Face-Recognition/util.py at master · UtkarshPrajapati/Face-Recognition · GitHub

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import numpy as np
import joblib
import json
import base64
import cv2
from wavelet import w2d

__class_name_to_number = {}
__class_number_to_name = {}
__model = None

face_cascade = cv2.CascadeClassifier(
    cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
eye_cascade = cv2.CascadeClassifier(
    cv2.data.haarcascades + 'haarcascade_eye.xml')


def classify_image(b64, path=None):
    imgs = face_detected(path, b64)
    result = []
    for img in imgs:
        scaled_raw_img = cv2.resize(img, (32, 32))
        img_har = w2d(img, "db1", 5)
        scaled_img_har = cv2.resize(img_har, (32, 32))
        combined_img = np.vstack((scaled_raw_img.reshape(
            32*32*3, 1), scaled_img_har.reshape(32*32, 1)))
        len_image_array = 32*32*3+32*32
        final = combined_img.reshape(1, len_image_array).astype(float)
        result.append({
            'class': class_number_to_name(__model.predict(final)[0]),
            'class_probability': np.around(__model.predict_proba(final)*100,2).tolist()[0],
            'class_dictionary': __class_name_to_number
        })
    return result

def class_number_to_name(class_num):
    return __class_number_to_name[class_num]

def load_saved_artifacts():
    print("loading saved artifacts...start")
    global __class_name_to_number
    global __class_number_to_name

    with open("./artifacts/class_dictionary.json", "r") as f:
        __class_name_to_number = json.load(f)
        __class_number_to_name = {v: k for k,
                                  v in __class_name_to_number.items()}

    global __model
    if __model is None:
        with open('./artifacts/saved_model.pkl', 'rb') as f:
            __model = joblib.load(f)
    print("loading saved artifacts...done")


def get_cv2_image_from_b64(b64):
    ed = b64.split(",")[1]
    nparr = np.frombuffer(base64.b64decode(ed), np.uint8)
    img = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
    return img


def face_detected(path, b64):
    if path:
        img = cv2.imread(path)
    else:
        img = get_cv2_image_from_b64(b64)
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    faces = face_cascade.detectMultiScale(gray, 1.3, 5)
    cropped_faces = []
    for (x, y, w, h) in faces:
        roi_gray = gray[y:y+h, x:x+w]
        roi_color = img[y:y+h, x:x+w]
        eyes = eye_cascade.detectMultiScale(roi_gray)
        cropped_faces.append(roi_color)
    return cropped_faces


def b64_image():
    with open("./b64.txt") as f:
        return f.read()


if __name__ == "__main__":
    load_saved_artifacts()
    x=classify_image( None,"./r.jpg")
    print(x)