本文整理汇总了Python中cv2.KNearest方法的典型用法代码示例。如果您正苦于以下问题：Python cv2.KNearest方法的具体用法？Python cv2.KNearest怎么用？Python cv2.KNearest使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类cv2的用法示例。
在下文中一共展示了cv2.KNearest方法的5个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: __init__
# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import KNearest [as 别名]
def __init__(self):
collect_dir = 'captcha/collect'
label = []
train_file = []
for i in os.listdir(collect_dir):
for y in os.listdir(collect_dir + '/' + i):
#print i
label.append(ord(i))
#print y
train_file.append(collect_dir + '/' + i + '/' + y)
train_data = [cv2.imread(i, 0) for i in train_file]
train = np.array(train_data).reshape(-1, 400).astype(np.float32)
label = np.array(label).reshape(-1)
self.knn = cv2.KNearest()
self.knn.train(train, label) 示例2: __init__
# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import KNearest [as 别名]
def __init__(self, k = 3):
self.k = k
self.model = cv2.KNearest() 示例3: train
# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import KNearest [as 别名]
def train(self, samples, responses):
self.model = cv2.KNearest()
self.model.train(samples, responses) 示例4: __init__
# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import KNearest [as 别名]
def __init__(self):
self.model = cv2.KNearest() 示例5: main
# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import KNearest [as 别名]
def main():
opencv_haystack =cv2.imread('adam.jpg')
opencv_needle = cv2.imread('adam_rightnostril.jpg')
ngrey = cv2.cvtColor(opencv_needle, cv2.COLOR_BGR2GRAY)
hgrey = cv2.cvtColor(opencv_haystack, cv2.COLOR_BGR2GRAY)
import pdb
pdb.set_trace()
# build feature detector and descriptor extractor
hessian_threshold = 175
detector = cv2.SURF(hessian_threshold)
(hkeypoints, hdescriptors) = detector.detect(hgrey, None, useProvidedKeypoints = False)
(nkeypoints, ndescriptors) = detector.detect(ngrey, None, useProvidedKeypoints = False)
# extract vectors of size 64 from raw descriptors numpy arrays
rowsize = len(hdescriptors) / len(hkeypoints)
if rowsize > 1:
hrows = numpy.array(hdescriptors, dtype = numpy.float32).reshape((-1, rowsize))
nrows = numpy.array(ndescriptors, dtype = numpy.float32).reshape((-1, rowsize))
print "haystack rows shape", hrows.shape
print "needle rows shape", nrows.shape
else:
print '*****************************************************8888'
hrows = numpy.array(hdescriptors, dtype = numpy.float32)
nrows = numpy.array(ndescriptors, dtype = numpy.float32)
rowsize = len(hrows[0])
# kNN training - learn mapping from hrow to hkeypoints index
samples = hrows
responses = numpy.arange(len(hkeypoints), dtype = numpy.float32)
print "sample length", len(samples), "response length", len(responses)
knn = cv2.KNearest()
knn.train(samples,responses)
# retrieve index and value through enumeration
for i, descriptor in enumerate(nrows):
descriptor = numpy.array(descriptor, dtype = numpy.float32).reshape((1, rowsize))
print i, 'descriptor shape', descriptor.shape, 'sample shape', samples[0].shape
retval, results, neigh_resp, dists = knn.find_nearest(descriptor, 1)
res, dist = int(results[0][0]), dists[0][0]
print 'result', res, 'distance', dist
if dist < 0.1:
# draw matched keypoints in red color
color = (0, 0, 255)
else:
# draw unmatched in blue color
color = (255, 0, 0)
# draw matched key points on haystack image
x,y = hkeypoints[res].pt
center = (int(x),int(y))
cv2.circle(opencv_haystack,center,2,color,-1)
# draw matched key points on needle image
x,y = nkeypoints[i].pt
center = (int(x),int(y))
cv2.circle(opencv_needle,center,2,color,-1)
cv2.imshow('haystack',opencv_haystack)
cv2.imshow('needle',opencv_needle)
cv2.waitKey(0)
cv2.destroyAllWindows()