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Samples Python:

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* Unused variables
* Bad identation
This commit is contained in:
tribta
2017-08-24 17:45:14 +01:00
parent 9bb17caa24
commit 7555ab194f
20 changed files with 43 additions and 45 deletions
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samples/python/letter_recog.py
+7 -8
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@@ -64,12 +64,11 @@ class RTrees(LetterStatModel):
self.model = cv2.ml.RTrees_create()
def train(self, samples, responses):
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()
@@ -81,7 +80,7 @@ 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()
@@ -90,7 +89,7 @@ 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)
@@ -101,7 +100,7 @@ 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)
@@ -118,7 +117,7 @@ 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()
@@ -127,7 +126,7 @@ 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])
@@ -141,7 +140,7 @@ 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)