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@takatakamanbou takatakamanbou/00.md Secret
Last active Jun 13, 2017

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#!/usr/bin/env python
# -*- coding: utf-8 -*-
import cv2
import numpy as np
# 画像を読み込む
img = cv2.imread( 'blackuni3.png' )
# img の画素値を反転 ==> 左右を反転
img2 = cv2.flip( 255 - img, 1 )
# img2 を 'Hoge' という名前のウインドウに表示
cv2.imshow( 'Hoge', img2 )
# 上記のウィンドウを選択して何かキーを押すまで待つ
cv2.waitKey( 0 )
# PNG形式の画像として保存する
cv2.imwrite( 'hoge.png', img2 )
#!/usr/bin/env python
# -*- coding: utf-8 -*-
from __future__ import print_function
from __future__ import division
import cv2
import numpy as np
# 画像 img に lines に格納されたパラメータの直線を重ねて描く
#
def drawLines( img, lines ):
for x in lines:
rho, theta = x[0]
# (rho, theta) の直線を画像の端までで描く
if np.pi/4 <= theta and theta <= 3*np.pi/4:
val = rho / np.sin( theta )
pt1 = ( 0, int( val ) )
pt2 = ( width, int( val - width / np.tan( theta ) ) )
else:
val = rho / np.cos( theta )
pt1 = ( int( val ), 0 )
pt2 = ( int( val - height * np.tan( theta ) ), height )
print( rho, theta, pt1, pt2 )
cv2.line( img, pt1, pt2, ( 0, 0, 255 ), thickness = 2 )
if __name__ == "__main__":
# Canny法の二つのしきい値 th1, th2 を変えると抽出される特徴点が変わり Hough 変換の結果も変わる
# Hough変換のしきい値 threshold を変えると検出される直線の数が変わる
FnSrc = 'kapibara640x480.jpg'
# 画像を読み込む
imgColor = cv2.imread( FnSrc )
height, width = imgColor.shape[0], imgColor.shape[1]
# グレイスケールに変換
imgGray = cv2.cvtColor( imgColor, cv2.COLOR_BGR2GRAY )
# Canny法でエッジを検出
th1, th2 = 100, 400 # 2つのしきい値
imgCanny = cv2.Canny( imgGray, th1, th2, apertureSize = 3 )
# Hough変換
rhoRes, thetaRes = 1, 1*np.pi/180 # (rho, theta)平面を分割する格子の間隔
threshold = 120 # 票数のしきい値
lines = cv2.HoughLines( imgCanny, rhoRes, thetaRes, threshold )
print( '( number of detected lines ) = ', lines.shape[0] )
# 元画像に検出した直線を重ねて描く
drawLines( imgColor, lines )
# 画像を表示
cv2.imshow( "imgCanny", imgCanny )
cv2.imshow( "imgColor", imgColor )
cv2.waitKey( 0 )
from __future__ import print_function
from __future__ import division
import cv2
import numpy as np
print( 'version of OpenCV:', cv2.__version__ )
print( 'version of NumPy:', np.__version__ )
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