Save matplotlib figure image

https://stackoverflow.com/questions/8218608/scipy-savefig-without-frames-axes-only-content


 import matplotlib.pyplot as plt

import cv2
from skimage import measure

image = cv2.imread("/data/temp/image.png")
mask = cv2.imread("/data/temp/mask.png", -1) * 255
contours = measure.find_contours(mask, 200)

fig = plt.figure(frameon=False)
# fig.set_size_inches(w, h)

ax = plt.Axes(fig, [0., 0., 1., 1.])
ax.set_axis_off()
fig.add_axes(ax)

ax.imshow(image, aspect="auto")
for contour in contours:
ax.plot(contour[:, 1], contour[:, 0], linewidth=1, label="class 1", color=)
ax.legend()

fig.savefig("/data/temp/temp.png", format="png")

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import os import torch import clip import matplotlib . pyplot as plt from PIL import Image from sklearn . manifold import TSNE from tqdm import tqdm from collections import defaultdict from random import shuffle   X = torch . cat ( X , dim = 0 ) X_emb = TSNE ( init = "pca" , perplexity = 30.0 ). fit_transform ( X . cpu ()) labels = [ "source" , "z_star/foggy-1" , "z_star/foggy-2" , "z_star/foggy-3" , "z_bar/foggy-1" , "z_bar/foggy-2" , "z_bar/foggy-3" ] fig , ax = plt . subplots ( 1 ) group_len = len ( zs ) for i in range ( 7 ): ax . scatter ( X_emb [ i * group_len :( i + 1 )* group_len , 0 ], X_emb [ i * group_len :( i + 1 )* group_len , 1 ], label = labels [ i ], s = 4 ) ax . legend () fig .savefig( "temp.png" )
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