src/face_demographics.py

import cv2
import yaml
import numpy as np
import os
from typing import Tuple
from src.cv_utils import get_image
from transformers import ViTImageProcessor, ViTForImageClassification
import urllib3


with open("parameters.yml", "r") as stream:
    try:
        parameters = yaml.safe_load(stream)
    except yaml.YAMLError as exc:
        print(exc)


class GetFaceDemographics:
    def __init__(self):
        pass

    @staticmethod
    def preprocess_image_for_caffe_cnn(image: np.array):
        model_mean = (
            78.4263377603,
            87.7689143744,
            114.895847746,
        )  # taken from the model page on Caffe
        blob = cv2.dnn.blobFromImage(image, 1.0, (227, 227), model_mean, swapRB=False)
        return blob

    @staticmethod
    def get_age_cnn(blob) -> Tuple:
        age_net = cv2.dnn.readNet(
            parameters["face_age"]["config"], parameters["face_age"]["model"]
        )
        age_list = [
            "(0-2)",
            "(4-6)",
            "(8-12)",
            "(15-20)",
            "(25-32)",
            "(38-43)",
            "(48-53)",
            "(60-100)",
        ]
        age_net.setInput(blob)
        age_preds = age_net.forward()
        i = age_preds[0].argmax()
        age = age_list[i]
        age_confidence_score = age_preds[0][i]
        return age, age_confidence_score

    @staticmethod
    def get_gender_cnn(blob) -> Tuple:
        gender_net = cv2.dnn.readNet(
            parameters["face_gender"]["config"], parameters["face_gender"]["model"]
        )
        gender_list = ["Male", "Female"]
        gender_net.setInput(blob)
        gender_preds = gender_net.forward()
        i = gender_preds[0].argmax()
        gender = gender_list[i]
        gender_confidence_score = gender_preds[0][i]
        return gender, gender_confidence_score

    @staticmethod
    def get_age_vit(image: np.array) -> Tuple:
        os.environ[
            "CURL_CA_BUNDLE"
        ] = ""  # fixes VPN issue when connecting to hugging face hub
        urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)
        id2label = {
            0: "0-2",
            1: "3-9",
            2: "10-19",
            3: "20-29",
            4: "30-39",
            5: "40-49",
            6: "50-59",
            7: "60-69",
            8: "more than 70",
        }
        model = ViTForImageClassification.from_pretrained("nateraw/vit-age-classifier")
        transforms = ViTImageProcessor.from_pretrained("nateraw/vit-age-classifier")
        inputs = transforms(image, return_tensors="pt")
        output = model(**inputs)
        proba = output.logits.softmax(1)
        preds = proba.argmax(1)
        age_confidence_score = round(max(proba[0]).item(), 2)
        age = id2label[int(preds)]
        return age, age_confidence_score

    @staticmethod
    def get_gender_vit(image: np.array) -> Tuple:
        os.environ[
            "CURL_CA_BUNDLE"
        ] = ""  # fixes VPN issue when connecting to hugging face hub
        urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)
        id2label = {
            0: "female",
            1: "male",
        }
        model = ViTForImageClassification.from_pretrained(
            "rizvandwiki/gender-classification"
        )
        transforms = ViTImageProcessor.from_pretrained(
            "rizvandwiki/gender-classification"
        )
        inputs = transforms(image, return_tensors="pt")
        output = model(**inputs)
        proba = output.logits.softmax(1)
        preds = proba.argmax(1)
        gender_confidence_score = round(max(proba[0]).item(), 2)
        gender = id2label[int(preds)]
        return gender, gender_confidence_score

    def main(self, image_input) -> dict:
        image = get_image(image_input)
        age, age_confidence_score = self.get_age_vit(image)
        gender, gender_confidence_score = self.get_gender_vit(image)
        d = {
            "age_range": age,
            "age_confidence": age_confidence_score,
            "gender": gender,
            "gender_confidence": gender_confidence_score,
        }
        return d


if __name__ == "__main__":
    path_to_images = "data/"
    image_files = os.listdir(path_to_images)
    for image in image_files:
        print(image)
        results = GetFaceDemographics().main(path_to_images + image)
        print(results)