caffe_functions.py 文件源码

def make_net(mean=None, net_dir='VGG_S_rgb'):
    # net_dir specifies a root directory containing a *.caffemodel file
    # Options in our setup are: VGG_S_[rgb / lbp / cyclic_lbp / cyclic_lbp_5 / cyclic_lbp_10]

    # This should hopefully already be in your system path, but just to be sure:
    caffe_root = '/home/Users/Dan/Development/caffe/'
    sys.path.insert(0, caffe_root + 'python')

    # Configure matplotlib
    plt.rcParams['figure.figsize'] = (10, 10)
    plt.rcParams['image.interpolation'] = 'nearest'
    plt.rcParams['image.cmap'] = 'gray'

    # Generate paths to the various model files
    net_root = 'models'
    net_pretrained = os.path.join(net_root, net_dir, 'EmotiW_VGG_S.caffemodel')
    net_model_file = os.path.join(net_root, net_dir, 'deploy.prototxt')

    # Construct Caffe network object
    VGG_S_Net = caffe.Classifier(net_model_file, net_pretrained,
                       mean=mean,
                       channel_swap=(2,1,0),
                       raw_scale=255,
                       image_dims=(256, 256))
    return VGG_S_Net

# Load a minibatch of images
# Inputs:   List of image filenames, 
#           Color boolean (true if images are in color), 
#           List of labels corresponding to each image, 
#           Index of first image to load
#           Number of images to load
# Output:   List of image numpy arrays of size Num x (W x H x 3)
#           List of labels for just the images in the batch