コード例 #1
0
def get_similar(image):
    db = Database()
    method = Color()
    samples = method.make_samples(db)
    # make sure image is ndarray
    query = {
        'img': str(uuid.uuid1()),
        'cls': 'TBD',
        'hist': method.histogram(image)
    }
    _, result = infer(query, samples=samples)
    return [r['url'] for r in result]
コード例 #2
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        for d in depths:
            APs = evaluate_class(db, f_instance=fusion, d_type=d_type, depth=d)
            cls_MAPs = []
            for cls, cls_APs in APs.items():
                MAP = np.mean(cls_APs)
                cls_MAPs.append(MAP)
            r = "{},{},{},{}".format(",".join(combination), d, d_type,
                                     np.mean(cls_MAPs))
            print(r)
            result.write('\n' + r)
        print()
    result.close()


if __name__ == "__main__":
    db = Database()

    # evaluate features double-wise
    evaluate_feats(db, N=2, d_type='d1')

    # evaluate features triple-wise
    evaluate_feats(db, N=3, d_type='d1')

    # evaluate features quadra-wise
    evaluate_feats(db, N=4, d_type='d1')

    # evaluate features penta-wise
    evaluate_feats(db, N=5, d_type='d1')

    # evaluate features hexa-wise
    evaluate_feats(db, N=6, d_type='d1')
コード例 #3
0
                    continue
                samples.append({
                    'img': d_img,
                    'cls': d_cls,
                    'url': d_url,
                    'hist': d_hist
                })
            cPickle.dump(
                samples, open(os.path.join(cache_dir, sample_cache), "wb",
                              True))

        return samples


if __name__ == "__main__":
    db = Database()
    data = db.get_data()
    color = Color()

    # test normalize
    print(os.path.join(DATASETS_PATH, data['cls'][0], data['img'][0]))
    hist = color.histogram(os.path.join(DATASETS_PATH, data['cls'][0],
                                        data['img'][0] + '.jpg'),
                           type='global')
    assert hist.sum() - 1 < 1e-9, "normalize false"

    # test histogram bins
    def sigmoid(z):
        a = 1.0 / (1.0 + np.exp(-1. * z))
        return a