def __init__(self, useSIFT = False, useHamming = True, ratio = 0.7, minMatches = 40): # store whether or not SIFT should be used as the feature # detector and extractor self.useSIFT = useSIFT self.useHamming = useHamming self.ratio = ratio self.minMatches = minMatches # if SIFT is to be used, then update the parameters if useSIFT: self.minMatches = 50 self.cd = CoverDescriptor(useSIFT = useSIFT) self.cv = CoverMatcher(self.cd, ratio = ratio, minMatches = minMatches, useHamming = useHamming)
# Converts np.array to TEXT when inserting
sqlite3.register_adapter(numpy.ndarray, adapt_array)
# Converts TEXT to np.array when selecting
sqlite3.register_converter("array", convert_array)
ap = argparse.ArgumentParser()
args = vars(ap.parse_args())
useSIFT = False
useHamming = True
# initialize the cover descriptor and cover matcher
cd = CoverDescriptor(useSIFT=useSIFT)
conn = sqlite3.connect('db', detect_types=sqlite3.PARSE_DECLTYPES)
cur = conn.cursor()
c = conn.cursor()
for imgPath in glob.glob("newimages/*.png"):
# load the query image, convert it to grayscale, and
# extract keypoints and descriptors
cover = cv2.imread(imgPath)
gray = cv2.cvtColor(cover, cv2.COLOR_BGR2GRAY)
(kps, descs) = CoverDescriptor.describe(cd, gray)
c.execute(
'INSERT INTO images(filename,keypoints,descriptors) VALUES(?,?,?)',
for l in csv.reader(open(args["db"])):
# update the database using the image ID as the key
db[l[0]] = l[1:]
# initialize the default parameters using BRISK is being used
useSIFT = args["sift"] > 0
useHamming = args["sift"] == 0
ratio = 0.7
minMatches = 40
# if SIFT is to be used, then update the parameters
if useSIFT:
minMatches = 50
# initialize the cover descriptor and cover matcher
cd = CoverDescriptor(useSIFT=useSIFT)
cv = CoverMatcher(cd,
glob.glob(args["covers"] + "/*.png"),
ratio=ratio,
minMatches=minMatches,
useHamming=useHamming)
# load the query image, convert it to grayscale, and extract
# keypoints and descriptors
queryImage = cv2.imread(args["query"])
gray = cv2.cvtColor(queryImage, cv2.COLOR_BGR2GRAY)
(queryKps, queryDescs) = cd.describe(gray)
# try to match the book cover to a known database of images
results = cv.search(queryKps, queryDescs)
# otherwise, load the video
else:
camera = cv2.VideoCapture(args["video"])
# initialize the database dictionary of covers
db = {}
# loop over the database, CSV file is opened and each line looped over.
# The db dictionary is updated with the unique filename of the book as the key and
# the title of the book and author as the value.
for l in csv.reader(open(args["db"])):
# update the database using the image ID as the key
db[l[0]] = l[1:]
# initialize the cover descriptor and cover matcher
cd = CoverDescriptor()
cv = CoverMatcher(cd, glob.glob(args["empaques"] + "/*.png"))
#########################################################################################################
# keep looping
while True:
# grab the current frame
(grabbed, frame) = camera.read()
# if we are viewing a video and we did not grab a
# frame, then we have reached the end of the video
if args.get("video") and not grabbed:
break
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
ap.add_argument("-c", "--covers", required = True,
help = "path to the directory that contains our book covers")
ap.add_argument("-q", "--query", required = True,
help = "path to the query book cover")
args = vars(ap.parse_args())
# initialize the database dictionary of covers
db = {}
# loop over the database
for l in csv.reader(open(args["db"])):
# update the database using the image ID as the key
db[l[0]] = l[1:]
# initialize the cover descriptor and cover matcher
cd = CoverDescriptor()
cv = CoverMatcher(cd, glob.glob(args["covers"] + "/*.png"))
# load the query image, convert it to grayscale, and extract
# keypoints and descriptors
queryImage = cv2.imread(args["query"])
gray = cv2.cvtColor(queryImage, cv2.COLOR_BGR2GRAY)
(queryKps, queryDescs) = cd.describe(gray)
# try to match the book cover to a known database of images
results = cv.search(queryKps, queryDescs)
# show the query cover
cv2.imshow("Query", queryImage)
# check to see if no results were found
# whether cropping is being performed or not
refPt = []
cropping = False
# initialize the database dictionary of covers
db = {}
# loop over the database, CSV file is opened and each line looped over.
# The db dictionary is updated with the unique filename of the book as the key and
# the title of the book and author as the value.
for l in csv.reader(open(args["db"])):
# update the database using the image ID as the key
db[l[0]] = l[1:]
# initialize the cover descriptor and cover matcher
cd = CoverDescriptor()
cv = CoverMatcher(cd, glob.glob(args["empaques"] + "/*.png"))
def click_search(event, x, y, flags, param):
if event == cv2.EVENT_LBUTTONDOWN:
print "Buscando .... "
elif event == cv2.EVENT_LBUTTONUP:
# grab the current frame
(grabbed, searched_image) = camera.read()
# if we are viewing a video and we did not grab a
# frame, then we have reached the end of the video
if args.get("video") and not grabbed:
# initialize the cover descriptor and cover matcher
cd = CoverDescriptor(useSIFT = useSIFT)
conn = sqlite3.connect('db', detect_types=sqlite3.PARSE_DECLTYPES)
cur = conn.cursor()
c = conn.cursor()
for imgPath in glob.glob("newimages/*.png"):
# load the query image, convert it to grayscale, and
# extract keypoints and descriptors
cover = cv2.imread(imgPath)
gray = cv2.cvtColor(cover, cv2.COLOR_BGR2GRAY)
(kps, descs) = CoverDescriptor.describe(cd, gray)
c.execute('INSERT INTO images(filename,keypoints,descriptors) VALUES(?,?,?)',
(imgPath, kps, descs))
# print(kps)
# print(descs)
conn.commit()
conn.close()
