Image preprocessing is a crucial step in preparing image data for neural networks. Proper preprocessing can significantly impact the performance of the model. Here’s a comprehensive guide on image preprocessing steps before feeding the data into a neural network:
1. Load and Resize Images:
Description: Ensure that all images are of the same size, which is necessary for most neural network architectures.
import cv2
import os
def load_and_resize_images(image_paths, target_size):
images = []
for path in image_paths:
img = cv2.imread(path)
img = cv2.resize(img, target_size)
images.append(img)
return images
# Example usage
image_paths = ["path/to/image1.jpg", "path/to/image2.jpg"]
target_size = (224, 224)
images = load_and_resize_images(image_paths, target_size)2. Data Augmentation:
Description: Generate augmented data by applying random transformations to increase the diversity of the training set and improve model generalization.
from keras.preprocessing.image import ImageDataGenerator
datagen = ImageDataGenerator(
rotation_range=40,
width_shift_range=0.2,
height_shift_range=0.2…
