SeetaPsych Attributes
Face and body based psychology analysis
SeetaPsych Lib is a Python library for face- and body-based psychology analysis. It provides a modular Pipeline/Runner runtime and an optional Streamlit WebUI.
This project is used to manage the specifications for various attribute outputs, providing a unified standard so that different algorithm implementations can produce interchangeable and reusable module outputs.
TypedDict Type Hints
Alongside the JSON schemas documented below, this project ships a set of ready-to-use
TypedDict declarations under seetapsych_attributes.types so that your IDE can
provide auto-completions and static type checks directly on the runner's report dict:
# -*- coding: utf-8 -*-
import json
import cv2
from seetapsych_lib.runtime.factory import Factory
from seetapsych_lib.runtime.pipeline import Pipeline
from seetapsych_lib.runtime.runner import Runner
from seetapsych_attributes.types import Report, BBox, FaceDetection
def main():
factory = Factory()
factory.load_builtin_modules()
pipeline = Pipeline(factory, attributes=['face/detection'])
pipeline.solve()
pipeline.install_requirements()
pipeline.cache_models()
runner = Runner(pipeline)
report: Report = runner.run(data={
'default': cv2.imread('data/a.jpg')
})
# IDE autocompletion + type inference for every attribute key:
detections: FaceDetection | None = report.get('face_detection')
if detections:
first: BBox = detections[0]
x1, y1, x2, y2 = first['xyxy']
score: float = first['score']
print(f"face at ({x1},{y1})-({x2},{y2}), score = {score:.3f}")
print(json.dumps(report, indent=2, ensure_ascii=False))
if __name__ == '__main__':
main()
The top-level Report TypedDict includes every attribute key defined in the
catalog (all fields are optional, because a pipeline may only request a subset).
Per-attribute element types such as BBox, Landmarks, Selection,
ActionUnits, Expression, HeartRate, HeadSocialGaze, etc. are also
exported individually.
Catalog
- face/detection Obtain the face detection results, represented as rectangular bounding boxes.
- face/landmarks Get facial landmarks for basic alignment, including the centers of the left and right eyes, the nose tip, and the positions of the left and right mouth corners.
- face/selection Indicates the result of face selection. It will update the properties of face/detection and face/landmarks.
- face/action_units Indicate the confidence level of each Action Unit. Not all Action Units' results may be output.
- face/expression Indicate the confidence level of each expression.
- face/dense_landmarks Predict 280 dense facial landmarks from bounding box with optional refinement.
- face/mesh Extract 468-point 3D face mesh landmarks with optional blendshapes.
- face/gaze_screen Estimate per-eye screen gaze coordinates and camera-space gaze vectors from face mesh.
- face/heart_rate Estimate heart rate (BPM) from face video frames using rPPG or model-based methods.
- face/dimensional_affect Continuous valence-arousal affect dimensions alongside discrete expressions and Action Units.
- head/detection YOLO-based multi-person head detection with configurable confidence and NMS thresholds.
- head/selection Select top-N head detections by size or confidence with spatial sorting options, and reorder head_detection accordingly.
- head/gaze_point Predict per-head 2D gaze target point on scene image using CoSI transformer model with heatmap.
- head/social_gaze Infer social gaze relations (looking-at, mutual, avert) between pairs of people via CoSI dyadic model.
face/detection
Properties
face_detection(array, required)- Items: Refer to #/$defs/BBox.
Definitions
Examples
{
"face_detection": [
{
"score": 0.5,
"xyxy": [
100,
200,
300,
400
]
}
]
}
face/landmarks
Properties
face_landmarks(array, required)- Items: Refer to #/$defs/Landmarks.
Definitions
Examples
{
"face_landmarks": [
{
"landmarks": [
100,
100,
200,
200,
300,
300,
400,
400,
500,
500
]
}
]
}
face/selection
Properties
face_selection(required): Refer to #/$defs/Selection.
Definitions
Examples
{
"face_detection": [
{
"score": 0.5,
"xyxy": [
100,
200,
300,
400
]
}
],
"face_selection": {
"pid": 1
}
}
face/action_units
Properties
face_action_units(array, required)- Items: Refer to #/$defs/ActionUnits.
Definitions
ActionUnits(object)AU1(number):[0, 1]. Inner Brow Raiser. Default:null.AU2(number):[0, 1]. Outer Brow Raiser. Default:null.AU4(number):[0, 1]. Brow Lowerer. Default:null.AU5(number):[0, 1]. Upper Lid Raiser. Default:null.AU6(number):[0, 1]. Cheek Raiser. Default:null.AU7(number):[0, 1]. Lid Tightener. Default:null.AU9(number):[0, 1]. Nose Wrinkler. Default:null.AU10(number):[0, 1]. Upper Lip Raiser. Default:null.AU12(number):[0, 1]. Lip Corner Puller. Default:null.AU15(number):[0, 1]. Lip Corner Depressor. Default:null.AU17(number):[0, 1]. Chin Raiser. Default:null.AU20(number):[0, 1]. Lip Stretcher. Default:null.AU23(number):[0, 1]. Lip Tightener. Default:null.AU24(number):[0, 1]. Lip Pressor. Default:null.AU25(number):[0, 1]. Lips Part. Default:null.AU26(number):[0, 1]. Jaw Drop. Default:null.
Examples
{
"face_action_units": [
{
"AU1": 0.5,
"AU10": 0.5,
"AU12": 0.5,
"AU15": 0.5,
"AU17": 0.5,
"AU2": 0.5,
"AU20": 0.5,
"AU23": 0.5,
"AU24": 0.5,
"AU25": 0.5,
"AU26": 0.5,
"AU4": 0.5,
"AU5": 0.5,
"AU6": 0.5,
"AU7": 0.5,
"AU9": 0.5
}
]
}
face/expression
Properties
face_expression(array, required)- Items: Refer to #/$defs/Expression.
Definitions
Expression(object)neutral(number): Confidence in[0, 1]. Default:null.anger(number): Confidence in[0, 1]. Default:null.disgust(number): Confidence in[0, 1]. Default:null.fear(number): Confidence in[0, 1]. Default:null.happy(number): Confidence in[0, 1]. Default:null.sad(number): Confidence in[0, 1]. Default:null.surprise(number): Confidence in[0, 1]. Default:null.
Examples
{
"face_expression": [
{
"anger": 0.01,
"disgust": 0.01,
"fear": 0.01,
"happy": 0.94,
"neutral": 0.01,
"sad": 0.01,
"surprise": 0.01
}
]
}
face/dense_landmarks
Properties
face_dense_landmarks(array, required)- Items: Refer to #/$defs/DenseLandmarks.
Definitions
Examples
{
"face_dense_landmarks": [
{
"landmarks": [
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]
}
]
}
face/mesh
Properties
face_mesh(array, required)- Items: Refer to #/$defs/MeshLandmarks.
Definitions
Examples
{
"face_mesh": [
{
"normalized_3d_landmarks": [
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]
}
]
}
face/gaze_screen
Properties
face_gaze_screen(array, required)- Items: Refer to #/$defs/GazeScreen.
Definitions
GazeData(object)success(boolean, required)gaze_screen_px(required): Refer to #/$defs/GazePoint.gaze_cm(required): Refer to #/$defs/GazePoint.
GazePoint(object)GazeScreen(object)gaze(required): Refer to #/$defs/GazeData.
Examples
{
"face_gaze_screen": [
{
"gaze": {
"gaze_cm": {
"left_eye": [
15.5,
5.0,
2.5
],
"right_eye": [
15.5,
5.0,
2.5
]
},
"gaze_screen_px": {
"left_eye": [
960.0,
540.0
],
"right_eye": [
960.0,
540.0
]
},
"success": true
}
}
]
}
face/heart_rate
Properties
face_heart_rate(required): Refer to #/$defs/HeartRate.
Definitions
Examples
{
"face_heart_rate": {
"fps": 30.0,
"hr_bpm": 72.5,
"wait_seconds": 0.0
}
}
{
"face_heart_rate": {
"fps": 30.0,
"wait_seconds": 5.2
}
}
face/dimensional_affect
Properties
face_dimensional_affect(array, required)- Items: Refer to #/$defs/DimensionalAffect.
Definitions
Examples
{
"face_dimensional_affect": [
{
"arousal": 0.32,
"valence": 0.85
}
]
}
head/detection
Properties
head_detection(array, required)- Items: Refer to #/$defs/HeadBBox.
Definitions
Examples
{
"head_detection": [
{
"score": 0.85,
"xyxy": [
100,
200,
300,
400
]
}
]
}
head/selection
Properties
head_selection(required): Refer to #/$defs/HeadSelection.
Definitions
Examples
{
"head_detection": [
{
"score": 0.85,
"xyxy": [
100,
200,
300,
400
]
}
],
"head_selection": {
"count": 1,
"selected_indices": [
0
]
}
}
head/gaze_point
Properties
head_gaze_point(array, required)- Items: Refer to #/$defs/HeadGazePoint.
Definitions
Examples
{
"head_gaze_point": [
{
"gaze_point_px": [
640.0,
360.0
],
"head_location_xyxy": [
100,
200,
300,
400
],
"heatmap": [
[
0.1,
0.2
],
[
0.3,
0.4
]
]
}
]
}
head/social_gaze
Properties
head_social_gaze(required): Refer to #/$defs/HeadSocialGaze.
Definitions
HeadSocialGaze(object)principal: Left-side / primary person in dyadic interaction. Default:null.- Any of
- : Refer to #/$defs/SocialGazePerson.
- null
- Any of
associate: Right-side / secondary person in dyadic interaction. Default:null.- Any of
- : Refer to #/$defs/SocialGazePerson.
- null
- Any of
success(boolean): Whether at least two heads were detected for social gaze inference. Default:true.
SocialGazePerson(object)head_location_xyxy(array, required): Length must be equal to 4.gaze_point_px(array, required): Length must be equal to 2.heatmap(array, required): 2D heatmap array of gaze likelihood.social_gaze_id(integer, required): Integer ID of the social gaze relation class.social_gaze_label(string, required): Human-readable label of the social gaze relation (e.g. looking-at, mutual, avert).
Examples
{
"head_social_gaze": {
"associate": {
"gaze_point_px": [
200.0,
300.0
],
"head_location_xyxy": [
600,
200,
800,
400
],
"heatmap": [
[
0.2,
0.1
],
[
0.4,
0.3
]
],
"social_gaze_id": 0,
"social_gaze_label": "looking-at"
},
"principal": {
"gaze_point_px": [
800.0,
300.0
],
"head_location_xyxy": [
100,
200,
300,
400
],
"heatmap": [
[
0.1,
0.2
],
[
0.3,
0.4
]
],
"social_gaze_id": 0,
"social_gaze_label": "looking-at"
},
"success": true
}
}
{
"head_social_gaze": {
"success": false
}
}
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