mxnet_predict_example.py 文件源码

def PreprocessImage(path, show_img=False):
    # load image
    img = io.imread(path)
    print("Original Image Shape: ", img.shape)
    # we crop image from center
    short_egde = min(img.shape[:2])
    yy = int((img.shape[0] - short_egde) / 2)
    xx = int((img.shape[1] - short_egde) / 2)
    crop_img = img[yy : yy + short_egde, xx : xx + short_egde]
    # resize to 224, 224
    resized_img = transform.resize(crop_img, (224, 224))
    if show_img:
        io.imshow(resized_img)
    # convert to numpy.ndarray
    sample = np.asarray(resized_img) * 255
    # swap axes to make image from (224, 224, 3) to (3, 224, 224)
    sample = np.swapaxes(sample, 0, 2)
    sample = np.swapaxes(sample, 1, 2)

    # sub mean
    normed_img = sample - mean_img
    normed_img.resize(1, 3, 224, 224)
    return normed_img

# Get preprocessed batch (single image batch)