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io.py
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io.py
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"""
Copyright © 2023 Howard Hughes Medical Institute, Authored by Carsen Stringer and Marius Pachitariu.
"""
import os, datetime, gc, warnings, glob, shutil, copy
from natsort import natsorted
import numpy as np
import cv2
import tifffile
import logging
import fastremap
from ..io import imread, imsave, outlines_to_text, add_model, remove_model, save_rois
from ..models import normalize_default, MODEL_DIR, MODEL_LIST_PATH, get_user_models
from ..utils import masks_to_outlines, outlines_list
try:
import qtpy
from qtpy.QtWidgets import QFileDialog
GUI = True
except:
GUI = False
try:
import matplotlib.pyplot as plt
MATPLOTLIB = True
except:
MATPLOTLIB = False
def _init_model_list(parent):
MODEL_DIR.mkdir(parents=True, exist_ok=True)
parent.model_list_path = MODEL_LIST_PATH
parent.model_strings = get_user_models()
def _add_model(parent, filename=None, load_model=True):
if filename is None:
name = QFileDialog.getOpenFileName(parent, "Add model to GUI")
filename = name[0]
add_model(filename)
fname = os.path.split(filename)[-1]
parent.ModelChooseC.addItems([fname])
parent.model_strings.append(fname)
for ind, model_string in enumerate(parent.model_strings[:-1]):
if model_string == fname:
_remove_model(parent, ind=ind + 1, verbose=False)
parent.ModelChooseC.setCurrentIndex(len(parent.model_strings))
if load_model:
parent.model_choose(custom=True)
def _remove_model(parent, ind=None, verbose=True):
if ind is None:
ind = parent.ModelChooseC.currentIndex()
if ind > 0:
ind -= 1
parent.ModelChooseC.removeItem(ind + 1)
del parent.model_strings[ind]
# remove model from txt path
modelstr = parent.ModelChooseC.currentText()
remove_model(modelstr)
if len(parent.model_strings) > 0:
parent.ModelChooseC.setCurrentIndex(len(parent.model_strings))
else:
parent.ModelChooseC.setCurrentIndex(0)
else:
print("ERROR: no model selected to delete")
def _get_train_set(image_names):
""" get training data and labels for images in current folder image_names"""
train_data, train_labels, train_files = [], [], []
restore = None
normalize_params = normalize_default
for image_name_full in image_names:
image_name = os.path.splitext(image_name_full)[0]
label_name = None
if os.path.exists(image_name + "_seg.npy"):
dat = np.load(image_name + "_seg.npy", allow_pickle=True).item()
masks = dat["masks"].squeeze()
if masks.ndim == 2:
fastremap.renumber(masks, in_place=True)
label_name = image_name + "_seg.npy"
else:
print(f"GUI_INFO: _seg.npy found for {image_name} but masks.ndim!=2")
if "img_restore" in dat:
data = dat["img_restore"].squeeze()
restore = dat["restore"]
else:
data = imread(image_name_full)
normalize_params = dat[
"normalize_params"] if "normalize_params" in dat else normalize_default
if label_name is not None:
train_files.append(image_name_full)
train_data.append(data)
train_labels.append(masks)
if restore:
print(f"GUI_INFO: using {restore} images (dat['img_restore'])")
return train_data, train_labels, train_files, restore, normalize_params
def _load_image(parent, filename=None, load_seg=True, load_3D=False):
""" load image with filename; if None, open QFileDialog """
if filename is None:
name = QFileDialog.getOpenFileName(parent, "Load image")
filename = name[0]
manual_file = os.path.splitext(filename)[0] + "_seg.npy"
load_mask = False
if load_seg:
if os.path.isfile(manual_file) and not parent.autoloadMasks.isChecked():
_load_seg(parent, manual_file, image=imread(filename), image_file=filename,
load_3D=load_3D)
return
elif parent.autoloadMasks.isChecked():
mask_file = os.path.splitext(filename)[0] + "_masks" + os.path.splitext(
filename)[-1]
mask_file = os.path.splitext(filename)[
0] + "_masks.tif" if not os.path.isfile(mask_file) else mask_file
load_mask = True if os.path.isfile(mask_file) else False
try:
print(f"GUI_INFO: loading image: {filename}")
image = imread(filename)
parent.loaded = True
except Exception as e:
print("ERROR: images not compatible")
print(f"ERROR: {e}")
if parent.loaded:
parent.reset()
parent.filename = filename
filename = os.path.split(parent.filename)[-1]
_initialize_images(parent, image, load_3D=load_3D)
parent.loaded = True
parent.enable_buttons()
if load_mask:
_load_masks(parent, filename=mask_file)
def _initialize_images(parent, image, load_3D=False):
""" format image for GUI """
load_3D = parent.load_3D if load_3D is False else load_3D
parent.nchan = 3
if image.ndim > 4:
image = image.squeeze()
if image.ndim > 4:
raise ValueError("cannot load 4D stack, reduce dimensions")
elif image.ndim == 1:
raise ValueError("cannot load 1D stack, increase dimensions")
if image.ndim == 4:
if not load_3D:
raise ValueError(
"cannot load 3D stack, run 'python -m cellpose --Zstack' for 3D GUI")
else:
# make tiff Z x channels x W x H
if image.shape[0] < 4:
# tiff is channels x Z x W x H
image = image.transpose((1, 2, 3, 0))
image = np.transpose(image, (0, 2, 3, 1))
elif image.ndim == 3:
if not load_3D:
# assume smallest dimension is channels and put last
c = np.array(image.shape).argmin()
image = image.transpose(((c + 1) % 3, (c + 2) % 3, c))
elif load_3D:
# assume smallest dimension is Z and put first if <3x max dim
shape = np.array(image.shape)
z = shape.argmin()
if shape[z] < shape.max()/3:
image = image.transpose((z, (z + 1) % 3, (z + 2) % 3))
image = image[..., np.newaxis]
elif image.ndim == 2:
if not load_3D:
image = image[..., np.newaxis]
else:
raise ValueError(
"cannot load 2D stack in 3D mode, run 'python -m cellpose' for 2D GUI")
if image.shape[-1] > 3:
print("WARNING: image has more than 3 channels, keeping only first 3")
image = image[..., :3]
elif image.shape[-1] == 2:
# fill in with blank channels to make 3 channels
shape = image.shape
image = np.concatenate(
(image, np.zeros((*shape[:-1], 3 - shape[-1]), dtype=np.uint8)), axis=-1)
parent.nchan = 2
elif image.shape[-1] == 1:
parent.nchan = 1
parent.stack = image
if load_3D:
parent.NZ = len(parent.stack)
parent.scroll.setMaximum(parent.NZ - 1)
else:
parent.NZ = 1
parent.stack = parent.stack[np.newaxis, ...]
img_min = image.min()
img_max = image.max()
parent.stack = parent.stack.astype(np.float32)
parent.stack -= img_min
if img_max > img_min + 1e-3:
parent.stack /= (img_max - img_min)
parent.stack *= 255
if load_3D:
print("GUI_INFO: converted to float and normalized values to 0.0->255.0")
del image
gc.collect()
parent.imask = 0
parent.Ly, parent.Lx = parent.stack.shape[-3:-1]
parent.Ly0, parent.Lx0 = parent.stack.shape[-3:-1]
parent.layerz = 255 * np.ones((parent.Ly, parent.Lx, 4), "uint8")
if hasattr(parent, "stack_filtered"):
parent.Lyr, parent.Lxr = parent.stack_filtered.shape[-3:-1]
elif parent.restore and "upsample" in parent.restore:
parent.Lyr, parent.Lxr = int(parent.Ly * parent.ratio), int(parent.Lx *
parent.ratio)
else:
parent.Lyr, parent.Lxr = parent.Ly, parent.Lx
parent.clear_all()
if not hasattr(parent, "stack_filtered") and parent.restore:
print("GUI_INFO: no 'img_restore' found, applying current settings")
parent.compute_restore()
if parent.autobtn.isChecked():
if parent.restore is None or parent.restore != "filter":
print(
"GUI_INFO: normalization checked: computing saturation levels (and optionally filtered image)"
)
parent.compute_saturation()
elif len(parent.saturation) != parent.NZ:
parent.saturation = []
for r in range(3):
parent.saturation.append([])
for n in range(parent.NZ):
parent.saturation[-1].append([0, 255])
parent.sliders[r].setValue([0, 255])
parent.compute_scale()
parent.track_changes = []
if load_3D:
parent.currentZ = int(np.floor(parent.NZ / 2))
parent.scroll.setValue(parent.currentZ)
parent.zpos.setText(str(parent.currentZ))
else:
parent.currentZ = 0
def _load_seg(parent, filename=None, image=None, image_file=None, load_3D=False):
""" load *_seg.npy with filename; if None, open QFileDialog """
if filename is None:
name = QFileDialog.getOpenFileName(parent, "Load labelled data", filter="*.npy")
filename = name[0]
try:
dat = np.load(filename, allow_pickle=True).item()
# check if there are keys in filename
dat["outlines"]
parent.loaded = True
except:
parent.loaded = False
print("ERROR: not NPY")
return
parent.reset()
if image is None:
found_image = False
if "filename" in dat:
parent.filename = dat["filename"]
if os.path.isfile(parent.filename):
parent.filename = dat["filename"]
found_image = True
else:
imgname = os.path.split(parent.filename)[1]
root = os.path.split(filename)[0]
parent.filename = root + "/" + imgname
if os.path.isfile(parent.filename):
found_image = True
if found_image:
try:
print(parent.filename)
image = imread(parent.filename)
except:
parent.loaded = False
found_image = False
print("ERROR: cannot find image file, loading from npy")
if not found_image:
parent.filename = filename[:-8]
print(parent.filename)
if "img" in dat:
image = dat["img"]
else:
print("ERROR: no image file found and no image in npy")
return
else:
parent.filename = image_file
parent.restore = None
parent.ratio = 1.
if "normalize_params" in dat:
parent.restore = None if "restore" not in dat else dat["restore"]
print(f"GUI_INFO: restore: {parent.restore}")
parent.set_normalize_params(dat["normalize_params"])
parent.set_restore_button()
if "img_restore" in dat:
img = dat["img_restore"]
img_min = img.min()
img_max = img.max()
parent.stack_filtered = img.astype("float32")
parent.stack_filtered -= img_min
if img_max > img_min + 1e-3:
parent.stack_filtered /= (img_max - img_min)
parent.stack_filtered *= 255
if parent.stack_filtered.ndim < 4:
parent.stack_filtered = parent.stack_filtered[np.newaxis,...]
if parent.stack_filtered.ndim < 4:
parent.stack_filtered = parent.stack_filtered[...,np.newaxis]
shape = parent.stack_filtered.shape
if shape[-1] == 2:
if "chan_choose" in dat:
channels = np.array(dat["chan_choose"]) - 1
img = np.zeros((*shape[:-1], 3), dtype="float32")
img[..., channels] = parent.stack_filtered
parent.stack_filtered = img
else:
parent.stack_filtered = np.concatenate(
(parent.stack_filtered, np.zeros((*shape[:-1], 1), dtype="float32")), axis=-1)
elif shape[-1] > 3:
parent.stack_filtered = parent.stack_filtered[..., :3]
parent.restore = dat["restore"]
parent.ViewDropDown.model().item(parent.ViewDropDown.count() -
1).setEnabled(True)
parent.view = parent.ViewDropDown.count() - 1
if parent.restore and "upsample" in parent.restore:
print(parent.stack_filtered.shape, image.shape)
parent.ratio = dat["ratio"]
parent.set_restore_button()
_initialize_images(parent, image, load_3D=load_3D)
print(parent.stack.shape)
if "chan_choose" in dat:
parent.ChannelChoose[0].setCurrentIndex(dat["chan_choose"][0])
parent.ChannelChoose[1].setCurrentIndex(dat["chan_choose"][1])
if "outlines" in dat:
if isinstance(dat["outlines"], list):
# old way of saving files
dat["outlines"] = dat["outlines"][::-1]
for k, outline in enumerate(dat["outlines"]):
if "colors" in dat:
color = dat["colors"][k]
else:
col_rand = np.random.randint(1000)
color = parent.colormap[col_rand, :3]
median = parent.add_mask(points=outline, color=color)
if median is not None:
parent.cellcolors = np.append(parent.cellcolors,
color[np.newaxis, :], axis=0)
parent.ncells += 1
else:
if dat["masks"].min() == -1:
dat["masks"] += 1
dat["outlines"] += 1
parent.ncells = dat["masks"].max()
if "colors" in dat and len(dat["colors"]) == dat["masks"].max():
colors = dat["colors"]
else:
colors = parent.colormap[:parent.ncells, :3]
_masks_to_gui(parent, dat["masks"], outlines=dat["outlines"], colors=colors)
parent.draw_layer()
if "est_diam" in dat:
parent.Diameter.setText("%0.1f" % dat["est_diam"])
parent.diameter = dat["est_diam"]
parent.compute_scale()
if "manual_changes" in dat:
parent.track_changes = dat["manual_changes"]
print("GUI_INFO: loaded in previous changes")
if "zdraw" in dat:
parent.zdraw = dat["zdraw"]
else:
parent.zdraw = [None for n in range(parent.ncells)]
parent.loaded = True
#print(f"GUI_INFO: {parent.ncells} masks found in {filename}")
else:
parent.clear_all()
parent.ismanual = np.zeros(parent.ncells, bool)
if "ismanual" in dat:
if len(dat["ismanual"]) == parent.ncells:
parent.ismanual = dat["ismanual"]
if "current_channel" in dat:
parent.color = (dat["current_channel"] + 2) % 5
parent.RGBDropDown.setCurrentIndex(parent.color)
if "flows" in dat:
parent.flows = dat["flows"]
try:
if parent.flows[0].shape[-3] != dat["masks"].shape[-2]:
Ly, Lx = dat["masks"].shape[-2:]
for i in range(len(parent.flows)):
parent.flows[i] = cv2.resize(
parent.flows[i].squeeze(), (Lx, Ly),
interpolation=cv2.INTER_NEAREST)[np.newaxis, ...]
if parent.NZ == 1:
parent.recompute_masks = True
else:
parent.recompute_masks = False
except:
try:
if len(parent.flows[0]) > 0:
parent.flows = parent.flows[0]
except:
parent.flows = [[], [], [], [], [[]]]
parent.recompute_masks = False
parent.enable_buttons()
parent.update_layer()
del dat
gc.collect()
def _load_masks(parent, filename=None):
""" load zeros-based masks (0=no cell, 1=cell 1, ...) """
if filename is None:
name = QFileDialog.getOpenFileName(parent, "Load masks (PNG or TIFF)")
filename = name[0]
print(f"GUI_INFO: loading masks: {filename}")
masks = imread(filename)
outlines = None
if masks.ndim > 3:
# Z x nchannels x Ly x Lx
if masks.shape[-1] > 5:
parent.flows = list(np.transpose(masks[:, :, :, 2:], (3, 0, 1, 2)))
outlines = masks[..., 1]
masks = masks[..., 0]
else:
parent.flows = list(np.transpose(masks[:, :, :, 1:], (3, 0, 1, 2)))
masks = masks[..., 0]
elif masks.ndim == 3:
if masks.shape[-1] < 5:
masks = masks[np.newaxis, :, :, 0]
elif masks.ndim < 3:
masks = masks[np.newaxis, :, :]
# masks should be Z x Ly x Lx
if masks.shape[0] != parent.NZ:
print("ERROR: masks are not same depth (number of planes) as image stack")
return
_masks_to_gui(parent, masks, outlines)
if parent.ncells > 0:
parent.draw_layer()
parent.toggle_mask_ops()
del masks
gc.collect()
parent.update_layer()
parent.update_plot()
def _masks_to_gui(parent, masks, outlines=None, colors=None):
""" masks loaded into GUI """
# get unique values
shape = masks.shape
masks = masks.flatten()
fastremap.renumber(masks, in_place=True)
masks = masks.reshape(shape)
masks = masks.astype(np.uint16) if masks.max() < 2**16 - 1 else masks.astype(
np.uint32)
if parent.restore and "upsample" in parent.restore:
parent.cellpix_resize = masks.copy()
parent.cellpix = parent.cellpix_resize.copy()
parent.cellpix_orig = cv2.resize(
masks.squeeze(), (parent.Lx0, parent.Ly0),
interpolation=cv2.INTER_NEAREST)[np.newaxis, :, :]
parent.resize = True
else:
parent.cellpix = masks
if parent.cellpix.ndim == 2:
parent.cellpix = parent.cellpix[np.newaxis, :, :]
if parent.restore and "upsample" in parent.restore:
if parent.cellpix_resize.ndim == 2:
parent.cellpix_resize = parent.cellpix_resize[np.newaxis, :, :]
if parent.cellpix_orig.ndim == 2:
parent.cellpix_orig = parent.cellpix_orig[np.newaxis, :, :]
print(f"GUI_INFO: {masks.max()} masks found")
# get outlines
if outlines is None: # parent.outlinesOn
parent.outpix = np.zeros_like(parent.cellpix)
if parent.restore and "upsample" in parent.restore:
parent.outpix_orig = np.zeros_like(parent.cellpix_orig)
for z in range(parent.NZ):
outlines = masks_to_outlines(parent.cellpix[z])
parent.outpix[z] = outlines * parent.cellpix[z]
if parent.restore and "upsample" in parent.restore:
outlines = masks_to_outlines(parent.cellpix_orig[z])
parent.outpix_orig[z] = outlines * parent.cellpix_orig[z]
if z % 50 == 0 and parent.NZ > 1:
print("GUI_INFO: plane %d outlines processed" % z)
if parent.restore and "upsample" in parent.restore:
parent.outpix_resize = parent.outpix.copy()
else:
parent.outpix = outlines
shape = parent.outpix.shape
fastremap.renumber(parent.outpix, in_place=True)
parent.outpix = np.reshape(parent.outpix, shape)
if parent.restore and "upsample" in parent.restore:
parent.outpix_resize = parent.outpix.copy()
parent.outpix_orig = np.zeros_like(parent.cellpix_orig)
for z in range(parent.NZ):
outlines = masks_to_outlines(parent.cellpix_orig[z])
parent.outpix_orig[z] = outlines * parent.cellpix_orig[z]
if z % 50 == 0 and parent.NZ > 1:
print("GUI_INFO: plane %d outlines processed" % z)
if parent.outpix.ndim == 2:
parent.outpix = parent.outpix[np.newaxis, :, :]
if parent.restore and "upsample" in parent.restore:
if parent.outpix_resize.ndim == 2:
parent.outpix_resize = parent.outpix_resize[np.newaxis, :, :]
if parent.outpix_orig.ndim == 2:
parent.outpix_orig = parent.outpix_orig[np.newaxis, :, :]
parent.ncells = parent.cellpix.max()
colors = parent.colormap[:parent.ncells, :3] if colors is None else colors
print("GUI_INFO: creating cellcolors and drawing masks")
parent.cellcolors = np.concatenate((np.array([[255, 255, 255]]), colors),
axis=0).astype(np.uint8)
if parent.ncells > 0:
parent.draw_layer()
parent.toggle_mask_ops()
parent.ismanual = np.zeros(parent.ncells, bool)
parent.zdraw = list(-1 * np.ones(parent.ncells, np.int16))
if hasattr(parent, "stack_filtered"):
parent.ViewDropDown.setCurrentIndex(parent.ViewDropDown.count() - 1)
print("set denoised/filtered view")
else:
parent.ViewDropDown.setCurrentIndex(0)
def _save_png(parent):
""" save masks to png or tiff (if 3D) """
filename = parent.filename
base = os.path.splitext(filename)[0]
if parent.NZ == 1:
if parent.cellpix[0].max() > 65534:
print("GUI_INFO: saving 2D masks to tif (too many masks for PNG)")
imsave(base + "_cp_masks.tif", parent.cellpix[0])
else:
print("GUI_INFO: saving 2D masks to png")
imsave(base + "_cp_masks.png", parent.cellpix[0].astype(np.uint16))
else:
print("GUI_INFO: saving 3D masks to tiff")
imsave(base + "_cp_masks.tif", parent.cellpix)
def _save_flows(parent):
""" save flows and cellprob to tiff """
filename = parent.filename
base = os.path.splitext(filename)[0]
if len(parent.flows) > 0:
imsave(base + "_cp_flows.tif", parent.flows[4][:-1])
imsave(base + "_cp_cellprob.tif", parent.flows[4][-1])
def _save_rois(parent):
""" save masks as rois in .zip file for ImageJ """
filename = parent.filename
if parent.NZ == 1:
print(
f"GUI_INFO: saving {parent.cellpix[0].max()} ImageJ ROIs to .zip archive.")
save_rois(parent.cellpix[0], parent.filename)
else:
print("ERROR: cannot save 3D outlines")
def _save_outlines(parent):
filename = parent.filename
base = os.path.splitext(filename)[0]
if parent.NZ == 1:
print(
"GUI_INFO: saving 2D outlines to text file, see docs for info to load into ImageJ"
)
outlines = outlines_list(parent.cellpix[0])
outlines_to_text(base, outlines)
else:
print("ERROR: cannot save 3D outlines")
def _save_sets_with_check(parent):
""" Save masks and update *_seg.npy file. Use this function when saving should be optional
based on the disableAutosave checkbox. Otherwise, use _save_sets """
if not parent.disableAutosave.isChecked():
_save_sets(parent)
def _save_sets(parent):
""" save masks to *_seg.npy. This function should be used when saving
is forced, e.g. when clicking the save button. Otherwise, use _save_sets_with_check
"""
filename = parent.filename
base = os.path.splitext(filename)[0]
flow_threshold, cellprob_threshold = parent.get_thresholds()
if parent.NZ > 1:
dat = {
"outlines":
parent.outpix,
"colors":
parent.cellcolors[1:],
"masks":
parent.cellpix,
"current_channel": (parent.color - 2) % 5,
"filename":
parent.filename,
"flows":
parent.flows,
"zdraw":
parent.zdraw,
"model_path":
parent.current_model_path
if hasattr(parent, "current_model_path") else 0,
"flow_threshold":
flow_threshold,
"cellprob_threshold":
cellprob_threshold,
"normalize_params":
parent.get_normalize_params(),
"restore":
parent.restore,
"ratio":
parent.ratio,
"diameter":
parent.diameter
}
print(dat["masks"].shape)
if parent.restore is not None:
dat["img_restore"] = parent.stack_filtered
np.save(base + "_seg.npy", dat)
else:
dat = {
"outlines":
parent.outpix.squeeze() if parent.restore is None or
not "upsample" in parent.restore else parent.outpix_resize.squeeze(),
"colors":
parent.cellcolors[1:],
"masks":
parent.cellpix.squeeze() if parent.restore is None or
not "upsample" in parent.restore else parent.cellpix_resize.squeeze(),
"chan_choose": [
parent.ChannelChoose[0].currentIndex(),
parent.ChannelChoose[1].currentIndex()
],
"filename":
parent.filename,
"flows":
parent.flows,
"ismanual":
parent.ismanual,
"manual_changes":
parent.track_changes,
"model_path":
parent.current_model_path
if hasattr(parent, "current_model_path") else 0,
"flow_threshold":
flow_threshold,
"cellprob_threshold":
cellprob_threshold,
"normalize_params":
parent.get_normalize_params(),
"restore":
parent.restore,
"ratio":
parent.ratio,
"diameter":
parent.diameter
}
print(dat["masks"].shape)
if parent.restore is not None:
dat["img_restore"] = parent.stack_filtered
np.save(base + "_seg.npy", dat)
del dat
#print(parent.point_sets)
print("GUI_INFO: %d ROIs saved to %s" % (parent.ncells, base + "_seg.npy"))