import numpy as np
import skimage.io
import cv2
def img2edges(imgFile, outFile):
img = skimage.io.imread(imgFile, as_gray=False)
# Blur the image with a Gaussian kernel
kernel_size = (5, 5)
img_blurred = img.copy() if kernel_size == (0,0) else cv2.GaussianBlur(img.copy(), kernel_size, 0)
# Find the edges from the blurred image
edges = np.max(np.array([edgedetect(img_blurred[:, :, 0]),
edgedetect(img_blurred[:, :, 1]),
edgedetect(img_blurred[:, :, 2])]), axis=0) # Sobel edge detection
# Process edges by zero-ing out pixels less than the mean
edges_zeroed = edges.copy()
edges_zeroed[edges.copy() <= np.mean(edges.copy())] = 0 # zero out pixels less than mean
img_edges = edges_zeroed
# Save
skimage.io.imsave(outFile, img_edges)
# edgedetect:
def edgedetect(channel):
sobelX = cv2.Sobel(channel, cv2.CV_16S, 1, 0)
sobelY = cv2.Sobel(channel, cv2.CV_16S, 0, 1)
sobel = np.hypot(sobelX, sobelY)
sobel[sobel > 255] = 255 # Some values seem to go above 255. However RGB channels has to be within 0-255
return sobel
