update

add sift and hough

SIFT.py

@@ -0,0 +1,76 @@
+# coding: utf-8
+
+import cv2
+import scipy as sp
+import numpy as np
+import random
+
+
+
+
+def visKeyPoints(img1, points1) :
+    tmp = np.zeros(img1.shape)
+    tmp = img1
+    vis = cv2.merge((tmp, tmp, tmp))
+
+    for p in points1:
+        if( p.size < 5 ) : continue
+        #temp = (p.pt, p.size, p.angle, p.response, p.octave, p.class_id)
+        color = (random.uniform(1,255), random.uniform(1,255), random.uniform(1,255))
+        cv2.circle(vis, (int(p.pt[0]),int(p.pt[1])), int(p.size), color, 2)
+    return vis
+
+
+
+def visMatching(img1, img2, points1, points2) :
+    H1, W1 = img1.shape[:2]
+    H2, W2 = img2.shape[:2]
+
+    print(img1.shape)
+    print(img2.shape)
+
+    gray = sp.zeros( (max(H1, H2), (W1 + W2 + 30)), sp.uint8)
+    gray[ :H1,   :W1] = img1
+    gray[ :H2, W1+ 30:] = img2
+    visImg = cv2.merge((gray, gray, gray))
+
+    for i in range(len(points1)):
+        if( i % 2 != 0) : continue
+        color = (random.uniform(1,255), random.uniform(1,255), random.uniform(1,255))
+        p1 = ( int(points1[i][0]     ), int(points1[i][1] ) )
+        p2 = ( int(points2[i][0] + W1+30), int(points2[i][1] ) )
+        cv2.line(visImg, p1, p2, color)
+
+    return visImg
+
+
+
+
+
+img1 =  cv2.imread("imgs/book1.jpg", 0) 
+img2 =  cv2.imread("imgs/book3.jpg", 0) 
+
+#compute SIFT
+sift = cv2.xfeatures2d.SIFT_create()
+key1, des1 = sift.detectAndCompute (img1, None      )
+key2, des2 = sift.detectAndCompute (img2, None      )
+
+#matching and triming
+matches       = cv2.BFMatcher( cv2.NORM_L2 ).match(des1, des2)
+threshold     = np.array([m.distance for m in matches]).mean() * 1.2
+matches_trim  = [m for m in matches if m.distance < threshold]
+
+
+point1  = np.array( [ np.array( key1[m.queryIdx].pt) for m in matches_trim  ])
+point2  = np.array( [ np.array( key2[m.trainIdx].pt) for m in matches_trim  ])
+imgMatch= visMatching(img1, img2, point1, point2)
+
+imgVis1 = visKeyPoints( img1, key1 )
+imgVis2 = visKeyPoints( img2, key2 )
+
+
+# wait key input
+cv2.imshow("key points1",imgVis1  )
+cv2.imshow("key points2",imgVis2  )
+cv2.imshow("key match"  ,imgMatch )
+cv2.waitKey(0)

hough.py

@@ -0,0 +1,41 @@
+# coding: utf-8
+
+import cv2
+import numpy as np
+
+
+
+img_orig  =  cv2.imread  ("imgs/book1.jpg") 
+img_gray  =  cv2.cvtColor(img_orig, cv2.COLOR_BGR2GRAY)
+img_canny =  cv2.Canny   (img_gray, 50,150,apertureSize = 3)
+img_vis   =  cv2.merge   ((img_gray,img_gray,img_gray))
+
+
+ACCURACY_RHO   = 1
+ACCURACY_THETA = np.pi/180.0
+THRESH_VOTE    = 150
+
+hough_lines = cv2.HoughLines(img_canny, ACCURACY_RHO, ACCURACY_THETA, THRESH_VOTE)
+
+for line in hough_lines : 
+    for r,t in  line : 
+        #r = x cos t + y sin t
+        a = np.cos(t)
+        b = np.sin(t)
+        x0 = a*r
+        y0 = b*r
+        x1 = int(x0 + 1000*(-b))
+        y1 = int(y0 + 1000*( a))
+        x2 = int(x0 - 1000*(-b))
+        y2 = int(y0 - 1000*( a))
+
+        cv2.line(img_vis, (x1,y1), (x2,y2), (0,0,255),1)
+
+
+
+
+cv2.imshow("c", img_canny)
+cv2.imshow("a", img_vis )
+
+
+cv2.waitKey(0)