preprocess.py 文件源码

def gen_data(name):
    reftracker = scio.loadmat('data/images_tracker.00047.mat')['tracker']
    desttracker = scio.loadmat('data/images_tracker/'+name+'.mat')['tracker']
    refpos = np.floor(np.mean(reftracker, 0))
    xxc, yyc = np.meshgrid(np.arange(1, 1801, dtype=np.int), np.arange(1, 2001, dtype=np.int))
    #normalize x and y channels
    xxc = (xxc - 600 - refpos[0]) * 1.0 / 600
    yyc = (yyc - 600 - refpos[1]) * 1.0 / 600
    maskimg = Image.open('data/meanmask.png')
    maskc = np.array(maskimg, dtype=np.float)
    maskc = np.pad(maskc, (600, 600), 'minimum')
    # warp is an inverse transform, and so src and dst must be reversed here
    tform = transform.estimate_transform('affine', desttracker + 600, reftracker + 600)

    img_data = skio.imread('data/images_data/'+name+'.jpg')
    # save org mat
    warpedxx = transform.warp(xxc, tform, output_shape=xxc.shape)
    warpedyy = transform.warp(yyc, tform, output_shape=xxc.shape)
    warpedmask = transform.warp(maskc, tform, output_shape=xxc.shape)
    warpedxx = warpedxx[600:1400, 600:1200, :]
    warpedyy = warpedyy[600:1400, 600:1200, :]
    warpedmask = warpedmask[600:1400, 600:1200, :]
    img_h, img_w, _ = img_data.shape
    mat = np.zeros((img_h, img_w, 6), dtype=np.float)
    mat[:, :, 0] = (img_data[2] * 1.0 - 104.008) / 255
    mat[:, :, 1] = (img_data[1] * 1.0 - 116.669) / 255
    mat[:, :, 2] = (img_data[0] * 1.0 - 122.675) / 255
    scio.savemat('portraitFCN_data/' + name + '.mat', {'img':mat})
    mat_plus = np.zeros((img_h, img_w, 6), dtype=np.float)
    mat_plus[:, :, 0:3] = mat
    mat_plus[:, :, 3] = warpedxx
    mat_plus[:, :, 4] = warpedyy
    mat_plus[:, :, 5] = warpedmask