start = time.time()
if show_images:
image_viewer = ImageViewer()
dataloader = DataLoader(path='Dataset/')
training_images, training_labels = dataloader.get_training_set(
shuffle=True)
validation_images, validation_labels = dataloader.get_validation_set(
shuffle=True)
if show_images:
image_viewer.add_to_plot(training_images[0], training_labels[0])
feature_extractor = FeatureExtractor(training_images)
training_keypoints = feature_extractor.get_keypoints(
method=feature_extractor_method)
if show_images:
keypoint_image = training_images[0].copy()
cv2.drawKeypoints(training_images[0], training_keypoints[0],
keypoint_image)
image_viewer.add_to_plot(
keypoint_image,
feature_extractor_method + " " + training_labels[0])
feature_descriptor = FeatureDescriptor(training_images, training_keypoints)
training_descriptors = feature_descriptor.get_SIFT_descriptors()
BOF = BagOfFeatures(training_descriptors, training_labels,
clustering_method)
BOF.train()
