main.py

from keras.models import load_model  # TensorFlow is required for Keras to work
import cv2  # Install opencv-python
import numpy as np

import cv_grab as vc
import mvsdk as sdk



# Disable scientific notation for clarity
np.set_printoptions(suppress=True)

# Load the model
model = load_model("keras_Model.h5", compile=False)
print(model)

# url ="http://192.168.0.253:8080/video"
# Load the labels
class_names = open("labels.txt", "r").readlines()

# CAMERA can be 0 or 1 based on default camera of your computer
camera = cv2.VideoCapture(1)
  

print(camera)

while True:
    # Grab the webcamera's image.
    ret, image = camera.read()


    # Resize the raw image into (224-height,224-width) pixels
    image = cv2.resize(image, (224, 224), interpolation=cv2.INTER_AREA)
    if ret:
    # Show the image in a window
        cv2.imshow("Webcam Image", image)

    # Make the image a numpy array and reshape it to the models input shape.
        image = np.asarray(image, dtype=np.float32).reshape(1, 224, 224, 3)

    # Normalize the image array
        image = (image / 127.5) - 1

        number_of_white_pix = np.sum(image == 224)

        # Predicts the model
        prediction = model.predict(image)
        index = np.argmax(prediction)
        class_name = class_names[index]
        confidence_score = prediction[0][index]

    # Print prediction and confidence score
    print("Class:", class_name[2:], end="")
    print("Confidence Score:", str(np.round(confidence_score * 100))[:-2], "%")

    # Listen to the keyboard for presses.
    keyboard_input = cv2.waitKey(1)

    # 27 is the ASCII for the esc key on your keyboard.
    if keyboard_input == 27:
        break

camera.release()
cv2.destroyAllWindows()