feat_ex.py 文件源码

def compute(i):
        img_file = files[i]
        img = cv2.imread(img_file, cv2.CV_LOAD_IMAGE_GRAYSCALE)
        if img is None:
            print 'img {} is None, path correct? --> skip'.format(img_file)
            return

        kpts = fe.detect(img)
        _, descriptors = fe.extract(img, kpts)

        if descriptors is None or len(descriptors) == 0:
            print 'WARNING: no descriptors extracted, skip image', img_file
            sys.exit(1)

        # Hellinger normalization
        descriptors += np.finfo(np.float32).eps
        descriptors /= np.sum(descriptors, axis=1)[:,np.newaxis]
        descriptors = np.sqrt(descriptors)

        # output
        new_basename = os.path.join(args.outputfolder,
                                    os.path.basename(os.path.splitext(img_file)[0]))
        feat_filename = new_basename + '_' + args.detector \
                        + '_' + args.feature + '.pkl.gz'
        with gzip.open(feat_filename, 'wb') as f:
            cPickle.dump(descriptors, f, -1)

        progress.update(i+1)