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python(test): enable pylint checks for tests

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This commit is contained in:
Alexander Alekhin
2017-09-03 11:17:15 +00:00
parent e1b102e9a6
commit 936234d5b1
20 changed files with 50 additions and 57 deletions
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modules/python/test/test_letter_recog.py
+8 -8
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@@ -59,12 +59,12 @@ class RTrees(LetterStatModel):
self.model = cv2.ml.RTrees_create()
def train(self, samples, responses):
sample_n, var_n = samples.shape
#sample_n, var_n = samples.shape
self.model.setMaxDepth(20)
self.model.train(samples, cv2.ml.ROW_SAMPLE, responses.astype(int))
def predict(self, samples):
ret, resp = self.model.predict(samples)
_ret, resp = self.model.predict(samples)
return resp.ravel()
@@ -76,7 +76,7 @@ class KNearest(LetterStatModel):
self.model.train(samples, cv2.ml.ROW_SAMPLE, responses)
def predict(self, samples):
retval, results, neigh_resp, dists = self.model.findNearest(samples, k = 10)
_retval, results, _neigh_resp, _dists = self.model.findNearest(samples, k = 10)
return results.ravel()
@@ -85,7 +85,7 @@ class Boost(LetterStatModel):
self.model = cv2.ml.Boost_create()
def train(self, samples, responses):
sample_n, var_n = samples.shape
_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)
@@ -96,7 +96,7 @@ class Boost(LetterStatModel):
def predict(self, samples):
new_samples = self.unroll_samples(samples)
ret, resp = self.model.predict(new_samples)
_ret, resp = self.model.predict(new_samples)
return resp.ravel().reshape(-1, self.class_n).argmax(1)
@@ -113,7 +113,7 @@ class SVM(LetterStatModel):
self.model.train(samples, cv2.ml.ROW_SAMPLE, responses.astype(int))
def predict(self, samples):
ret, resp = self.model.predict(samples)
_ret, resp = self.model.predict(samples)
return resp.ravel()
@@ -122,7 +122,7 @@ class MLP(LetterStatModel):
self.model = cv2.ml.ANN_MLP_create()
def train(self, samples, responses):
sample_n, var_n = samples.shape
_sample_n, var_n = samples.shape
new_responses = self.unroll_responses(responses).reshape(-1, self.class_n)
layer_sizes = np.int32([var_n, 100, 100, self.class_n])
@@ -136,7 +136,7 @@ class MLP(LetterStatModel):
self.model.train(samples, cv2.ml.ROW_SAMPLE, np.float32(new_responses))
def predict(self, samples):
ret, resp = self.model.predict(samples)
_ret, resp = self.model.predict(samples)
return resp.argmax(-1)
from tests_common import NewOpenCVTests