str = 'info')

Bases: object

load_preset_image(deep_learning_model: int, RootDir: str, preset_face_imagesDir: str, upsampling: int = 0, jitters: int = 100, mode: str = 'hog', model: str = 'small') → Tuple[List[ndarray], List[str]]

face01lib.load_preset_image_bk module

License for the Code.

Copyright Owner: Yoshitsugu Kesamaru Please refer to the separate license file for the license of the code.

class face01lib.load_preset_image_bk.LoadPresetImage(log_level: str = 'info')

Bases: object

load_preset_image(deep_learning_model: int, RootDir: str, preset_face_imagesDir: str, upsampling: int = 0, jitters: int = 100, mode: str = 'hog', model: str = 'small') → Tuple[List, List]

Load face image from preset_face_images folder.

Parameters

deep_learning_model (int) – You can select from 0 or 1. 0 is Dlib, 1 is Efficientnetv2_arcface.onnx.
RootDir (str) – Root directory. npKnown.npzが作成されるディレクトリ。
preset_face_imagesDir (str) – Path to preset_face_images folder. pngファイルが存在するディレクトリ。
upsampling (int, optional) – Value of upsampling. Defaults to 0.
jitters (int, optional) – Value of jitters. Defaults to 100.
mode (str, optional) – You can select from hog or cnn. Defaults to ‘hog’.
model (str, optional) – You cannot modify this value.

Returns

known_face_encodings_list, known_face_names_list

known_face_encodings_list
- List of encoded many face images as ndarray
known_face_names_list
- List of name which encoded as ndarray

Return type

Tuple[List, List]

Example

>>> known_face_encodings, known_face_names = LoadPresetImage().load_preset_image(
    self,
    self.conf_dict["RootDir"],
    self.conf_dict["preset_face_imagesDir"]
)

face01lib.logger module

License for the Code.

Copyright Owner: Yoshitsugu Kesamaru Please refer to the separate license file for the license of the code.

class face01lib.logger.Logger(log_level: str = 'info')

Bases: object

Set log level.

logger(name: str, dir: str)

Manage log.

Parameters

name (str) – File name
dir (str) – Directory name. (Usually the root directory of FACE01)

Returns

logger

Return type

Logger object

Note

parent_dir in the above example refers to the root directory of FACE01.

i.e. CONFIG[‘RootDir’] in the code below.

# Initialize
CONFIG: Dict =  Initialize('LIGHTWEIGHT_GUI', 'info').initialize()
# Set up logger
logger = Logger(CONFIG['log_level']).logger(__file__, CONFIG['RootDir'])

face01lib.return_face_image module

License for the Code.

Copyright Owner: Yoshitsugu Kesamaru Please refer to the separate license file for the license of the code.

class face01lib.return_face_image.Return_face_image

Bases: object

This class include a method for return face image function.

return_face_image(resized_frame: ndarray[Any, dtype[uint8]], face_location: Tuple[int, int, int, int]) → ndarray[Any, dtype[uint8]]

Return face image array which contain ndarray.

Parameters

resized_frame (numpy.ndarray) – frame data
face_location (tuple) – face location which ordered top, right, bottom, left

Returns

face image of ndarray or empty array

Return type

list ( npt.NDArray[np.uint8])

face01lib.spoof module

Spoof Module.

Note

This module is experimental.

このモジュールは、顔の特徴点の検出、オブジェクトの検出、およびQRコードの生成に関連する機能を提供します。

主なクラス:

Spoof: 顔の特徴点の検出、オブジェクトの検出、およびQRコードの生成を行うメソッドを持つクラス。

主なメソッド:

iris: 顔の虹彩を検出し、その特徴点を描画します。
obj_detect: オブジェクトを検出し、その特徴点を描画します。
make_qr_code: QRコードを生成します。

使用例:

spoof_obj = Spoof() spoof_obj.iris() spoof_obj.obj_detect() spoof_obj.make_qr_code()

注意:

このモジュールは、実験的なものです。

License:

Copyright Owner: Yoshitsugu Kesamaru Please refer to the separate license file for the license of the code.

class face01lib.spoof.Spoof(log_level: str = 'info')

Bases: object

class EyeState(value)

Bases: Enum

An enumeration.

BLINKING = 3

CLOSED = 2

OPEN = 1

detect_eye_blinks(frame_datas_array, CONFIG) → bool

Return True if eye blink is detected.

Parameters: frame_datas_array (array[Dict]) – frame_datas_array
Returns: If eye blink is detected, return True. Otherwise, return False.
Return type: bool

iris()

make_qr_code()

obj_detect()

face01lib.utils module

License for the Code.

Copyright Owner: Yoshitsugu Kesamaru Please refer to the separate license file for the license of the code.

class face01lib.utils.Utils(log_level: str = 'info')

Bases: object

Utils class.

contains convenience methods

align_and_resize_maintain_aspect_ratio(path: str, upper_limit_length: int = 1024, padding: float = 0.4, size: int = 224, contain: str = '') → List[str]

Align and resize input image with maintain aspect ratio.

Parameters

path (str) – file path which contain file name. (‘.jpg’ or ‘.jpeg’ or ‘.png’. These must be lower case.) If path is directory, All files contained in this directory are targeted.
upper_limit_length (int, optional) – Upper limit length of width. Defaults to 1024.
padding (float, optional) – Padding around the face. Large = 0.8, Medium = 0.4, Small = 0.25, tiny = 0.1. Default = 0.4
size (int, optional) – Resized size of image data. Default is 224.
contain (str, optional) – Contain word in the directory

Returns

List of files that failed to align and resize.

Return type

error_files (list)

Result:

Note

If the width of the input image file exceeds ‘1024px’, it will be resized to ‘1024px’ while maintaining the aspect ratio.

create_concat_images(img: str, size: int = 224) → None

Create tile images.

Parameters

img (str) – absolute file path
size (int) – image size. Default is 224.

Result:

distort_barrel(dir_path: str, align_and_resize_bool: bool = False, size: int = 224, padding: float = 0.1, initial_value: float = -0.1, closing_value: float = 0.1, step_value: float = 0.1) → List[str]

Distort barrel.

Takes a path which contained png, jpg, jpeg files in the directory, distort barrel and saves them.

Parameters

dir_path (str) – absolute path of target directory.
align_and_resize_bool (bool, optional) – Whether to align and resize. Defaults to False.
size (int, optional) – Width and height. Defaults to 224.
padding (float, optional) – Padding. Defaults to 0.1.
initial_value (float) – Initial value. Default is -0.1.
closing_value (float) – Closing value. Default is 0.1.
step_value (float) – Step value. Default is 0.1.

Returns

Path list of processed files.

Note

ImageMagick must be installed on your system. - See ImageMagick https://imagemagick.org/script/download.php

Result:

get_face_encoding(deep_learning_model: int, image_path: str, num_jitters: int = 0, number_of_times_to_upsample: int = 0, mode: str = 'cnn', model: str = 'small')

get_face_encoding : get face encoding from image file.

Parameters

deep_learning_model (int) – dli model: 0, efficientnetv2_arcface model: 1
image_path (str) – image file path.
num_jitters (int, optional) – Number of jitters. Defaults to 0.
number_of_times_to_upsample (int, optional) – Number of times to upsample the image looking for faces. Defaults to 0.
mode (str, optional) – cnn or hog. Defaults to ‘cnn’.
model (str, optional) – small or large. Defaults to ‘small’.

Returns

face encoding data or None if not detected face.

Return type

NDArray data (npt.NDArray[np.float32])

get_files_from_path(path: str, contain: str = 'resize') → list

Receive path, return files.

Parameters

path (str) – Directory path
contain (str) – Contain word. If you want to get all files, set *. Default is resize.

Returns

Files in received path (absolute path)

Return type

list

get_jitter_image(dir_path: str, num_jitters: int = 10, size: int = 224, disturb_color: bool = True)

Jitter images at the specified path.

Parameters

dir_path (str) – path of target directory.
num_jitters (int, optional) – Number of jitters. Defaults to 10.
size (int, optional) – Resize image to size(px). Defaults to 224px.
disturb_color (bool, optional) – Disturb color. Defaults to True.

Note: This method is based on davisking/dlib/python_example/face_jitter.py. https://github.com/davisking/dlib/blob/master/python_examples/face_jitter.py

resize_image(img: ndarray, upper_limit_length: int = 1024) → ndarray

resize_image : resize image.

The input np.ndarray format image data is resized to fit the specified width or height. In this process, the aspect ratio is maintained by resizing based on the longer side of the width and height. The default maximum values for width and height are 1024px.

Parameters

img (np.ndarray) – image data.
upper_limit_length (int, optional) – upper limit length. Defaults to 1024.

Returns

resized image data.

Return type

np.ndarray

return_qr_code(face_encodings) → List[ndarray]

return_qr_code : return qr code.

Summary:: This method returns a QR code based on the face encoding list.

Parameters: face_encodings (List) – face encoding list.
Returns: qr code.
Return type: List

face01lib.video_capture module

License for the Code.

Copyright Owner: Yoshitsugu Kesamaru Please refer to the separate license file for the license of the code.

class face01lib.video_capture.VidCap(log_level: str = 'info')

Bases: object

VidCap class.

contains methods that initially process the input video data

finalize(vcap) → None

Release vcap and Destroy window.

Parameters: vcap (cv2.VideoCapture) – vcap which is handle of input video process

frame_generator(CONFIG: Dict) → Generator

Generator: Return resized frame data.

Parameters: CONFIG (Dict) – CONFIG
Raises: StopIteration – ret == False, then raise StopIteration
Yields: Generator – Resized frame data (npt.NDArray[np.uint8])

frame_imshow_for_debug(frame: ndarray[Any, dtype[uint8]]) → None

Used for debugging.

Display the given frame data in a GUI window for 3 seconds.

Parameters: frame (npt.NDArray[np.uint8]) – Image data called ‘frame’
Returns: None

resize_frame(set_width: int, set_height: int, frame: ndarray[Any, dtype[uint8]]) → ndarray[Any, dtype[uint8]]

Return resized frame data.

Parameters

set_width (int) – Width described in config.ini
set_height (int) – Height described in config.ini
frame (npt.NDArray[np.uint8]) – Image data

Returns

small_frame

Return type

npt.NDArray[np.uint8]

return_movie_property(set_width: int, vcap) → Tuple[int, ...]

Return input movie file’s property.

Parameters

set_width (int) – Width which set in config.ini
vcap (cv2.VideoCapture) – Handle of input movie processing

Returns

self.set_width, fps, height, width, set_height

Return type

Tuple[int,…]

return_vcap(movie: str) → VideoCapture

Return vcap object.

Parameters: movie (str) – movie
Returns: cv2.VideoCapture
Return type: object

Module contents

License for the Code.

Copyright Owner: Yoshitsugu Kesamaru Please refer to the separate license file for the license of the code.