Identify specific populations and expand these masks - Single object#
By employing the CosMx data, we sought to identify gamma delta (γδ) T cells, a rare immune cell subset that is otherwise difficult to detect through clustering and phenotyping of segmented cells
To achieve this, we applied GRIDGEN’s convolutional approach to identify putative regions enriched for γδ T cell transcripts. Specifically, we identified areas where TRGC and TRDC transcripts, encoding for the T cell receptor (TCR) constant regions of γδ T cells, were within 5.5 µm of each other, with contours highlighting these regions. Beyond the presence of TRGC and TRDC transcripts in a confined area, specific criteria were applied, including thresholds on transcript counts, contour size, and transcript combinations, specifically requiring the sum of TRGC and TRDC counts to be higher than the sum of TRAC and TRBC (encoding for the TCR of γδ T cells), with at least one TRGC and one TRDC .
The resulting regions represent putative γδ T cell areas that can be subjected to further analysis.
%load_ext autoreload
import os
import sys
import time
import logging
import re
from tqdm import tqdm
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from PIL import Image
import json
from natsort import natsorted
import matplotlib.pyplot as plt
import matplotlib.colors as mcolors
import matplotlib.cm as cm
from PIL import Image, ImageDraw
import json
import math
import matplotlib.patches as mpatches
import matplotlib.patches as patches
from skimage.measure import label
sys.path.append(os.path.dirname(os.getcwd()))
from gridgene import get_arrays as ga
from gridgene import contours, get_masks
from gridgene.mask_properties import MaskAnalysisPipeline, MaskDefinition, MorphologyExtractor
from gridgene.get_masks import MultiClassObjectAnalysis
from helper_plot import plot_TRGC_TRDC_points_polygons, plot_TRGC_TRDC_points_contours, plot_TRGC_TRDC_points_mask
The autoreload extension is already loaded. To reload it, use:
%reload_ext autoreload
define looger – important to save info of the runs
# define the logger : can be None, and is set to INFO
# Custom logger setup
logger = logging.getLogger('contour_logger')
handler = logging.StreamHandler()
formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
handler.setFormatter(formatter)
logger.addHandler(handler)
logger.setLevel(logging.INFO)
define files
cosmx_path_s3 = '../../cosmx_data/S3/S3/20230628_151317_S3/AnalysisResults/yxyz3r7ufm'
folder_names_s3 = [folder_name for folder_name in os.listdir(cosmx_path_s3) if
os.path.isdir(os.path.join(cosmx_path_s3, folder_name))]
target_files_s3 = [
os.path.join(cosmx_path_s3, folder, file)
for folder in os.listdir(cosmx_path_s3)
if os.path.isdir(os.path.join(cosmx_path_s3, folder))
for file in os.listdir(os.path.join(cosmx_path_s3, folder))
if '__target_call_coord.csv' in file
]
files_cosmx = natsorted(target_files_s3)
len(files_cosmx)
dapi_folder = '/home/martinha/PycharmProjects/phd/spatial_transcriptomics/cosmx_data/S3/S3/20230628_151317_S3/CellStatsDir/Morphology2D/'
annotation_cellpose = '/home/martinha/PycharmProjects/phd/spatial_transcriptomics/cosmx_data/S3/Slide4_S3_CosMx_CellPose_All_annotated.csv'
annotation_df = pd.read_csv(annotation_cellpose)
print(annotation_df['Final_label'].value_counts())
print(len(annotation_df))
Final_label
Epithelial cells 20558
T cells 9436
Myeloid cells 6688
Fibroblasts 6133
Other cells 4988
Plasma cells 1656
Endothelial cells 1508
Name: count, dtype: int64
50967
2. Define GD
This intends to, based on defined genes, find γδ T cells.
We need to define:
Parameters for γδ contours
Genes to consider
density
minimum area
kernel size
this will define for an overlapping area with kernel size a minimum number of density genes of interest. The contiguous area of the contour will have at least minimum area.
You may also need to define extra parameters to filter out wrong contours
Define arrays for an example FOV
target_gd = ['TRGC1/TRGC2', 'TRDC']
target_ab = ['TRBC1/TRBC2', 'TRAC']
target_cd8 = ['CD8A','CD8B', 'TRBC1/TRBC2', 'TRAC']
target_cd4 = ['CD4']
target_tum = ['EPCAM', 'CEACAM6', 'CLDN4', 'CDH1', 'RNF43', 'SPINK1', 'SOX9', 'CD24', 'KRT19', 'AREG',
'REG1A', 'AGR2', 'PLAC8', 'CALB1', 'S100P', 'ITGA6', 'DMBT1', 'DUSP4',
'KRT8', 'S100A6', 'RPL37', 'RPL32', 'KRT18', 'OLFM4',
'PRSS2', 'CD55', 'EPHB4', 'ADGRL1', 'KRT17', 'ITGB8', 'ADGRE5', 'GDF15', 'IL27RA', 'AZGP1'
] # cadherin 'PIGR', 'LYZ','SERPINA1'
fov = 'FOV010'
file_csv = [file for file in files_cosmx if fov in file][0]
df_total = pd.read_csv(file_csv)
df_total = df_total.rename(columns={'x': 'X', 'y': 'Y'})
df_total = df_total[~df_total['target'].str.contains('System|egative')]
n_genes = len(df_total['target'].unique())
height = int(max(df_total['X'])) + 1
width = int(max(df_total['Y'])) + 1
print(f'n genes: {n_genes}')
print(f'shape: {height}, {width}')
print(f'n hits {len(df_total)}')
target_dict_total = {target: index for index, target in enumerate(df_total['target'].unique())}
array_total = ga.transform_df_to_array(df = df_total, target_dict=target_dict_total, array_shape = (height, width,len(target_dict_total))).astype(np.int8)
# creating subsets
df_subset_gd, array_subset_gd, target_indices_subset_gd = ga.get_subset_arrays(df_total, array_total,
target_dict_total,
target_list=target_gd,
target_col='target')
df_subset_ab, array_subset_ab, target_indices_subset_ab = ga.get_subset_arrays(df_total, array_total,
target_dict_total,
target_list=target_ab,
target_col='target')
df_subset_g, array_subset_g, target_indices_subset_g = ga.get_subset_arrays(df_total, array_total,
target_dict_total,
target_list=['TRGC1/TRGC2'],
target_col='target')
df_subset_d, array_subset_d, target_indices_subset_d = ga.get_subset_arrays(df_total, array_total,
target_dict_total,
target_list=['TRDC'],
target_col='target')
df_subset_tum, array_subset_tum, target_indices_subset_tum = ga.get_subset_arrays(df_total, array_total,target_dict_total,
target_list=target_tum, target_col = 'target')
df_subset_cd8, array_subset_cd8, target_indices_subset_cd8 = ga.get_subset_arrays(df_total, array_total,
target_dict_total,
target_list=target_cd8,
target_col='target')
df_subset_TRAC_gene, array_subset_TRAC_gene, target_indices_subset_TRAC_gene = ga.get_subset_arrays(df_total,
array_total,
target_dict_total,
target_list= ['TRAC'],
target_col='target')
df_subset_TRBC_gene, array_subset_TRBC_gene, target_indices_subset_TRBC_gene = ga.get_subset_arrays(df_total,
array_total,
target_dict_total,
target_list= ['TRBC1/TRBC2'],
target_col='target')
df_subset_cd8_gene, array_subset_cd8_gene, target_indices_subset_cd8_gene = ga.get_subset_arrays(df_total,
array_total,
target_dict_total,
target_list= ['CD8A', 'CD8B'],
target_col='target')
df_subset_cd4_gene, array_subset_cd4_gene, target_indices_subset_cd4_gene = ga.get_subset_arrays(df_total,
array_total,
target_dict_total,
target_list= ['CD4'],
target_col='target')
n genes: 999
shape: 4246, 4246
n hits 1715888
T cells are usually between 5-10 um diameter, in CosMx this will be between 43 px and 85 px. We will find regions possible with γδ using the convolution approach.
density_th_gd = 2
min_area_th_gd = 5 # 40
kernel_size_gd = 45 #90
CGD = contours.ConvolutionContours(array_subset_gd, contour_name='GD', logger=logger)
CGD.get_conv_sum(kernel_size=kernel_size_gd, kernel_shape='square') #
CGD.contours_from_sum(density_threshold=density_th_gd,
min_area_threshold=min_area_th_gd, directionality='higher')
fig, axs = plt.subplots(1, 2, figsize=(10, 5))
CGD.plot_contours_scatter(path=None, show=False, s=0.05, alpha=0.5, linewidth=1,
c_points='blue', c_contours='red', ax=axs[0])
axs[0].set_title('γδ transcript points and γδ contours')
CGD.plot_conv_sum(cmap='plasma', c_countour='white', path=None, ax=axs[1])
axs[1].set_title('γδ transcript density and contours for γδ')
plt.show()
print('total contours found ', CGD.total_valid_contours)
print('total contours after filtering by area', CGD.contours_filtered_area)
2025-06-16 15:22:02,599 - contour_logger - INFO - Initialized GetContour
get_conv_sum took 0.4387 seconds
2025-06-16 15:22:03,311 - contour_logger - INFO - Number of contours after filtering no counts: 26
contours_from_sum took 0.2731 seconds
total contours found 0
total contours after filtering by area 0
In this case, we are considering a possible object of γδ T cells (one or more) if there is at least 3 counts (>2) of the genes [‘TRGC1’,’TRGC2’,’TRDC’] in a kernel size 45*45.
This is a very lenient threshold. One could use more stringent thresholds but risk to possible loose some cells. One shoudl design these parameters accordingly to the genes behaviour. γδ transcripts in CosMx are not express much.
As we are using light restrictions, some of the contours are not true γδ cells. For example, an artifact cluster of 3 TRGC1 genes would be considered. Also, in biological terms, it is necessary to also guarantee that the TRGC and TRDC are more expressed than the receptors B and C of other T cells.
We will define some filtering rules:
* AB < GD : counts of AB total counts in the contour needs to be lower than tota GD point counts.
* The contour should have at least 1 count of G and one count of D. G >0, D>0.
filter_contours_by_gene_comparison -> Filters contours based on the comparison of gene counts between two gene arrays.Contours are kept if the gene count in gene_array1 is greater than the gene count in gene_array2 (gene_array1>gene_array2)
filter_contours_by_gene_threshold -> Filters contours based on a gene count threshold. Contours are kept if the gene count is greater than or equal to the threshold (gene_array>=threshold).
### Filtering
CGD.filter_contours_by_gene_comparison(gene_array1 = array_subset_gd, gene_array2 = array_subset_ab,
gene_name1 = "gd", gene_name2 = "ab") # gene 1 > gene2 --> valid contour
# G>0 D>0
CGD.filter_contours_by_gene_threshold(gene_array = array_subset_d.squeeze(), threshold = 1, gene_name = 'TRDC')# >= 1
CGD.filter_contours_by_gene_threshold(gene_array = np.sum(array_subset_g, axis=-1), threshold = 1, gene_name = 'TRGC1_2')# >= 1
print('total contours found ', len(CGD.contours))
2025-06-16 15:22:05,681 - contour_logger - INFO - Excluding contour 7. gd count 3.00 ≤ ab count 4.00
2025-06-16 15:22:06,079 - contour_logger - INFO - Excluding contour 13. gd count 1.00 ≤ ab count 2.00
2025-06-16 15:22:06,146 - contour_logger - INFO - Excluding contour 14. gd count 3.00 ≤ ab count 3.00
2025-06-16 15:22:06,892 - contour_logger - INFO - Contours remaining after gene comparison: 23
2025-06-16 15:22:06,963 - contour_logger - INFO - Excluding contour 1. Gene TRDC count 0.0 is below threshold 1
2025-06-16 15:22:06,997 - contour_logger - INFO - Excluding contour 2. Gene TRDC count 0.0 is below threshold 1
2025-06-16 15:22:07,413 - contour_logger - INFO - Excluding contour 14. Gene TRDC count 0.0 is below threshold 1
2025-06-16 15:22:07,690 - contour_logger - INFO - Excluding contour 22. Gene TRDC count 0.0 is below threshold 1
2025-06-16 15:22:07,690 - contour_logger - INFO - Number of contours remaining: 19
2025-06-16 15:22:07,822 - contour_logger - INFO - Excluding contour 2. Gene TRGC1_2 count 0.0 is below threshold 1
2025-06-16 15:22:07,857 - contour_logger - INFO - Excluding contour 3. Gene TRGC1_2 count 0.0 is below threshold 1
2025-06-16 15:22:07,927 - contour_logger - INFO - Excluding contour 5. Gene TRGC1_2 count 0.0 is below threshold 1
2025-06-16 15:22:07,962 - contour_logger - INFO - Excluding contour 6. Gene TRGC1_2 count 0.0 is below threshold 1
2025-06-16 15:22:08,030 - contour_logger - INFO - Excluding contour 8. Gene TRGC1_2 count 0.0 is below threshold 1
2025-06-16 15:22:08,099 - contour_logger - INFO - Excluding contour 10. Gene TRGC1_2 count 0.0 is below threshold 1
2025-06-16 15:22:08,170 - contour_logger - INFO - Excluding contour 12. Gene TRGC1_2 count 0.0 is below threshold 1
2025-06-16 15:22:08,274 - contour_logger - INFO - Excluding contour 15. Gene TRGC1_2 count 0.0 is below threshold 1
2025-06-16 15:22:08,310 - contour_logger - INFO - Excluding contour 16. Gene TRGC1_2 count 0.0 is below threshold 1
2025-06-16 15:22:08,345 - contour_logger - INFO - Excluding contour 17. Gene TRGC1_2 count 0.0 is below threshold 1
2025-06-16 15:22:08,379 - contour_logger - INFO - Excluding contour 18. Gene TRGC1_2 count 0.0 is below threshold 1
2025-06-16 15:22:08,380 - contour_logger - INFO - Number of contours remaining: 8
total contours found 8
Lets plot the transcripts on top of DAPI images for better visuaization.
# # plot polygons with TRDC and TRGC points for cell boundaries
# img_points_dapi = plot_TRGC_TRDC_points_mask(segmentation_file_path, filtered_data_gd,dapi_file_path, figsize=(10, 10))
df_total = pd.read_csv(file_csv)
target_list = ['TRGC1/TRGC2', 'TRDC','TRBC1/TRBC2', ] # 'TRAC', 'CD8A', 'CD8B', 'CD4'
filtered_data_gd = df_total[df_total['target'].isin(target_list)]
dapi_file = [file for file in os.listdir(dapi_folder) if str(fov[3:]) in file][0]
dapi_file_path = os.path.join(dapi_folder, dapi_file)
img_gd_contour = plot_TRGC_TRDC_points_contours(CGD.contours, filtered_data_gd, dapi_file_path)
# GM = get_masks.GetMasks(image_shape=(height, width))
# SA = get_masks.SingleClassObjectAnalysis(GM,contours_object=CGD.contours)
# SA.get_mask_objects(exclude_masks=None) # Not excluding any tum or empty
# SA.get_objects_expansion(expansions_pixels=[10, 20, 30],
# exclude_masks=None) # exclude from empty? or from tumour? Not excluding
# mask_GD = SA.mask_object_SA
# morpho_properties= MorphologyExtractor().extract_per_object_features(labeled_mask= label(mask_GD))
# centroids = [(obj['centroid_x'], obj['centroid_y']) for obj in morpho_properties]
# print('gd', centroids)
zoom_coords = [(1000,3100), (1000,3450), (1300,0), (2000,3300),
(3300,1500),(3900,1000)]
zoom_size = 500 # size of the square to crop
# Plot full image with red rectangles ===
fig, ax = plt.subplots(figsize=(10, 10))
ax.imshow(img_gd_contour, origin='lower')
ax.axis('off')
ax.set_title("Full Image with Zoom Boxes")
# Add red dotted rectangles
for zoom_x, zoom_y in zoom_coords:
rect = patches.Rectangle(
(zoom_x, zoom_y),
zoom_size,
zoom_size,
linewidth=2,
edgecolor='red',
facecolor='none',
linestyle='dotted'
)
ax.add_patch(rect)
plt.show()
# Plot zoomed-in regions in a grid with 3 columns ===
num_coords = len(zoom_coords)
num_cols = 3
num_rows = (num_coords + num_cols - 1) // num_cols # ceil division
fig, axes = plt.subplots(num_rows, num_cols, figsize=(5 * num_cols, 5 * num_rows))
# If axes is 1D (single row), make it 2D for uniformity
if num_rows == 1:
axes = [axes]
for idx, (zoom_x, zoom_y) in enumerate(zoom_coords):
row = idx // num_cols
col = idx % num_cols
ax = axes[row][col] if num_rows > 1 else axes[col]
# Define bounding box for cropping
box = (zoom_x, zoom_y, zoom_x + zoom_size, zoom_y + zoom_size)
cropped_img = img_gd_contour.crop(box)
ax.imshow(cropped_img, origin='lower')
ax.axis('off')
ax.set_title(f"Zoom {idx + 1} ({zoom_x}, {zoom_y})")
# Hide any unused subplots
for idx in range(num_coords, num_rows * num_cols):
row = idx // num_cols
col = idx % num_cols
ax = axes[row][col] if num_rows > 1 else axes[col]
ax.axis('off')
plt.tight_layout()
plt.show()
<built-in method max of numpy.ndarray object at 0x7f26e399cb10>
Check a different FOV
fov = 'FOV006'
file_csv = [file for file in files_cosmx if fov in file][0]
df_total = pd.read_csv(file_csv)
df_total = df_total.rename(columns={'x': 'X', 'y': 'Y'})
df_total = df_total[~df_total['target'].str.contains('System|egative')]
n_genes = len(df_total['target'].unique())
height = int(max(df_total['X'])) + 1
width = int(max(df_total['Y'])) + 1
target_dict_total = {target: index for index, target in enumerate(df_total['target'].unique())}
array_total = ga.transform_df_to_array(df = df_total, target_dict=target_dict_total, array_shape = (height, width,len(target_dict_total))).astype(np.int8)
# creating subsets
df_subset_gd, array_subset_gd, target_indices_subset_gd = ga.get_subset_arrays(df_total, array_total,
target_dict_total,
target_list=target_gd,
target_col='target')
df_subset_ab, array_subset_ab, target_indices_subset_ab = ga.get_subset_arrays(df_total, array_total,
target_dict_total,
target_list=target_ab,
target_col='target')
df_subset_g, array_subset_g, target_indices_subset_g = ga.get_subset_arrays(df_total, array_total,
target_dict_total,
target_list=['TRGC1/TRGC2'],
target_col='target')
df_subset_d, array_subset_d, target_indices_subset_d = ga.get_subset_arrays(df_total, array_total,
target_dict_total,
target_list=['TRDC'],
target_col='target')
df_subset_cd8, array_subset_cd8, target_indices_subset_cd8 = ga.get_subset_arrays(df_total, array_total,
target_dict_total,
target_list=target_cd8,
target_col='target')
df_subset_TRAC_gene, array_subset_TRAC_gene, target_indices_subset_TRAC_gene = ga.get_subset_arrays(df_total,
array_total,
target_dict_total,
target_list= ['TRAC'],
target_col='target')
df_subset_TRBC_gene, array_subset_TRBC_gene, target_indices_subset_TRBC_gene = ga.get_subset_arrays(df_total,
array_total,
target_dict_total,
target_list= ['TRBC1/TRBC2'],
target_col='target')
CGD = contours.ConvolutionContours(array_subset_gd, contour_name='GD', logger=logger)
CGD.get_conv_sum(kernel_size=kernel_size_gd, kernel_shape='square') #
CGD.contours_from_sum(density_threshold=density_th_gd,
min_area_threshold=min_area_th_gd, directionality='higher')
fig, axs = plt.subplots(1, 2, figsize=(10, 5))
CGD.plot_contours_scatter(path=None, show=False, s=0.05, alpha=0.5, linewidth=1,
c_points='blue', c_contours='red', ax=axs[0])
axs[0].set_title('γδ transcript points and γδ contours')
CGD.plot_conv_sum(cmap='plasma', c_countour='white', path=None, ax=axs[1])
axs[1].set_title('γδ transcript density and contours for γδ')
plt.show()
print('total contours found ', CGD.total_valid_contours)
print('total contours after filtering by area', CGD.contours_filtered_area)
### Filtering
CGD.filter_contours_by_gene_comparison(gene_array1 = array_subset_gd, gene_array2 = array_subset_ab,
gene_name1 = "gd", gene_name2 = "ab") # gene 1 > gene2 --> valid contour
# G>0 D>0
CGD.filter_contours_by_gene_threshold(gene_array = array_subset_d.squeeze(), threshold = 1, gene_name = 'TRDC')# >= 1
CGD.filter_contours_by_gene_threshold(gene_array = np.sum(array_subset_g, axis=-1), threshold = 1, gene_name = 'TRGC1_2')# >= 1
print('total contours found ', len(CGD.contours))
# # plot polygons with TRDC and TRGC points for cell boundaries
# img_points_dapi = plot_TRGC_TRDC_points_mask(segmentation_file_path, filtered_data_gd,dapi_file_path, figsize=(10, 10))
df_total = pd.read_csv(file_csv)
target_list = ['TRGC1/TRGC2', 'TRDC','TRBC1/TRBC2', ] # 'TRAC', 'CD8A', 'CD8B', 'CD4'
filtered_data_gd = df_total[df_total['target'].isin(target_list)]
dapi_file = [file for file in os.listdir(dapi_folder) if str(fov[3:]) in file][0]
dapi_file_path = os.path.join(dapi_folder, dapi_file)
img_gd_contour = plot_TRGC_TRDC_points_contours(CGD.contours, filtered_data_gd, dapi_file_path)
2025-06-16 15:24:35,654 - contour_logger - INFO - Initialized GetContour
get_conv_sum took 0.4348 seconds
2025-06-16 15:24:36,311 - contour_logger - INFO - Number of contours after filtering no counts: 17
contours_from_sum took 0.2220 seconds
total contours found 0
total contours after filtering by area 0
2025-06-16 15:24:38,412 - contour_logger - INFO - Excluding contour 4. gd count 4.00 ≤ ab count 4.00
2025-06-16 15:24:38,481 - contour_logger - INFO - Excluding contour 5. gd count 3.00 ≤ ab count 3.00
2025-06-16 15:24:39,233 - contour_logger - INFO - Contours remaining after gene comparison: 15
2025-06-16 15:24:39,438 - contour_logger - INFO - Excluding contour 5. Gene TRDC count 0.0 is below threshold 1
2025-06-16 15:24:39,742 - contour_logger - INFO - Number of contours remaining: 14
2025-06-16 15:24:39,871 - contour_logger - INFO - Excluding contour 2. Gene TRGC1_2 count 0.0 is below threshold 1
2025-06-16 15:24:39,906 - contour_logger - INFO - Excluding contour 3. Gene TRGC1_2 count 0.0 is below threshold 1
2025-06-16 15:24:39,976 - contour_logger - INFO - Excluding contour 5. Gene TRGC1_2 count 0.0 is below threshold 1
2025-06-16 15:24:40,044 - contour_logger - INFO - Excluding contour 7. Gene TRGC1_2 count 0.0 is below threshold 1
2025-06-16 15:24:40,114 - contour_logger - INFO - Excluding contour 9. Gene TRGC1_2 count 0.0 is below threshold 1
2025-06-16 15:24:40,251 - contour_logger - INFO - Number of contours remaining: 9
total contours found 9
<built-in method max of numpy.ndarray object at 0x7f26e3954ab0>
# centroids for GD
GM = get_masks.GetMasks(image_shape=(height, width))
SA = get_masks.SingleClassObjectAnalysis(GM,contours_object=CGD.contours)
SA.get_mask_objects(exclude_masks=None) # Not excluding any tum or empty
SA.get_objects_expansion(expansions_pixels=[10, 20, 30],
)
mask_GD = SA.mask_object_SA
morpho_properties= MorphologyExtractor().extract_per_object_features(labeled_mask= label(mask_GD))
centroids = [(obj['centroid_x'], obj['centroid_y']) for obj in morpho_properties]
# print('gd', centroids)
zoom_coords = [(y-250,x-250) for (x,y) in centroids]
# zoom_coords = centroids
zoom_size = 500 # size of the square to crop
# Plot full image with red rectangles ===
fig, ax = plt.subplots(figsize=(10, 10))
ax.imshow(img_gd_contour, origin='upper')
ax.axis('off')
ax.set_title("Full Image with Zoom Boxes")
# Add red dotted rectangles
for zoom_x, zoom_y in zoom_coords:
rect = patches.Rectangle(
(zoom_x, zoom_y),
zoom_size,
zoom_size,
linewidth=2,
edgecolor='red',
facecolor='none',
linestyle='dotted'
)
ax.add_patch(rect)
plt.show()
# Plot zoomed-in regions in a grid with 3 columns ===
num_coords = len(zoom_coords)
num_cols = 3
num_rows = (num_coords + num_cols - 1) // num_cols # ceil division
fig, axes = plt.subplots(num_rows, num_cols, figsize=(5 * num_cols, 5 * num_rows))
# If axes is 1D (single row), make it 2D for uniformity
if num_rows == 1:
axes = [axes]
for idx, (zoom_x, zoom_y) in enumerate(zoom_coords):
row = idx // num_cols
col = idx % num_cols
ax = axes[row][col] if num_rows > 1 else axes[col]
# Define bounding box for cropping
box = (zoom_x, zoom_y, zoom_x + zoom_size, zoom_y + zoom_size)
cropped_img = img_gd_contour.crop(box)
ax.imshow(cropped_img, origin='lower')
ax.axis('off')
ax.set_title(f"Zoom {idx + 1} ({zoom_x}, {zoom_y})")
# Hide any unused subplots
for idx in range(num_coords, num_rows * num_cols):
row = idx // num_cols
col = idx % num_cols
ax = axes[row][col] if num_rows > 1 else axes[col]
ax.axis('off')
plt.tight_layout()
plt.show()
2025-06-16 15:28:56,115 - gridgen.get_masks.GetMasks - INFO - Initialized GetMasks
2025-06-16 15:28:56,118 - gridgen.get_masks.GetMasks - INFO - Mask for objects created.
