Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

added optional params cp_path and logfile_name to the setup_logger fu… #862

Open
wants to merge 1 commit into
base: main
Choose a base branch
from
Open

added optional params cp_path and logfile_name to the setup_logger fu… #862

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Jump to
Jump to file
Failed to load files.
Diff view
Diff view
128 changes: 64 additions & 64 deletions cellpose/io.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -45,13 +45,13 @@
SERVER_UPLOAD = True
except:
SERVER_UPLOAD = False

io_logger = logging.getLogger(__name__)

def logger_setup():
cp_dir = pathlib.Path.home().joinpath('.cellpose')
def logger_setup(cp_path=".cellpose", logfile_name="run.log"):
cp_dir = pathlib.Path.home().joinpath(cp_path)
cp_dir.mkdir(exist_ok=True)
log_file = cp_dir.joinpath('run.log')
log_file = cp_dir.joinpath(logfile_name)
try:
log_file.unlink()
except:
Expand All @@ -73,7 +73,7 @@ def logger_setup():

from . import utils, plot, transforms

# helper function to check for a path; if it doesn't exist, make it
# helper function to check for a path; if it doesn't exist, make it
def check_dir(path):
if not os.path.isdir(path):
os.mkdir(path)
Expand Down Expand Up @@ -157,9 +157,9 @@ def imread(filename):
img = np.zeros((ltif, *shape), dtype=dtype)
for i,page in enumerate(tqdm(tif.series[0])):
img[i] = page.asarray()
img = img.reshape(full_shape)
img = img.reshape(full_shape)
return img
elif ext == '.dax':
elif ext == '.dax':
img = load_dax(filename)
return img
elif ext == '.nd2':
Expand Down Expand Up @@ -240,7 +240,7 @@ def get_image_files(folder, mask_filter, imf=None, look_one_level_down=False):
image_names = []
if imf is None:
imf = ''

folders = []
if look_one_level_down:
folders = natsorted(glob.glob(os.path.join(folder, "*/")))
Expand All @@ -255,7 +255,7 @@ def get_image_files(folder, mask_filter, imf=None, look_one_level_down=False):
image_names.extend(glob.glob(folder + f'/*{imf}{ext}'))
image_names.extend(glob.glob(folder + f'/*{imf}{ext.upper()}'))
l0 += len(image_names)

# return error if no files found
if al==0:
raise ValueError('ERROR: no files in --dir folder ')
Expand All @@ -266,24 +266,24 @@ def get_image_files(folder, mask_filter, imf=None, look_one_level_down=False):
imn = []
for im in image_names:
imfile = os.path.splitext(im)[0]
igood = all([(len(imfile) > len(mask_filter) and imfile[-len(mask_filter):] != mask_filter) or len(imfile) <= len(mask_filter)
igood = all([(len(imfile) > len(mask_filter) and imfile[-len(mask_filter):] != mask_filter) or len(imfile) <= len(mask_filter)
for mask_filter in mask_filters])
if len(imf)>0:
igood &= imfile[-len(imf):]==imf
if igood:
imn.append(im)

image_names = imn

# remove duplicates
image_names = [*set(image_names)]
image_names = natsorted(image_names)

if len(image_names)==0:
raise ValueError('ERROR: no images in --dir folder without _masks or _flows ending')

return image_names

def get_label_files(image_names, mask_filter, imf=None):
nimg = len(image_names)
label_names0 = [os.path.splitext(image_names[n])[0] for n in range(nimg)]
Expand All @@ -292,7 +292,7 @@ def get_label_files(image_names, mask_filter, imf=None):
label_names = [label_names0[n][:-len(imf)] for n in range(nimg)]
else:
label_names = label_names0

# check for flows
if os.path.exists(label_names0[0] + '_flows.tif'):
flow_names = [label_names0[n] + '_flows.tif' for n in range(nimg)]
Expand All @@ -301,7 +301,7 @@ def get_label_files(image_names, mask_filter, imf=None):
if not all([os.path.exists(flow) for flow in flow_names]):
io_logger.info('not all flows are present, running flow generation for all images')
flow_names = None

# check for masks
if mask_filter =='_seg.npy':
label_names = [label_names[n] + mask_filter for n in range(nimg)]
Expand Down Expand Up @@ -333,10 +333,10 @@ def get_label_files(image_names, mask_filter, imf=None):
def load_images_labels(tdir, mask_filter='_masks', image_filter=None, look_one_level_down=False):
image_names = get_image_files(tdir, mask_filter, image_filter, look_one_level_down)
nimg = len(image_names)

# training data
label_names, flow_names = get_label_files(image_names, mask_filter, imf=image_filter)

images = []
labels = []
k = 0
Expand All @@ -350,7 +350,7 @@ def load_images_labels(tdir, mask_filter='_masks', image_filter=None, look_one_l
print(flow_names[n])
flow = imread(flow_names[n])
if flow.shape[0]<4:
label = np.concatenate((label[np.newaxis,:,:], flow), axis=0)
label = np.concatenate((label[np.newaxis,:,:], flow), axis=0)
else:
label = flow
images.append(image)
Expand All @@ -361,7 +361,7 @@ def load_images_labels(tdir, mask_filter='_masks', image_filter=None, look_one_l

def load_train_test_data(train_dir, test_dir=None, image_filter=None, mask_filter='_masks', look_one_level_down=False):
images, labels, image_names = load_images_labels(train_dir, mask_filter, image_filter, look_one_level_down)

# testing data
test_images, test_labels, test_image_names = None, None, None
if test_dir is not None:
Expand All @@ -372,12 +372,12 @@ def load_train_test_data(train_dir, test_dir=None, image_filter=None, mask_filte


def masks_flows_to_seg(images, masks, flows, file_names, diams=30., channels=None):
""" save output of model eval to be loaded in GUI
""" save output of model eval to be loaded in GUI

can be list output (run on multiple images) or single output (run on single image)

saved to file_names[k]+'_seg.npy'

Parameters
-------------

Expand All @@ -387,7 +387,7 @@ def masks_flows_to_seg(images, masks, flows, file_names, diams=30., channels=Non
masks: (list of) 2D arrays, int
masks output from Cellpose.eval, where 0=NO masks; 1,2,...=mask labels

flows: (list of) list of ND arrays
flows: (list of) list of ND arrays
flows output from Cellpose.eval

diams: float array
Expand All @@ -397,13 +397,13 @@ def masks_flows_to_seg(images, masks, flows, file_names, diams=30., channels=Non
names of files of images

channels: list of int (optional, default None)
channels used to run Cellpose
channels used to run Cellpose

"""

if channels is None:
channels = [0,0]

if isinstance(masks, list):
if not isinstance(diams, (list, np.ndarray)):
diams = diams * np.ones(len(masks), np.float32)
Expand All @@ -416,14 +416,14 @@ def masks_flows_to_seg(images, masks, flows, file_names, diams=30., channels=Non

if len(channels)==1:
channels = channels[0]

flowi = []
if flows[0].ndim==3:
Ly, Lx = masks.shape[-2:]
flowi.append(cv2.resize(flows[0], (Lx, Ly), interpolation=cv2.INTER_NEAREST)[np.newaxis,...])
else:
flowi.append(flows[0])

if flows[0].ndim==3:
cellprob = (np.clip(transforms.normalize99(flows[2]),0,1) * 255).astype(np.uint8)
cellprob = cv2.resize(cellprob, (Lx, Ly), interpolation=cv2.INTER_NEAREST)
Expand Down Expand Up @@ -459,13 +459,13 @@ def masks_flows_to_seg(images, masks, flows, file_names, diams=30., channels=Non
'ismanual': np.zeros(masks.max(), bool),
'filename': file_names,
'flows': flowi,
'est_diam': diams})
'est_diam': diams})

def save_to_png(images, masks, flows, file_names):
""" deprecated (runs io.save_masks with png=True)
""" deprecated (runs io.save_masks with png=True)

does not work for 3D images

"""
save_masks(images, masks, flows, file_names, png=True)

Expand Down Expand Up @@ -499,7 +499,7 @@ def save_rois(masks, file_name):

# Now saves flows, masks, etc. to separate folders.
def save_masks(images, masks, flows, file_names, png=True, tif=False, channels=[0,0],
suffix='',save_flows=False, save_outlines=False, save_ncolor=False,
suffix='',save_flows=False, save_outlines=False, save_ncolor=False,
dir_above=False, in_folders=False, savedir=None, save_txt=False, save_mpl=False):
""" save masks + nicely plotted segmentation image to png and/or tiff

Expand All @@ -510,7 +510,7 @@ def save_masks(images, masks, flows, file_names, png=True, tif=False, channels=[
if png and matplotlib installed, full segmentation figure is saved to file_names[k]+'_cp.png'

only tif option works for 3D data, and only tif option works for empty masks

Parameters
-------------

Expand All @@ -520,15 +520,15 @@ def save_masks(images, masks, flows, file_names, png=True, tif=False, channels=[
masks: (list of) 2D arrays, int
masks output from Cellpose.eval, where 0=NO masks; 1,2,...=mask labels

flows: (list of) list of ND arrays
flows: (list of) list of ND arrays
flows output from Cellpose.eval

file_names: (list of) str
names of files of images

savedir: str
absolute path where images will be saved. Default is none (saves to image directory)

save_flows, save_outlines, save_ncolor, save_txt: bool
Can choose which outputs/views to save.
ncolor is a 4 (or 5, if 4 takes too long) index version of the labels that
Expand All @@ -538,7 +538,7 @@ def save_masks(images, masks, flows, file_names, png=True, tif=False, channels=[
save_mpl: bool
If True, saves a matplotlib figure of the original image/segmentation/flows. Does not work for 3D.
This takes a long time for large images. Default is False.

"""

if isinstance(masks, list):
Expand All @@ -547,28 +547,28 @@ def save_masks(images, masks, flows, file_names, png=True, tif=False, channels=[
save_flows=save_flows,save_outlines=save_outlines,save_ncolor=save_ncolor,
savedir=savedir,save_txt=save_txt,in_folders=in_folders, save_mpl=save_mpl)
return

if masks.ndim > 2 and not tif:
raise ValueError('cannot save 3D outputs as PNG, use tif option instead')

if masks.max() == 0:
io_logger.warning('no masks found, will not save PNG or outlines')
if not tif:
return
else:
png = False
save_outlines=False
png = False
save_outlines=False
save_flows=False
save_txt=False
if savedir is None:

if savedir is None:
if dir_above:
savedir = Path(file_names).parent.parent.absolute() #go up a level to save in its own folder
else:
savedir = Path(file_names).parent.absolute()
check_dir(savedir)

check_dir(savedir)

basename = os.path.splitext(os.path.basename(file_names))[0]
if in_folders:
maskdir = os.path.join(savedir,'masks')
Expand All @@ -582,19 +582,19 @@ def save_masks(images, masks, flows, file_names, png=True, tif=False, channels=[
txtdir = savedir
ncolordir = savedir
flowdir = savedir
check_dir(maskdir)

check_dir(maskdir)

exts = []
if masks.ndim > 2:
png = False
tif = True
if png:
if png:
if masks.max() < 2**16:
masks = masks.astype(np.uint16)
masks = masks.astype(np.uint16)
exts.append('.png')
else:
png = False
png = False
tif = True
io_logger.warning('found more than 65535 masks in each image, cannot save PNG, saving as TIF')
if tif:
Expand All @@ -604,7 +604,7 @@ def save_masks(images, masks, flows, file_names, png=True, tif=False, channels=[
with warnings.catch_warnings():
warnings.simplefilter("ignore")
for ext in exts:

imsave(os.path.join(maskdir,basename + '_cp_masks' + suffix + ext), masks)

if save_mpl and png and MATPLOTLIB and not min(images.shape) > 3:
Expand All @@ -615,21 +615,21 @@ def save_masks(images, masks, flows, file_names, png=True, tif=False, channels=[
img = img[:,:,np.newaxis]
elif img.shape[0]<8:
np.transpose(img, (1,2,0))

fig = plt.figure(figsize=(12,3))
plot.show_segmentation(fig, img, masks, flows[0])
fig.savefig(os.path.join(savedir,basename + '_cp_output' + suffix + '.png'), dpi=300)
plt.close(fig)

# ImageJ txt outline files
# ImageJ txt outline files
if masks.ndim < 3 and save_txt:
check_dir(txtdir)
outlines = utils.outlines_list(masks)
outlines_to_text(os.path.join(txtdir,basename), outlines)

# RGB outline images
if masks.ndim < 3 and save_outlines:
check_dir(outlinedir)
if masks.ndim < 3 and save_outlines:
check_dir(outlinedir)
outlines = utils.masks_to_outlines(masks)
outX, outY = np.nonzero(outlines)
img0 = transforms.normalize99(images)
Expand All @@ -641,19 +641,19 @@ def save_masks(images, masks, flows, file_names, png=True, tif=False, channels=[
if img0.max()<=50.0:
img0 = np.uint8(np.clip(img0*255, 0, 1))
imgout= img0.copy()
imgout[outX, outY] = np.array([255,0,0]) #pure red
imgout[outX, outY] = np.array([255,0,0]) #pure red
imsave(os.path.join(outlinedir, basename + '_outlines' + suffix + '.png'), imgout)

# save RGB flow picture
if masks.ndim < 3 and save_flows:
check_dir(flowdir)
imsave(os.path.join(flowdir, basename + '_flows' + suffix + '.tif'), (flows[0]*(2**16 - 1)).astype(np.uint16))
#save full flow data
imsave(os.path.join(flowdir, basename + '_dP' + suffix + '.tif'), flows[1])
imsave(os.path.join(flowdir, basename + '_dP' + suffix + '.tif'), flows[1])

def save_server(parent=None, filename=None):
""" Uploads a *_seg.npy file to the bucket.

Parameters
----------------
parent: PyQt.MainWindow (optional, default None)
Expand Down Expand Up @@ -694,4 +694,4 @@ def save_server(parent=None, filename=None):
"File {} uploaded to {}.".format(
source_file_name, destination_blob_name
)
)
)