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test_output.py
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test_output.py
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from cellpose import io, models, metrics, plot
from pathlib import Path
from subprocess import check_output, STDOUT
import os, shutil
import numpy as np
try:
import matplotlib.pyplot as plt
MATPLOTLIB = True
except:
MATPLOTLIB = False
r_tol, a_tol = 1e-2, 1e-2
def clear_output(data_dir, image_names):
data_dir_2D = data_dir.joinpath('2D')
data_dir_3D = data_dir.joinpath('2D')
for image_name in image_names:
if '2D' in image_name:
cached_file = str(data_dir_2D.joinpath(image_name))
ext = '.png'
else:
cached_file = str(data_dir_3D.joinpath(image_name))
ext = '.tif'
name, ext = os.path.splitext(cached_file)
output = name + '_cp_masks' + ext
if os.path.exists(output):
os.remove(output)
def test_class_2D(data_dir, image_names):
clear_output(data_dir, image_names)
image_name = 'rgb_2D.png'
img = io.imread(str(data_dir.joinpath('2D').joinpath(image_name)))
model_types = ['nuclei']
chan = [1]
chan2 = [0]
for m,model_type in enumerate(model_types):
model = models.Cellpose(model_type=model_type)
masks, flows, _, _ = model.eval(img, diameter=0, mask_threshold=0, channels=[chan[m],chan2[m]],
net_avg=False, resample=False)
io.imsave(str(data_dir.joinpath('2D').joinpath('rgb_2D_cp_masks.png')), masks)
# io.imsave('/home/kcutler/DataDrive/cellpose_debug/rgb_2D_cp_masks.png', masks)
compare_masks(data_dir, [image_name], '2D', model_type)
clear_output(data_dir, image_names)
if MATPLOTLIB:
fig = plt.figure(figsize=(8,3))
plot.show_segmentation(fig, img, masks, flows[0], channels=[chan[m],chan2[m]])
def test_cyto2_to_seg(data_dir, image_names):
clear_output(data_dir, image_names)
image_names = ['rgb_2D.png', 'rgb_2D_tif.tif']
file_names = [str(data_dir.joinpath('2D').joinpath(image_name)) for image_name in image_names]
imgs = [io.imread(file_name) for file_name in file_names]
model_type = 'cyto2'
model = models.Cellpose(model_type=model_type)
channels = [2,1]
masks, flows, styles, diams = model.eval(imgs, diameter=30, channels=channels, net_avg=False)
io.masks_flows_to_seg(imgs, masks, flows, diams, file_names)
def test_class_3D(data_dir, image_names):
clear_output(data_dir, image_names)
img = io.imread(str(data_dir.joinpath('3D').joinpath('rgb_3D.tif')))
model_types = ['nuclei']
chan = [1]
chan2 = [0]
for m,model_type in enumerate(model_types):
model = models.Cellpose(model_type='nuclei')
masks = model.eval(img, do_3D=True, diameter=25, channels=[chan[m],chan2[m]], net_avg=False)[0]
io.imsave(str(data_dir.joinpath('3D').joinpath('rgb_3D_cp_masks.tif')), masks)
compare_masks(data_dir, ['rgb_3D.tif'], '3D', model_type)
clear_output(data_dir, image_names)
def test_cli_2D(data_dir, image_names):
clear_output(data_dir, image_names)
model_types = ['cyto']
chan = [2]
chan2 = [1]
for m,model_type in enumerate(model_types):
cmd = 'python -m cellpose --dir %s --pretrained_model %s --fast_mode --chan %d --chan2 %d --diameter 0 --no_interp --save_png'%(str(data_dir.joinpath('2D')), model_type, chan[m], chan2[m])
try:
cmd_stdout = check_output(cmd, stderr=STDOUT, shell=True).decode()
print(cmd_stdout)
except Exception as e:
print(e)
raise ValueError(e)
compare_masks(data_dir, image_names, '2D', model_type)
clear_output(data_dir, image_names)
def test_cli_3D(data_dir, image_names):
clear_output(data_dir, image_names)
model_types = ['cyto']
chan = [2]
chan2 = [1]
for m,model_type in enumerate(model_types):
cmd = 'python -m cellpose --dir %s --do_3D --pretrained_model %s --fast_mode --mask_threshold 0 --chan %d --chan2 %d --diameter 25 --save_tif'%(str(data_dir.joinpath('3D')), model_type, chan[m], chan2[m])
try:
cmd_stdout = check_output(cmd, stderr=STDOUT, shell=True).decode()
except Exception as e:
print(e)
raise ValueError(e)
compare_masks(data_dir, image_names, '3D', model_type)
clear_output(data_dir, image_names)
def compare_masks(data_dir, image_names, runtype, model_type):
"""
Helper function to check if outputs given by a test are exactly the same
as the ground truth outputs.
"""
data_dir_2D = data_dir.joinpath('2D')
data_dir_3D = data_dir.joinpath('3D')
for image_name in image_names:
check=False
if '2D' in runtype and '2D' in image_name:
image_file = str(data_dir_2D.joinpath(image_name))
name = os.path.splitext(image_file)[0]
output_test = name + '_cp_masks.png'
output_true = name + '_%s_masks.png'%model_type
check = True
elif '3D' in runtype and '3D' in image_name:
image_file = str(data_dir_3D.joinpath(image_name))
name = os.path.splitext(image_file)[0]
output_test = name + '_cp_masks.tif'
output_true = name + '_%s_masks.tif'%model_type
check = True
if check:
if os.path.exists(output_test):
print('checking output %s'%output_test)
masks_test = io.imread(output_test)
masks_true = io.imread(output_true)
print('masks',np.unique(masks_test),np.unique(masks_true),output_test,output_true)
ap = metrics.average_precision(masks_true, masks_test)[0]
print('average precision of [%0.3f %0.3f %0.3f]'%(ap[0],ap[1],ap[2]))
ap_precision = np.allclose(ap, np.ones(3), rtol=r_tol, atol=a_tol)
matching_pix = np.logical_and(masks_test>0, masks_true>0).mean()
all_pix = (masks_test>0).mean()
pix_precision = np.allclose(all_pix, matching_pix, rtol=r_tol, atol=a_tol)
assert all([ap_precision, pix_precision])
else:
print('ERROR: no output file of name %s found'%output_test)
assert False