Spaces:
Running
Running
File size: 2,935 Bytes
38d65f9 0f5237c 6ebce6b 38d65f9 6ebce6b 0f5237c 6ebce6b 1d65b9f 0f5237c 6ebce6b 0f5237c 1d65b9f 0f5237c 6ebce6b 1d65b9f 0f5237c 6ebce6b 1d65b9f 0f5237c 38d65f9 1d65b9f 0f5237c 6ebce6b 0f5237c 38d65f9 0f5237c 6ebce6b 0f5237c 38d65f9 04fbf12 38d65f9 0f5237c 38d65f9 0f5237c 38d65f9 0f5237c 38d65f9 |
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 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 |
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 build_poisson_matrix(img, alpha, img_h, img_w):
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)
e = 0
for y in range(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[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[y, x] - img[n_y, n_x])
e += 1
return A_data[:e], A_row[:e], A_col[:e], b[:e], e
def poisson_sharpening(img: np.ndarray, alpha: float) -> np.ndarray:
img_h, img_w = img.shape
A_data, A_row, A_col, b, e = build_poisson_matrix(img, alpha, img_h, img_w)
A = sp.sparse.csr_matrix((A_data, (A_row, A_col)), shape=(e, img_h * img_w))
v = sp.sparse.linalg.lsqr(A, b)[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() |