Create app.py

app.py

@@ -0,0 +1,132 @@
+import gradio as gr
+import cv2
+import matplotlib.pyplot as plt
+from scipy import ndimage
+from scipy.ndimage.filters import convolve
+import numpy as np
+
+def hysteresis(img, weak = 75, strong=255):
+    M, N = img.shape  
+    for i in range(1, M-1):
+        for j in range(1, N-1):
+            if (img[i,j] == weak):
+                try:
+                    if ((img[i+1, j-1] == strong) or (img[i+1, j] == strong) or (img[i+1, j+1] == strong)
+                        or (img[i, j-1] == strong) or (img[i, j+1] == strong)
+                        or (img[i-1, j-1] == strong) or (img[i-1, j] == strong) or (img[i-1, j+1] == strong)):
+                        img[i, j] = strong
+                    else:
+                        img[i, j] = 0
+                except IndexError as e:
+                    pass
+    return img
+
+def threshold(img, lowThresholdRatio=0.05, highThresholdRatio=0.09):
+    
+    highThreshold = img.max() * highThresholdRatio;
+    lowThreshold = highThreshold * lowThresholdRatio;
+    
+    M, N = img.shape
+    res = np.zeros((M,N), dtype=np.int32)
+    
+    weak = np.int32(25)
+    strong = np.int32(255)
+    
+    strong_i, strong_j = np.where(img >= highThreshold)
+    zeros_i, zeros_j = np.where(img < lowThreshold)
+    
+    weak_i, weak_j = np.where((img <= highThreshold) & (img >= lowThreshold))
+    
+    res[strong_i, strong_j] = strong
+    res[weak_i, weak_j] = weak
+    
+    return (res)
+
+def non_max_suppression(img, D):
+    M, N = img.shape
+    Z = np.zeros((M,N), dtype=np.int32)
+    angle = D * 180. / np.pi
+    angle[angle < 0] += 180
+
+    
+    for i in range(1,M-1):
+        for j in range(1,N-1):
+            try:
+                q = 255
+                r = 255
+                
+               #angle 0
+                if (0 <= angle[i,j] < 22.5) or (157.5 <= angle[i,j] <= 180):
+                    q = img[i, j+1]
+                    r = img[i, j-1]
+                #angle 45
+                elif (22.5 <= angle[i,j] < 67.5):
+                    q = img[i+1, j-1]
+                    r = img[i-1, j+1]
+                #angle 90
+                elif (67.5 <= angle[i,j] < 112.5):
+                    q = img[i+1, j]
+                    r = img[i-1, j]
+                #angle 135
+                elif (112.5 <= angle[i,j] < 157.5):
+                    q = img[i-1, j-1]
+                    r = img[i+1, j+1]
+
+                if (img[i,j] >= q) and (img[i,j] >= r):
+                    Z[i,j] = img[i,j]
+                else:
+                    Z[i,j] = 0
+
+            except IndexError as e:
+                pass
+    
+    return Z
+
+def gaussian_kernel(size, sigma=1):
+    size = int(size) // 2
+    x, y = np.mgrid[-size:size+1, -size:size+1]
+    normal = 1 / (2.0 * np.pi * sigma**2)
+    g =  np.exp(-((x**2 + y**2) / (2.0*sigma**2))) * normal
+    return g
+
+def sobel_filters(img):
+    Kx = np.array([[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]], np.float32)
+    Ky = np.array([[1, 2, 1], [0, 0, 0], [-1, -2, -1]], np.float32)
+    
+    Ix = ndimage.filters.convolve(img, Kx)
+    Iy = ndimage.filters.convolve(img, Ky)
+    
+    G = np.hypot(Ix, Iy)
+    G = G / G.max() * 255
+    theta = np.arctan2(Iy, Ix)
+    
+    return (G, theta)
+
+def canny(img, kernel, sigma):
+    img_color = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+    img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+
+    img_gaussian = convolve(img_gray, gaussian_kernel(kernel, sigma))
+    G, theta = sobel_filters(img_gaussian)
+    img_nonmax = non_max_suppression(G, theta)
+    img_threshold = threshold(img_nonmax)
+    img_final = hysteresis(img_threshold)
+
+    return img_final
+
+
+interface = gr.Interface(
+    title = "Canny Edge Detector 🤖",
+    description = "<h3>The Canny edge detector is an edge detection operator that uses a multi-stage algorithm to detect a wide range of edges in images.</h3> <br> <b>Select an image 🖼</b>",
+    article='<a href="#"> Hello </a>',
+    allow_flagging = "never",
+    fn = canny, 
+    inputs = [
+        gr.Image(),
+        gr.Slider(1, 9, step = 1, value=3, label = "Kernel Size"),
+        gr.Slider(1, 20, step = 5, value=10, label = "Sigma"),
+    ],
+    outputs = "image"
+)
+
+interface.launch(share = False)