python类fromimage()的实例源码

def original_color_transform(content, generated, mask=None, hist_match=0, mode='YCbCr'):
    generated = fromimage(toimage(generated, mode='RGB'), mode=mode)  # Convert to YCbCr color space

    if mask is None:
        if hist_match == 1:
            for channel in range(3):
                generated[:, :, channel] = match_histograms(generated[:, :, channel], content[:, :, channel])
        else:
            generated[:, :, 1:] = content[:, :, 1:]
    else:
        width, height, channels = generated.shape

        for i in range(width):
            for j in range(height):
                if mask[i, j] == 1:
                    if hist_match == 1:
                        for channel in range(3):
                            generated[i, j, channel] = match_histograms(generated[i, j, channel], content[i, j, channel])
                    else:
                        generated[i, j, 1:] = content[i, j, 1:]

    generated = fromimage(toimage(generated, mode=mode), mode='RGB')  # Convert to RGB color space
    return generated


# util function to load masks

def original_color_transform(content, generated, mask=None):
    generated = fromimage(toimage(generated, mode='RGB'), mode='YCbCr')  # Convert to YCbCr color space

    if mask is None:
        generated[:, :, 1:] = content[:, :, 1:]  # Generated CbCr = Content CbCr
    else:
        width, height, channels = generated.shape

        for i in range(width):
            for j in range(height):
                if mask[i, j] == 1:
                    generated[i, j, 1:] = content[i, j, 1:]

    generated = fromimage(toimage(generated, mode='YCbCr'), mode='RGB')  # Convert to RGB color space
    return generated

def original_color_transform(content, generated):
    generated = fromimage(toimage(generated, mode='RGB'), mode='YCbCr')  # Convert to YCbCr color space
    generated[:, :, 1:] = content[:, :, 1:]  # Generated CbCr = Content CbCr
    generated = fromimage(toimage(generated, mode='YCbCr'), mode='RGB')  # Convert to RGB color space
    return generated

def original_color_transform(content, generated):
    generated = fromimage(toimage(generated, mode='RGB'), mode='YCbCr')  # Convert to YCbCr color space
    generated[:, :, 1:] = content[:, :, 1:]  # Generated CbCr = Content CbCr
    generated = fromimage(toimage(generated, mode='YCbCr'), mode='RGB')  # Convert to RGB color space
    return generated

def original_color_transform(content, generated, mask=None):
    generated = fromimage(toimage(generated, mode='RGB'), mode='YCbCr')  # Convert to YCbCr color space

    if mask is None:
        generated[:, :, 1:] = content[:, :, 1:]  # Generated CbCr = Content CbCr
    else:
        width, height, channels = generated.shape

        for i in range(width):
            for j in range(height):
                if mask[i, j] == 1:
                    generated[i, j, 1:] = content[i, j, 1:]

    generated = fromimage(toimage(generated, mode='YCbCr'), mode='RGB')  # Convert to RGB color space
    return generated

def original_color_transform(content, generated, mask=None):
    generated = fromimage(toimage(generated, mode='RGB'), mode='YCbCr')  # Convert to YCbCr color space

    if mask is None:
        generated[:, :, 1:] = content[:, :, 1:]  # Generated CbCr = Content CbCr
    else:
        width, height, channels = generated.shape

        for i in range(width):
            for j in range(height):
                if mask[i, j] == 1:
                    generated[i, j, 1:] = content[i, j, 1:]

    generated = fromimage(toimage(generated, mode='YCbCr'), mode='RGB')  # Convert to RGB color space
    return generated

def find_dominant_colors(image):
    """Cluster the colors of the image in CLUSTER_NUMBER of clusters. Returns
    an array of dominant colors reverse sorted by cluster size.
    """

    array = img_as_float(fromimage(image))

    # Reshape from MxNx4 to Mx4 array
    array = array.reshape(scipy.product(array.shape[:2]), array.shape[2])

    # Remove transparent pixels if any (channel 4 is alpha)
    if array.shape[-1] > 3:
        array = array[array[:, 3] == 1]

    # Finding centroids (centroids are colors)
    centroids, _ = kmeans(array, CLUSTER_NUMBER)

    # Allocate pixel to a centroid cluster
    observations, _ = vq(array, centroids)

    # Calculate the number of pixels in a cluster
    histogram, _ = scipy.histogram(observations, len(centroids))

    # Sort centroids by number of pixels in their cluster
    sorted_centroids = sorted(zip(centroids, histogram),
                              key=lambda x: x[1],
                              reverse=True)

    sorted_colors = tuple((couple[0] for couple in sorted_centroids))

    return sorted_colors

def original_color_transform(content, generated, mask=None):
    generated = fromimage(toimage(generated, mode='RGB'), mode='YCbCr')  # Convert to YCbCr color space

    if mask is None:
        generated[:, :, 1:] = content[:, :, 1:]  # Generated CbCr = Content CbCr
    else:
        width, height, channels = generated.shape

        for i in range(width):
            for j in range(height):
                if mask[i, j] == 1:
                    generated[i, j, 1:] = content[i, j, 1:]

    generated = fromimage(toimage(generated, mode='YCbCr'), mode='RGB')  # Convert to RGB color space
    return generated

def draw_label(label, img, n_class, label_titles, bg_label=0):
    """Convert label to rgb with label titles.

    @param label_title: label title for each labels.
    @type label_title: dict
    """
    from PIL import Image
    from scipy.misc import fromimage
    from skimage.color import label2rgb
    from skimage.transform import resize
    colors = labelcolormap(n_class)
    label_viz = label2rgb(label, img, colors=colors[1:], bg_label=bg_label)
    # label 0 color: (0, 0, 0, 0) -> (0, 0, 0, 255)
    label_viz[label == 0] = 0

    # plot label titles on image using matplotlib
    plt.subplots_adjust(left=0, right=1, top=1, bottom=0,
                        wspace=0, hspace=0)
    plt.margins(0, 0)
    plt.gca().xaxis.set_major_locator(plt.NullLocator())
    plt.gca().yaxis.set_major_locator(plt.NullLocator())
    plt.axis('off')
    # plot image
    plt.imshow(label_viz)
    # plot legend
    plt_handlers = []
    plt_titles = []
    for label_value in np.unique(label):
        if label_value not in label_titles:
            continue
        fc = colors[label_value]
        p = plt.Rectangle((0, 0), 1, 1, fc=fc)
        plt_handlers.append(p)
        plt_titles.append(label_titles[label_value])
    plt.legend(plt_handlers, plt_titles, loc='lower right', framealpha=0.5)
    # convert plotted figure to np.ndarray
    f = StringIO.StringIO()
    plt.savefig(f, bbox_inches='tight', pad_inches=0)
    result_img_pil = Image.open(f)
    result_img = fromimage(result_img_pil, mode='RGB')
    result_img = resize(result_img, img.shape, preserve_range=True)
    result_img = result_img.astype(img.dtype)
    return result_img