#!--*-- coding=utf-8 --*--
import cv2
import os
import argparse
'''
基于OpenCV Canny 算子的提取图像边缘
'''
#
def edge_extract(in_file, out_file, lowThreshold=10, Gaussianblur_size=19):
'''
:param in_file:
:param out_file:
:param lowThreshold: 阈值, 如：low - 10; medium - 23; high - 60
'''
img = cv2.imread(in_file)
if img is None:
return 'Please check input image.'
# 对BGR三个通道分别检测边缘
grayB = img[:, :, 0]
grayG = img[:, :, 1]
grayR = img[:, :, 2]
# 高斯模糊过滤掉部分细节
blurB = cv2.GaussianBlur(grayB, (Gaussianblur_size, Gaussianblur_size), 0)
blurG = cv2.GaussianBlur(grayG, (Gaussianblur_size, Gaussianblur_size), 0)
blurR = cv2.GaussianBlur(grayR, (Gaussianblur_size, Gaussianblur_size), 0)
# opencv Canny算法
highThreshold = lowThreshold * 2 # Canny算子的阈值上限
edges_B = cv2.Canny(blurB, lowThreshold, highThreshold, apertureSize=3)
edges_G = cv2.Canny(blurG, lowThreshold, highThreshold, apertureSize=3)
edges_R = cv2.Canny(blurR, lowThreshold, highThreshold, apertureSize=3)
# 3个通道分别提取的边缘融合到一块
edge = cv2.bitwise_or(edges_B, edges_G)
edge = cv2.bitwise_or(edge, edges_R)
# 默认生成的是黑底白边缘线, 故黑白反转,使背景为白色,边缘线为黑色
edge = 255 - edge
cv2.imwrite(out_file, edge)
def parse_args():
"""Parse input arguments."""
parser = argparse.ArgumentParser()
parser.add_argument('-i', dest='input')
parser.add_argument('-o', dest='output')
args = parser.parse_args()
return args
if __name__ == '__main__':
args = parse_args()
edge_extract(args.input, args.output)
print('[INFO]Done.')
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