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")