import matplotlib.pyplot as plt
from datetime import datetime as d
# initialize the image descriptor
cd = ColorDescriptor()
# load the query image
query = cv2.imread('queries/3_301.jpg')
# Set the timer
now = d.now()
current = now.strftime("%H:%M:%S")
print(current)
# describe LBP features and perform the search
lbp_features = cd.calculate_lbp(query)
lbp_searcher = Searcher("lbp_value.csv")
lbp_results = lbp_searcher.search(lbp_features)
print(lbp_results)
# Set the timer
now = d.now()
current = now.strftime("%H:%M:%S")
print(current)
# Plot result images from LBP
filenames = []
for (score, resultID) in lbp_results:
filenames.append("Corel/" + resultID)
print(filenames)
dir_images = 'Corel'
imgs = os.listdir(dir_images)
# Set the timer
now = d.now()
current = now.strftime("%H:%M:%S")
print(current)
# Store imageID and LBP features
output = open("lbp_value.csv", "w")
for imgnm in imgs:
img_rgb = plt.imread(os.path.join(dir_images,imgnm))
imageID = imgnm
features = cd.calculate_lbp(img_rgb)
# write the features to file
features = [str(f) for f in features]
output.write("%s,%s\n" % (imageID, ",".join(features)))
output.close()
# Set the timer
now = d.now()
current = now.strftime("%H:%M:%S")
print(current)
# Store imageID and BEM features
i = 0
output = open("bem_value.csv", "w")
