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added some constants to python cv2 api

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This commit is contained in:
Alexander Mordvintsev 2011-06-06 14:18:25 +00:00
parent cd2f3786f0
commit 6dc7ae0ff6
4 changed files with 21 additions and 20 deletions
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8
modules/python/src2/cv2.cpp
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@ -856,6 +856,14 @@ void initcv2()
PUBLISH(GC_INIT_WITH_MASK); PUBLISH(GC_INIT_WITH_MASK);
PUBLISH(GC_EVAL); 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" #include "pyopencv_generated_const_reg.h"
} }

12
samples/python2/browse.py
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@ -16,14 +16,14 @@ if len(sys.argv) > 1:
img = cv2.imread(fn) img = cv2.imread(fn)
else: else:
sz = 4096 sz = 4096
print 'generating %dx%d precudural image ...' % (sz, sz) print 'generating %dx%d procudural image ...' % (sz, sz)
img = np.zeros((sz, sz), np.uint8) img = np.zeros((sz, sz), np.uint8)
track = np.cumsum(np.random.rand(1000000, 2)-0.5, axis=0) track = np.cumsum(np.random.rand(500000, 2)-0.5, axis=0)
track = np.int32(track*20 + (sz/2, sz/2)) track = np.int32(track*10 + (sz/2, sz/2))
cv2.polylines(img, [track], 0, 255, 1, cv.CV_AA) cv2.polylines(img, [track], 0, 255, 1, cv.CV_AA)
small = img small = img
for i in xrange(4): for i in xrange(3):
small = cv2.pyrDown(small) small = cv2.pyrDown(small)
def onmouse(event, x, y, flags, param): def onmouse(event, x, y, flags, param):
@ -33,6 +33,6 @@ def onmouse(event, x, y, flags, param):
zoom = cv2.getRectSubPix(img, (800, 600), (x+0.5, y+0.5)) zoom = cv2.getRectSubPix(img, (800, 600), (x+0.5, y+0.5))
cv2.imshow('zoom', zoom) cv2.imshow('zoom', zoom)
cv2.imshow('small', small) cv2.imshow('preview', small)
cv.SetMouseCallback('small', onmouse, None) cv.SetMouseCallback('preview', onmouse, None)
cv2.waitKey() cv2.waitKey()

4
samples/python2/gaussian_mix.py
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@ -1,6 +1,6 @@
import numpy as np import numpy as np
from numpy import random from numpy import random
import cv2, cv import cv2
def make_gaussians(cluster_n, img_size): def make_gaussians(cluster_n, img_size):
@ -22,7 +22,7 @@ def draw_gaussain(img, mean, cov, color):
w, u, vt = cv2.SVDecomp(cov) w, u, vt = cv2.SVDecomp(cov)
ang = np.rad2deg( np.arctan2(u[1, 0], u[0, 0]) ) ang = np.rad2deg( np.arctan2(u[1, 0], u[0, 0]) )
s1, s2 = np.sqrt(w)*3.0 s1, s2 = np.sqrt(w)*3.0
cv2.ellipse(img, (x, y), (s1, s2), ang, 0, 360, color, 1, cv.CV_AA) cv2.ellipse(img, (x, y), (s1, s2), ang, 0, 360, color, 1, cv2.CV_AA)
if __name__ == '__main__': if __name__ == '__main__':

17
samples/python2/letter_recog.py
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@ -6,13 +6,6 @@ def load_base(fn):
samples, responses = a[:,1:], a[:,0] samples, responses = a[:,1:], a[:,0]
return samples, responses return samples, responses
# TODO move these to cv2
CV_ROW_SAMPLE = 1
CV_VAR_NUMERICAL = 0
CV_VAR_ORDERED = 0
CV_VAR_CATEGORICAL = 1
class LetterStatModel(object): class LetterStatModel(object):
train_ratio = 0.5 train_ratio = 0.5
def load(self, fn): def load(self, fn):
@ -26,10 +19,10 @@ class RTrees(LetterStatModel):
def train(self, samples, responses): def train(self, samples, responses):
sample_n, var_n = samples.shape sample_n, var_n = samples.shape
var_types = np.array([CV_VAR_NUMERICAL] * var_n + [CV_VAR_CATEGORICAL], np.uint8) var_types = np.array([cv2.CV_VAR_NUMERICAL] * var_n + [cv2.CV_VAR_CATEGORICAL], np.uint8)
#CvRTParams(10,10,0,false,15,0,true,4,100,0.01f,CV_TERMCRIT_ITER)); #CvRTParams(10,10,0,false,15,0,true,4,100,0.01f,CV_TERMCRIT_ITER));
params = dict(max_depth=10 ) params = dict(max_depth=10 )
self.model.train(samples, CV_ROW_SAMPLE, responses, varType = var_types, params = params) self.model.train(samples, cv2.CV_ROW_SAMPLE, responses, varType = var_types, params = params)
def predict(self, samples): def predict(self, samples):
return np.float32( [self.model.predict(s) for s in samples] ) return np.float32( [self.model.predict(s) for s in samples] )
@ -56,10 +49,10 @@ class Boost(LetterStatModel):
sample_n, var_n = samples.shape sample_n, var_n = samples.shape
new_samples = self.unroll_samples(samples) new_samples = self.unroll_samples(samples)
new_responses = self.unroll_responses(responses) new_responses = self.unroll_responses(responses)
var_types = np.array([CV_VAR_NUMERICAL] * var_n + [CV_VAR_CATEGORICAL, CV_VAR_CATEGORICAL], np.uint8) var_types = np.array([cv2.CV_VAR_NUMERICAL] * var_n + [cv2.CV_VAR_CATEGORICAL, cv2.CV_VAR_CATEGORICAL], np.uint8)
#CvBoostParams(CvBoost::REAL, 100, 0.95, 5, false, 0 ) #CvBoostParams(CvBoost::REAL, 100, 0.95, 5, false, 0 )
params = dict(max_depth=5) #, use_surrogates=False) params = dict(max_depth=5) #, use_surrogates=False)
self.model.train(new_samples, CV_ROW_SAMPLE, new_responses, varType = var_types, params=params) self.model.train(new_samples, cv2.CV_ROW_SAMPLE, new_responses, varType = var_types, params=params)
def predict(self, samples): def predict(self, samples):
new_samples = self.unroll_samples(samples) new_samples = self.unroll_samples(samples)
@ -105,7 +98,7 @@ if __name__ == '__main__':
parser = argparse.ArgumentParser() parser = argparse.ArgumentParser()
parser.add_argument('-model', default='rtrees', choices=models.keys()) parser.add_argument('-model', default='rtrees', choices=models.keys())
parser.add_argument('-data', nargs=1, default='letter-recognition.data') parser.add_argument('-data', nargs=1, default='../cpp/letter-recognition.data')
parser.add_argument('-load', nargs=1) parser.add_argument('-load', nargs=1)
parser.add_argument('-save', nargs=1) parser.add_argument('-save', nargs=1)
args = parser.parse_args() args = parser.parse_args()