test_rotation_new.py

from tensorflow.keras.models import load_model
import numpy as np
import cv2
import random
import glob
import pickle


def show_image(img,title=""):
    cv2.imshow(title, img)
    cv2.waitKey(0)
    cv2.destroyAllWindows()

# Load the trained model
model = load_model('/home/lucassoares/Desktop/projects/biometrid/dev_document_rotation/rotation_model')

# Load and preprocess the image
for image_path in glob.glob("/home/lucassoares/Desktop/projects/biometrid/dev_document_rotation/data/eval_images/*"):
    print("Image Path: ")
    print(image_path)
    img = cv2.imread(image_path)
    img = cv2.resize(img, (600, 600)) # Resize the image to match input size used in training
    img = img.astype('float32') / 255.0 # Normalize pixel values
    
        
    # Generate prediction on the image
    prediction = model.predict(np.expand_dims(img, axis=0))[0]
    print("Prediction: ", prediction)
    # Convert prediction from probabilities to class label
    class_label = np.argmax(prediction)

    # Load the labels from the pickle file
    with open('./lastClassesCore.pickle', 'rb') as f:
        labels = pickle.load(f)

    predicted_label = labels.classes_[class_label]
    print("Predicted Label: ", predicted_label)
    show_image(img, title=predicted_label)