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

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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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samples/python/digits.py
+18 -18
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@@ -30,7 +30,7 @@ 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
@@ -55,18 +55,18 @@ def split2d(img, cell_size, flatten=True):
def load_digits(fn):
print('loading "%s" ...' % fn)
digits_img = cv2.imread(fn, 0)
digits_img = cv.imread(fn, 0)
digits = split2d(digits_img, (SZ, SZ))
labels = np.repeat(np.arange(CLASS_N), len(digits)/CLASS_N)
return digits, labels
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):
@@ -78,10 +78,10 @@ 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)
@@ -89,14 +89,14 @@ 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()
@@ -116,7 +116,7 @@ def evaluate_model(model, digits, samples, labels):
vis = []
for img, flag in zip(digits, resp == labels):
img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
img = cv.cvtColor(img, cv.COLOR_GRAY2BGR)
if not flag:
img[...,:2] = 0
vis.append(img)
@@ -128,9 +128,9 @@ 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:]
@@ -163,7 +163,7 @@ if __name__ == '__main__':
samples = preprocess_hog(digits2)
train_n = int(0.9*len(samples))
cv2.imshow('test set', mosaic(25, digits[train_n:]))
cv.imshow('test set', mosaic(25, digits[train_n:]))
digits_train, digits_test = np.split(digits2, [train_n])
samples_train, samples_test = np.split(samples, [train_n])
labels_train, labels_test = np.split(labels, [train_n])
@@ -173,14 +173,14 @@ if __name__ == '__main__':
model = KNearest(k=4)
model.train(samples_train, labels_train)
vis = evaluate_model(model, digits_test, samples_test, labels_test)
cv2.imshow('KNearest test', vis)
cv.imshow('KNearest test', vis)
print('training SVM...')
model = SVM(C=2.67, gamma=5.383)
model.train(samples_train, labels_train)
vis = evaluate_model(model, digits_test, samples_test, labels_test)
cv2.imshow('SVM test', vis)
cv.imshow('SVM test', vis)
print('saving SVM as "digits_svm.dat"...')
model.save('digits_svm.dat')
cv2.waitKey(0)
cv.waitKey(0)