我们从Python开源项目中，提取了以下9个代码示例，用于说明如何使用scipy.misc.fromimage()。
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 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 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
