How to convert Matplotlib figure to PIL Image object (without saving image)
Solution 1
EDIT # 2
PIL.Image.frombytes('RGB',
fig.canvas.get_width_height(),fig.canvas.tostring_rgb())
takes around 2ms compared to the 35/40ms of the below.
This is the fastest way I can find so far.
I've been looking at this also today.
In the matplotlib docs the savefig function had this.
pil_kwargsdict, optional Additional keyword arguments that are passed to PIL.Image.save when saving the figure. Only applicable for formats that are saved using Pillow, i.e. JPEG, TIFF, and (if the keyword is set to a non-None value) PNG.
This must mean it's already a pil image before saving but I can't see it.
You could follow this
Matplotlib: save plot to numpy array
To get it into a numpy array and then do
PIL.Image.fromarray(array)
You might need to reverse the channels from BGR TO RGB with array [:, :, ::-1]
EDIT:
I've tested each way come up with so far.
import io
def save_plot_and_get():
fig.savefig("test.jpg")
img = cv2.imread("test.jpg")
return PIL.Image.fromarray(img)
def buffer_plot_and_get():
buf = io.BytesIO()
fig.savefig(buf)
buf.seek(0)
return PIL.Image.open(buf)
def from_canvas():
lst = list(fig.canvas.get_width_height())
lst.append(3)
return PIL.Image.fromarray(np.fromstring(fig.canvas.tostring_rgb(),dtype=np.uint8).reshape(lst))
Results
%timeit save_plot_and_get()
35.5 ms ± 148 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)
%timeit save_plot_and_get()
35.5 ms ± 142 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)
%timeit buffer_plot_and_get()
40.4 ms ± 152 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)
Solution 2
I use the following function:
def fig2img(fig):
"""Convert a Matplotlib figure to a PIL Image and return it"""
import io
buf = io.BytesIO()
fig.savefig(buf)
buf.seek(0)
img = Image.open(buf)
return img
Example usage:
import numpy as np
import matplotlib.pyplot as plt
from PIL import Image
x = np.arange(-3,3)
plt.plot(x)
fig = plt.gcf()
img = fig2img(fig)
img.show()
Solution 3
I flagged it as a duplicate and then closed it because I used the wrong link.
Anyway the answer may be here:
how to save a pylab figure into in-memory file which can be read into PIL image?
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Zach
Updated on June 03, 2021Comments
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Zach almost 3 years
As the title states, I am trying to convert a
fig
to aPIL.Image
. I am currently able to do so by first saving thefig
to disk and then opening that file usingImage.open()
but the process is taking longer than expected and I am hoping that by skipping the saving locally step it will be a bit faster.Here is what I have so far:
# build fig figsize, dpi = self._calc_fig_size_res(img_height) fig = plt.Figure(figsize=figsize) canvas = FigureCanvas(fig) ax = fig.add_subplot(111) ax.imshow(torch.from_numpy(S).flip(0), cmap = cmap) fig.subplots_adjust(left = 0, right = 1, bottom = 0, top = 1) ax.axis('tight'); ax.axis('off') # export fig.savefig(export_path, dpi = dpi) # open image as PIL object img = Image.open(export_path)
I have tried doing this after I build the fig (it would be right before the export stage):
pil_img = Image.frombytes('RGB', canvas.get_width_height(), canvas.tostring_rgb())
But it's not showing the entire image. It looks like it's a crop of the top left corner, but it could just be a weird representation of the data -- I'm working with spectrograms so the images are fairly abstract.
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Zach almost 5 yearsThanks! This is definitely an improvement, as it is an
Image
object and it's showing the right image and I'm not saving it to disk. However, for some reason the resolution of the image is much lower than if I were to save it to disk and then reload it, a problem that I also had when following this process: icare.univ-lille1.fr/tutorials/convert_a_matplotlib_figure | I'll edit my post with examples -
Zach almost 5 yearsI take that back -- I just forgot to include the
dpi
argument, which makes sense why the resolution was lower. So thank you!! I wouldn't mark it as a duplicate because the problem is slightly different but if you feel it is then you can flag. Either way, that solved it :) -
Afflatus about 3 yearsThis worked and was extremely efficient, but why does the "img.show()" function open the images in preview instead of within the jupyter notebook?
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Heinrich about 3 yearswhere is "deepcopy"?
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Elliot Young almost 3 yearsNote that this can be matplotlib backend-dependent - some backends (such as QTAgg) require the canvas to be drawn via fig.canvas.draw() to initialize the renderer before using tostring_rgb(). See stackoverflow.com/a/35407794/10342097