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# Define basic float and int types to patch sctypes without numpy
if not hasattr(__builtins__, 'sctypes'):
sctypes = {
"float": [float],
"int": [int],
"uint": [int] # Handle 'uint' as normal 'int' since it's not natively supported without numpy
}
# Now, import imgaug after patching
import imgaug.augmenters as iaa
import cv2
import matplotlib.pyplot as plt
import gradio as gr
def augment_image(image, flip, rotate, brightness, noise_scale, elastic_alpha, elastic_sigma):
# Ensure the image is a NumPy array (required by imgaug)
image = np.array(image)
# Apply augmentations based on user inputs
if flip:
flip_aug = iaa.Fliplr(1.0) # flips horizontally 100% of the time
image = flip_aug.augment_image(image)
rotate_aug = iaa.Affine(rotate=rotate) # Rotate by specified degrees
image = rotate_aug.augment_image(image)
brightness_aug = iaa.Multiply(brightness) # Adjust brightness
image = brightness_aug.augment_image(image)
noise_aug = iaa.AdditiveGaussianNoise(scale=(noise_scale)) # Gaussian noise
image = noise_aug.augment_image(image)
elastic_aug = iaa.ElasticTransformation(alpha=elastic_alpha, sigma=elastic_sigma) # Elastic transformation
image = elastic_aug.augment_image(image)
return image
def gradio_interface(image, flip, rotate, brightness, noise_scale, elastic_alpha, elastic_sigma):
augmented_image = augment_image(image, flip, rotate, brightness, noise_scale, elastic_alpha, elastic_sigma)
return augmented_image
# Gradio UI
inputs = [
gr.Image(type="pil"), # Image input
gr.Checkbox(label="Flip Image Horizontally"), # Flip input
gr.Slider(minimum=-180, maximum=180, step=1, value=0, label="Rotate Image (degrees)"), # Rotation input
gr.Slider(minimum=0.1, maximum=2.0, step=0.1, value=1.0, label="Adjust Brightness"), # Brightness input
gr.Slider(minimum=0, maximum=100, step=1, value=10, label="Gaussian Noise Scale"), # Noise input
gr.Slider(minimum=0, maximum=200, step=10, value=100, label="Elastic Transformation Alpha"), # Elastic Alpha input
gr.Slider(minimum=0.1, maximum=10.0, step=0.1, value=3.0, label="Elastic Transformation Sigma") # Elastic Sigma input
]
iface = gr.Interface(
fn=gradio_interface,
inputs=inputs,
outputs=gr.Image(type="numpy"),
title="Image Augmentation Demo",
description="Try out different data augmentation techniques on your image.",
)
iface.launch()
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