14_DeepDream.py 文件源码

def resize_image(image, size=None, factor=None):
    # If a rescaling-factor is provided then use it.
    if factor is not None:
        # Scale the numpy array's shape for height and width.
        size = np.array(image.shape[0:2]) * factor

        # The size is floating-point because it was scaled.
        # PIL requires the size to be integers.
        size = size.astype(int)
    else:
        # Ensure the size has length 2.
        size = size[0:2]

    # The height and width is reversed in numpy vs. PIL.
    size = tuple(reversed(size))

    # Ensure the pixel-values are between 0 and 255.
    img = np.clip(image, 0.0, 255.0)

    # Convert the pixels to 8-bit bytes.
    img = img.astype(np.uint8)

    # Create PIL-object from numpy array.
    img = PIL.Image.fromarray(img)

    # Resize the image.
    img_resized = img.resize(size, PIL.Image.LANCZOS)

    # Convert 8-bit pixel values back to floating-point.
    img_resized = np.float32(img_resized)

    return img_resized


# ## DeepDream Algorithm

# ### Gradient

# The following helper-functions calculate the gradient of an input image for use in the DeepDream algorithm. The Inception 5h model can accept images of any size, but very large images may use many giga-bytes of RAM. In order to keep the RAM-usage low we will split the input image into smaller tiles and calculate the gradient for each of the tiles. 
# 
# However, this may result in visible lines in the final images produced by the DeepDream algorithm. We therefore choose the tiles randomly so the locations of the tiles are always different. This makes the seams between the tiles invisible in the final DeepDream image.

# This is a helper-function for determining an appropriate tile-size. The desired tile-size is e.g. 400x400 pixels, but the actual tile-size will depend on the image-dimensions.

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