From e90dc20361fdf104c829fca4d16440c712e3ed1a Mon Sep 17 00:00:00 2001 From: Vladislav Sovrasov Date: Wed, 3 Feb 2016 11:22:32 +0300 Subject: [PATCH] Update letter_recog sample to current version of opencv interfaces --- samples/python/letter_recog.py | 59 +++++++++++++++++----------------- 1 file changed, 30 insertions(+), 29 deletions(-) diff --git a/samples/python/letter_recog.py b/samples/python/letter_recog.py index e68c095bc6..4e166cbbd6 100755 --- a/samples/python/letter_recog.py +++ b/samples/python/letter_recog.py @@ -65,13 +65,12 @@ class RTrees(LetterStatModel): def train(self, samples, responses): sample_n, var_n = samples.shape - var_types = np.array([cv2.ml.VAR_NUMERICAL] * var_n + [cv2.ml.VAR_CATEGORICAL], np.uint8) - #CvRTParams(10,10,0,false,15,0,true,4,100,0.01f,CV_TERMCRIT_ITER)); - params = dict(max_depth=10 ) - self.model.train(samples, cv2.ml.ROW_SAMPLE, responses, varType = var_types, params = params) + self.model.setMaxDepth(20) + self.model.train(samples, cv2.ml.ROW_SAMPLE, responses.astype(int)) def predict(self, samples): - return [self.model.predict(s) for s in samples] + ret, resp = self.model.predict(samples) + return resp.ravel() class KNearest(LetterStatModel): @@ -79,10 +78,10 @@ class KNearest(LetterStatModel): self.model = cv2.ml.KNearest_create() def train(self, samples, responses): - self.model.train(samples, responses) + self.model.train(samples, cv2.ml.ROW_SAMPLE, responses) def predict(self, samples): - retval, results, neigh_resp, dists = self.model.find_nearest(samples, k = 10) + retval, results, neigh_resp, dists = self.model.findNearest(samples, k = 10) return results.ravel() @@ -95,15 +94,15 @@ class Boost(LetterStatModel): 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) - #CvBoostParams(CvBoost::REAL, 100, 0.95, 5, false, 0 ) - params = dict(max_depth=5) #, use_surrogates=False) - self.model.train(new_samples, cv2.ml.ROW_SAMPLE, new_responses, varType = var_types, params=params) + + self.model.setMaxDepth(5) + self.model.train(cv2.ml.TrainData_create(new_samples, cv2.ml.ROW_SAMPLE, new_responses.astype(int), varType = var_types)) def predict(self, samples): new_samples = self.unroll_samples(samples) - pred = np.array( [self.model.predict(s, returnSum = True) for s in new_samples] ) - pred = pred.reshape(-1, self.class_n).argmax(1) - return pred + ret, resp = self.model.predict(new_samples) + + return resp.ravel().reshape(-1, self.class_n).argmax(1) class SVM(LetterStatModel): @@ -111,13 +110,14 @@ class SVM(LetterStatModel): self.model = cv2.ml.SVM_create() def train(self, samples, responses): - params = dict( kernel_type = cv2.ml.SVM_LINEAR, - svm_type = cv2.ml.SVM_C_SVC, - C = 1 ) - self.model.train(samples, responses, params = params) + self.model.setType(cv2.ml.SVM_C_SVC) + self.model.setC(1) + self.model.setKernel(cv2.ml.SVM_LINEAR) + self.model.train(samples, cv2.ml.ROW_SAMPLE, responses.astype(int)) def predict(self, samples): - return self.model.predict_all(samples).ravel() + ret, resp = self.model.predict(samples) + return resp.ravel() class MLP(LetterStatModel): @@ -127,22 +127,23 @@ class MLP(LetterStatModel): 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.create(layer_sizes) - # CvANN_MLP_TrainParams::BACKPROP,0.001 - params = dict( term_crit = (cv2.TERM_CRITERIA_COUNT, 300, 0.01), - train_method = cv2.ml.ANN_MLP_TRAIN_PARAMS_BACKPROP, - bp_dw_scale = 0.001, - bp_moment_scale = 0.0 ) - self.model.train(samples, np.float32(new_responses), None, params = params) + self.model.setLayerSizes(layer_sizes) + self.model.setTrainMethod(cv2.ml.ANN_MLP_BACKPROP) + self.model.setBackpropMomentumScale(0) + self.model.setBackpropWeightScale(0.001) + self.model.setTermCriteria((cv2.TERM_CRITERIA_COUNT, 300, 0.01)) + self.model.setActivationFunction(cv2.ml.ANN_MLP_SIGMOID_SYM) + + self.model.train(samples, cv2.ml.ROW_SAMPLE, np.float32(new_responses)) def predict(self, samples): ret, resp = self.model.predict(samples) return resp.argmax(-1) + if __name__ == '__main__': import getopt import sys @@ -155,7 +156,7 @@ if __name__ == '__main__': args, dummy = getopt.getopt(sys.argv[1:], '', ['model=', 'data=', 'load=', 'save=']) args = dict(args) - args.setdefault('--model', 'rtrees') + args.setdefault('--model', 'svm') args.setdefault('--data', '../data/letter-recognition.data') print('loading data %s ...' % args['--data']) @@ -173,8 +174,8 @@ if __name__ == '__main__': model.train(samples[:train_n], responses[:train_n]) print('testing...') - train_rate = np.mean(model.predict(samples[:train_n]) == responses[:train_n]) - test_rate = np.mean(model.predict(samples[train_n:]) == responses[train_n:]) + train_rate = np.mean(model.predict(samples[:train_n]) == responses[:train_n].astype(int)) + test_rate = np.mean(model.predict(samples[train_n:]) == responses[train_n:].astype(int)) print('train rate: %f test rate: %f' % (train_rate*100, test_rate*100))