Created
March 27, 2023 18:06
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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) | |
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