trainhog.py 文件源码

def train_svm(svm_save_path, descriptors, labels):
    # train_data = convert_to_ml(descriptors)
    train_data = np.array(descriptors)
    responses = np.array(labels, dtype=np.int32)

    print "Start training..."
    svm = cv2.ml.SVM_create()
    # Default values to train SVM
    svm.setCoef0(0.0)
    svm.setDegree(3)
    # svm.setTermCriteria(TermCriteria(cv2.TERMCRIT_ITER + cv2.TERMCRIT_EPS, 1000, 1e-3))
    svm.setTermCriteria((cv2.TERM_CRITERIA_MAX_ITER + cv2.TERM_CRITERIA_EPS, 1000, 1e-3))
    svm.setGamma(0)
    svm.setKernel(cv2.ml.SVM_LINEAR)
    svm.setNu(0.5)
    svm.setP(0.1) # for EPSILON_SVR, epsilon in loss function?
    svm.setC(0.01) # From paper, soft classifier
    svm.setType(cv2.ml.SVM_EPS_SVR) # C_SVC; # EPSILON_SVR; # may be also NU_SVR; # do regression task
    svm.train(train_data, cv2.ml.ROW_SAMPLE, responses)
    print "...[done]"

    svm.save(svm_save_path)

# def test_classifier(svm_file_path, window_dims):
#     #  Set the trained svm to my_hog
#     hog_detector = get_svm_detector(svm_file_path)
#     hog = get_hog_object(window_dims)
#     hog.setSVMDetector(hog_detector)
#
#     locations = hog.detectMultiScale(img)