added some constants to python cv2 api

diff --git a/modules/python/src2/cv2.cpp b/modules/python/src2/cv2.cpp
index 511a3f0..e9c2aa4 100644 (file)
--- a/modules/python/src2/cv2.cpp
+++ b/modules/python/src2/cv2.cpp
@@ -856,6 +856,14 @@ void initcv2()
   PUBLISH(GC_INIT_WITH_MASK);
   PUBLISH(GC_EVAL);
 
+  PUBLISH(CV_ROW_SAMPLE);
+  PUBLISH(CV_VAR_NUMERICAL);
+  PUBLISH(CV_VAR_ORDERED);
+  PUBLISH(CV_VAR_CATEGORICAL);
+
+  PUBLISH(CV_AA);
+
+
 #include "pyopencv_generated_const_reg.h"
 }
 

diff --git a/samples/python2/browse.py b/samples/python2/browse.py
index ed2353f..721fbaa 100644 (file)
--- a/samples/python2/browse.py
+++ b/samples/python2/browse.py
@@ -16,14 +16,14 @@ if len(sys.argv) > 1:
     img = cv2.imread(fn)\r
 else:\r
     sz = 4096\r
-    print 'generating %dx%d precudural image ...' % (sz, sz)\r
+    print 'generating %dx%d procudural image ...' % (sz, sz)\r
     img = np.zeros((sz, sz), np.uint8)\r
-    track = np.cumsum(np.random.rand(1000000, 2)-0.5, axis=0)\r
-    track = np.int32(track*20 + (sz/2, sz/2))\r
+    track = np.cumsum(np.random.rand(500000, 2)-0.5, axis=0)\r
+    track = np.int32(track*10 + (sz/2, sz/2))\r
     cv2.polylines(img, [track], 0, 255, 1, cv.CV_AA)\r
 \r
 small = img\r
-for i in xrange(4):\r
+for i in xrange(3):\r
     small = cv2.pyrDown(small)\r
 \r
 def onmouse(event, x, y, flags, param):\r
@@ -33,6 +33,6 @@ def onmouse(event, x, y, flags, param):
     zoom = cv2.getRectSubPix(img, (800, 600), (x+0.5, y+0.5))\r
     cv2.imshow('zoom', zoom)\r
 \r
-cv2.imshow('small', small)\r
-cv.SetMouseCallback('small', onmouse, None)\r
+cv2.imshow('preview', small)\r
+cv.SetMouseCallback('preview', onmouse, None)\r
 cv2.waitKey()\r

diff --git a/samples/python2/gaussian_mix.py b/samples/python2/gaussian_mix.py
index 6f08e88..d7aaa21 100644 (file)
--- a/samples/python2/gaussian_mix.py
+++ b/samples/python2/gaussian_mix.py
@@ -1,6 +1,6 @@
 import numpy as np\r
 from numpy import random\r
-import cv2, cv\r
+import cv2\r
 \r
 \r
 def make_gaussians(cluster_n, img_size):\r
@@ -22,7 +22,7 @@ def draw_gaussain(img, mean, cov, color):
     w, u, vt = cv2.SVDecomp(cov)\r
     ang = np.rad2deg( np.arctan2(u[1, 0], u[0, 0]) )\r
     s1, s2 = np.sqrt(w)*3.0\r
-    cv2.ellipse(img, (x, y), (s1, s2), ang, 0, 360, color, 1, cv.CV_AA)\r
+    cv2.ellipse(img, (x, y), (s1, s2), ang, 0, 360, color, 1, cv2.CV_AA)\r
 \r
 \r
 if __name__ == '__main__':\r

diff --git a/samples/python2/letter_recog.py b/samples/python2/letter_recog.py
index efd4297..e0fabc5 100644 (file)
--- a/samples/python2/letter_recog.py
+++ b/samples/python2/letter_recog.py
@@ -6,13 +6,6 @@ def load_base(fn):
     samples, responses = a[:,1:], a[:,0]\r
     return samples, responses\r
 \r
-# TODO move these to cv2\r
-CV_ROW_SAMPLE = 1\r
-CV_VAR_NUMERICAL   = 0\r
-CV_VAR_ORDERED     = 0\r
-CV_VAR_CATEGORICAL = 1\r
-\r
-\r
 class LetterStatModel(object):\r
     train_ratio = 0.5\r
     def load(self, fn):\r
@@ -26,10 +19,10 @@ class RTrees(LetterStatModel):
 \r
     def train(self, samples, responses):\r
         sample_n, var_n = samples.shape\r
-        var_types = np.array([CV_VAR_NUMERICAL] * var_n + [CV_VAR_CATEGORICAL], np.uint8)\r
+        var_types = np.array([cv2.CV_VAR_NUMERICAL] * var_n + [cv2.CV_VAR_CATEGORICAL], np.uint8)\r
         #CvRTParams(10,10,0,false,15,0,true,4,100,0.01f,CV_TERMCRIT_ITER));\r
         params = dict(max_depth=10 )\r
-        self.model.train(samples, CV_ROW_SAMPLE, responses, varType = var_types, params = params)\r
+        self.model.train(samples, cv2.CV_ROW_SAMPLE, responses, varType = var_types, params = params)\r
 \r
     def predict(self, samples):\r
         return np.float32( [self.model.predict(s) for s in samples] )\r
@@ -56,10 +49,10 @@ class Boost(LetterStatModel):
         sample_n, var_n = samples.shape\r
         new_samples = self.unroll_samples(samples)\r
         new_responses = self.unroll_responses(responses)\r
-        var_types = np.array([CV_VAR_NUMERICAL] * var_n + [CV_VAR_CATEGORICAL, CV_VAR_CATEGORICAL], np.uint8)\r
+        var_types = np.array([cv2.CV_VAR_NUMERICAL] * var_n + [cv2.CV_VAR_CATEGORICAL, cv2.CV_VAR_CATEGORICAL], np.uint8)\r
         #CvBoostParams(CvBoost::REAL, 100, 0.95, 5, false, 0 )\r
         params = dict(max_depth=5) #, use_surrogates=False)\r
-        self.model.train(new_samples, CV_ROW_SAMPLE, new_responses, varType = var_types, params=params)\r
+        self.model.train(new_samples, cv2.CV_ROW_SAMPLE, new_responses, varType = var_types, params=params)\r
 \r
     def predict(self, samples):\r
         new_samples = self.unroll_samples(samples)\r
@@ -105,7 +98,7 @@ if __name__ == '__main__':
     \r
     parser = argparse.ArgumentParser()\r
     parser.add_argument('-model', default='rtrees', choices=models.keys())\r
-    parser.add_argument('-data', nargs=1, default='letter-recognition.data')\r
+    parser.add_argument('-data', nargs=1, default='../cpp/letter-recognition.data')\r
     parser.add_argument('-load', nargs=1)\r
     parser.add_argument('-save', nargs=1)\r
     args = parser.parse_args()\r