def findSimilarImages(self):
self.allResults = []
qrCodeDetector = cv2.QRCodeDetector()
similarFile = open(self.similarImageFile, "w")
uniqueFile = open(self.uniqueImageFile, "w")
print("Finding similar images and decoding QR codes if any.... ")
for (i, imagePath) in enumerate(self.imagePaths):
# load the image
image = cv2.imread(imagePath)
arr = []
# convert image to hash
queryHash = dhash(image)
queryHash = convert_hash(queryHash)
results = self.treePickle.get_all_in_range(queryHash, 21)
for (count, (d, h)) in enumerate(results):
resultPaths = self.hashPickle.get(h, "")
arr.append(resultPaths[0])
if len(arr) > 1:
similarFile.write(", ".join(arr) + os.linesep)
elif len(arr) == 1:
uniqueFile.write(arr[0] + os.linesep)
similarFile.close()
uniqueFile.close()
def formHashes(self):
for (i, imagePath) in enumerate(self.imagePaths):
# load the input image
print("[INFO] processing image {}/{} {}".format(
i + 1, len(self.imagePaths), imagePath))
image = cv2.imread(imagePath)
# compute the hash for the image and convert it
h = dhash(image)
h = convert_hash(h)
# update the hashes dictionary
tmp = self.hashes.get(h, [])
tmp.append(imagePath)
self.hashes[h] = tmp
default=10,
help="maximum hamming distance")
args = vars(ap.parse_args())
# load the VP-Tree and hashes dictionary
print("[INFO] loading VP-Tree and hashes...")
tree = pickle.loads(open(args["tree"], "rb").read())
hashes = pickle.loads(open(args["hashes"], "rb").read())
# load the input query image
image = cv2.imread(args["query"])
cv2.imshow("Query", image)
# compute the hash for the query image, then convert it
queryHash = dhash(image)
queryHash = convert_hash(queryHash)
# perform the search
print("[INFO] performing search...")
start = time.time()
results = tree.get_all_in_range(queryHash, args["distance"])
results = sorted(results)
end = time.time()
print("[INFO] search took {} seconds".format(end - start))
# loop over the results
for (d, h) in results:
# grab all image paths in our dataset with the same hash
resultPaths = hashes.get(h, [])
print("[INFO] {} total image(s) with d: {}, h: {}".format(
len(resultPaths), d, h))
args = vars(ap.parse_args())
# grab the paths to the input images and initialize the dictionary
# of hashes
imagePaths = list(paths.list_images(args["images"]))
hashes = {}
# loop over the image paths
for (i, imagePath) in enumerate(imagePaths):
# load the input image
print("[INFO] processing image {}/{}".format(i + 1, len(imagePaths)))
image = cv2.imread(imagePath)
# compute the hash for the image and convert it
h = dhash(image)
h = convert_hash(h)
# update the hashes dictionary
l = hashes.get(h, [])
l.append(imagePath)
hashes[h] = l
# build the VP-Tree
print("[INFO] building VP-Tree...")
points = list(hashes.keys())
tree = vptree.VPTree(points, hamming)
# serialize the VP-Tree to disk
print("[INFO] serializing VP-Tree...")
f = open(args["tree"], "wb")
f.write(pickle.dumps(tree))
