def recognize(self):
if not self.filename:
QtWidgets.QMessageBox.critical(self.centralwidget,
'Selection error',
'You have to load a query image !')
return None
queryImage = cv2.imread(self.filename)
desc = RGBHistogram([8, 8, 8], 5)
queryFeatures = desc.describe(queryImage)
# load the index perform the search
if not os.path.exists("index.pkl"):
QtWidgets.QMessageBox.critical(
self.centralwidget, 'Indexing error',
'You have to Index the images features from the dataset !')
self.open_indexer()
return None
with open("index.pkl", 'rb') as handle:
index = pickle.load(handle)
searcher = Searcher(index)
results = searcher.search(queryFeatures)
# loop over the top ten results
images = [x[1] for x in results][:10]
self.thumbnail.update(images)
def index_imgs(self):
self.index_images.setEnabled(False)
if not self.images:
QtGui.QMessageBox.critical(self, 'Selection error',
'No image selected')
return
self.overlay.show()
index = {}
# initialize our image descriptor -- a 3D RGB histogram with
# 8 bins per channel
desc = RGBHistogram([8, 8, 8], 5)
# use glob to grab the image paths and loop over them
for imagePath in self.images:
# load the image, describe it using our RGB histogram
# descriptor, and update the index
image = cv2.imread(imagePath)
features = desc.describe(image)
index[imagePath] = features
# we are now done indexing our image -- now we can write our
# index to disk
filename = 'index.pkl'
with open(filename, 'wb') as handle:
pickle.dump(index, handle)
self.overlay.hide()
QtWidgets.QMessageBox.information(
self, 'Indexing complete',
'%s images indexed. Indexed file saves as %s' %
(len(self.images), filename))
def Match(queryname,k):
folder="static"
queryImage = cv2.imread(os.path.join(folder,queryname))
desc = RGBHistogram([8, 8, 8])
# load the index perform the search
#index = cPickle.loads(open("index.txt").read())
d = min(division(queryImage),7)
folder='indexIMG'
index = shelve.open(os.path.join(folder,'f%d.shelve'%d))
searcher = Searcher(index)
queryFeatures = desc.describe(queryImage)
results = searcher.search(queryFeatures)
l=len(results)
lastRes=results[:min(k,l)]
return lastRes
def search(image):
args = {'dataset': 'images', 'index': 'index.cpickle'}
queryImage = image
# cv2.imshow("Query", queryImage)
# print("query: {}".format(args["query"]))
desc = RGBHistogram([8, 8, 8])
queryFeatures = desc.describe(queryImage)
index = pickle.loads(open(args["index"], "rb").read())
searcher = Searcher(index)
results = searcher.search(queryFeatures)
# montageA = np.zeros((166 * 5, 400, 3), dtype="uint8")
# montageB = np.zeros((166 * 5, 400, 3), dtype="uint8")
for j in range(0, 1):
(score, imageName) = results[j]
path = os.path.join(args["dataset"], imageName)
result = cv2.imread(path)
print("\t{}. {} : {:.3f}".format(j + 1, imageName, score))
# construct the argument parser and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-d", "--dataset", required = True,
help = "Path to the directory that contains the images to be indexed")
ap.add_argument("-i", "--index", required = True,
help = "Path to where the computed index will be stored")
args = vars(ap.parse_args())
# initialize the index dictionary to store our our quantifed
# images, with the 'key' of the dictionary being the image
# filename and the 'value' our computed features
index = {}
# initialize our image descriptor -- a 3D RGB histogram with
# 8 bins per channel
desc = RGBHistogram([8, 8, 8])
# use glob to grab the image paths and loop over them
for imagePath in glob.glob(args["dataset"] + "/*.*"):
# extract our unique image ID (i.e. the filename)
k = imagePath[imagePath.rfind("/") + 1:]
# load the image, describe it using our RGB histogram
# descriptor, and update the index
image = cv2.imread(imagePath)
features = desc.describe(image)
index[k] = features
# we are now done indexing our image -- now we can write our
# index to disk
f = open(args["index"], "w")
ap.add_argument("-i",
"--index",
required=True,
help="Path to where we stored our index")
ap.add_argument("-q", "--query", required=True, help="Path to query image")
args = vars(ap.parse_args())
# load the query image and show it
queryImage = cv2.imread(args["query"])
cv2.imshow("Query", queryImage)
print "query: %s" % (args["query"])
# describe the query in the same way that we did in
# index.py -- a 3D RGB histogram with 8 bins per
# channel
desc = RGBHistogram([8, 8, 8])
queryFeatures = desc.describe(queryImage)
# load the index perform the search
index = cPickle.loads(open(args["index"]).read())
searcher = Searcher(index)
results = searcher.search(queryFeatures)
# initialize the two montages to display our results --
# we have a total of 25 images in the index, but let's only
# display the top 10 results; 5 images per montage, with
# images that are 400x166 pixels
# montageA = np.zeros((166 * 5, 400, 3), dtype = "uint8")
# montageB = np.zeros((166 * 5, 400, 3), dtype = "uint8")
# montageA = np.zeros((40 * 5, 100, 3), dtype = "uint8")
help = "Path to the directory that contains the images we just indexed")
ap.add_argument("-i", "--index", required = True,
help = "Path to where we stored our index")
ap.add_argument("-q", "--query", required = True,
help = "Path to query image")
args = vars(ap.parse_args())
# load the query image and show it
queryImage = cv2.imread(args["query"])
cv2.imshow("Query", queryImage)
print "query: %s" % (args["query"])
# describe the query in the same way that we did in
# index.py -- a 3D RGB histogram with 8 bins per
# channel
desc = RGBHistogram([8, 8, 8])
queryFeatures = desc.describe(queryImage)
# load the index perform the search
index = cPickle.loads(open(args["index"]).read())
searcher = Searcher(index)
results = searcher.search(queryFeatures)
# initialize the two montages to display our results --
# we have a total of 25 images in the index, but let's only
# display the top 10 results; 5 images per montage, with
# images that are 400x166 pixels
# montageA = np.zeros((166 * 5, 400, 3), dtype = "uint8")
# montageB = np.zeros((166 * 5, 400, 3), dtype = "uint8")
# montageA = np.zeros((40 * 5, 100, 3), dtype = "uint8")
