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DeepHP_reconstructing_HPS_slides_main.py
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DeepHP_reconstructing_HPS_slides_main.py
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# -*- coding: utf-8 -*-
#============================================================
#
# Deep HistoPathology (DeepHP)
# Test Full HPS Image reconstruction and cancer detection
#
# author: Francisco Perdigon Romero
# email: fperdigon88@gmail.com
# github id: fperdigon
#
#===========================================================
import Data_Preparation.data_preparation as data_preparation
import DeepHP.dl_pipeline as dl_pipeline
import Utils.utils as utils
import matplotlib.pyplot as plt
import matplotlib
import matplotlib.gridspec as gridspec
import os
from tqdm import tqdm
import numpy as np
from scipy.ndimage import gaussian_filter, grey_dilation
if __name__ == "__main__":
data_folder = './data'
recons_folder = 'reconst_data'
train_list_file = './Data_Preparation/cases_train.txt'
val_list_file = './Data_Preparation/cases_val.txt'
test_list_file = './Data_Preparation/cases_test.txt'
# Reading hps from test to generate images
test_list = []
with open(test_list_file) as fp:
Lines = fp.readlines()
for line in Lines:
test_list.append(line.strip())
# Generate HPS image from patches
hps_path_list = []
pbar = tqdm(total=len(test_list))
pbar.set_description(desc=' Generating HPS slide image')
for hps in test_list:
df = data_folder + '/' + data_preparation.uzip_folder
# Determines if the hps folder exists
if os.path.exists(df + '/' + hps):
# determine if the image was previously reconstructed
if not os.path.exists(data_folder + '/' + recons_folder + '/' + hps + '_hps.png'):
[img_big, class_patches] = utils.hps_image_reconst_from_patches(hps, df)
hps_path = utils.save_reconst_hps_to_png(img=img_big,
hps_slide=hps,
save_folder=data_folder + '/' + recons_folder)
hps_path_list.append(hps_path)
else:
hps_path_list.append(data_folder + '/' + recons_folder + '/' + hps + '_hps.png')
else:
print(hps + ' folder does not exist')
pbar.update(1)
pbar.close()
# Predict Cancer on the HPS images and Generate Heatmap
pbar = tqdm(total=len(hps_path_list))
pbar.set_description(desc=' Generating HPS Predictions heatmaps')
for hps_path in hps_path_list:
# Generate patches from HPS image
patch_size = (50, 50)
out_dict = utils.load_img_generate_patch_array(hps_path, patch_size)
patch_size = out_dict['patch_size']
patch_per_row = out_dict['patch_per_row']
patch_per_column = out_dict['patch_per_column']
patches_array = out_dict['patches_array']
org_hps = out_dict['org_hps']
# Apply RGB normalization to each patch
# for i in range(patches_array.shape[0]):
# patches_array[i, :, :, :] = dl_pipeline.normalize_rgb(patches_array[i, :, :, :])
# Deep Learning Model Prediction
predictions = dl_pipeline.inference_dl(patches_array, model_filepath='weights.best.hdf5')
# predictions = dl_pipeline.inference_dl(patches_array, model_filepath='norm_weights.best.hdf5')
# Get cancer cell prediction
cancer_cells_prediction = predictions[:, 1]
# Heatmap generation
heatmap = utils.heatmap_img_from_predictions(org_hps, cancer_cells_prediction, patch_size, patch_per_row, patch_per_column)
# Dilate operation to reduce the fading intrudiced by the gaussian filter
for i in range(10):
heatmap = grey_dilation(heatmap, footprint=np.ones((3, 3)))
# Apply a Gaussian filter to smooth the heatmap
heatmap = gaussian_filter(heatmap, sigma=25)
# Generating images
def transparent_cmap(cmap, alpha_th=70, N=255):
"Copy colormap and set alpha values"
mycmap = cmap.__copy__()
mycmap._init()
alpha_values = np.linspace(0, 1, N + 4)
alpha_values[alpha_values < alpha_th / N] = 0
alpha_values[alpha_values >= alpha_th / N] = 1
mycmap._lut[:, -1] = alpha_values
return mycmap
# Make the figure
f = plt.figure()
gs = gridspec.GridSpec(nrows=1, ncols=3, left=0.1, bottom=0.25, right=0.95, top=0.95,
wspace=0.05, hspace=0., width_ratios=[1, 1, 0.03])
a0 = plt.subplot(gs[0])
a1 = plt.subplot(gs[1])
a2 = plt.subplot(gs[2])
# Using subplots (It works as well)
# f, (a0, a1, a2) = plt.subplots()
# f, (a0, a1, a2) = plt.subplots(1, 3, gridspec_kw={'width_ratios': [1, 1, 0.03]})
# plt.tight_layout()
# Axis 0
a0.imshow(org_hps)
a0.axis('off')
# Axis 1
a1.imshow(org_hps)
mycmap = transparent_cmap(cmap=plt.get_cmap('jet'), alpha_th=int(0.5 * 255), N=255)
aa1 = a1.imshow(heatmap, cmap=mycmap, alpha=0.3)
#a1.imshow(heatmap)
a1.axis('off')
# # Axis 2
norm = matplotlib.colors.Normalize(vmin=0, vmax=1)
cbar = f.colorbar(matplotlib.cm.ScalarMappable(norm=norm, cmap='jet'),
ax=a2, pad=.05, extend='neither', fraction=1)
for t in cbar.ax.get_yticklabels():
t.set_fontsize(5)
a2.axis('off')
fig1 = plt.gcf()
plt.show(block=False)
# plt.show(block=True)
print('\nSaving image ...')
fig1.savefig(hps_path + '_predictions.jpg', dpi=500, bbox_inches='tight', pad_inches=0.5)
print('\nSaving image DONE.')
plt.close()
pbar.update(1)
pbar.close()