math.pi -> np.pi

squares.py sample added

samples/python2/gaussian_mix.py

 import numpy as np
-import math
 from numpy import random
 import cv2
 
@@ -20,7 +19,7 @@ def make_gaussians(cluster_n, img_size):
 def draw_gaussain(img, mean, cov, color):
     x, y = np.int32(mean)
     w, u, vt = cv2.SVDecomp(cov)
-    ang = np.arctan2(u[1, 0], u[0, 0])*(180/math.pi)
+    ang = np.arctan2(u[1, 0], u[0, 0])*(180/np.pi)
     s1, s2 = np.sqrt(w)*3.0
     cv2.ellipse(img, (x, y), (s1, s2), ang, 0, 360, color, 1, cv2.CV_AA)
 

samples/python2/lk_track.py

@@ -13,9 +13,6 @@ Keys:
   SPACE - reset features
 '''
 
-
-
-
 lk_params = dict( winSize  = (21, 21), 
                   maxLevel = 2, 
                   criteria = (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 0.03),
@@ -35,7 +32,7 @@ def calc_flow_old(img0, img1, p0):
     np.asarray(img1_cv)[:] = img1
     t = clock()
     features, status, error  = cv.CalcOpticalFlowPyrLK(img0_cv, img1_cv, None, None, p0, 
-        lk_params['winSize'], lk_params['maxLevel'], (cv.CV_TERMCRIT_EPS | cv.CV_TERMCRIT_ITER, 10, 0.03), 0, p0)
+        lk_params['winSize'], lk_params['maxLevel'], lk_params['criteria'], 0, p0)
     return np.float32(features), status, error, clock()-t
 
 def main():
@@ -60,7 +57,7 @@ def main():
                 p1,  st, err, dt = calc_flow_old(img0, img1, p0)
             else:
                 t = clock()
-                p1,  st, err = cv2.calcOpticalFlowPyrLK(img0, img1, p0, **lk_params)
+                p1,  st, err = cv2.calcOpticalFlowPyrLK(img0, img1, p0, None, **lk_params)
                 dt = clock()-t
             for tr, (x, y) in zip(tracks, p1.reshape(-1, 2)):
                 tr.append((x, y))

samples/python2/opt_flow.py

 import numpy as np
-import math
 import cv2
 import cv2.cv as cv
 import video
@@ -31,7 +30,7 @@ def draw_hsv(flow):
     ang = np.arctan2(fy, fx) + np.pi
     v = np.sqrt(fx*fx+fy*fy)
     hsv = np.zeros((h, w, 3), np.uint8)
-    hsv[...,0] = ang*(180/math.pi/2)
+    hsv[...,0] = ang*(180/np.pi/2)
     hsv[...,1] = 255
     hsv[...,2] = np.minimum(v*4, 255)
     bgr = cv2.cvtColor(hsv, cv.CV_HSV2BGR)
@@ -61,7 +60,7 @@ if __name__ == '__main__':
     while True:
         ret, img = cam.read()
         gray = cv2.cvtColor(img, cv.CV_BGR2GRAY)
-        flow = cv2.calcOpticalFlowFarneback(prevgray, gray, 0.5, 3, 15, 3, 5, 1.2, 0)
+        flow = cv2.calcOpticalFlowFarneback(prevgray, gray, None, 0.5, 3, 15, 3, 5, 1.2, 0)
         prevgray = gray
         
         cv2.imshow('flow', draw_flow(gray, flow))

samples/python2/squares.py

+import numpy as np
+import cv2
+
+
+def angle_cos(p0, p1, p2):
+    d1, d2 = p0-p1, p2-p1
+    return abs( np.dot(d1, d2) / np.sqrt( np.dot(d1, d1)*np.dot(d2, d2) ) )
+
+def find_squares(img):
+    img = cv2.GaussianBlur(img, (5, 5), 0)
+    squares = []
+    for gray in cv2.split(img):
+        for thrs in xrange(0, 255, 26):
+            if thrs == 0:
+                bin = cv2.Canny(gray, 0, 50, apertureSize=5)
+                bin = cv2.dilate(bin, None)
+            else:
+                retval, bin = cv2.threshold(gray, thrs, 255, cv2.THRESH_BINARY)
+            contours, hierarchy = cv2.findContours(bin, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
+            for cnt in contours:
+                cnt_len = cv2.arcLength(cnt, True)
+                cnt = cv2.approxPolyDP(cnt, 0.02*cnt_len, True)
+                if len(cnt) == 4 and cv2.contourArea(cnt) > 1000 and cv2.isContourConvex(cnt):
+                    cnt = cnt.reshape(-1, 2)
+                    max_cos = np.max([angle_cos( cnt[i], cnt[(i+1) % 4], cnt[(i+2) % 4] ) for i in xrange(4)])
+                    if max_cos < 0.3:
+                        squares.append(cnt)
+    return squares
+
+if __name__ == '__main__':
+    from glob import glob
+    for fn in glob('../cpp/pic*.png'):
+        img = cv2.imread(fn)
+        squares = find_squares(img)
+        cv2.drawContours( img, squares, -1, (0, 255, 0), 3 )
+        cv2.imshow('squares', img)
+        ch = cv2.waitKey()
+        if ch == 27:
+            break
\ No newline at end of file

samples/python2/watershed.py

@@ -52,7 +52,7 @@ class App:
                 print 'auto_update if', ['off', 'on'][self.auto_update]
             if ch in [ord('r'), ord('R')]:
                 self.markers[:] = 0
-                self.markers_vis[:] = self.img.copy()
+                self.markers_vis[:] = self.img
                 self.sketch.show()