def parallel_testing(test_image, test_label, codebook, svm, scaler, pca):
gray = io.load_grayscale_image(test_image)
kpt, des = feature_extraction.dense(gray)
labels = np.array([test_label] * des.shape[0])
ind = np.array([0] * des.shape[0])
vis_word, _ = bovw.visual_words(des, labels, ind, codebook)
prediction_prob = classification.predict_svm(vis_word, svm, std_scaler=scaler, pca=pca)
predicted_class = lin_svm.classes_[np.argmax(prediction_prob)]
return predicted_class == test_label, predicted_class, np.ravel(prediction_prob)
def parallel_testing(test_image, test_label, codebook, svm, scaler, pca):
gray = io.load_grayscale_image(test_image)
kpt, des = feature_extraction.dense(gray)
kpt_pos = np.array([kpt[i].pt for i in range(0, len(kpt))], dtype=np.float64)
labels = np.array([test_label] * des.shape[0])
ind = np.array([0] * des.shape[0])
vis_word, _ = bovw.visual_words(des, labels, ind, codebook, spatial_pyramid=True)
prediction_prob = classification.predict_svm(vis_word, svm, std_scaler=scaler, pca=pca)
predicted_class = svm.classes_[np.argmax(prediction_prob)]
return predicted_class == test_label, predicted_class, np.ravel(prediction_prob)
def parallel_testing(test_image, test_label, svm, scaler, gmm):
gray = io.load_grayscale_image(test_image)
kpt, des = feature_extraction.dense(gray)
labels = np.array([test_label] * des.shape[0])
ind = np.array([0] * des.shape[0])
fisher, _ = bovw.fisher_vectors(des, labels, ind, gmm)
prediction_prob = classification.predict_svm(fisher, svm, std_scaler=scaler)
predicted_class = svm.classes_[np.argmax(prediction_prob)]
return predicted_class == test_label, predicted_class, np.ravel(prediction_prob)