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utils.py
=======================
General utilities that still need to be broken up into preprocessing, machine learning input preparation, and output submodules.
Ú N)┌
BeautifulSoup)┌Point)┌Polygon)┌join)┌Image┌ ImageDrawg _áB)┌Dataset)┌train_test_split)┌deepzoom)┌SafeDataLoaderc C s* t j| â}tjdj|â|dŹ}|jâ |S )zÍLoad pandas dataframe from SQL, accessing particular patch size within SQL.
Parameters
----------
sql_file:str
SQL db.
patch_size:int
Patch size.
Returns
-------
dataframe
Patch level information.
zselect * from "{}";)┌con)┌sqlite3┌connect┌pd┌read_sql┌format┌close)┌sql_file┌
patch_size┌conn┌dfę r ˙A/Users/joshualevy/Documents/GitHub/PathFlowAI/pathflowai/utils.py┌ load_sql_df* s
r ┌replacec C s0 t j|â}| jdâjt|â||dŹ |jâ dS )z˝Write dataframe containing patch level information to SQL db.
Parameters
----------
df:dataframe
Dataframe containing patch information.
sql_file:str
SQL database.
patch_size:int
Size of patches.
mode:str
Replace or append.
┌index)r ┌ if_existsN)r
r ┌ set_index┌to_sql┌strr )r r r ┌moder r r r ┌df2sql? s
r! ÚŔ TFc
sť t j| â}tj|||ddŹëtłjâd ëłjł \}ëççfddä}|ddâ}|jëtj |ddŹët
j ç ççççfdd ät |łsćdnd âD âł d
Ź} | S ) aô Convert SVS, TIF or TIFF to dask array.
Parameters
----------
svs_file:str
Image file.
tile_size:int
Size of chunk to be read in.
overlap:int
Do not modify, overlap between neighboring tiles.
remove_last:bool
Remove last tile because it has a custom size.
allow_unknown_chunksizes: bool
Allow different chunk sizes, more flexible, but slowdown.
Returns
-------
dask.array
Dask Array.
>>> arr=svs2dask_array(svs_file, tile_size=1000, overlap=0, remove_last=True, allow_unknown_chunksizes=False)
>>> arr2=arr.compute()
>>> arr3=to_pil(cv2.resize(arr2, dsize=(1440,700), interpolation=cv2.INTER_CUBIC))
>>> arr3.save(test_image_name)T)┌ tile_size┌overlapZ limit_boundsÚ c s t jł jł| |fââjdâS )Nr% r Ú )r% r r& )┌np┌array┌get_tile┌ transpose)┌i┌j)┌gen┌ max_levelr r ┌<lambda>s s z svs2dask_array.<locals>.<lambda>r )Zpurec s> g | ]6ë t jçç çfd dätłłs&dnd âD âłddŹĹqS )c s" g | ]}t jł ł|âłtjâĹqS r )┌daZ from_delayedr' ┌uint8)┌.0r, )┌
dask_get_tiler+ ┌sample_tile_shaper r ˙
<listcomp>w s z-svs2dask_array.<locals>.<listcomp>.<listcomp>r r% )┌allow_unknown_chunksizes┌axis)r0 ┌ concatenate┌range)r2 )r6 r3 ┌ n_tiles_y┌ remove_lastr4 )r+ r r5 w s z"svs2dask_array.<locals>.<listcomp>)r6 )
┌ openslideZ
open_slider
ZDeepZoomGenerator┌lenZlevel_dimensionsZ level_tiles┌shape┌dask┌delayedr0 r8 r9 )
┌svs_filer# r$ r; r6 ┌imgZ n_tiles_xr) Z sample_tile┌arrr )r6 r3 r- r. r: r; r4 r ┌svs2dask_arrayV s
4rD c C s, t | dj|ââ}t|â}tj||jâ â |S )z┘Convert SVS, TIF, TIFF to NPY.
Parameters
----------
input_dir:str
Output file dir.
basename:str
Basename of output file
svs_file:str
SVS, TIF, TIFF file input.
Returns
-------
str
NPY output file.
z{}.npy)r r rD r' ┌save┌compute)┌ input_dir┌basenamerA Z npy_out_filerC r r r ┌img2npy_z s rI c C s" t j| â}tjtj|âdâ|jfS )zčLoad SVS, TIF, TIFF
Parameters
----------
svs_file:type
Description of parameter `svs_file`.
Returns
-------
type
Description of returned object.
r% r )r% r )r ┌openr' r* r( ┌size)rA ┌imr r r ┌
load_imageÉ s
rM c C sÜ | ddůddůdf | ddůddůdf | ddůddůdf }}}d| d| d| }d| }drľ|rľ|j â }tj|d jâ |d jâ gâj}|S ) a< Create a gray scale intensity mask. This will be changed soon to support other thresholding QC methods.
Parameters
----------
arr:dask.array
Dask array containing image information.
img_size:int
Deprecated.
sparse:bool
Deprecated
Returns
-------
dask.array
Intensity, grayscale array over image.
Nr r% r& g┼Ć1w-!Ë?gbX9┤╚Ô?g╔vżč/Ż?g Óo@)┌nonzeror' r( rF ┌T)rC ┌img_size┌sparse┌r┌b┌g┌gray┌maskr r r ┌create_purple_maská s F rW c C s t j| t| âfddŹS )zÂOptional add intensity mask to the dask array.
Parameters
----------
arr:dask.array
Image data.
Returns
-------
array
Image data with intensity added as forth channel.
r )r7 )r' r8 rW )rC r r r ┌add_purple_maskŻ s rX c s$ çfddä|D âë ç fddä|D âS )a2 Convert annotation xml to shapely objects and store in dictionary.
Parameters
----------
xml_file:str
XML file containing annotations.
img_size:int
Deprecated.
annotations:list
Annotations to look for in xml export.
Returns
-------
dict
Dictionary with annotation-shapely object pairs.
c s i | ]}t ł |d dŹ|ôqS )F)┌annotation_name┌
return_coords)┌parse_coord_return_boxes)r2 ┌
annotation)┌xml_filer r ˙
<dictcomp>▀ s z3create_sparse_annotation_arrays.<locals>.<dictcomp>c s i | ]}ł | |ôqS r r )r2 r\ )┌interior_points_dictr r r^ Ó s r )r] rP ┌ annotationsr )r_ r] r ┌ create_sparse_annotation_arrays═ s ra c C sl | j dârt| ân t| dddŹ}|jddů }i }|dk rN|jt|||dŹâ |dk rd|jd|iâ ||fS ) a~ Load SVS-like image (including NPY), segmentation/classification annotations, generate dask array and dictionary of annotations.
Parameters
----------
svs_file:str
Image file
xml_file:str
Annotation file.
npy_mask:array
Numpy segmentation mask.
annotations:list
List of annotations in xml.
Returns
-------
array
Dask array of image.
dict
Annotation masks.
z.npyiŔ r )r# r$ Nr& )r` r` )┌endswith┌npy2darD r> ┌updatera )rA r] ┌npy_maskr` rC rP ┌masksr r r ┌load_process_imageÔ s rg c C s* | j dâj|ddŹ tj|t|dââ dS )z Saves dask array image, dictionary of annotations to zarr and pickle respectively.
Parameters
----------
arr:array
Image.
masks:dict
Dictionary of annotation shapes.
out_zarr:str
Zarr output file for image.
out_pkl:str
Pickle output file.
r1 T)┌ overwrite┌wbN)┌astypeZto_zarr┌pickle┌dumprJ )rC rf ┌out_zarr┌out_pklr r r ┌ save_dataset s ro zoutput_zarr.zarrz
output.pklc C s$ t | |||â\}}t||||â dS )aÇ Run preprocessing pipeline. Store image into zarr format, segmentations maintain as npy, and xml annotations as pickle.
Parameters
----------
svs_file:str
Input image file.
xml_file:str
Input annotation file.
npy_mask:str
NPY segmentation mask.
annotations:list
List of annotations.
out_zarr:str
Output zarr for image.
out_pkl:str
Output pickle for annotations.
N)rg ro )rA r] re r` rm rn rC rf r r r ┌run_preprocessing_pipeline s rp c C sś ddl m} ddlm} tj|â}t| â}|dk}|d
dk|d dk@ |d dk@ dk} || tj||fâd Ź}
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ax Fixes segmentation masks to reduce coarse annotations over empty regions.
Parameters
----------
mask_file:str
NPY segmentation mask.
dask_img_array_file:str
Dask image file.
out_npy:str
Output numpy file.
n_neighbors:int
Number nearest neighbors for dilation and erosion of mask from background to not background.
Returns
-------
str
Output numpy file.
r )┌binary_opening)┌Clientg .g i@r% r& )┌ structure).r ).r% ).r& ) Zdask_image.ndmorphrq ┌dask.distributedrr r0 ┌ from_zarrrc ┌onesr' rE rF ) Z mask_fileZdask_img_array_fileZout_npy┌ n_neighborsrq rr Zdask_img_arrayrV Zis_tissue_maskZis_tissue_mask_img┌openingr r r ┌ adjust_mask3 s
(ry z./c C sr t j|ddŹ | jdâd jdâd }t| |â\}}tjt|dj|ââ|â tj |t
t|dj|ââd âd
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Ź d S )z¨Store images into npy format and store annotations into pickle dictionary.
Parameters
----------
svs_file:str
Image file.
xml_file:str
Annotations file.
annotations:list
List of annotations in image.
output_dir:str
Output directory.
T)┌exist_ok˙/r% ┌.r z{}.npyz{}.pklri )┌protocolNÚ r~ ) ┌os┌makedirs┌splitrg r' rE r r rk rl rJ )rA r] r` ┌
output_dirrH rC rf r r r ┌ process_svsV s
râ c C s t j| âtjt|dââfS )z╬Load ZARR image and annotations pickle.
Parameters
----------
in_zarr:str
Input image.
in_pkl:str
Input annotations.
Returns
-------
dask.array
Image array.
dict
Annotations dictionary.
┌rb)r0 ru rk ┌loadrJ )Zin_zarrZin_pklr r r ┌ load_datasetl s rć š Ó?c C s2 t | |â || | | ů||| ůf |kjâ |kS )z,Deprecated, computes whether patch is valid.)┌print┌mean)┌xs┌ysr ┌ purple_mask┌intensity_threshold┌ thresholdr r r ┌is_valid_patchÇ s
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Ér. }! zHt |!â |d! d7 < |d! |kÉr t&d"âént d#â t'f |Ä W Y dd}!~!X nX łS )$a Final step of preprocessing pipeline. Break up image into patches, include if not background and of a certain intensity, find area of each annotation type in patch, spatial information, image ID and dump data to SQL table.
Parameters
----------
basename:str
Patient ID.
input_dir:str
Input directory.
annotations:list
List of annotations to record, these can be different tissue types, must correspond with XML labels.
threshold:float
Value between 0 and 1 that indicates the minimum amount of patch that musn't be background for inclusion.
patch_size:int
Patch size of patches; this will become one of the tables.
generate_finetune_segmentation:bool
Deprecated.
target_class:int
Number of segmentation classes desired, from 0th class to target_class-1 will be annotated in SQL.
intensity_threshold:float
Value between 0 and 255 that represents minimum intensity to not include as background. Will be modified with new transforms.
target_threshold:float
Deprecated.
adj_mask:str
Adjusted mask if performed binary opening operations in previous preprocessing step.
basic_preprocess:bool
Do not store patch level information.
tries:int
Number of tries in case there is a Dask timeout, run again.
Returns
-------
dataframe
Patch information.
r N)┌ dataframe)┌ unary_union)┌ MultiPolygon)┌productÚ ) rH rG r` rÄ r ┌ generate_finetune_segmentation┌ target_classrŹ ┌target_threshold┌adj_mask┌basic_preprocess┌triesz{}.zarrz {}_mask.pklr` Tz {}_mask.npyFr% c s< g | ]4\}}ł|ł |ł łd gdgłr,łnt ł â ĹqS )ZNAg )r= )r2 r+ r, )r` rH r ┌ segmentationrś r r r5 Ň s z-extract_patch_information.<locals>.<listcomp>┌ID┌x┌yr r\ c S s g | ] }t |âĹqS r )r )r2 r+ r r r r5 Ň s )┌columnszValid Patches Complete┌segment)r9 Zbinsc s& g | ] }ł j tâłłgłł| âĹqS r )r@ ┌is_coords_in_box)r2 r\ )r? rf r rŐ rő r r r5 Ű s zArea Info CompleteÚ c s ł łj | dd ůf jjâ S )NrĄ )┌iloc┌values┌argmax)r+ )┌annot┌
patch_infor r r/ ÷ s z+extract_patch_information.<locals>.<lambda>rť z&Exceeded past maximum number of tries.z Restarting preprocessing again.)(r? ┌timerĺ ┌
dask.arrayr( ┌multiprocessingZ shapely.opsrô ┌shapely.geometryrö ┌ itertoolsrĽ ┌dictrć r r rc rW ┌floatr> ┌intr ┌ DataFramer9 ┌listrą rŽ ┌tolist┌appendrë r' rF rł ┌loc┌ histogramZfloat16┌ vectorize┌arange┌ Exception┌extract_patch_information)"rH rG r` rÄ r rŚ rś rŹ rÖ rÜ rŤ rť r¬ ┌ddr0 rČ rô rö rĽ Z max_triesZkargsrC Zsegmentation_maskrî Zx_maxZy_maxZx_stepsZy_stepsr\ Z
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patch_info.dbc
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jâ dS )a▄ Short summary.
Parameters
----------
basename:str
Patient ID.
input_dir:str
Input directory.
annotations:list
List of annotations to record, these can be different tissue types, must correspond with XML labels.
threshold:float
Value between 0 and 1 that indicates the minimum amount of patch that musn't be background for inclusion.
patch_size:int
Patch size of patches; this will become one of the tables.
out_db:str
Output SQL database.
generate_finetune_segmentation:bool
Deprecated.
target_class:int
Number of segmentation classes desired, from 0th class to target_class-1 will be annotated in SQL.
intensity_threshold:float
Value between 0 and 255 that represents minimum intensity to not include as background. Will be modified with new transforms.
target_threshold:float
Deprecated.
adj_mask:str
Adjusted mask if performed binary opening operations in previous preprocessing step.
basic_preprocess:bool
Do not store patch level information.
)rŚ rś rŹ rÖ rÜ rŤ rÁ )r r N)r╗ r
r r r r )rH rG r` rÄ r ┌out_dbrŚ rś rŹ rÖ rÜ rŤ rę r r r r ┌generate_patch_pipeline s
r┐ zpatch_info.pklc s4 t jç çççfddä| D ââjddŹ}|j|â dS )z Deprecated.c s g | ]}t |łł łłâĹqS r )r╗ )r2 rH )r` rG r rÄ r r r5 * s z'save_all_patch_info.<locals>.<listcomp>T)┌dropN)r ┌concatZ reset_index┌ to_pickle)Z basenamesrG r` rÄ r Z
output_pklr r )r` rG r rÄ r ┌save_all_patch_info( s &r├ c C sĘ t jj| ârtj| â}nîtj|â}tjdj|â|dŹ}|j â |d j
â }tj |dgdŹ}t |â\}}t |â\}}d|d< d|d< d|d< tj
|||gâ}|j| â |S ) aO Create dataframe that splits slides into training validation and test.
Parameters
----------
train_val_test_pkl:str
Pickle for training validation and test slides.
input_info_db:str
Patch information SQL database.
patch_size:int
Patch size looking to access.
Returns
-------
dataframe
Train test validation splits.
zselect * from "{}";)r r× )rí Ztrain┌set┌val┌test)r ┌path┌existsr Z read_pickler
r r r r ┌uniquer▓ r r┴ r┬ ) Ztrain_val_test_pkl┌
input_info_dbr ZIDsr r Z IDs_trainZIDs_testZIDs_valr r r ┌create_train_val_test0 s
r╦ r% c s└ t j| â} tjdj|â| dŹë | jâ ł jâ ë ł jtj ł d |j
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|k ë n(d ł d< |d kÉr╝ł jł t|â |k ë ł S )a7 Modify the patch information to get ready for deep learning, incorporate whole slide labels if needed.
Parameters
----------
input_info_db:str
SQL DB file.
slide_labels:dataframe
Dataframe with whole slide labels.
pos_annotation_class:str
Tissue/annotation label to label with whole slide image label, if not supplied, any slide's patches receive the whole slide label.
patch_size:int
Patch size.
segmentation:bool
Segmentation?
other_annotations:list
Other annotations to access from patch information.
target_segmentation_class:int
Segmentation class to threshold.
target_threshold:float
Include patch if patch has target area greater than this.
classify_annotations:bool
Classifying annotations for pretraining, or final model?
Returns
-------
dataframe
Modified patch information.
zselect * from "{}";)r r× r\ r% NrĄ rĹ g c s ł j | ł j | d S )Nr\ )rą )r+ )r r r r/ ő s z#modify_patch_info.<locals>.<lambda>r ┌area┌target)r
r r r r r Zdrop_duplicatesr r' ┌isinr r╔ r┤ r= r│ rą ┌type┌copy┌deepcopy┌extend┌sumrŽ rŞ r╣ r> r )r╩ Z slide_labelsZpos_annotation_classr rŁ Zother_annotations┌target_segmentation_classrÖ Zclassify_annotationsr ┌targetsZincluded_annotationsr═ ZslideZ
slide_boolr )r r ┌modify_patch_infoS s:
* &
rÍ c C s t jtj| ddŹâS )zÉNumpy to dask array.
Parameters
----------
npy_file:str
Input npy file.
Returns
-------
dask.array
Converted numpy array to dask.
zr+)Z mmap_mode)r0 Z
from_arrayr' rů )Znpy_filer r r rc ö s rc c C sB i }x8|D ]0}yt | |ddŹ||< W q
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W |S )z Deprecated.F)rZ )r[ )r] rP r` Zinterior_point_dictr\ r r r ┌grab_interior_pointsĄ s
rÎ c C sD t jd| dâ}x"|D ]}tj|âj|dddŹ qW tj|âjâ }|S )z Deprecated.┌Lr r% )┌outline┌fill)r ┌newr ZDraw┌polygonr' r( rN )rP ZpolygonsrB r▄ rV r r r ┌boxes2interior« s
rŢ c C sŐ g }t t| âdâ}xr|jdâD ]d}|d |kr xR|jdâD ]D}ddä |jdâD â}|rb|j|â q:|jttj|âjtjâââ q:W q W |S )a Get list of shapely objects for each annotation in the XML object.
Parameters
----------
xml_file:str
Annotation file.
annotation_name:str
Name of xml annotation.
return_coords:bool
Just return list of coords over shapes.
Returns
-------
list
List of shapely objects.
┌htmlr\ Z partofgroup┌ coordinatesc S s g | ]}|d |d fĹqS )rč rá r )r2 ┌
coordinater r r r5 Đ s z,parse_coord_return_boxes.<locals>.<listcomp>rÓ ) r rJ ZfindAllrÁ r r' r( rj r░ )r] rY rZ ┌boxesZxml_datar\ r▀ ┌coordsr r r r[ Ě s $r[ c C sZ t |ârRttjddgddgddgddggâ| | â}|j|d âjt|jâ }nd}|S )zŔGet area of annotation in patch.
Parameters
----------
coords:array
X,Y coordinates of patch.
patch_size:int
Patch size.
boxes:list
Shapely objects for annotations.
Returns
-------
float
Area of annotation type.
r r% g )r= r r' r( ┌ intersectionr╠ r░ )rÔ r rß ┌pointsr╠ r r r rú ě s
. rú c s ç çfddäłD âS )z■Find if image intersects with annotations.
Parameters
----------
image_coord_dict:dict
Dictionary of patches.
boxes:list
Shapely annotation shapes.
Returns
-------
dict
Dictionary of whether image intersects with any of the annotations.
c s* i | ]"ë t ttç çfd däłâââł ôqS )c s | j łł âS )N)Z
intersects)rč )┌image┌image_coord_dictr r r/ s z.is_image_in_boxes.<locals>.<dictcomp>.<lambda>)┌anyr│ ┌map)r2 )rß rŠ )rň r r^ s z%is_image_in_boxes.<locals>.<dictcomp>r )rŠ rß r )rß rŠ r ┌is_image_in_boxes˝ s rÚ c s ç fddä| D âS )┌
Deprecatedc s i | ]}t |ł â|ôqS r )┌ image2coords)r2 rň )┌ output_pointr r r^ s z%images2coord_dict.<locals>.<dictcomp>r )┌imagesrý r )rý r ┌images2coord_dict s rţ c C s t j t| dââS )rŕ z*.jpg)┌globr )┌ image_dirr r r ┌
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Parameters
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y_pred:list
List of patch segmentation masks
patch_info:dataframe
Patch information from DB.
segmentation_map:array
Existing segmentation mask.
npy_output:str
Output npy file.
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