import tensorflow.keras
from PIL import Image, ImageOps
import numpy as np
import cv2
import serial

camera = cv2.VideoCapture(1)
# Disable scientific notation for clarity
np.set_printoptions(suppress=True)

# Load the model
model = tensorflow.keras.models.load_model('keras_model.h5')
# model.compile(
#     optimizer='rmsprop', loss=None, metrics=None, loss_weights=None,
#     sample_weight_mode=None, weighted_metrics=None, target_tensors=None,
#     distribute=None
# )

with serial.Serial('COM5', 9600) as ser:
    spraying = False
    while True:
        ret, frame = camera.read()
        if not ret:
            continue
        # cv2.imshow('frame', frame)

        image = Image.fromarray(frame)
        # Create the array of the right shape to feed into the keras model
        # The 'length' or number of images you can put into the array is
        # determined by the first position in the shape tuple, in this case 1.
        data = np.ndarray(shape=(1, 224, 224, 3), dtype=np.float32)

        # Replace this with the path to your image
        # image = Image.open('test_photo.jpg')

        # resize the image to a 224x224 with the same strategy as in TM2:
        # resizing the image to be at least 224x224 and then cropping from the center
        size = (224, 224)
        image = ImageOps.fit(image, size, Image.ANTIALIAS)

        # turn the image into a numpy array
        image_array = np.asarray(image)

        # display the resized image
        #image.show()

        # Normalize the image
        normalized_image_array = (image_array.astype(np.float32) / 127.0) - 1

        # Load the image into the array
        data[0] = normalized_image_array

        # run the inference
        prediction = model.predict(data)
        print(float(prediction[0][1]))
        if float(prediction[0][1]) > 0.5:
            # armpittt
            print("armpit")
            if not spraying:
                ser.write(b"80a")
                spraying = True
                print("sprayyyy")
        else:
            # notpit
            if spraying:
                ser.write(b"100a")
                spraying = False