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| def load_dataset_images(directory): | |
| import pickle | |
| import os | |
| import cv2 | |
| import numpy as np | |
| # Set the paths to the dataset folders | |
| dataset_dir = "general" | |
| train_dir = os.path.join(dataset_dir, "PANTOLON") | |
| # Set the image size to be used as the input data for the network | |
| image_size = (224, 224) | |
| preprocessed_images_file = os.path.join(directory, 'preprocessed_images.pkl') | |
| labels_file = os.path.join(directory, 'labels.pkl') | |
| filenames_file = os.path.join(directory, 'filenames.pkl') | |
| # Check if preprocessed images, labels and filenames already exist | |
| if os.path.exists(preprocessed_images_file) and os.path.exists(labels_file) and os.path.exists(filenames_file): | |
| # Load preprocessed images, labels and filenames from disk | |
| with open(preprocessed_images_file, 'rb') as f: | |
| images = pickle.load(f) | |
| with open(labels_file, 'rb') as f: | |
| labels = pickle.load(f) | |
| with open(filenames_file, 'rb') as f: | |
| filenames = pickle.load(f) | |
| else: | |
| # Preprocess images, labels and filenames and save them to disk | |
| images = [] | |
| labels = [] | |
| filenames = [] | |
| for filename in os.listdir(directory): | |
| if filename.endswith(".txt"): | |
| with open(os.path.join(directory, filename), "r", encoding="utf-8") as file: | |
| product = file.readline().strip().replace("Product: ", "") | |
| labels.append(product) | |
| if filename.endswith(".jpg") or filename.endswith(".JPG") or filename.endswith( | |
| ".jpeg") or filename.endswith(".png"): | |
| img_path = os.path.join(directory, filename) | |
| img = cv2.imread(img_path) | |
| img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) | |
| img = cv2.resize(img, image_size) | |
| img = img.astype("float32") / 255.0 # Normalize the pixel values | |
| images.append(img) | |
| filenames.append(filename) | |
| images = np.array(images) | |
| # Save preprocessed images, labels and filenames to disk | |
| with open(preprocessed_images_file, 'wb') as f: | |
| pickle.dump(images, f) | |
| with open(labels_file, 'wb') as f: | |
| pickle.dump(labels, f) | |
| with open(filenames_file, 'wb') as f: | |
| pickle.dump(filenames, f) | |
| return images, labels, filenames |