from utilities.coverdescriptor import CoverDescriptor

from utilities.covermatcher import CoverMatcher

import glob

import csv

import cv2

import ntpath

# Define paths

database_path = 'books.csv'

covers_path = 'covers'

query_path = 'queries/query01.png'

# Initialize the default parameters using BRISK is being used

use_sift = False

use_hamming = True

ratio = 0.7

min_matches = 40

# Initialize the database dictionary of covers

database = {}

# Loop over the database

for l in csv.reader(open(database_path)):

# Update the database using the image ID as the key

database[l[0]] = l[1:]

# If SIFT is to be used, then update the parameters

if use_sift:

min_matches = 50

# Initialize the cover descriptor and cover matcher

cover_descriptor = CoverDescriptor(use_sift=use_sift)

cover_matcher = CoverMatcher(cover_descriptor, glob.glob(covers_path + "/*.png"), ratio=ratio, min_matches=min_matches,

use_hamming=use_hamming)

# Load the query image, convert it to greyscale, and extract keypoints and descriptors

query_image = cv2.imread(query_path)

gray = cv2.cvtColor(query_image, cv2.COLOR_BGR2GRAY)

(query_keypoints, query_descriptors) = cover_descriptor.describe(gray)

# Try to match the book cover to a known database of images

results = cover_matcher.search(query_keypoints, query_descriptors)

# Show the query cover

cv2.imshow("Query", query_image)

# Check to see if no results were found

if len(results) == 0:

print("A match could not be found for that cover!")

cv2.waitKey(0)

# Otherwise, matches were found

else:

# Loop over the results

for (i, (score, cover_path)) in enumerate(results):

# Grab the book information

(author, title) = database[ntpath.basename(cover_path)]

print("{}. {:.2f}% : {} - {}".format(i+1, score*100, author, title))

# Load the result image and show it

result = cv2.imread(cover_path)

cv2.imshow("Result", result)

cv2.waitKey(0)