image_classification_mobilenet.py

import cv2
import numpy as np
from keras.applications.mobilenet import MobileNet
from keras.preprocessing import image
from keras.applications.mobilenet import preprocess_input, decode_predictions

IMG_SIZE = 224
IMG_NAME = 'photo.jpg'

model = MobileNet(weights='imagenet')
cap = cv2.VideoCapture(0)

while cap.isOpened():
    ret, frame = cap.read()
  
    height, width = frame.shape[:2]
    crop_img = frame[
            int(round((height/2)-IMG_SIZE)):int(round((height/2)+IMG_SIZE)),
            int(round((height/2)-IMG_SIZE)):int(round((height/2)+IMG_SIZE))]
    cv2.imwrite(IMG_NAME, crop_img)

    img = image.load_img(IMG_NAME, target_size=(IMG_SIZE, IMG_SIZE))
    x = image.img_to_array(img)
    x = np.expand_dims(x, axis=0)
    x = preprocess_input(x)

    preds = model.predict(x)
    print('Predicted:', decode_predictions(preds, top=3)[0])

    cv2.imshow('sample', crop_img)
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

cap.release()
cv2.destroyAllWindows