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import cv2
import numpy as np
import scipy as sp
import scipy.sparse.linalg
from numba import njit, prange
import gradio as gr
@njit
def neighbours(y, x, max_y, max_x):
neighbors = []
if y > 0:
neighbors.append((y-1, x))
if y < max_y:
neighbors.append((y+1, x))
if x > 0:
neighbors.append((y, x-1))
if x < max_x:
neighbors.append((y, x+1))
return neighbors
@njit
def poisson_sharpening(img, alpha):
img_h, img_w = img.shape
img_s = img.copy()
im2var = np.arange(img_h * img_w).reshape(img_h, img_w)
A_data = np.zeros(img_h * img_w * 5, dtype=np.float64)
A_row = np.zeros(img_h * img_w * 5, dtype=np.int32)
A_col = np.zeros(img_h * img_w * 5, dtype=np.int32)
b = np.zeros(img_h * img_w * 5, dtype=np.float64)
return _poisson_sharpening_inner(img_s, alpha, im2var, A_data, A_row, A_col, b, img_h, img_w)
@njit(parallel=True)
def _poisson_sharpening_inner(img_s, alpha, im2var, A_data, A_row, A_col, b, img_h, img_w):
e = 0
for y in prange(img_h):
for x in range(img_w):
A_data[e] = 1
A_row[e] = e
A_col[e] = im2var[y, x]
b[e] = img_s[y, x]
e += 1
for n_y, n_x in neighbours(y, x, img_h-1, img_w-1):
A_data[e] = 1
A_row[e] = e
A_col[e] = im2var[y, x]
e += 1
A_data[e] = -1
A_row[e] = e - 1
A_col[e] = im2var[n_y, n_x]
b[e-1] = alpha * (img_s[y, x] - img_s[n_y, n_x])
e += 1
A = sp.sparse.csr_matrix((A_data[:e], (A_row[:e], A_col[:e])), shape=(e, img_h * img_w))
v = sp.sparse.linalg.lsqr(A, b[:e])[0]
return np.clip(v.reshape(img_h, img_w), 0, 1)
@njit(parallel=True)
def sharpen_image_channels(img, alpha):
sharpen_img = np.zeros_like(img)
for b in prange(3):
sharpen_img[:,:,b] = poisson_sharpening(img[:,:,b], alpha)
return sharpen_img
def get_image(img):
return cv2.cvtColor(img, cv2.COLOR_BGR2RGB).astype('float32') / 255.0
def sharpen_image(input_img, alpha):
img = get_image(input_img)
sharpen_img = sharpen_image_channels(img, alpha)
return (sharpen_img * 255).astype(np.uint8)
# Create examples list
examples = [
["img1.jpg", 9.0],
["img2.PNG", 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=0.01, value=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() |