File size: 1,798 Bytes
4661f75 43c52cc 4661f75 43c52cc 4661f75 43c52cc 233c4fc 43c52cc 4661f75 43c52cc 4661f75 233c4fc 4661f75 43c52cc 4661f75 43c52cc 4661f75 43c52cc 233c4fc 43c52cc 4661f75 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 |
import gradio as gr
from tensorflow.keras.preprocessing.image import img_to_array, ImageDataGenerator
from PIL import Image
import numpy as np
import os
import zipfile
import tempfile
def augment_images(image_file, num_duplicates):
datagen = ImageDataGenerator(
rotation_range=40,
width_shift_range=0.2,
height_shift_range=0.2,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True,
fill_mode='nearest')
# Convert to RGB and resize the image
img = Image.open(image_file).convert('RGB')
img = img.resize((256, 256))
x = img_to_array(img) # Convert image to numpy array
x = x.reshape((1,) + x.shape) # Reshape for data generator
with tempfile.TemporaryDirectory() as temp_dir:
i = 0
for _ in datagen.flow(x, batch_size=1, save_to_dir=temp_dir, save_prefix='aug', save_format='jpeg'):
i += 1
if i >= num_duplicates:
break
# Zip the augmented images
zip_name = os.path.join(tempfile.gettempdir(), 'augmented_images.zip')
with zipfile.ZipFile(zip_name, 'w', zipfile.ZIP_DEFLATED) as zipf:
for root, _, files in os.walk(temp_dir):
for file in files:
zipf.write(os.path.join(root, file), arcname=file)
return zip_name
iface = gr.Interface(
fn=augment_images,
inputs=[
gr.Image(tool="editor", label="Upload Image"),
gr.Slider(minimum=1, maximum=20, default=5, label="Number of Augmented Samples")
],
outputs=gr.File(label="Download Augmented Images"),
title="Image Augmentation App",
description="Upload an image to generate augmented versions. Select the number of augmented duplicates you want for the image."
)
iface.launch()
|