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Main.py
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import cv2
import os
import argparse
import matplotlib.pyplot as plt
import os
from time import time
from sklearn import metrics
import Dataset as dataset
import bag_of_visual_words as bovw
import mpeg7_color_layout as mpeg7
import candidate_selection as cand_selec
import constraint_creation as gen_cons
import sift as SIFT
import PC_Kmeans as PCK
import COP_Kmeans as COPK
import cluster_evaluation as clus_eval
import numpy as np
# --------Accepting Input----------
# Initialize parser
parser = argparse.ArgumentParser()
# Adding arguments
parser.add_argument("--k", required=True)
parser.add_argument("--query", required=True)
# ---------------------------------
# -------------- Parameter Declaration -------------------
img_folder_path = '\\VOCtrainval_06-Nov-2007\\VOCdevkit\\VOC2007\\JPEGImages\\'
img_class_path = '\\VOCtrainval_06-Nov-2007\\VOCdevkit\\VOC2007\\ImageSets\\Main\\processed_files\\'
img_class_set_names = [{'name': 'aeroplane', 'type': 'vehicle'},
{'name': 'bicycle', 'type': 'vehicle'},
{'name': 'bird', 'type': 'animal'},
{'name': 'boat', 'type': 'vehicle'},
{'name': 'bottle', 'type': 'furniture'},
{'name': 'bus', 'type': 'vehicle'},
{'name': 'car', 'type': 'vehicle'},
{'name': 'cat', 'type': 'animal'},
{'name': 'chair', 'type': 'furniture'},
{'name': 'cow', 'type': 'animal'},
{'name': 'diningtable', 'type': 'furniture'},
{'name': 'dog', 'type': 'animal'},
{'name': 'horse', 'type': 'animal'},
{'name': 'motorbike', 'type': 'vehicle'},
{'name': 'person', 'type': 'person'},
{'name': 'pottedplant', 'type': 'furniture'},
{'name': 'sheep', 'type': 'animal'},
{'name': 'sofa', 'type': 'furniture'},
{'name': 'train', 'type': 'vehicle'},
{'name': 'tvmonitor', 'type': 'electronics'}]
# BOVW parameters
bovw_kmeans_model = None
tam_patch = (15, 15)
n_patches = 250
random_state = 1
n_dic = 50 # size of the dictionary
def gen_clus(c_name, cand_img, cons, f_name):
'''Generate and Print Constrainted Cluster\n
Parameters
c_name: cluster name
cand_img: candidate image list
cons: Constraint class object
f_name: feature name
'''
if c_name == "COP":
'''---------------- COP-Kmeans ---------------'''
c_kmeans = COPK.COP_KMeans(
len(cons.descripList), cons.ml_g, cons.cl_g, f_name)
else:
'''---------------- PC-Kmeans ---------------'''
c_kmeans = PCK.PC_Kmeans(len(cons.descripList), cons.ml_g, cons.cl_g,
cons.neighborhoods, cons.y, len(cand_img[0].feature), f_name)
c_kmeans.fit(cons.x)
labels = c_kmeans.predict(cons.x)
print(f'{c_name} Kmeans Clusters:')
for i in range(len(c_kmeans.clusters)):
print(f'Cluster {i + 1} :')
count = 0
for index in c_kmeans.clusters[i]:
count += 1
if count != len(c_kmeans.clusters[i]):
print(f'{cand_img[index].filename}, ', end='')
else:
print(f'{cand_img[index].filename}.')
return c_kmeans, labels
def gen_feats(f_name, query_img, img_dataset, n_imgs):
'''Generate and Print Constrainted Cluster\n
Parameters
f_name: cluster name
query_img: the query image
k: k neighbours for kNN
img_classSet: label class array
img_dataset: image dataset
n_imgs: Length of img List
'''
if f_name == "BOVW":
print('----------- Bag of Visual Words -----------')
t0 = time()
bovw_param = bovw.BOVW(random_state=random_state,
n_patches=n_patches, tam_patch=tam_patch, n_dic=n_dic)
features = bovw.execute_Bovw(
img_dataset, bovw_param, n_imgs)
query_feature = bovw.get_bovw_features(query_img, bovw_param)
print("BOVW features Creation time: %0.3fs" % (time() - t0))
elif f_name == "SIFT":
print('----------- SIFT -----------')
t0 = time()
sift_keypoints, features = SIFT.execute_sift(img_dataset, 1000)
query_feature = SIFT.get_sift_features(query_img)
print("SIFT features Creation time: %0.3fs" % (time() - t0))
elif f_name == "MPEG7":
print('----------- MPEG7 -----------')
t0 = time()
features = mpeg7.execute_mpeg7(img_dataset)
query_feature = mpeg7.get_mpeg7_features(query_img)
print("MPEG7 features Creation time: %0.3fs" % (time() - t0))
return features, query_feature
def main():
args = parser.parse_args()
k = int(args.k)
query = args.query
path_query = os.getcwd() + img_folder_path + query + '.jpg'
query_img = cv2.imread(path_query)
'''----------- Creating Data Set ------------------'''
t0 = time()
# image_classSet = dataset.extract_imageDescrip(img_class_set_names, img_class_path)
# image_dataset, n_imgs = dataset.create_dataset(img_folder_path)
image_classSet = dataset.extract_random_image_descrip(img_class_set_names)
image_dataset, n_imgs = dataset.create_random_dataset()
print("Data Set Creation time: %0.3fs" % (time() - t0))
'''Extract image features, candidates using knn & create constraints for given feature name'''
f_name_arr = ["MPEG7"]
for f_name in f_name_arr:
features, query_feature = gen_feats(
f_name, query_img, image_dataset, n_imgs)
'''----------- Candidate Selection ---------------'''
t0 = time()
cand_img, cand_features = cand_selec.select_candidates(
f_name, k, features, query_feature, image_dataset, n_imgs)
print("Candidate Selection time: %0.3fs" % (time() - t0))
'''----------- Constraint Creation ---------------'''
t0 = time()
cand_img, cons, dist_matrix = gen_cons.generate_constraints(
cand_img, cand_features, image_classSet, f_name)
print("Constraint Creation time: %0.3fs" % (time() - t0))
'''Generate Clusters'''
copk_clus, copk_labels = gen_clus("COP", cand_img, cons, f_name)
pck_clus, pck_labels = gen_clus("PC", cand_img, cons, f_name)
'''Evaluate Clustering'''
print(
f'COPKMeans Silhouette Score(n={k}): {clus_eval.silhouette_score(f_name, cons.x, copk_labels, len(cons.descripList), dist_matrix)}')
print(
f'PCKMeans Silhouette Score(n={k}): {clus_eval.silhouette_score(f_name, cons.x, pck_labels, len(cons.descripList), dist_matrix)}')
print(f'COPKMeans V-Measure Score(n={k}): {clus_eval.my_v_measure_score(cons.y, copk_labels)}')
print(f'PCKMeans V-Measure Score(n={k}): {clus_eval.my_v_measure_score(cons.y, pck_labels)}')
print(
f'COPKMeans Calinski Harabasz Score(n={k}): {clus_eval.my_calinski_harabasz_score(f_name, cons.x, copk_labels)}')
print(
f'PCKMeans SCalinski Harabasz Score(n={k}): {clus_eval.my_calinski_harabasz_score(f_name, cons.x, pck_labels)}')
if __name__ == "__main__":
main()