def main():
feature_extraction_model = task6.feature_extraction_model
dimension_reduction_model = task6.dimension_reduction_model
k_value_for_ss_similarity = 10
given_k_value = get_input_k()
print(global_constants.LINE_SEPARATOR)
print("User Inputs summary")
print(global_constants.LINE_SEPARATOR)
print("k-value: {}".format(given_k_value))
print(global_constants.LINE_SEPARATOR)
dim_reduction = DimensionReduction(feature_extraction_model, dimension_reduction_model, k_value_for_ss_similarity)
# original feature vectors
obj_feat_matrix = dim_reduction.get_object_feature_matrix()
# get the img IDs from the database for images in the fit model
img_set = pd.DataFrame({"imageId": obj_feat_matrix['imageId']})
# get the metadata for each image with given subject id
subject_data = dim_reduction.get_metadata("imageName", list(set(img_set["imageId"].tolist())))
# unique subject IDs in dataset
dataset_subject_ids = set((subject_data)["id"])
subject_subject_matrix = []
m_value = len(img_set)
starttime = time.time()
model = task6.load_model(dim_reduction, feature_extraction_model, dimension_reduction_model,
k_value_for_ss_similarity)
folder = path.basename(path.dirname(obj_feat_matrix['path'][0]))
for i, subjectid in enumerate(dataset_subject_ids):
given_subject_images = dim_reduction.get_metadata("id", list([subjectid]))["imageName"].tolist()
image_list_for_subject = list(set(given_subject_images).intersection(set(img_set["imageId"].tolist())))
similar_subjects = task6.find_similar_subjects(subjectid, image_list_for_subject, model,
img_set, dim_reduction, m_value, folder)
subject_subject_matrix.append(np.asarray(list(similar_subjects.values())))
print("\nTime taken to create subject subject matrix: {}\n".format(time.time() - starttime))
# perform nmf on subject_subject_matrix
# given_k_value = 1
matrix = pd.DataFrame(data={'imageId': list(dataset_subject_ids), 'featureVector': subject_subject_matrix})
dim_red = DimensionReduction(None, "NMF", given_k_value, subject_subject=True, matrix=matrix)
w, h, model = dim_red.execute()
# display latent semantics
# printing the term weight
print_tw(w, h, subject_subject=True)
# save to csv
filename = "task7" + '_' + str(given_k_value)
CSVReader().save_to_csv(w, None, filename, subject_subject=True)
print("Please check the CSV file: output/{}.csv".format(filename))
def get_y(fea_ext_mod,
dim_red_mod,
k_value,
collection_name,
red_dim=None,
obj_lat=None):
dim_red = DimensionReduction(fea_ext_mod, dim_red_mod, k_value)
if collection_name == 'unlabelled':
aspect = dim_red.get_metadata(
"imageName", red_dim['imageId'].tolist())['aspectOfHand'].tolist()
else:
aspect = dim_red.get_metadata_collection(
"imageName", obj_lat['imageId'].tolist(),
collection_name)['aspectOfHand'].tolist()
return [i.split(' ')[0] for i in aspect]
def main():
# given subject id
given_subject_id = get_input_subject_id()
k_value = 40
master_folder = "Hands"
dim_reduction = DimensionReduction(feature_extraction_model,
dimension_reduction_model, k_value)
# original feature vectors
obj_feat_matrix = dim_reduction.get_object_feature_matrix()
# extract model saved from task 1
model = load_model(dim_reduction, feature_extraction_model,
dimension_reduction_model, k_value)
# get the img IDs from the database for images in the fit model
img_set = pd.DataFrame({"imageId": obj_feat_matrix['imageId']})
# image count to rank against current image
m_value = len(img_set)
print(global_constants.LINE_SEPARATOR)
print("User Inputs summary")
print(global_constants.LINE_SEPARATOR)
print("Query Subject Id: {}".format(given_subject_id))
print(global_constants.LINE_SEPARATOR)
# given_subject_id = 55
# similar subjects to find
similar_subject_count = 3
# get metadata for given subject's images
metadata = dim_reduction.get_metadata("id", list([given_subject_id]))
# get a list of img IDs for the particular subject in the dataset
image_list_for_given_subject = random.sample(
list(set(metadata["imageName"].tolist())), 5)
image_list = list(set(img_set["imageId"].tolist()))
starttime = time.time()
# method call to find similar subjects
subject_similarity = find_similar_subjects(given_subject_id,
image_list_for_given_subject,
model, img_set, dim_reduction,
m_value, master_folder)
# sort the similarity scores in descending order
sorted_subject_similarity = sorted(subject_similarity.items(),
key=operator.itemgetter(1),
reverse=True)
print()
print("Subject : Score")
list_subjects = []
max = similar_subject_count
counter = 0
while counter < max:
subject = sorted_subject_similarity[counter]
if subject[0] != given_subject_id:
print(subject[0], " : ", subject[1])
list_subjects.append([subject[0], subject[1]])
else:
max += 1
counter += 1
print()
# print(sorted_subject_similarity)
image_list_for_similar_subjects_abs_path = []
similarity_scores = []
folder_path = os.path.dirname(obj_feat_matrix['path'][0])
# create list of images for each subject to visualize most similar subjects
for subject in (sorted_subject_similarity):
if subject[0] != given_subject_id:
metadata = dim_reduction.get_metadata("id", list([subject[0]]))
similarity_scores.append(subject[1])
image_list_for_similar_subject = list(
set(metadata["imageName"].tolist()).intersection(
set(img_set["imageId"].tolist())))
image_list_for_one_similar_subject_abs_path = []
for image in image_list_for_similar_subject:
image_list_for_one_similar_subject_abs_path.append(
(os.path.join(folder_path, image)))
image_list_for_similar_subjects_abs_path.append(
image_list_for_one_similar_subject_abs_path)
similar_subject_count -= 1
if (similar_subject_count <= 0):
break
# Create image list for given subject
image_list_for_given_subject_abs_path = []
# pick 5 images of given subject at random from master dataset
for image in image_list_for_given_subject:
image_list_for_given_subject_abs_path.append(
os.path.abspath(os.path.join(master_folder, image)))
output_path = os.path.abspath(os.path.join("output"))
if not os.path.exists(output_path):
os.makedirs(output_path)
fig_filename = os.path.join(
output_path,
"task6_{0}_{1}_{2}_{3}_{4}.png".format(feature_extraction_model,
dimension_reduction_model,
str(k_value), dist_func,
given_subject_id))
# show images on a plot
imgvwr.show_subjectwise_images(given_subject_id,
image_list_for_given_subject_abs_path,
list_subjects,
image_list_for_similar_subjects_abs_path,
fig_filename)
print("\nTime taken for task 6: {}\n".format(time.time() - starttime))