INNER CODE UNIT · Python

idx

MITDeepLearning/introtodeeplearning · mitdeeplearning/lab2.py:104

        idx = np.argsort(prob)[::-1]
        most_prob_inds = self.pos_train_inds[idx[: 10 * n : 10]]
        return (self.images[most_prob_inds, ...] / 255.0).astype(np.float32)

    def get_all_train_faces(self):
        return self.images[self.pos_train_inds]


def get_test_faces(channels_last=True):
    cwd = os.path.dirname(__file__)
    images = {"LF": [], "LM": [], "DF": [], "DM": []}
    for key in images.keys():
        files = glob.glob(os.path.join(cwd, "data", "faces", key, "*.png"))
        for file in sorted(files):
            image = cv2.resize(cv2.imread(file), (64, 64))[:, :, ::-1] / 255.0
            if not channels_last:
                image = np.transpose(image, (2, 0, 1))
            images[key].append(image)

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