Sepia Filter

Runnable examples for the frozen Gradio Lite 5.45.0 browser runtime.

Apply a NumPy color transform to an image in the browser.

This coding playground loads a sample image from scikit-learn and applies a sepia matrix with NumPy. Click Run, then submit the sample or upload another image.

Code

import numpy as np
import gradio as gr
from sklearn.datasets import load_sample_images

# Get started with a sample image
sample_image = load_sample_images().images[0]

def sepia(input_img):
    sepia_filter = np.array([
        [0.393, 0.769, 0.189],
        [0.349, 0.686, 0.168],
        [0.272, 0.534, 0.131]
    ])
    sepia_img = input_img.dot(sepia_filter.T)
    sepia_img /= sepia_img.max()
    return sepia_img




demo = gr.Interface(
    sepia,
    gr.Image(
        value=sample_image,
        label="Input Image",
    ),
    gr.Image(label="Sepia Output"),
    flagging_mode="never",
)

demo.launch()

import numpy as np import gradio as gr from sklearn.datasets import load_sample_images # Get started with a sample image sample_image = load_sample_images().images[0] def sepia(input_img): sepia_filter = np.array([ [0.393, 0.769, 0.189], [0.349, 0.686, 0.168], [0.272, 0.534, 0.131] ]) sepia_img = input_img.dot(sepia_filter.T) sepia_img /= sepia_img.max() return sepia_img demo = gr.Interface( sepia, gr.Image( value=sample_image, label="Input Image", ), gr.Image(label="Sepia Output"), flagging_mode="never", ) demo.launch()