Merge pull request #3636 from kaingwade:ml_to_contrib

Move ml to opencv_contrib #3636

Main PR: opencv/opencv#25017

modules/ml/misc/python/test/test_letter_recog.py

@@ -0,0 +1,171 @@
+#!/usr/bin/env python
+
+'''
+The sample demonstrates how to train Random Trees classifier
+(or Boosting classifier, or MLP, or Knearest, or Support Vector Machines) using the provided dataset.
+
+We use the sample database letter-recognition.data
+from UCI Repository, here is the link:
+
+Newman, D.J. & Hettich, S. & Blake, C.L. & Merz, C.J. (1998).
+UCI Repository of machine learning databases
+[http://www.ics.uci.edu/~mlearn/MLRepository.html].
+Irvine, CA: University of California, Department of Information and Computer Science.
+
+The dataset consists of 20000 feature vectors along with the
+responses - capital latin letters A..Z.
+The first 10000 samples are used for training
+and the remaining 10000 - to test the classifier.
+======================================================
+  Models: RTrees, KNearest, Boost, SVM, MLP
+'''
+
+# Python 2/3 compatibility
+from __future__ import print_function
+
+import numpy as np
+import cv2 as cv
+
+def load_base(fn):
+    a = np.loadtxt(fn, np.float32, delimiter=',', converters={ 0 : lambda ch : ord(ch)-ord('A') })
+    samples, responses = a[:,1:], a[:,0]
+    return samples, responses
+
+class LetterStatModel(object):
+    class_n = 26
+    train_ratio = 0.5
+
+    def load(self, fn):
+        self.model.load(fn)
+    def save(self, fn):
+        self.model.save(fn)
+
+    def unroll_samples(self, samples):
+        sample_n, var_n = samples.shape
+        new_samples = np.zeros((sample_n * self.class_n, var_n+1), np.float32)
+        new_samples[:,:-1] = np.repeat(samples, self.class_n, axis=0)
+        new_samples[:,-1] = np.tile(np.arange(self.class_n), sample_n)
+        return new_samples
+
+    def unroll_responses(self, responses):
+        sample_n = len(responses)
+        new_responses = np.zeros(sample_n*self.class_n, np.int32)
+        resp_idx = np.int32( responses + np.arange(sample_n)*self.class_n )
+        new_responses[resp_idx] = 1
+        return new_responses
+
+class RTrees(LetterStatModel):
+    def __init__(self):
+        self.model = cv.ml.RTrees_create()
+
+    def train(self, samples, responses):
+        #sample_n, var_n = samples.shape
+        self.model.setMaxDepth(20)
+        self.model.train(samples, cv.ml.ROW_SAMPLE, responses.astype(int))
+
+    def predict(self, samples):
+        _ret, resp = self.model.predict(samples)
+        return resp.ravel()
+
+
+class KNearest(LetterStatModel):
+    def __init__(self):
+        self.model = cv.ml.KNearest_create()
+
+    def train(self, samples, responses):
+        self.model.train(samples, cv.ml.ROW_SAMPLE, responses)
+
+    def predict(self, samples):
+        _retval, results, _neigh_resp, _dists = self.model.findNearest(samples, k = 10)
+        return results.ravel()
+
+
+class Boost(LetterStatModel):
+    def __init__(self):
+        self.model = cv.ml.Boost_create()
+
+    def train(self, samples, responses):
+        _sample_n, var_n = samples.shape
+        new_samples = self.unroll_samples(samples)
+        new_responses = self.unroll_responses(responses)
+        var_types = np.array([cv.ml.VAR_NUMERICAL] * var_n + [cv.ml.VAR_CATEGORICAL, cv.ml.VAR_CATEGORICAL], np.uint8)
+
+        self.model.setWeakCount(15)
+        self.model.setMaxDepth(10)
+        self.model.train(cv.ml.TrainData_create(new_samples, cv.ml.ROW_SAMPLE, new_responses.astype(int), varType = var_types))
+
+    def predict(self, samples):
+        new_samples = self.unroll_samples(samples)
+        _ret, resp = self.model.predict(new_samples)
+
+        return resp.ravel().reshape(-1, self.class_n).argmax(1)
+
+
+class SVM(LetterStatModel):
+    def __init__(self):
+        self.model = cv.ml.SVM_create()
+
+    def train(self, samples, responses):
+        self.model.setType(cv.ml.SVM_C_SVC)
+        self.model.setC(1)
+        self.model.setKernel(cv.ml.SVM_RBF)
+        self.model.setGamma(.1)
+        self.model.train(samples, cv.ml.ROW_SAMPLE, responses.astype(int))
+
+    def predict(self, samples):
+        _ret, resp = self.model.predict(samples)
+        return resp.ravel()
+
+
+class MLP(LetterStatModel):
+    def __init__(self):
+        self.model = cv.ml.ANN_MLP_create()
+
+    def train(self, samples, responses):
+        _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])
+
+        self.model.setLayerSizes(layer_sizes)
+        self.model.setTrainMethod(cv.ml.ANN_MLP_BACKPROP)
+        self.model.setBackpropMomentumScale(0)
+        self.model.setBackpropWeightScale(0.001)
+        self.model.setTermCriteria((cv.TERM_CRITERIA_COUNT, 20, 0.01))
+        self.model.setActivationFunction(cv.ml.ANN_MLP_SIGMOID_SYM, 2, 1)
+
+        self.model.train(samples, cv.ml.ROW_SAMPLE, np.float32(new_responses))
+
+    def predict(self, samples):
+        _ret, resp = self.model.predict(samples)
+        return resp.argmax(-1)
+
+from tests_common import NewOpenCVTests
+
+class letter_recog_test(NewOpenCVTests):
+
+    def test_letter_recog(self):
+
+        eps = 0.01
+
+        models = [RTrees, KNearest, Boost, SVM, MLP]
+        models = dict( [(cls.__name__.lower(), cls) for cls in models] )
+        testErrors = {RTrees: (98.930000, 92.390000), KNearest: (94.960000, 92.010000),
+         Boost: (85.970000, 74.920000), SVM: (99.780000, 95.680000), MLP: (90.060000, 87.410000)}
+
+        for model in models:
+            Model = models[model]
+            classifier = Model()
+
+            samples, responses = load_base(self.repoPath + '/samples/data/letter-recognition.data')
+            train_n = int(len(samples)*classifier.train_ratio)
+
+            classifier.train(samples[:train_n], responses[:train_n])
+            train_rate = np.mean(classifier.predict(samples[:train_n]) == responses[:train_n].astype(int))
+            test_rate  = np.mean(classifier.predict(samples[train_n:]) == responses[train_n:].astype(int))
+
+            self.assertLess(train_rate - testErrors[Model][0], eps)
+            self.assertLess(test_rate - testErrors[Model][1], eps)
+
+
+if __name__ == '__main__':
+    NewOpenCVTests.bootstrap()