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import cv2
import numpy as np
import scipy as sp
import scipy.sparse.linalg
import gradio as gr
def get_image(img):
return cv2.cvtColor(img, cv2.COLOR_BGR2RGB).astype('double') / 255.0
def neighbours(i, j, max_i, max_j):
pairs = []
for n in [-1, 1]:
if 0 <= i+n <= max_i:
pairs.append((i+n, j))
if 0 <= j+n <= max_j:
pairs.append((i, j+n))
return pairs
def poisson_sharpening(img, alpha):
img_h, img_w = img.shape[:2]
img_s = img.copy()
im2var = np.arange(img_h * img_w).reshape(img_h, img_w)
A = sp.sparse.lil_matrix((img_h*img_w*4*2, img_h*img_w))
b = np.zeros(img_h*img_w*4*2)
e = 0
for y in range(img_h):
for x in range(img_w):
A[e, im2var[y][x]] = 1
b[e] = img_s[y][x]
e += 1
for n_y, n_x in neighbours(y, x, img_h-1, img_w-1):
A[e, im2var[y][x]] = 1
A[e, im2var[n_y][n_x]] = -1
b[e] = alpha * (img_s[y][x] - img_s[n_y][n_x])
e += 1
A = sp.sparse.csr_matrix(A)
v = sp.sparse.linalg.lsqr(A, b)[0]
return np.clip(v.reshape(img_h, img_w), 0, 1)
def sharpen_image(input_img, alpha):
img = get_image(input_img)
sharpen_img = np.zeros(img.shape)
for b in range(3):
sharpen_img[:,:,b] = poisson_sharpening(img[:,:,b], alpha)
return (sharpen_img * 255).astype(np.uint8)
# Create examples list using the images from the original code
examples = [
["samples/img1.jpg", 9.0],
["samples/img2.jpg", 7.0],
]
# 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=0.1, default=9.0, 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() |