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Patchify branch (DO NOT MERGE) #10

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b84e5e5
try patchify
Jun 4, 2023
ba1acf0
try patchify
Jun 4, 2023
add88a2
add slurm script
Jun 4, 2023
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293 changes: 185 additions & 108 deletions histomicstk/segmentationschool/Codes/IterativePredict_1X.py
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Original file line number Diff line number Diff line change
@@ -1,4 +1,5 @@
import cv2
from patchify import patchify, unpatchify
import numpy as np
import os
import json
Expand Down Expand Up @@ -36,8 +37,7 @@
from scipy.ndimage import zoom
# import warnings
import torch


from torch.utils.data import DataLoader

from skimage.color import rgb2hsv
from skimage.filters import gaussian
Expand Down Expand Up @@ -93,6 +93,25 @@ def decode_panoptic(image,segments_info,organType,args):
return out.astype('uint8')


class NewPredictor(DefaultPredictor):
def __call__(self, original_images:list):
with torch.no_grad(): # https://github.com/sphinx-doc/sphinx/issues/4258
# Apply pre-processing to image.
if self.input_format == "RGB":
# whether the model expects BGR inputs or RGB
for original_image in original_images:
original_image = original_image[:, :, ::-1]
inputs_list = []
for original_image in original_images:
height, width = original_image.shape[:2]
original_image = zoom(original_image,(4,4,1),order=1)
image = self.aug.get_transform(original_image).apply_image(original_image)
image = torch.as_tensor(image.astype("float32").transpose(2, 0, 1))

inputs = {"image":image, "height": height, "width": width}
inputs_list.append(inputs)
predictions = self.model(inputs_list)
return predictions

def predict(args):
# define folder structure dict
Expand Down Expand Up @@ -188,7 +207,8 @@ def predict(args):
# cfg.MODEL.PANOPTIC_FPN.INSTANCES_CONFIDENCE_THRESH = args.roi_thresh
# cfg.MODEL.PANOPTIC_FPN.OVERLAP_THRESH = 1

predictor = DefaultPredictor(cfg)
#predictor = DefaultPredictor(cfg)
new_predictor = NewPredictor(cfg)
broken_slides=[]
for wsi in [args.files]:
print(wsi.split('/')[-1])
Expand All @@ -208,8 +228,7 @@ def predict(args):
slide=openslide.TiffSlide(wsi)
print(wsi,'here/s the silde')
# slide = ti.imread(wsi)

# except:
# except:
# broken_slides.append(wsi)
# continue
# continue
Expand Down Expand Up @@ -254,86 +273,162 @@ def predict(args):
binary=binary_fill_holes(binary)

print('Segmenting tissue ...\n')
totalpatches=len(index_x)*len(index_y)
with tqdm(total=totalpatches,unit='image',colour='green',desc='Total WSI progress') as pbar:
for i,j in coordinate_pairs(index_y,index_x):


image_height, image_width = slide.dimensions

channel_count = 3
patch_height, patch_width = 2048,2048
print(step)
print(thumbIm.shape)
patch_shape = (patch_height, patch_width, channel_count)
patches = patchify(np.array(slide.get_thumbnail((fullSize[0],fullSize[1]))), patch_shape, step=step)
patches[0][0]
count = 1
zoomed_patches = []
#output_patches = np.empty((patch_height, patch_width)).astype(np.uint8)
maskparts=[]
for i in range(patches.shape[0]):
for j in range(patches.shape[1]):
im = patches[i, j, 0]

print(im.shape)
zoomed_patches.append(im)
if count%3 == 0:
#print(len(zoomed_patches))
#predictions = (new_predictor(zoomed_patches))
predictions = new_predictor(zoomed_patches)
for prediction in predictions:
panoptic_seg, segments_info = prediction["panoptic_seg"]
maskpart = decode_panoptic(panoptic_seg.to("cpu").numpy(),segments_info,'kidney',args)
maskpart = zoom(maskpart,(0.25,0.25),order=0)
maskparts.append(maskpart)
zoomed_patches = []
# output_patches[i,j,0] = maskpart

count+=1



# zoomed_patches=[]
# for i in range(patches.shape[0]):
# for j in range(patches.shape[1]):
# im = patches[i, j, 0]
# im = zoom(im,(4,4,1),order=1)
# print(im.shape)
# zoomed_patches.append(im)
# print(len(zoomed_patches))
# totalpatches=len(index_x)*len(index_y)
# all_patches = []
# with tqdm(total=totalpatches,unit='image',colour='green',desc='Total WSI progress') as pbar:
# for i,j in coordinate_pairs(index_y,index_x):

yEnd = min(dim_y+offsety,i+region_size)
xEnd = min(dim_x+offsetx,j+region_size)
# yStart_small = int(np.round((i-offsety)/resRatio))
# yStop_small = int(np.round(((i-offsety)+args.boxSize)/resRatio))
# xStart_small = int(np.round((j-offsetx)/resRatio))
# xStop_small = int(np.round(((j-offsetx)+args.boxSize)/resRatio))
yStart_small = int(np.round((i-offsety)/resRatio))
yStop_small = int(np.round(((yEnd-offsety))/resRatio))
xStart_small = int(np.round((j-offsetx)/resRatio))
xStop_small = int(np.round(((xEnd-offsetx))/resRatio))
box_total=(xStop_small-xStart_small)*(yStop_small-yStart_small)
pbar.update(1)
if np.sum(binary[yStart_small:yStop_small,xStart_small:xStop_small])>(args.white_percent*box_total):

xLen=xEnd-j
yLen=yEnd-i

dxS=j
dyS=i
dxE=j+xLen
dyE=i+yLen
print(xLen,yLen)
print('here is the length')
im=np.array(slide.read_region((dxS,dyS),0,(xLen,yLen)))[:,:,:3]
#print(sys.getsizeof(im), 'first')
#UPSAMPLE
im = zoom(im,(4,4,1),order=1)
print(sys.getsizeof(im), 'second')
panoptic_seg, segments_info = predictor(im)["panoptic_seg"]
del im
torch.cuda.empty_cache()
print(sys.getsizeof(panoptic_seg), 'third')
print(sys.getsizeof(segments_info), 'forth')
maskpart=decode_panoptic(panoptic_seg.to("cpu").numpy(),segments_info,'kidney',args)
del panoptic_seg, segments_info
#outImageName=basename+'_'.join(['',str(dxS),str(dyS)])
#print(sys.getsizeof(maskpart), 'fifth')
#DOWNSAMPLE
maskpart=zoom(maskpart,(0.25,0.25),order=0)
#print(sys.getsizeof(maskpart), 'sixth')
# yEnd = min(dim_y+offsety,i+region_size)
# xEnd = min(dim_x+offsetx,j+region_size)
# # yStart_small = int(np.round((i-offsety)/resRatio))
# # yStop_small = int(np.round(((i-offsety)+args.boxSize)/resRatio))
# # xStart_small = int(np.round((j-offsetx)/resRatio))
# # xStop_small = int(np.round(((j-offsetx)+args.boxSize)/resRatio))
# yStart_small = int(np.round((i-offsety)/resRatio))
# yStop_small = int(np.round(((yEnd-offsety))/resRatio))
# xStart_small = int(np.round((j-offsetx)/resRatio))
# xStop_small = int(np.round(((xEnd-offsetx))/resRatio))
# box_total=(xStop_small-xStart_small)*(yStop_small-yStart_small)
# pbar.update(1)
# if np.sum(binary[yStart_small:yStop_small,xStart_small:xStop_small])>(args.white_percent*box_total):

# xLen=xEnd-j
# yLen=yEnd-i

# dxS=j
# dyS=i
# dxE=j+xLen
# dyE=i+yLen
# print(xLen,yLen)
# print('here is the length')
# im=np.array(slide.read_region((dxS,dyS),0,(xLen,yLen)))[:,:,:3]
# #im = zoom(im,(4,4,1),order=1)
# all_patches.append(im)
# #print(sys.getsizeof(im), 'first')
# #UPSAMPLE
# #im = zoom(im,(4,4,1),order=1)
# count=0
# test_1,test_2 = new_predictor(all_patches)#["panoptic_seg"]
# print(len(test_1))
# print(len(test_2))
# with tqdm(total=totalpatches,unit='image',colour='green',desc='Total WSI progress') as pbar:
# for i,j in coordinate_pairs(index_y,index_x):
# yEnd = min(dim_y+offsety,i+region_size)
# xEnd = min(dim_x+offsetx,j+region_size)
# # yStart_small = int(np.round((i-offsety)/resRatio))
# # yStop_small = int(np.round(((i-offsety)+args.boxSize)/resRatio))
# # xStart_small = int(np.round((j-offsetx)/resRatio))
# # xStop_small = int(np.round(((j-offsetx)+args.boxSize)/resRatio))
# yStart_small = int(np.round((i-offsety)/resRatio))
# yStop_small = int(np.round(((yEnd-offsety))/resRatio))
# xStart_small = int(np.round((j-offsetx)/resRatio))
# xStop_small = int(np.round(((xEnd-offsetx))/resRatio))
# box_total=(xStop_small-xStart_small)*(yStop_small-yStart_small)
# pbar.update(1)
# if np.sum(binary[yStart_small:yStop_small,xStart_small:xStop_small])>(args.white_percent*box_total):

# xLen=xEnd-j
# yLen=yEnd-i

# dxS=j
# dyS=i
# dxE=j+xLen
# dyE=i+yLen

# panoptic_seg, segments_info =test_1[count][0]["panoptic_seg"],test_2[count]
# count+=1
# print(test_1,test_2,'newmodel')
# #del im
# # torch.cuda.empty_cache()

# imsave(outImageName+'_p.png',maskpart)
if dxE != dim_x:
maskpart[:,-int(args.bordercrop/2):]=0
if dyE != dim_y:
maskpart[-int(args.bordercrop/2):,:]=0

if dxS != offsetx:
maskpart[:,:int(args.bordercrop/2)]=0
if dyS != offsety:
maskpart[:int(args.bordercrop/2),:]=0

# xmlbuilder.deconstruct(maskpart,dxS-offsetx,dyS-offsety,args)
# plt.subplot(121)
# plt.imshow(im)
# plt.subplot(122)
# plt.imshow(maskpart)
# plt.show()

dyE-=offsety
dyS-=offsety
dxS-=offsetx
dxE-=offsetx

wsiMask[dyS:dyE,dxS:dxE]=np.maximum(maskpart,
wsiMask[dyS:dyE,dxS:dxE])

del maskpart
torch.cuda.empty_cache()
# wsiMask[dyS:dyE,dxS:dxE]=maskpart

# print('showing mask')
# plt.imshow(wsiMask)
# plt.show()
slide.close()
print('\n\nStarting XML construction: ')
# maskpart=decode_panoptic(panoptic_seg.to("cpu").numpy(),segments_info,'kidney',args)
# #del panoptic_seg, segments_info
# #outImageName=basename+'_'.join(['',str(dxS),str(dyS)])
# #print(sys.getsizeof(maskpart), 'fifth')
# #DOWNSAMPLE
# #maskpart=zoom(maskpart,(0.25,0.25),order=0)
# #print(sys.getsizeof(maskpart), 'sixth')

# # imsave(outImageName+'_p.png',maskpart)
# if dxE != dim_x:
# maskpart[:,-int(args.bordercrop/2):]=0
# if dyE != dim_y:
# maskpart[-int(args.bordercrop/2):,:]=0

# if dxS != offsetx:
# maskpart[:,:int(args.bordercrop/2)]=0
# if dyS != offsety:
# maskpart[:int(args.bordercrop/2),:]=0

# # xmlbuilder.deconstruct(maskpart,dxS-offsetx,dyS-offsety,args)
# # plt.subplot(121)
# # plt.imshow(im)
# # plt.subplot(122)
# # plt.imshow(maskpart)
# # plt.show()

# dyE-=offsety
# dyS-=offsety
# dxS-=offsetx
# dxE-=offsetx

# wsiMask[dyS:dyE,dxS:dxE]=np.maximum(maskpart,
# wsiMask[dyS:dyE,dxS:dxE])

# del maskpart
# #torch.cuda.empty_cache()
# # wsiMask[dyS:dyE,dxS:dxE]=maskpart

# # print('showing mask')
# # plt.imshow(wsiMask)
# # plt.show()
# slide.close()
# print('\n\nStarting XML construction: ')

# wsiMask=np.swapaxes(wsiMask,0,1)
# print('swapped axes')
Expand Down Expand Up @@ -439,25 +534,7 @@ def xml_suey(wsiMask, dirs, args, classNum, downsample,glob_offset):

# save xml
folder = args.base_dir
girder_folder_id = folder.split('/')[-2]
_ = os.system("printf 'Using data from girder_client Folder: {}\n'".format(folder))
file_name = dirs['fileID'] + dirs['extension']
print(file_name)
gc = girder_client.GirderClient(apiUrl=args.girderApiUrl)
gc.setToken(args.girderToken)
files = list(gc.listItem(girder_folder_id))
# dict to link filename to gc id
item_dict = dict()
for file in files:
d = {file['name']:file['_id']}
item_dict.update(d)
print(item_dict)
print(item_dict[file_name])
annots = convert_xml_json(Annotations, NAMES)
for annot in annots:
_ = gc.post(path='annotation',parameters={'itemId':item_dict[file_name]}, data = json.dumps(annot))
print('uploating layers')
print('annotation uploaded...\n')
xml_save(Annotations=Annotations, filename=folder+'/test_data/'+dirs['fileID']+'.xml')



Expand Down Expand Up @@ -524,12 +601,12 @@ def xml_add_region(Annotations, pointList, annotationID=-1, regionID=None): # ad
ET.SubElement(Vertices, 'Vertex', attrib={'X': str(pointList[0]['X']), 'Y': str(pointList[0]['Y']), 'Z': '0'})
return Annotations

# def xml_save(Annotations, filename):
# xml_data = ET.tostring(Annotations, pretty_print=True)
# #xml_data = Annotations.toprettyxml()
# f = open(filename, 'w')
# f.write(xml_data.decode())
# f.close()
def xml_save(Annotations, filename):
xml_data = ET.tostring(Annotations, pretty_print=True)
#xml_data = Annotations.toprettyxml()
f = open(filename, 'wb')
f.write(xml_data)
f.close()

# def read_xml(filename):
# # import xml file
Expand Down
35 changes: 35 additions & 0 deletions histomicstk/segmentationschool/slurm_script.sh
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Original file line number Diff line number Diff line change
@@ -0,0 +1,35 @@
#!/bin/sh
#SBATCH --account=pinaki.sarder
#SBATCH --nodes=1
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=12
#SBATCH --mem-per-cpu=16gb
#SBATCH --partition=gpu
#SBATCH --gpus=a100
#SBATCH --time=72:00:00
#SBATCH --output=./slurm_log.out
#SBATCH --job-name="segmentation_frozen"
echo "SLURM_JOBID="$SLURM_JOBID
echo "SLURM_JOB_NODELIST="$SLURM_JOB_NODELIST
echo "SLURM_NNODES="$SLURM_NNODES
echo "SLURMTMPDIR="$SLURMTMPDIR

echo "working directory = "$SLURM_SUBMIT_DIR
ulimit -s unlimited
module load singularity
ls
ml

# Add your userid here:
USER=sayat.mimar
# Add the name of the folder containing WSIs here
PROJECT=multic_segment

CODESDIR=/blue/pinaki.sarder/sayat.mimar/multi_compart_segment/Multi-Compartment-Segmentation/histomicstk/segmentationschool

DATADIR=/$CODESDIR/test_data
MODELDIR=$CODESDIR/pretrained_model

CONTAINER=$CODESDIR/multic_segment.sif
CUDA_LAUNCH_BLOCKING=1
singularity exec --nv -B $(pwd):/exec/,$DATADIR/:/data,$MODELDIR/:/model/ $CONTAINER python3 /exec/segmentation_school.py --option 'predict' --base_dir $CODESDIR --modelfile /model/model_0214999.pth --files /data/18-142_PAS_6of6.svs