image_output_prediction.py

from tensorflow.keras.preprocessing.image import img_to_array
from tensorflow.keras.models import load_model
import numpy as np
import cv2
import os
import cvlib as cv

classes = ['man','woman']

# load model
model = load_model('gender_detection.model')

frame = cv2.imread("face_0.jpg")
# man_img = cv2.imread("face_1.jpg")

# apply face detection
face, confidence = cv.detect_face(frame)
# face_m, confidence_m = cv.detect_face(man_img)

for idx, f in enumerate(face):

    # get corner points of face rectangle
    (startX, startY) = f[0], f[1]
    (endX, endY) = f[2], f[3]

    # draw rectangle over face
    cv2.rectangle(frame, (startX,startY), (endX,endY), (0,255,0), 2)

    # crop the detected face region
    face_crop = np.copy(frame[startY:endY,startX:endX])

    if (face_crop.shape[0]) < 10 or (face_crop.shape[1]) < 10:
        continue

    # preprocessing for gender detection model
    face_crop = cv2.resize(face_crop, (96,96))
    face_crop = face_crop.astype("float") / 255.0
    face_crop = img_to_array(face_crop)
    face_crop = np.expand_dims(face_crop, axis=0)

    # apply gender detection on face
    conf = model.predict(face_crop)[0] # model.predict return a 2D matrix, ex: [[9.9993384e-01 7.4850512e-05]]

    # get label with max accuracy
    idx = np.argmax(conf)
    label = classes[idx]

    label = "{}: {:.2f}%".format(label, conf[idx] * 100)
    print(label)
    Y = startY - 10 if startY - 10 > 10 else startY + 10

    # write label and confidence above face rectangle
    cv2.putText(frame, label, (startX, Y),  cv2.FONT_HERSHEY_SIMPLEX,
                0.7, (0, 255, 0), 2)

# display output
# cv2.imshow("gender detection", frame)