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python: 'cv2.' -> 'cv.' via 'import cv2 as cv'

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This commit is contained in:
Alexander Alekhin
2017-12-11 12:55:03 +03:00
parent 9665dde678
commit 5560db73bf
162 changed files with 2083 additions and 2084 deletions
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modules/python/test/test_letter_recog.py
+17 -17
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@@ -24,7 +24,7 @@ and the remaining 10000 - to test the classifier.
from __future__ import print_function
import numpy as np
import cv2
import cv2 as cv
def load_base(fn):
a = np.loadtxt(fn, np.float32, delimiter=',', converters={ 0 : lambda ch : ord(ch)-ord('A') })
@@ -56,12 +56,12 @@ class LetterStatModel(object):
class RTrees(LetterStatModel):
def __init__(self):
self.model = cv2.ml.RTrees_create()
self.model = cv.ml.RTrees_create()
def train(self, samples, responses):
#sample_n, var_n = samples.shape
self.model.setMaxDepth(20)
self.model.train(samples, cv2.ml.ROW_SAMPLE, responses.astype(int))
self.model.train(samples, cv.ml.ROW_SAMPLE, responses.astype(int))
def predict(self, samples):
_ret, resp = self.model.predict(samples)
@@ -70,10 +70,10 @@ class RTrees(LetterStatModel):
class KNearest(LetterStatModel):
def __init__(self):
self.model = cv2.ml.KNearest_create()
self.model = cv.ml.KNearest_create()
def train(self, samples, responses):
self.model.train(samples, cv2.ml.ROW_SAMPLE, responses)
self.model.train(samples, cv.ml.ROW_SAMPLE, responses)
def predict(self, samples):
_retval, results, _neigh_resp, _dists = self.model.findNearest(samples, k = 10)
@@ -82,17 +82,17 @@ class KNearest(LetterStatModel):
class Boost(LetterStatModel):
def __init__(self):
self.model = cv2.ml.Boost_create()
self.model = cv.ml.Boost_create()
def train(self, samples, responses):
_sample_n, var_n = samples.shape
new_samples = self.unroll_samples(samples)
new_responses = self.unroll_responses(responses)
var_types = np.array([cv2.ml.VAR_NUMERICAL] * var_n + [cv2.ml.VAR_CATEGORICAL, cv2.ml.VAR_CATEGORICAL], np.uint8)
var_types = np.array([cv.ml.VAR_NUMERICAL] * var_n + [cv.ml.VAR_CATEGORICAL, cv.ml.VAR_CATEGORICAL], np.uint8)
self.model.setWeakCount(15)
self.model.setMaxDepth(10)
self.model.train(cv2.ml.TrainData_create(new_samples, cv2.ml.ROW_SAMPLE, new_responses.astype(int), varType = var_types))
self.model.train(cv.ml.TrainData_create(new_samples, cv.ml.ROW_SAMPLE, new_responses.astype(int), varType = var_types))
def predict(self, samples):
new_samples = self.unroll_samples(samples)
@@ -103,14 +103,14 @@ class Boost(LetterStatModel):
class SVM(LetterStatModel):
def __init__(self):
self.model = cv2.ml.SVM_create()
self.model = cv.ml.SVM_create()
def train(self, samples, responses):
self.model.setType(cv2.ml.SVM_C_SVC)
self.model.setType(cv.ml.SVM_C_SVC)
self.model.setC(1)
self.model.setKernel(cv2.ml.SVM_RBF)
self.model.setKernel(cv.ml.SVM_RBF)
self.model.setGamma(.1)
self.model.train(samples, cv2.ml.ROW_SAMPLE, responses.astype(int))
self.model.train(samples, cv.ml.ROW_SAMPLE, responses.astype(int))
def predict(self, samples):
_ret, resp = self.model.predict(samples)
@@ -119,7 +119,7 @@ class SVM(LetterStatModel):
class MLP(LetterStatModel):
def __init__(self):
self.model = cv2.ml.ANN_MLP_create()
self.model = cv.ml.ANN_MLP_create()
def train(self, samples, responses):
_sample_n, var_n = samples.shape
@@ -127,13 +127,13 @@ class MLP(LetterStatModel):
layer_sizes = np.int32([var_n, 100, 100, self.class_n])
self.model.setLayerSizes(layer_sizes)
self.model.setTrainMethod(cv2.ml.ANN_MLP_BACKPROP)
self.model.setTrainMethod(cv.ml.ANN_MLP_BACKPROP)
self.model.setBackpropMomentumScale(0)
self.model.setBackpropWeightScale(0.001)
self.model.setTermCriteria((cv2.TERM_CRITERIA_COUNT, 20, 0.01))
self.model.setActivationFunction(cv2.ml.ANN_MLP_SIGMOID_SYM, 2, 1)
self.model.setTermCriteria((cv.TERM_CRITERIA_COUNT, 20, 0.01))
self.model.setActivationFunction(cv.ml.ANN_MLP_SIGMOID_SYM, 2, 1)
self.model.train(samples, cv2.ml.ROW_SAMPLE, np.float32(new_responses))
self.model.train(samples, cv.ml.ROW_SAMPLE, np.float32(new_responses))
def predict(self, samples):
_ret, resp = self.model.predict(samples)