Python edge detection and curvature calculation

Question

I know the edge detection problem has been posted before (in Java: Count the number of objects in an Image, language independent: Image edge detection), but I want to know how t

Answer 1

There are different edge detectors you can use: Canny, Sobel, Laplacian, Scharr, Prewitt, Roberts. You can do it with OpenCV:

import cv2
import numpy as np

img = cv2.imread('your_image.jpg', 0)

# Canny
edges_canny = cv2.Canny(img, 100, 100)

# Sobel
sobel_x = cv2.Sobel(img, cv2.CV_64F, 1, 0, ksize=5)
sobel_y = cv2.Sobel(img, cv2.CV_64F, 0, 1, ksize=5)
edges_sobel = np.hypot(sobel_x, sobel_y)
edges_sobel *= 255.0 / np.max(edges_sobel)

# Laplacian
edges_laplacian = cv2.Laplacian(img, cv2.CV_64F)

# Scharr
schar_x = cv2.Scharr(img, cv2.CV_64F, 1, 0)
schar_y = cv2.Scharr(img, cv2.CV_64F, 0, 1)
edges_scharr = np.hypot(schar_x, schar_y)
edges_scharr *= 255.0 / np.max(edges_scharr)

or with scikit-image:

import cv2
from skimage import feature, filters

img = cv2.imread('your_image.jpg', 0)

edges_canny = feature.canny(img) # Canny
edges_sobel = filters.sobel(img) # Sobel
edges_laplace = filters.laplace(img) # Laplacian
edges_scharr = filters.scharr(img) # Scharr
edges_prewitt = filters.prewitt(img) # Prewitt
edges_roberts = filters.roberts(img) # Roberts

Canny edge detector is probably the most commonly used and most effective method, but also the most complex. For more details on what is the difference between the mentioned methods check this blog post.