def job():
# initialize the keypoint detector, local invariant descriptor, and the descriptor pipeline
detector = FeatureDetector_create("SURF")
descriptor = DescriptorExtractor_create("RootSIFT")
dad = DetectAndDescribe(detector, descriptor)
# loop over the lines of input
for line in Mapper.parse_input(sys.stdin):
# parse the line into the image ID, path, and image
imageID, path, image = Mapper.handle_input(line.strip())
# describe the image and initialize the output list
image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
image = imutils.resize(image, width=320)
(kps, descs) = dad.describe(image)
output = []
# loop over the keypoints and descriptors
for kp, vec in zip(kps, descs):
# update the output list as a 2-tuple of the keypoint (x,y)-coordinates and the feature vector
output.append((kp.tolist(), vec.tolist()))
# output the row to the reducer
Mapper.output_row(imageID, path, output, sep="\t")
default=500,
help="Approximate # of images in the dataset")
ap.add_argument(
"-b",
"--max-buffer-size",
type=int,
default=50000,
help="Maximum buffer size for # of features to be stored in memory")
ap.add_argument("-z", "--feature-detector", default="GFTT")
ap.add_argument("-e", "--extractor", default="BRISK")
args = vars(ap.parse_args())
# initialize the keypoint detector, local invariant descriptor, and the descriptor
# pipeline
detector = FeatureDetector_create(args["feature_detector"])
descriptor = DescriptorExtractor_create(args["extractor"])
dad = DetectAndDescribe(detector, descriptor)
print("Using {} feature detector".format(args["feature_detector"]))
print("Using {} descriptor extractor".format(args["extractor"]))
# initialize the feature indexer, then grab the image paths and randomly shuffle
# them
fi = FeatureIndexer(args["features_db"],
estNumImages=args["approx_images"],
maxBufferSize=args["max_buffer_size"],
verbose=True)
imagePaths = list(paths.list_images(args["dataset"]))
random.shuffle(imagePaths)
# loop over the images in the dataset
help="Path to the bag-of-visual-words database")
ap.add_argument("-c", "--codebook", required=True, help="Path to the codebook")
ap.add_argument("-i",
"--idf",
type=str,
help="Path to inverted document frequencies array")
ap.add_argument("-r",
"--relevant",
required=True,
help="Path to relevant dictionary")
args = vars(ap.parse_args())
# Initialize the keypoints detector, local invariant descriptor, descriptor pipeline,
# distance metric, and inverted document frequency array
detector = FeatureDetector_create("SURF")
descriptor = DescriptorExtractor_create("RootSIFT")
dad = DetectAndDescribe(detector, descriptor)
distanceMetric = dists.chi2_distance
idf = None
# If the path to the inverted document frequency array was supplied, then load the
# idf array and update the distance metric
if args["idf"] is not None:
idf = pickle.loads(open(args["idf"], "rb").read())
distanceMetric = distance.cosine
# Load the codebook vocabulary and initialize the bag-of-visual-words transformer
vocab = pickle.loads(open(args["codebook"], "rb").read())
bovw = BagOfVisualWords(vocab)
# Connect to redis and initialize the searcher
default=50000,
help="Maximum buffer size for # of features to be stored in memory")
args = vars(ap.parse_args())
##################
# CREATE KEYPOINT FEATURES
##################
print('\n\n STARTING FEATURE EXTRACTION \n\n')
# initialize the keypoint detector, local invariant descriptor, and the descriptor
# pipeline
detector = FeatureDetector_create('GFTT')
# RootSIFT was originally used in results presented in blog/README
# ORB was substituted since RootSIFT is not currently shipped with opencv-contrib-python
descriptor = DescriptorExtractor_create('ORB')
dad = DetectAndDescribe(detector, descriptor)
# initialize the feature indexer, then grab the image paths and sort
fi = FeatureIndexer('{}/features.hdf5'.format(args["output"]),
estNumImages=args["approx_images"],
maxBufferSize=args["max_buffer_size"],
verbose=True)
imagePaths = list(paths.list_images(args["dataset"]))
imagePaths.sort()
# loop over the images in the dataset
for (i, imagePath) in enumerate(imagePaths):
# check to see if progress should be displayed
if i > 0 and i % 50 == 0:
fi._debug("processed {} images".format(i), msgType="[PROGRESS]")
# author: Adrian Rosebrock
# website: http://www.pyimagesearch.com
# USAGE
# python feature_demo.py
# import the necessary packages
from imutils.feature import DescriptorExtractor_create
from imutils.feature import FeatureDetector_create
from imutils.feature import corners_to_keypoints
# ensure the keypoint detection and local invariant descriptors are
# working properly
detector = FeatureDetector_create("SIFT")
extractor = DescriptorExtractor_create("SIFT")
print(detector)
print(extractor)
print(corners_to_keypoints)
"-b",
"--max-buffer-size",
type=int,
default=50000,
help="Maximum buffer size for # of features to be stored in memory")
args = vars(ap.parse_args())
##################
# CREATE KEYPOINT FEATURES
##################
print('\n\n STARTING FEATURE EXTRACTION \n\n')
# initialize the keypoint detector, local invariant descriptor, and the descriptor
# pipeline
detector = FeatureDetector_create('GFTT')
descriptor = DescriptorExtractor_create('RootSIFT')
dad = DetectAndDescribe(detector, descriptor)
# initialize the feature indexer, then grab the image paths and sort
fi = FeatureIndexer('{}/features.hdf5'.format(args["output"]),
estNumImages=args["approx_images"],
maxBufferSize=args["max_buffer_size"],
verbose=True)
imagePaths = list(paths.list_images(args["dataset"]))
imagePaths.sort()
# loop over the images in the dataset
for (i, imagePath) in enumerate(imagePaths):
# check to see if progress should be displayed
if i > 0 and i % 50 == 0:
fi._debug("processed {} images".format(i), msgType="[PROGRESS]")
ap.add_argument("-a",
"--approx-images",
type=int,
default=500,
help="Approximate # of images in the dataset")
ap.add_argument(
"-b",
"--max-buffer-size",
type=int,
default=50000,
help="Maximum buffer size for # of features to be stored in memory")
args = vars(ap.parse_args())
# Initialize the keypoint detector, local invariant descriptor, and the descriptor pipeline
detector = FeatureDetector_create("BRISK")
descriptor = DescriptorExtractor_create("BRISK")
dad = DetectAndDescribe(detector, descriptor)
# Initialize the feature indexer
fi = FeatureIndexer(args["features_db"],
estNumImages=args["approx_images"],
maxBufferSize=args["max_buffer_size"],
verbose=True)
# Loop over the images in the dataset
for (i, imagePath) in enumerate(sorted(paths.list_images(args["dataset"]))):
# Check to see if progress should be displayed
if i > 0 and i % 10 == 0:
fi._debug("processed {} images".format(i), msgType="[PROGRESS]")
# Extract the image filename (i.e. the unique image ID) from the image path, then load the image itself
filename = imagePath[imagePath.rfind("\\") + 1:]