|
- #! /usr/bin/python
- # -*- coding: utf-8 -*-
-
- import os
-
- import imageio
- import numpy as np
- import tensorlayer as tl
- from tensorlayer.lazy_imports import LazyImport
- import colorsys, random
-
- cv2 = LazyImport("cv2")
-
- # Uncomment the following line if you got: _tkinter.TclError: no display name and no $DISPLAY environment variable
- # import matplotlib
- # matplotlib.use('Agg')
-
- __all__ = [
- 'read_image', 'read_images', 'save_image', 'save_images', 'draw_boxes_and_labels_to_image',
- 'draw_mpii_people_to_image', 'frame', 'CNN2d', 'images2d', 'tsne_embedding', 'draw_weights', 'W',
- 'draw_boxes_and_labels_to_image_with_json'
- ]
-
-
- def read_image(image, path=''):
- """Read one image.
-
- Parameters
- -----------
- image : str
- The image file name.
- path : str
- The image folder path.
-
- Returns
- -------
- numpy.array
- The image.
-
- """
- return imageio.imread(os.path.join(path, image))
-
-
- def read_images(img_list, path='', n_threads=10, printable=True):
- """Returns all images in list by given path and name of each image file.
-
- Parameters
- -------------
- img_list : list of str
- The image file names.
- path : str
- The image folder path.
- n_threads : int
- The number of threads to read image.
- printable : boolean
- Whether to print information when reading images.
-
- Returns
- -------
- list of numpy.array
- The images.
-
- """
- imgs = []
- for idx in range(0, len(img_list), n_threads):
- b_imgs_list = img_list[idx:idx + n_threads]
- b_imgs = tl.prepro.threading_data(b_imgs_list, fn=read_image, path=path)
- # tl.logging.info(b_imgs.shape)
- imgs.extend(b_imgs)
- if printable:
- tl.logging.info('read %d from %s' % (len(imgs), path))
- return imgs
-
-
- def save_image(image, image_path='_temp.png'):
- """Save a image.
-
- Parameters
- -----------
- image : numpy array
- [w, h, c]
- image_path : str
- path
-
- """
- try: # RGB
- imageio.imwrite(image_path, image)
- except Exception: # Greyscale
- imageio.imwrite(image_path, image[:, :, 0])
-
-
- def save_images(images, size, image_path='_temp.png'):
- """Save multiple images into one single image.
-
- Parameters
- -----------
- images : numpy array
- (batch, w, h, c)
- size : list of 2 ints
- row and column number.
- number of images should be equal or less than size[0] * size[1]
- image_path : str
- save path
-
- Examples
- ---------
- >>> import numpy as np
- >>> import tensorlayer as tl
- >>> images = np.random.rand(64, 100, 100, 3)
- >>> tl.visualize.save_images(images, [8, 8], 'temp.png')
-
- """
- if len(images.shape) == 3: # Greyscale [batch, h, w] --> [batch, h, w, 1]
- images = images[:, :, :, np.newaxis]
-
- def merge(images, size):
- h, w = images.shape[1], images.shape[2]
- img = np.zeros((h * size[0], w * size[1], 3), dtype=images.dtype)
- for idx, image in enumerate(images):
- i = idx % size[1]
- j = idx // size[1]
- img[j * h:j * h + h, i * w:i * w + w, :] = image
- return img
-
- def imsave(images, size, path):
- if np.max(images) <= 1 and (-1 <= np.min(images) < 0):
- images = ((images + 1) * 127.5).astype(np.uint8)
- elif np.max(images) <= 1 and np.min(images) >= 0:
- images = (images * 255).astype(np.uint8)
-
- return imageio.imwrite(path, merge(images, size))
-
- if len(images) > size[0] * size[1]:
- raise AssertionError("number of images should be equal or less than size[0] * size[1] {}".format(len(images)))
-
- return imsave(images, size, image_path)
-
-
- def draw_boxes_and_labels_to_image(
- image, classes, coords, scores, classes_list, is_center=True, is_rescale=True, save_name=None
- ):
- """Draw bboxes and class labels on image. Return or save the image with bboxes, example in the docs of ``tl.prepro``.
-
- Parameters
- -----------
- image : numpy.array
- The RGB image [height, width, channel].
- classes : list of int
- A list of class ID (int).
- coords : list of int
- A list of list for coordinates.
- - Should be [x, y, x2, y2] (up-left and botton-right format)
- - If [x_center, y_center, w, h] (set is_center to True).
- scores : list of float
- A list of score (float). (Optional)
- classes_list : list of str
- for converting ID to string on image.
- is_center : boolean
- Whether the coordinates is [x_center, y_center, w, h]
- - If coordinates are [x_center, y_center, w, h], set it to True for converting it to [x, y, x2, y2] (up-left and botton-right) internally.
- - If coordinates are [x1, x2, y1, y2], set it to False.
- is_rescale : boolean
- Whether to rescale the coordinates from pixel-unit format to ratio format.
- - If True, the input coordinates are the portion of width and high, this API will scale the coordinates to pixel unit internally.
- - If False, feed the coordinates with pixel unit format.
- save_name : None or str
- The name of image file (i.e. image.png), if None, not to save image.
-
- Returns
- -------
- numpy.array
- The saved image.
-
- References
- -----------
- - OpenCV rectangle and putText.
- - `scikit-image <http://scikit-image.org/docs/dev/api/skimage.draw.html#skimage.draw.rectangle>`__.
-
- """
- if len(coords) != len(classes):
- raise AssertionError("number of coordinates and classes are equal")
-
- if len(scores) > 0 and len(scores) != len(classes):
- raise AssertionError("number of scores and classes are equal")
-
- # don't change the original image, and avoid error https://stackoverflow.com/questions/30249053/python-opencv-drawing-errors-after-manipulating-array-with-numpy
- image = image.copy()
-
- imh, imw = image.shape[0:2]
- thick = int((imh + imw) // 430)
-
- for i, _v in enumerate(coords):
- if is_center:
- x, y, x2, y2 = tl.prepro.obj_box_coord_centroid_to_upleft_butright(coords[i])
- else:
- x, y, x2, y2 = coords[i]
-
- if is_rescale: # scale back to pixel unit if the coords are the portion of width and high
- x, y, x2, y2 = tl.prepro.obj_box_coord_scale_to_pixelunit([x, y, x2, y2], (imh, imw))
-
- cv2.rectangle(
- image,
- (int(x), int(y)),
- (int(x2), int(y2)), # up-left and botton-right
- [0, 255, 0],
- thick
- )
-
- cv2.putText(
- image,
- classes_list[classes[i]] + ((" %.2f" % (scores[i])) if (len(scores) != 0) else " "),
- (int(x), int(y)), # button left
- 0,
- 1.5e-3 * imh, # bigger = larger font
- [0, 0, 256], # self.meta['colors'][max_indx],
- int(thick / 2) + 1
- ) # bold
-
- if save_name is not None:
- # cv2.imwrite('_my.png', image)
- save_image(image, save_name)
- # if len(coords) == 0:
- # tl.logging.info("draw_boxes_and_labels_to_image: no bboxes exist, cannot draw !")
- return image
-
-
- def draw_mpii_pose_to_image(image, poses, save_name='image.png'):
- """Draw people(s) into image using MPII dataset format as input, return or save the result image.
-
- This is an experimental API, can be changed in the future.
-
- Parameters
- -----------
- image : numpy.array
- The RGB image [height, width, channel].
- poses : list of dict
- The people(s) annotation in MPII format, see ``tl.files.load_mpii_pose_dataset``.
- save_name : None or str
- The name of image file (i.e. image.png), if None, not to save image.
-
- Returns
- --------
- numpy.array
- The saved image.
-
- Examples
- --------
- >>> import pprint
- >>> import tensorlayer as tl
- >>> img_train_list, ann_train_list, img_test_list, ann_test_list = tl.files.load_mpii_pose_dataset()
- >>> image = tl.vis.read_image(img_train_list[0])
- >>> tl.vis.draw_mpii_pose_to_image(image, ann_train_list[0], 'image.png')
- >>> pprint.pprint(ann_train_list[0])
-
- References
- -----------
- - `MPII Keyponts and ID <http://human-pose.mpi-inf.mpg.de/#download>`__
- """
- # import skimage
- # don't change the original image, and avoid error https://stackoverflow.com/questions/30249053/python-opencv-drawing-errors-after-manipulating-array-with-numpy
- image = image.copy()
-
- imh, imw = image.shape[0:2]
- thick = int((imh + imw) // 430)
- # radius = int(image.shape[1] / 500) + 1
- radius = int(thick * 1.5)
-
- if image.max() < 1:
- image = image * 255
-
- for people in poses:
- # Pose Keyponts
- joint_pos = people['joint_pos']
- # draw sketch
- # joint id (0 - r ankle, 1 - r knee, 2 - r hip, 3 - l hip, 4 - l knee,
- # 5 - l ankle, 6 - pelvis, 7 - thorax, 8 - upper neck,
- # 9 - head top, 10 - r wrist, 11 - r elbow, 12 - r shoulder,
- # 13 - l shoulder, 14 - l elbow, 15 - l wrist)
- #
- # 9
- # 8
- # 12 ** 7 ** 13
- # * * *
- # 11 * 14
- # * * *
- # 10 2 * 6 * 3 15
- # * *
- # 1 4
- # * *
- # 0 5
-
- lines = [
- [(0, 1), [100, 255, 100]],
- [(1, 2), [50, 255, 50]],
- [(2, 6), [0, 255, 0]], # right leg
- [(3, 4), [100, 100, 255]],
- [(4, 5), [50, 50, 255]],
- [(6, 3), [0, 0, 255]], # left leg
- [(6, 7), [255, 255, 100]],
- [(7, 8), [255, 150, 50]], # body
- [(8, 9), [255, 200, 100]], # head
- [(10, 11), [255, 100, 255]],
- [(11, 12), [255, 50, 255]],
- [(12, 8), [255, 0, 255]], # right hand
- [(8, 13), [0, 255, 255]],
- [(13, 14), [100, 255, 255]],
- [(14, 15), [200, 255, 255]] # left hand
- ]
- for line in lines:
- start, end = line[0]
- if (start in joint_pos) and (end in joint_pos):
- cv2.line(
- image,
- (int(joint_pos[start][0]), int(joint_pos[start][1])),
- (int(joint_pos[end][0]), int(joint_pos[end][1])), # up-left and botton-right
- line[1],
- thick
- )
- # rr, cc, val = skimage.draw.line_aa(int(joint_pos[start][1]), int(joint_pos[start][0]), int(joint_pos[end][1]), int(joint_pos[end][0]))
- # image[rr, cc] = line[1]
- # draw circles
- for pos in joint_pos.items():
- _, pos_loc = pos # pos_id, pos_loc
- pos_loc = (int(pos_loc[0]), int(pos_loc[1]))
- cv2.circle(image, center=pos_loc, radius=radius, color=(200, 200, 200), thickness=-1)
- # rr, cc = skimage.draw.circle(int(pos_loc[1]), int(pos_loc[0]), radius)
- # image[rr, cc] = [0, 255, 0]
-
- # Head
- head_rect = people['head_rect']
- if head_rect: # if head exists
- cv2.rectangle(
- image,
- (int(head_rect[0]), int(head_rect[1])),
- (int(head_rect[2]), int(head_rect[3])), # up-left and botton-right
- [0, 180, 0],
- thick
- )
-
- if save_name is not None:
- # cv2.imwrite(save_name, image)
- save_image(image, save_name)
- return image
-
-
- draw_mpii_people_to_image = draw_mpii_pose_to_image
-
-
- def frame(I=None, second=5, saveable=True, name='frame', cmap=None, fig_idx=12836):
- """Display a frame. Make sure OpenAI Gym render() is disable before using it.
-
- Parameters
- ----------
- I : numpy.array
- The image.
- second : int
- The display second(s) for the image(s), if saveable is False.
- saveable : boolean
- Save or plot the figure.
- name : str
- A name to save the image, if saveable is True.
- cmap : None or str
- 'gray' for greyscale, None for default, etc.
- fig_idx : int
- matplotlib figure index.
-
- Examples
- --------
- >>> env = gym.make("Pong-v0")
- >>> observation = env.reset()
- >>> tl.visualize.frame(observation)
-
- """
- import matplotlib.pyplot as plt
- if saveable is False:
- plt.ion()
- plt.figure(fig_idx) # show all feature images
-
- if len(I.shape) and I.shape[-1] == 1: # (10,10,1) --> (10,10)
- I = I[:, :, 0]
-
- plt.imshow(I, cmap)
- plt.title(name)
- # plt.gca().xaxis.set_major_locator(plt.NullLocator()) # distable tick
- # plt.gca().yaxis.set_major_locator(plt.NullLocator())
-
- if saveable:
- plt.savefig(name + '.pdf', format='pdf')
- else:
- plt.draw()
- plt.pause(second)
-
-
- def CNN2d(CNN=None, second=10, saveable=True, name='cnn', fig_idx=3119362):
- """Display a group of RGB or Greyscale CNN masks.
-
- Parameters
- ----------
- CNN : numpy.array
- The image. e.g: 64 5x5 RGB images can be (5, 5, 3, 64).
- second : int
- The display second(s) for the image(s), if saveable is False.
- saveable : boolean
- Save or plot the figure.
- name : str
- A name to save the image, if saveable is True.
- fig_idx : int
- The matplotlib figure index.
-
- Examples
- --------
- >>> tl.visualize.CNN2d(network.all_params[0].eval(), second=10, saveable=True, name='cnn1_mnist', fig_idx=2012)
-
- """
- import matplotlib.pyplot as plt
- # tl.logging.info(CNN.shape) # (5, 5, 3, 64)
- # exit()
- n_mask = CNN.shape[3]
- n_row = CNN.shape[0]
- n_col = CNN.shape[1]
- n_color = CNN.shape[2]
- row = int(np.sqrt(n_mask))
- col = int(np.ceil(n_mask / row))
- plt.ion() # active mode
- fig = plt.figure(fig_idx)
- count = 1
- for _ir in range(1, row + 1):
- for _ic in range(1, col + 1):
- if count > n_mask:
- break
- fig.add_subplot(col, row, count)
- # tl.logging.info(CNN[:,:,:,count-1].shape, n_row, n_col) # (5, 1, 32) 5 5
- # exit()
- # plt.imshow(
- # np.reshape(CNN[count-1,:,:,:], (n_row, n_col)),
- # cmap='gray', interpolation="nearest") # theano
- if n_color == 1:
- plt.imshow(np.reshape(CNN[:, :, :, count - 1], (n_row, n_col)), cmap='gray', interpolation="nearest")
- elif n_color == 3:
- plt.imshow(
- np.reshape(CNN[:, :, :, count - 1], (n_row, n_col, n_color)), cmap='gray', interpolation="nearest"
- )
- else:
- raise Exception("Unknown n_color")
- plt.gca().xaxis.set_major_locator(plt.NullLocator()) # distable tick
- plt.gca().yaxis.set_major_locator(plt.NullLocator())
- count = count + 1
- if saveable:
- plt.savefig(name + '.pdf', format='pdf')
- else:
- plt.draw()
- plt.pause(second)
-
-
- def images2d(images=None, second=10, saveable=True, name='images', dtype=None, fig_idx=3119362):
- """Display a group of RGB or Greyscale images.
-
- Parameters
- ----------
- images : numpy.array
- The images.
- second : int
- The display second(s) for the image(s), if saveable is False.
- saveable : boolean
- Save or plot the figure.
- name : str
- A name to save the image, if saveable is True.
- dtype : None or numpy data type
- The data type for displaying the images.
- fig_idx : int
- matplotlib figure index.
-
- Examples
- --------
- >>> X_train, y_train, X_test, y_test = tl.files.load_cifar10_dataset(shape=(-1, 32, 32, 3), plotable=False)
- >>> tl.visualize.images2d(X_train[0:100,:,:,:], second=10, saveable=False, name='cifar10', dtype=np.uint8, fig_idx=20212)
-
- """
- import matplotlib.pyplot as plt
- # tl.logging.info(images.shape) # (50000, 32, 32, 3)
- # exit()
- if dtype:
- images = np.asarray(images, dtype=dtype)
- n_mask = images.shape[0]
- n_row = images.shape[1]
- n_col = images.shape[2]
- n_color = images.shape[3]
- row = int(np.sqrt(n_mask))
- col = int(np.ceil(n_mask / row))
- plt.ion() # active mode
- fig = plt.figure(fig_idx)
- count = 1
- for _ir in range(1, row + 1):
- for _ic in range(1, col + 1):
- if count > n_mask:
- break
- fig.add_subplot(col, row, count)
- # tl.logging.info(images[:,:,:,count-1].shape, n_row, n_col) # (5, 1, 32) 5 5
- # plt.imshow(
- # np.reshape(images[count-1,:,:,:], (n_row, n_col)),
- # cmap='gray', interpolation="nearest") # theano
- if n_color == 1:
- plt.imshow(np.reshape(images[count - 1, :, :], (n_row, n_col)), cmap='gray', interpolation="nearest")
- # plt.title(name)
- elif n_color == 3:
- plt.imshow(images[count - 1, :, :], cmap='gray', interpolation="nearest")
- # plt.title(name)
- else:
- raise Exception("Unknown n_color")
- plt.gca().xaxis.set_major_locator(plt.NullLocator()) # distable tick
- plt.gca().yaxis.set_major_locator(plt.NullLocator())
- count = count + 1
- if saveable:
- plt.savefig(name + '.pdf', format='pdf')
- else:
- plt.draw()
- plt.pause(second)
-
-
- def tsne_embedding(embeddings, reverse_dictionary, plot_only=500, second=5, saveable=False, name='tsne', fig_idx=9862):
- """Visualize the embeddings by using t-SNE.
-
- Parameters
- ----------
- embeddings : numpy.array
- The embedding matrix.
- reverse_dictionary : dictionary
- id_to_word, mapping id to unique word.
- plot_only : int
- The number of examples to plot, choice the most common words.
- second : int
- The display second(s) for the image(s), if saveable is False.
- saveable : boolean
- Save or plot the figure.
- name : str
- A name to save the image, if saveable is True.
- fig_idx : int
- matplotlib figure index.
-
- Examples
- --------
- >>> see 'tutorial_word2vec_basic.py'
- >>> final_embeddings = normalized_embeddings.eval()
- >>> tl.visualize.tsne_embedding(final_embeddings, labels, reverse_dictionary,
- ... plot_only=500, second=5, saveable=False, name='tsne')
-
- """
- import matplotlib.pyplot as plt
-
- def plot_with_labels(low_dim_embs, labels, figsize=(18, 18), second=5, saveable=True, name='tsne', fig_idx=9862):
-
- if low_dim_embs.shape[0] < len(labels):
- raise AssertionError("More labels than embeddings")
-
- if saveable is False:
- plt.ion()
- plt.figure(fig_idx)
-
- plt.figure(figsize=figsize) # in inches
-
- for i, label in enumerate(labels):
- x, y = low_dim_embs[i, :]
- plt.scatter(x, y)
- plt.annotate(label, xy=(x, y), xytext=(5, 2), textcoords='offset points', ha='right', va='bottom')
-
- if saveable:
- plt.savefig(name + '.pdf', format='pdf')
- else:
- plt.draw()
- plt.pause(second)
-
- try:
- from sklearn.manifold import TSNE
- from six.moves import xrange
-
- tsne = TSNE(perplexity=30, n_components=2, init='pca', n_iter=5000)
- # plot_only = 500
- low_dim_embs = tsne.fit_transform(embeddings[:plot_only, :])
- labels = [reverse_dictionary[i] for i in xrange(plot_only)]
- plot_with_labels(low_dim_embs, labels, second=second, saveable=saveable, name=name, fig_idx=fig_idx)
-
- except ImportError:
- _err = "Please install sklearn and matplotlib to visualize embeddings."
- tl.logging.error(_err)
- raise ImportError(_err)
-
-
- def draw_weights(W=None, second=10, saveable=True, shape=None, name='mnist', fig_idx=2396512):
- """Visualize every columns of the weight matrix to a group of Greyscale img.
-
- Parameters
- ----------
- W : numpy.array
- The weight matrix
- second : int
- The display second(s) for the image(s), if saveable is False.
- saveable : boolean
- Save or plot the figure.
- shape : a list with 2 int or None
- The shape of feature image, MNIST is [28, 80].
- name : a string
- A name to save the image, if saveable is True.
- fig_idx : int
- matplotlib figure index.
-
- Examples
- --------
- >>> tl.visualize.draw_weights(network.all_params[0].eval(), second=10, saveable=True, name='weight_of_1st_layer', fig_idx=2012)
-
- """
- if shape is None:
- shape = [28, 28]
-
- import matplotlib.pyplot as plt
- if saveable is False:
- plt.ion()
- fig = plt.figure(fig_idx) # show all feature images
- n_units = W.shape[1]
-
- num_r = int(np.sqrt(n_units)) # 每行显示的个数 若25个hidden unit -> 每行显示5个
- num_c = int(np.ceil(n_units / num_r))
- count = int(1)
- for _row in range(1, num_r + 1):
- for _col in range(1, num_c + 1):
- if count > n_units:
- break
- fig.add_subplot(num_r, num_c, count)
- # ------------------------------------------------------------
- # plt.imshow(np.reshape(W[:,count-1],(28,28)), cmap='gray')
- # ------------------------------------------------------------
- feature = W[:, count - 1] / np.sqrt((W[:, count - 1]**2).sum())
- # feature[feature<0.0001] = 0 # value threshold
- # if count == 1 or count == 2:
- # print(np.mean(feature))
- # if np.std(feature) < 0.03: # condition threshold
- # feature = np.zeros_like(feature)
- # if np.mean(feature) < -0.015: # condition threshold
- # feature = np.zeros_like(feature)
- plt.imshow(
- np.reshape(feature, (shape[0], shape[1])), cmap='gray', interpolation="nearest"
- ) # , vmin=np.min(feature), vmax=np.max(feature))
- # plt.title(name)
- # ------------------------------------------------------------
- # plt.imshow(np.reshape(W[:,count-1] ,(np.sqrt(size),np.sqrt(size))), cmap='gray', interpolation="nearest")
- plt.gca().xaxis.set_major_locator(plt.NullLocator()) # distable tick
- plt.gca().yaxis.set_major_locator(plt.NullLocator())
- count = count + 1
- if saveable:
- plt.savefig(name + '.pdf', format='pdf')
- else:
- plt.draw()
- plt.pause(second)
-
-
- W = draw_weights
-
-
- def draw_boxes_and_labels_to_image_with_json(image, json_result, class_list, save_name=None):
- """Draw bboxes and class labels on image. Return the image with bboxes.
-
- Parameters
- -----------
- image : numpy.array
- The RGB image [height, width, channel].
- json_result : list of dict
- The object detection result with json format.
- classes_list : list of str
- For converting ID to string on image.
- save_name : None or str
- The name of image file (i.e. image.png), if None, not to save image.
-
- Returns
- -------
- numpy.array
- The saved image.
-
- References
- -----------
- - OpenCV rectangle and putText.
- - `scikit-image <http://scikit-image.org/docs/dev/api/skimage.draw.html#skimage.draw.rectangle>`__.
-
- """
- image_h, image_w, _ = image.shape
- num_classes = len(class_list)
- hsv_tuples = [(1.0 * x / num_classes, 1., 1.) for x in range(num_classes)]
- colors = list(map(lambda x: colorsys.hsv_to_rgb(*x), hsv_tuples))
- colors = list(map(lambda x: (int(x[0] * 255), int(x[1] * 255), int(x[2] * 255)), colors))
- random.seed(0)
- random.shuffle(colors)
- random.seed(None)
- bbox_thick = int(0.6 * (image_h + image_w) / 600)
- fontScale = 0.5
-
- for bbox_info in json_result:
- image_name = bbox_info['image']
- category_id = bbox_info['category_id']
- if category_id < 0 or category_id > num_classes: continue
- bbox = bbox_info['bbox'] # the order of coordinates is [x1, y2, x2, y2]
- score = bbox_info['score']
-
- bbox_color = colors[category_id]
- c1, c2 = (int(bbox[0]), int(bbox[1])), (int(bbox[2]), int(bbox[3]))
- cv2.rectangle(image, c1, c2, bbox_color, bbox_thick)
-
- bbox_mess = '%s: %.2f' % (class_list[category_id], score)
- t_size = cv2.getTextSize(bbox_mess, 0, fontScale, thickness=bbox_thick // 2)[0]
- c3 = (c1[0] + t_size[0], c1[1] - t_size[1] - 3)
- cv2.rectangle(image, c1, (np.float32(c3[0]), np.float32(c3[1])), bbox_color, -1)
-
- cv2.putText(
- image, bbox_mess, (c1[0], np.float32(c1[1] - 2)), cv2.FONT_HERSHEY_SIMPLEX, fontScale, (0, 0, 0),
- bbox_thick // 2, lineType=cv2.LINE_AA
- )
-
- if save_name is not None:
- save_image(image, save_name)
-
- return image
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