2017年robomaster比赛中，大神符环节使用的是
# decoding:utf-8
import os
import cv2
import numpy
as np
import codecs
from cv2.ml
import VAR_ORDERED
import codecs
from cv2.ml
import VAR_ORDERED
from canny
import *
from find_contours
import *
import numpy
as np
import cPickle
import gzip
def vectorized_result(j
):
e
= np.zeros
((10,
1))
e
[j
] = 1.0
return e
def load_data():
mnist
= gzip.open
(os.path.join
('data',
'mnist.pkl.gz'),
'rb')
training_data, classification_data, test_data
= cPickle.load
(mnist
)
mnist.close
()
return training_data, classification_data, test_data
def wrap_data():
tr_d, va_d, te_d
= load_data
()
# print type(tr_d), type(va_d), type(te_d)
training_inputs
= [np.reshape
(x,
(784,
1)) for x
in tr_d
[0]]
training_results
= [vectorized_result
(y
) for y
in tr_d
[1]]
training_data
= zip(training_inputs, training_results
)
validation_inputs
= [np.reshape
(x,
(784,
1)) for x
in va_d
[0]]
validation_data
= zip(validation_inputs, va_d
[1])
test_input
= [np.reshape
(x,
(784,
1)) for x
in te_d
[0]]
test_data
= zip(test_input, te_d
[1])
return training_data, validation_data, test_data
def train_svm(train_file
='train_data.txt', test_file
= 'train_result.txt'):
svm
= cv2.ml.SVM_create
()
svm.setType
(cv2.ml.SVM_C_SVC
)
#自己设置一下SVM参数
svm.setKernel
(cv2.ml.SVM_POLY
)
t_d
= np.loadtxt
(train_file, np.float32
)
m_d
= np.loadtxt
(test_file, np.int32
)
train_data
= cv2.ml.TrainData_create
(t_d, cv2.ml.ROW_SAMPLE, m_d
)
svm.train
(train_data
)
return svm
def svm_test(svm, test_data
):
le
= len(test_data
)
sum_tem
= 0
for i
in range(le
):
sample = np.array
([test_data
[i
][0].ravel
()],
dtype=np.float32
).reshape
(28,
28)
a, b
=svm.predict
(np.array
([test_data
[i
][0].ravel
()],
dtype=np.float32
))
if b
[0][0] == test_data
[i
][1] or test_data
[i
][1] == 0:
sum_tem
+= 1
print '正确率 ',
float(sum_tem
* 1.0/ le
)
def svm_predict(svm, sample
):
resized
= sample.copy
()
rows, cols
= resized.shape
if (rows
!= 28 or cols
!= 28) and rows
* cols
> 0:
resized
= cv2.resize
(resized,
(28,
28),
interpolation=cv2.INTER_CUBIC
)
return svm.predict
(np.array
([resized.ravel
()],
dtype=np.float32
))
if __name__
== '__main__':
tr, val, test
= wrap_data
()
save_path
= os.path.join
('data', '自己想个文件名')
if os.path.exists
(save_path
):
print 'find it'
svm
= cv2.ml.SVM_load
(save_path
)
else:
svm
= train_svm
()
svm.save
(save_path
)
svm_test
(svm, test
)
