PersonaScan is a simple and fast API designed to analyze and extract various attributes from images, including gender, race, emotion, age, glasses presence, facial hair, iris color, and hair color. It leverages various state-of-the-art computer vision and deep learning techniques such as Dlib, OpenCV, Deepface Library and some other pretrained models to provide detailed insights into facial features.
- Gender Detection: Identifies the dominant gender in the image.
- Race Detection: Analyzes the person's race in the image.
- Emotion Detection: Detects the person's emotion in the image.
- Age Estimation: Estimates the age of the individual in the image.
- Glasses Detection: Determines if the person is wearing glasses in the image.
- Facial Hair Detection: Identifies the presence of facial hair in the image.
- Iris Color Detection: Determines the color of the iris in the image.
- Hair Color Detection: Analyzes the color of the hair in the image. (Still in development)
To use the PersonaScan API, follow these steps:
- Python 3.x
- Flask
- OpenCV
- dlib
- DeepFace
- PIL
- imutils
- colorthief
- webcolors
- Clone the repository:
git clone https://github.com/yourusername/personascan-api.git cd personascan-api- Install the required packages
pip install -r requirements.txtNote: Install the Dlib library for your python version from here.
- Start the flask server.
python app.pyThe server will run on http://127.0.0.1:5000.
Analyzes the attributes of the given image. It takes the input from the request body which is a base64 string of the image. The response is json containing the description of various facial features.
{ "b64_string": ".....base64....string...of...the....image......" }{ "Gender": "Male", "Race": "Caucasian", "Emotion": "Happy", "Age": 29, "Glasses": "Present", "Facial Hair": "Absent", "Iris Color": "#7F3F6D", "Hair Color": "#C9A29D" }201 Created- If the analysis is successful.400 Bad Request- If the request format is incorrect or required fields are missing.
