Extract features and save to a file.

vgg_face_demo.py

import numpy as np
import caffe

PROTOTEXT = "VGG_FACE_deploy.prototxt"
CAFFEMODEL = "VGG_FACE.caffemodel"
IMG_PATH = "ma_images/"
IMG_FILES = os.listdir(IMG_PATH)
OUTPUT_FILE = "train.csv"
#OUTPUT_FILE = "test.csv"

with open(OUTPUT_FILE, "w") as f:
    for i, img_file in enumerate(IMG_FILES):
        img = caffe.io.load_image(IMG_PATH + img_file)
        img = img[:,:,::-1]*255.0 # convert RGB->BGR
        avg = np.array([129.1863,104.7624,93.5940])
        img = img - avg # subtract mean (numpy takes care of dimensions :)

        # adopt caffe format
        img = img.transpose((2,0,1))
        img = img[None,:] # add singleton dimension
        net = caffe.Net(PROTOTEXT, CAFFEMODEL, caffe.TEST)
        net.forward_all( data = img )
        out = net.blobs["fc7"].data[0].flatten().tolist()
        # write header
        if i == 0:
            headers = ["feature" + str(i) for i in range(len(out))]
            header = ",".join(headers)
            f.write("label," + header + "\n")

        out = ",".join(map(str, out))
        label = "_".join(img_file[:-4].split("_")[2:])
        f.write(label + "," + out + "\n")

        # reset net
        net = None