MAV_Compute.py 文件源码

def compute_mean_vector(category_name, labellist, layer = 'fc8'):
    print category_name
    featurefile_list = glob.glob('%s/%s/*.mat' %(featurefilepath, category_name))

    # gather all the training samples for which predicted category
    # was the category under consideration
    correct_features = []
    for featurefile in featurefile_list:
        try:
            img_arr = loadmat(featurefile)
            predicted_category = labellist[img_arr['scores'].argmax()]
            if predicted_category == category_name:
                correct_features += [img_arr[layer]]
        except TypeError:
            continue

    # Now compute channel wise mean vector
    channel_mean_vec = []
    for channelid in range(correct_features[0].shape[0]):
        channel = []
        for feature in correct_features:
            channel += [feature[channelid, :]]
        channel = sp.asarray(channel)
        assert len(correct_features) == channel.shape[0]
        # Gather mean over each channel, to get mean channel vector
        channel_mean_vec += [sp.mean(channel, axis=0)]

    # this vector contains mean computed over correct classifications
    # for each channel separately
    channel_mean_vec = sp.asarray(channel_mean_vec)
    savemat('%s.mat' %category_name, {'%s'%category_name: channel_mean_vec})