def test_codebook():
dataset = pickle.load(open(constants.DATASET_OBJ_FILENAME, "rb"))
option = input("Enter [1] for using ORB features or [2] to use SIFT features.\n")
start = time.time()
des = descriptors.all_descriptors(dataset, dataset.get_train_set(), option)
end = time.time()
elapsed_time = utils.humanize_time(end - start)
print("Elapsed time getting all the descriptors is {0}".format(elapsed_time))
k = 64
des_name = constants.ORB_FEAT_NAME if option == constants.ORB_FEAT_OPTION else constants.SIFT_FEAT_NAME
codebook_filename = "codebook_{0}_{1}.csv".format(k, des_name)
start = time.time()
codebook = descriptors.gen_codebook(dataset, des, k)
end = time.time()
elapsed_time = utils.humanize_time(end - start)
print("Elapsed time calculating the k means for the codebook is {0}".format(elapsed_time))
np.savetxt(codebook_filename, codebook, delimiter=constants.NUMPY_DELIMITER)
print("Codebook loaded in {0}, press any key to exit ...".format(constants.CODEBOOK_FILE_NAME))
cv2.waitKey()
def main(is_interactive=True,
k=64,
des_option=constants.ORB_FEAT_OPTION,
svm_kernel=cv2.SVM_LINEAR):
if not is_interactive:
experiment_start = time.time()
# Check for the dataset of images
if not os.path.exists(constants.DATASET_PATH):
print("Dataset not found, please copy one.")
return
dataset = Dataset(constants.DATASET_PATH)
dataset.generate_sets()
# Check for the directory where stores generated files
if not os.path.exists(constants.FILES_DIR_NAME):
os.makedirs(constants.FILES_DIR_NAME)
if is_interactive:
des_option = input(
"Enter [1] for using ORB features or [2] to use SIFT features.\n")
k = input(
"Enter the number of cluster centers you want for the codebook.\n")
svm_option = input(
"Enter [1] for using SVM kernel Linear or [2] to use RBF.\n")
svm_kernel = cv2.SVM_LINEAR if svm_option == 1 else cv2.SVM_RBF
des_name = constants.ORB_FEAT_NAME if des_option == constants.ORB_FEAT_OPTION else constants.SIFT_FEAT_NAME
log = Log(k, des_name, svm_kernel)
codebook_filename = filenames.codebook(k, des_name)
if is_interactive:
codebook_option = input(
"Enter [1] for generating a new codebook or [2] to load one.\n")
else:
codebook_option = constants.GENERATE_OPTION
if codebook_option == constants.GENERATE_OPTION:
# Calculate all the training descriptors to generate the codebook
start = time.time()
des = descriptors.all_descriptors(dataset, dataset.get_train_set(),
des_option)
end = time.time()
log.train_des_time(end - start)
# Generates the codebook using K Means
print("Generating a codebook using K-Means with k={0}".format(k))
start = time.time()
codebook = descriptors.gen_codebook(dataset, des, k)
end = time.time()
log.codebook_time(end - start)
# Stores the codebook in a file
utils.save(codebook_filename, codebook)
print("Codebook saved in {0}".format(codebook_filename))
else:
# Load a codebook from a file
print("Loading codebook ...")
codebook = utils.load(codebook_filename)
print("Codebook with shape = {0} loaded.".format(codebook.shape))
# Train and test the dataset
classifier = Classifier(dataset, log)
svm = classifier.train(svm_kernel,
codebook,
des_option=des_option,
is_interactive=is_interactive)
print("Training ready. Now beginning with testing")
result, labels = classifier.test(codebook,
svm,
des_option=des_option,
is_interactive=is_interactive)
# Store the results from the test
classes = dataset.get_classes()
log.classes(classes)
log.classes_counts(dataset.get_classes_counts())
result_filename = filenames.result(k, des_name, svm_kernel)
test_count = len(dataset.get_test_set()[0])
result_matrix = np.reshape(result, (len(classes), test_count))
utils.save_csv(result_filename, result_matrix)
# Create a confusion matrix
confusion_matrix = np.zeros((len(classes), len(classes)), dtype=np.uint32)
for i in range(len(result)):
predicted_id = int(result[i])
real_id = int(labels[i])
confusion_matrix[real_id][predicted_id] += 1
print("Confusion Matrix =\n{0}".format(confusion_matrix))
log.confusion_matrix(confusion_matrix)
log.save()
print("Log saved on {0}.".format(filenames.log(k, des_name, svm_kernel)))
if not is_interactive:
experiment_end = time.time()
elapsed_time = utils.humanize_time(experiment_end - experiment_start)
print("Total time during the experiment was {0}".format(elapsed_time))
else:
# Show a plot of the confusion matrix on interactive mode
utils.show_conf_mat(confusion_matrix)
raw_input("Press [Enter] to exit ...")
def main(is_interactive=True, k=64, des_option=constants.ORB_FEAT_OPTION, svm_kernel=cv2.SVM_LINEAR):
if not is_interactive:
experiment_start = time.time()
# Check for the dataset of images
if not os.path.exists(constants.DATASET_PATH):
print("Dataset not found, please copy one.")
return
dataset = Dataset(constants.DATASET_PATH)
dataset.generate_sets()
# Check for the directory where stores generated files
if not os.path.exists(constants.FILES_DIR_NAME):
os.makedirs(constants.FILES_DIR_NAME)
if is_interactive:
des_option = input("Enter [1] for using ORB features or [2] to use SIFT features.\n")
k = input("Enter the number of cluster centers you want for the codebook.\n")
svm_option = input("Enter [1] for using SVM kernel Linear or [2] to use RBF.\n")
svm_kernel = cv2.SVM_LINEAR if svm_option == 1 else cv2.SVM_RBF
des_name = constants.ORB_FEAT_NAME if des_option == constants.ORB_FEAT_OPTION else constants.SIFT_FEAT_NAME
log = Log(k, des_name, svm_kernel)
codebook_filename = filenames.codebook(k, des_name)
if is_interactive:
codebook_option = input("Enter [1] for generating a new codebook or [2] to load one.\n")
else:
codebook_option = constants.GENERATE_OPTION
if codebook_option == constants.GENERATE_OPTION:
# Calculate all the training descriptors to generate the codebook
start = time.time()
des = descriptors.all_descriptors(dataset, dataset.get_train_set(), des_option)
end = time.time()
log.train_des_time(end - start)
# Generates the codebook using K Means
print("Generating a codebook using K-Means with k={0}".format(k))
start = time.time()
codebook = descriptors.gen_codebook(dataset, des, k)
end = time.time()
log.codebook_time(end - start)
# Stores the codebook in a file
utils.save(codebook_filename, codebook)
print("Codebook saved in {0}".format(codebook_filename))
else:
# Load a codebook from a file
print("Loading codebook ...")
codebook = utils.load(codebook_filename)
print("Codebook with shape = {0} loaded.".format(codebook.shape))
# Train and test the dataset
classifier = Classifier(dataset, log)
svm = classifier.train(svm_kernel, codebook, des_option=des_option, is_interactive=is_interactive)
print("Training ready. Now beginning with testing")
result, labels = classifier.test(codebook, svm, des_option=des_option, is_interactive=is_interactive)
# Store the results from the test
classes = dataset.get_classes()
log.classes(classes)
log.classes_counts(dataset.get_classes_counts())
result_filename = filenames.result(k, des_name, svm_kernel)
test_count = len(dataset.get_test_set()[0])
result_matrix = np.reshape(result, (len(classes), test_count))
utils.save_csv(result_filename, result_matrix)
# Create a confusion matrix
confusion_matrix = np.zeros((len(classes), len(classes)), dtype=np.uint32)
for i in range(len(result)):
predicted_id = int(result[i])
real_id = int(labels[i])
confusion_matrix[real_id][predicted_id] += 1
print("Confusion Matrix =\n{0}".format(confusion_matrix))
log.confusion_matrix(confusion_matrix)
log.save()
print("Log saved on {0}.".format(filenames.log(k, des_name, svm_kernel)))
if not is_interactive:
experiment_end = time.time()
elapsed_time = utils.humanize_time(experiment_end - experiment_start)
print("Total time during the experiment was {0}".format(elapsed_time))
else:
# Show a plot of the confusion matrix on interactive mode
utils.show_conf_mat(confusion_matrix)
raw_input("Press [Enter] to exit ...")
