Created
December 12, 2022 16:41
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Test script for cellpose MPS branch
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| # %% | |
| %env PYTORCH_ENABLE_MPS_FALLBACK=1 | |
| # %% | |
| import numpy as np | |
| import time, os, sys | |
| from urllib.parse import urlparse | |
| import skimage.io | |
| import matplotlib.pyplot as plt | |
| import matplotlib as mpl | |
| %matplotlib inline | |
| mpl.rcParams['figure.dpi'] = 300 | |
| from urllib.parse import urlparse | |
| from cellpose import models, core | |
| # %% | |
| use_GPU = core.use_gpu() | |
| print(f'>>> GPU activated? {use_GPU}') | |
| # call logger_setup to have output of cellpose written | |
| from cellpose.io import logger_setup | |
| logger_setup(); | |
| # %% | |
| from cellpose import utils | |
| # I will download images from website | |
| urls = ['https://www.cellpose.org/static/images/img02.png', | |
| 'https://www.cellpose.org/static/images/img03.png', | |
| 'https://www.cellpose.org/static/images/img05.png', | |
| 'https://www.cellpose.org/static/data/rgb_3D.tif'] | |
| files = [] | |
| for url in urls: | |
| parts = urlparse(url) | |
| filename = os.path.basename(parts.path) | |
| if not os.path.exists(filename): | |
| sys.stderr.write('Downloading: "{}" to {}\n'.format(url, filename)) | |
| utils.download_url_to_file(url, filename) | |
| files.append(filename) | |
| # REPLACE FILES WITH YOUR IMAGE PATHS | |
| # files = ['img0.tif', 'img1.tif'] | |
| imgs = [skimage.io.imread(f) for f in files] | |
| nimg = len(imgs) | |
| plt.figure(figsize=(8,4)) | |
| for k,img in enumerate(imgs[:-1]): | |
| plt.subplot(1,3,k+1) | |
| plt.imshow(img) | |
| # %% | |
| # RUN CELLPOSE | |
| imgs_2D = imgs[:-1] | |
| #from skimage.data import coins, gravel | |
| #imgs_2D = [gravel()] | |
| # %% | |
| # DEFINE CELLPOSE MODEL | |
| # model_type='cyto' or model_type='nuclei' | |
| model = models.Cellpose(gpu=True, model_type='cyto') | |
| # %% | |
| # define CHANNELS to run segementation on | |
| # grayscale=0, R=1, G=2, B=3 | |
| # channels = [cytoplasm, nucleus] | |
| # if NUCLEUS channel does not exist, set the second channel to 0 | |
| # channels = [0,0] | |
| # IF ALL YOUR IMAGES ARE THE SAME TYPE, you can give a list with 2 elements | |
| # channels = [0,0] # IF YOU HAVE GRAYSCALE | |
| # channels = [2,3] # IF YOU HAVE G=cytoplasm and B=nucleus | |
| # channels = [2,1] # IF YOU HAVE G=cytoplasm and R=nucleus | |
| # or if you have different types of channels in each image | |
| channels = [[2,3], [0,0], [0,0]] | |
| #channels = [0,0] | |
| # if diameter is set to None, the size of the cells is estimated on a per image basis | |
| # you can set the average cell `diameter` in pixels yourself (recommended) | |
| # diameter can be a list or a single number for all images | |
| masks, flows, styles, diams = model.eval(imgs_2D, flow_threshold=None, channels=channels) | |
| # %% | |
| from cellpose import plot | |
| nimg = len(imgs_2D) | |
| for idx in range(nimg): | |
| maski = masks[idx] | |
| flowi = flows[idx][0] | |
| fig = plt.figure(figsize=(12,5)) | |
| plot.show_segmentation(fig, imgs_2D[idx], maski, flowi, channels=channels) | |
| plt.tight_layout() | |
| plt.show() | |
| # %% |
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