| 88 | return results.ravel() |
| 89 | |
| 90 | class SVM(StatModel): |
| 91 | def __init__(self, C = 1, gamma = 0.5): |
| 92 | self.model = cv2.ml.SVM_create() |
| 93 | self.model.setGamma(gamma) |
| 94 | self.model.setC(C) |
| 95 | self.model.setKernel(cv2.ml.SVM_RBF) |
| 96 | self.model.setType(cv2.ml.SVM_C_SVC) |
| 97 | |
| 98 | def train(self, samples, responses): |
| 99 | self.model.train(samples, cv2.ml.ROW_SAMPLE, responses) |
| 100 | |
| 101 | def predict(self, samples): |
| 102 | return self.model.predict(samples)[1].ravel() |
| 103 | |
| 104 | |
| 105 | def evaluate_model(model, digits, samples, labels): |