def main():
"""Main function for the task 4"""
feature_extraction_model = get_input_feature_extractor_model()
dimension_reduction_model = get_input_dimensionality_reduction_model()
k_value = get_input_k()
label = get_input_image_label()
folder = get_input_folder()
image_name = get_input_image(folder)
m_value = get_input_m()
if dimension_reduction_model != "NMF":
dist_func = "euclidean"
elif feature_extraction_model in ["CM", "LBP"]:
dist_func = "nvsc1"
else:
dist_func = "euclidean"
# dist_func = "cosine"
# dist_func = "chebyshev"
# dist_func = "manhattan"
# dist_func = "chi_square"
# dist_func = "euclidean"
print(global_constants.LINE_SEPARATOR)
print("User Inputs summary")
print(global_constants.LINE_SEPARATOR)
print("Feature Extraction Model: {}\nDimensionality Reduction Model: {}\nk-value: {}\nLabel: {}\nFolder: {}\n"
"Image: {}\nm-value: {}".format(feature_extraction_model, dimension_reduction_model, k_value, label, folder,
image_name, m_value))
print(global_constants.LINE_SEPARATOR)
# Saves the returned model
filename = "{0}_{1}_{2}_{3}".format(feature_extraction_model, dimension_reduction_model, label.replace(" ", ''),
str(k_value))
model = model_interact.load_model(filename=filename)
if not model:
print("Please run Task 3 for {}, {}, {} and {}".format(feature_extraction_model, dimension_reduction_model,
label, k_value))
sys.exit(1)
# Compute the reduced dimensions for the new query image and find m similar images
dim_reduction = DimensionReduction(feature_extraction_model, dimension_reduction_model, k_value, label)
result = dim_reduction.find_m_similar_images(model, m_value, folder, image_name, dist_func)
print(global_constants.LINE_SEPARATOR)
print("Similar Images")
print(global_constants.LINE_SEPARATOR)
for rec in result:
print(rec)
print(global_constants.LINE_SEPARATOR)
title = {
"Feature Extraction": feature_extraction_model,
"Dimension Reduction": dimension_reduction_model,
"k": k_value,
"Label": label,
"Distance": dist_func
}
show_images(os.path.abspath(os.path.join(folder, image_name)), result, title)
def run_task4(feature_extraction_model, dimension_reduction_model, folder,
image_name, dist_func, label, k_value, m_value):
"""Main function for the Task4"""
# Saves the returned model
filename = "{0}_{1}_{2}_{3}".format(feature_extraction_model,
dimension_reduction_model,
label.replace(" ", ''), str(k_value))
model = model_interact.load_model(filename=filename)
# Compute the reduced dimensions for the new query image and find m similar images
dim_reduction = DimensionReduction(feature_extraction_model,
dimension_reduction_model, k_value,
label)
obj_feature = dim_reduction.get_object_feature_matrix()
result = dim_reduction.find_m_similar_images(model, m_value, folder,
image_name, dist_func)
return result