Update app.py

app.py

@@ -1,62 +1,35 @@
 import cv2
 import numpy as np
-import scipy as sp
-import scipy.sparse.linalg
+from numba import jit, prange
 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()
+@jit(nopython=True, parallel=True)
+def fast_poisson_sharpening(img, alpha):
+    img_h, img_w = img.shape
+    output = np.zeros_like(img)
     
-    im2var = np.arange(img_h * img_w).reshape(img_h, img_w) 
+    for y in prange(1, img_h - 1):
+        for x in range(1, img_w - 1):
+            laplacian = (
+                img[y-1, x] + img[y+1, x] + img[y, x-1] + img[y, x+1] - 4 * img[y, x]
+            )
+            output[y, x] = img[y, x] + alpha * laplacian
     
-    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)
+    return np.clip(output, 0, 1)
 
 def sharpen_image(input_img, alpha):
-    img = get_image(input_img)
+    img = cv2.cvtColor(input_img, cv2.COLOR_BGR2RGB).astype('float32') / 255.0
     
-    sharpen_img = np.zeros(img.shape)
+    sharpen_img = np.zeros_like(img)
     for b in range(3):
-        sharpen_img[:,:,b] = poisson_sharpening(img[:,:,b], alpha)
+        sharpen_img[:,:,b] = fast_poisson_sharpening(img[:,:,b], alpha)
     
     return (sharpen_img * 255).astype(np.uint8)
 
-# Create examples list using the images from the original code
+# Create examples list
 examples = [
-    ["img1.jpg", 9.0],
-    ["img2.PNG", 7.0],
+    ["img1.jpg", 0.5],
+    ["img2.PNG", 0.3],
 ]
 
 # Create the Gradio interface
@@ -64,10 +37,10 @@ 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)")
+        gr.Slider(minimum=0.1, maximum=1.0, step=0.01, value=0.5, label="Sharpening Strength (alpha)")
     ],
     outputs=gr.Image(label="Sharpened Image"),
-    title="Poisson Image Sharpening",
+    title="Fast 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,