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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='Step-by-step on GitHub <a href="https://github.com/Ivanrs297/machine-learning-projects/blob/main/computer-vision/edge_detection/main.ipynb"> notebook </a> <br> ~ Ivanrs',
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) |