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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_digits.py
+14 -14
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@@ -28,7 +28,7 @@ from __future__ import print_function
# built-in modules
from multiprocessing.pool import ThreadPool
import cv2
import cv2 as cv
import numpy as np
from numpy.linalg import norm
@@ -48,12 +48,12 @@ def split2d(img, cell_size, flatten=True):
return cells
def deskew(img):
m = cv2.moments(img)
m = cv.moments(img)
if abs(m['mu02']) < 1e-2:
return img.copy()
skew = m['mu11']/m['mu02']
M = np.float32([[1, skew, -0.5*SZ*skew], [0, 1, 0]])
img = cv2.warpAffine(img, M, (SZ, SZ), flags=cv2.WARP_INVERSE_MAP | cv2.INTER_LINEAR)
img = cv.warpAffine(img, M, (SZ, SZ), flags=cv.WARP_INVERSE_MAP | cv.INTER_LINEAR)
return img
class StatModel(object):
@@ -65,10 +65,10 @@ class StatModel(object):
class KNearest(StatModel):
def __init__(self, k = 3):
self.k = k
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, self.k)
@@ -76,14 +76,14 @@ class KNearest(StatModel):
class SVM(StatModel):
def __init__(self, C = 1, gamma = 0.5):
self.model = cv2.ml.SVM_create()
self.model = cv.ml.SVM_create()
self.model.setGamma(gamma)
self.model.setC(C)
self.model.setKernel(cv2.ml.SVM_RBF)
self.model.setType(cv2.ml.SVM_C_SVC)
self.model.setKernel(cv.ml.SVM_RBF)
self.model.setType(cv.ml.SVM_C_SVC)
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):
return self.model.predict(samples)[1].ravel()
@@ -105,9 +105,9 @@ def preprocess_simple(digits):
def preprocess_hog(digits):
samples = []
for img in digits:
gx = cv2.Sobel(img, cv2.CV_32F, 1, 0)
gy = cv2.Sobel(img, cv2.CV_32F, 0, 1)
mag, ang = cv2.cartToPolar(gx, gy)
gx = cv.Sobel(img, cv.CV_32F, 1, 0)
gy = cv.Sobel(img, cv.CV_32F, 0, 1)
mag, ang = cv.cartToPolar(gx, gy)
bin_n = 16
bin = np.int32(bin_n*ang/(2*np.pi))
bin_cells = bin[:10,:10], bin[10:,:10], bin[:10,10:], bin[10:,10:]
@@ -190,8 +190,8 @@ class digits_test(NewOpenCVTests):
[ 0, 0, 0, 0, 0, 0, 0, 0, 47, 0],
[ 0, 1, 0, 1, 0, 0, 0, 0, 1, 45]]
self.assertLess(cv2.norm(confusionMatrixes[0] - confusionKNN, cv2.NORM_L1), normEps)
self.assertLess(cv2.norm(confusionMatrixes[1] - confusionSVM, cv2.NORM_L1), normEps)
self.assertLess(cv.norm(confusionMatrixes[0] - confusionKNN, cv.NORM_L1), normEps)
self.assertLess(cv.norm(confusionMatrixes[1] - confusionSVM, cv.NORM_L1), normEps)
self.assertLess(errors[0] - 0.034, eps)
self.assertLess(errors[1] - 0.018, eps)