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Update app.py
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import numpy as np
import cv2
import matplotlib.pyplot as plt
from numba import jit
import gradio as gr
@jit(nopython=True, parallel=True)
def poisson_sharpening_rgb(image, alpha):
height, width, channels = image.shape
sharpened = np.zeros_like(image, dtype=np.float32)
for c in range(channels):
for i in range(height):
for j in range(width):
# Compute indices for neighboring pixels
left = max(0, j - 1)
right = min(width - 1, j + 1)
top = max(0, i - 1)
bottom = min(height - 1, i + 1)
# Compute differences with neighboring pixels
diff_left = float(image[i, j, c]) - float(image[i, left, c])
diff_right = float(image[i, j, c]) - float(image[i, right, c])
diff_top = float(image[i, j, c]) - float(image[top, j, c])
diff_bottom = float(image[i, j, c]) - float(image[bottom, j, c])
# Compute sharpened pixel value
sharpened[i, j, c] = min(max(
float(image[i, j, c]) + alpha * (diff_left + diff_right + diff_top + diff_bottom),
0.0), 255.0)
return sharpened.astype(np.uint8)
def sharpen_image(image, alpha):
# Ensure the image is in RGB format
if image.shape[2] == 4: # If RGBA, convert to RGB
image = cv2.cvtColor(image, cv2.COLOR_RGBA2RGB)
elif len(image.shape) == 2: # If grayscale, convert to RGB
image = cv2.cvtColor(image, cv2.COLOR_GRAY2RGB)
# Apply sharpening
sharpened = poisson_sharpening_rgb(image, alpha)
return sharpened
# Create examples list
examples = [
["img1.jpg", 2.0],
["img2.PNG", 2.0],
]
# Create the Gradio interface
iface = gr.Interface(
fn=sharpen_image,
inputs=[
gr.Image(label="Input Image", type="numpy"),
gr.Slider(minimum=1.0, maximum=15.0, step=0.01, value=2.0, label="Sharpening Strength (alpha)")
],
outputs=gr.Image(label="Sharpened Image"),
title="Poisson Image Sharpening",
description="Upload an image or choose from the examples, then adjust the sharpening strength to enhance edges and details.",
theme='bethecloud/storj_theme',
examples=examples,
cache_examples=True
)
iface.launch()