def parallel_testing(test_image, test_label, lin_svm, std_scaler):
gray = io.load_grayscale_image(test_image)
kpt, des = feature_extraction.sift(gray)
predictions = classification.predict_svm(des,
lin_svm,
std_scaler=std_scaler)
values, counts = np.unique(predictions, return_counts=True)
predicted_class = values[np.argmax(counts)]
return predicted_class == test_label
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.sift(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)
def parallel_testing(test_image, test_label, svm, std_scaler, pca):
gray = io.load_grayscale_image(test_image)
kpt, des = feature_extraction.sift(gray)
predictions = classification.predict_svm(des,
svm,
std_scaler=std_scaler,
pca=pca,
probability=True)
probabilities = np.sum(predictions, axis=0)
predicted_class = svm.classes_[np.argmax(probabilities)]
return predicted_class == test_label, predicted_class, test_label
def parallel_testing(test_image, test_label, lin_svm, std_scaler, pca):
probs = [0, 0, 0, 0, 0, 0, 0, 0]
gray = io.load_grayscale_image(test_image)
kpt, des = feature_extraction.sift(gray)
predictions = classification.predict_svm(des,
lin_svm,
std_scaler=std_scaler,
pca=pca,
probability=True)
for j in range(0, len(predictions)):
probs = probs + predictions[j]
predicted_class = lin_svm.classes_[np.argmax(probs)]
return predicted_class == test_label, predicted_class, test_label
def parallel_testing(test_image, test_label, svm, scaler, gmm, model, pca):
D, _, _ = feature_extraction.compute_CNN_features(None, test_image, None,
model)
D_pca = pca.transform(D)
D_pca = np.float32(D_pca)
labels = np.array([test_label] * D.shape[0])
ind = np.array([0] * D.shape[0])
fisher, _ = bovw.fisher_vectors(D_pca,
labels,
ind,
gmm,
normalization='l2')
prediction_prob = classification.predict_svm(fisher,
svm,
std_scaler=scaler)
# print prediction_prob
# print prediction_prob.shape
predicted_class = svm.classes_[np.argmax(prediction_prob)]
return predicted_class == test_label, predicted_class, np.ravel(
prediction_prob)
D, L, degree=p1, coef0=p2)
elif kernel == 'rbf':
svm, std_scaler, pca = classification.train_rbf_svm(
D, L, gamma=p1)
elif kernel == 'sigmoid':
svm, std_scaler, pca = classification.train_sigmoid_svm(
D, L, gamma=p1, coef0=p2)
else:
svm, std_scaler, pca = classification.train_poly_svm(
D, L, degree=p1, coef0=p2)
print('Time spend: {:.2f} s'.format(time.time() - temp))
temp = time.time()
# Predict labels for all sift descriptors
print('Predicting labels for all descriptors...')
predicted_labels = classification.predict_svm(
D_t, svm, std_scaler=std_scaler, pca=pca)
print('Time spend: {:.2f} s'.format(time.time() - temp))
temp = time.time()
# Aggregate predictions
print(
'Aggregating predictions of descriptors to obtain a single label...'
)
num_correct = 0
for i in range(len(test_images_filenames)):
predictions_image = predicted_labels[I_t == i]
values, counts = np.unique(predictions_image,
return_counts=True)
predicted_class = values[np.argmax(counts)]
if predicted_class == test_labels[i]:
num_correct += 1
print('Training the SVM classifier...')
lin_svm, std_scaler, pca = classification.train_linear_svm(
D, L, model_name='linsvm_sift_30s')
print('Time spend: {:.2f} s'.format(time.time() - temp))
temp = time.time()
# Read the test set
test_images_filenames, test_labels = io.load_test_set()
print('Loaded {} test images.'.format(len(test_images_filenames)))
# Feature extraction with sift
print('Predicting test data...')
D, L, I = feature_extraction.seq_sift(test_images_filenames, test_labels)
# Predict labels for all sift descriptors
predicted_labels = classification.predict_svm(D, lin_svm, std_scaler)
# Aggregate predictions
num_correct = 0
for i in range(len(test_images_filenames)):
predictions_image = predicted_labels[I == i]
values, counts = np.unique(predictions_image, return_counts=True)
predicted_class = values[np.argmax(counts)]
if predicted_class == test_labels[i]:
num_correct += 1
print('Time spend: {:.2f} s'.format(time.time() - temp))
temp = time.time()
# Compute accuracy
accuracy = num_correct * 100.0 / len(test_images_filenames)
