FaceMaskDetection/detector.py at master · zanuarts/FaceMaskDetection · GitHub

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import tensorflow as tf
from tensorflow.keras.models import Model, load_model
import numpy as np
from IPython.display import display
import glob
import os
import cv2
from tensorflow.keras.preprocessing.image import img_to_array
from tensorflow.keras.preprocessing.image import load_img
import matplotlib.pyplot as plt
import imutils

def detectPredictMask(pic):

    print('masuk detectPredictMask')

    my_model = load_model('model/model_cnn.h5')
    prototxt_path = "detector/deploy.prototxt"
    weight_path = "detector/res10_300x300_ssd_iter_140000.caffemodel"
    faceNet = cv2.dnn.readNet(prototxt_path, weight_path)

    pic = imutils.resize(pic)
    (h, w) = pic.shape[:2]
    blob = cv2.dnn.blobFromImage(pic, 1.0, (300, 300), (104.0, 177.0, 123.0))
    faceNet.setInput(blob)
    detections = faceNet.forward()

    for i in range(0, detections.shape[2]):
        box = detections[0, 0, 0, 3:7] * np.array([w, h, w, h])
        (startX, startY, endX, endY) = box.astype("int")

        (startX, startY) = (max(0, startX), max(0, startY))
        (endX, endY) = (min(w - 1, endX), min(h - 1, endY))

        face = pic[startY:endY, startX:endX]
        face = cv2.cvtColor(face, cv2.COLOR_BGR2RGB)
        face = cv2.resize(face, (224, 224))
        img = np.array(face, dtype='float')
        img = img.reshape(1, 224, 224, 3)

        preds = my_model.predict(img)
        result = preds

        color = (0, 255, 0)
        status =" Wearing Mask"
        if (result == 1):
            status =" Not Wearing Mask"
            color = (0, 0, 255)

        font = cv2.FONT_HERSHEY_DUPLEX

        stroke = 1
        cv2.putText(image, status, (startX, startY - 10), font, 0.5, color, stroke, cv2.LINE_AA)

        stroke = 2
        cv2.rectangle(image, (startX, startY), (endX, endY), color, stroke)

    return image