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phiseg_sample_construction.py
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phiseg_sample_construction.py
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import glob
import logging
import os
from importlib.machinery import SourceFileLoader
import cv2
import numpy as np
import config.system as sys_config
import utils
from data.data_switch import data_switch
from phiseg.phiseg_model import phiseg
logging.basicConfig(level=logging.INFO, format='%(asctime)s %(message)s')
import matplotlib.pyplot as plt
import itertools
def findsubsets(S,m):
return list(itertools.combinations(S, m))
def main(model_path, exp_config):
# Make and restore vagan model
phiseg_model = phiseg(exp_config=exp_config)
phiseg_model.load_weights(model_path, type='best_dice')
data_loader = data_switch(exp_config.data_identifier)
data = data_loader(exp_config)
outfolder = '/home/baumgach/Reports/ETH/MICCAI2019_segvae/raw_figures'
ims = exp_config.image_size
# x_b, s_b = data.test.next_batch(1)
# heart 100
# prostate 165
index = 165 # 100 is a normal image, 15 is a very good slice
x_b = data.test.images[index, ...].reshape([1] + list(exp_config.image_size))
if exp_config.data_identifier == 'lidc':
s_b = data.test.labels[index, ...]
if np.sum(s_b[..., 0]) > 0:
s_b = s_b[..., 0]
elif np.sum(s_b[..., 1]) > 0:
s_b = s_b[..., 1]
elif np.sum(s_b[..., 2]) > 0:
s_b = s_b[..., 2]
else:
s_b = s_b[..., 3]
s_b = s_b.reshape([1] + list(exp_config.image_size[0:2]))
elif exp_config.data_identifier == 'uzh_prostate':
s_b = data.test.labels[index, ...]
s_b = s_b[..., 0]
s_b = s_b.reshape([1] + list(exp_config.image_size[0:2]))
else:
s_b = data.test.labels[index, ...].reshape([1] + list(exp_config.image_size[0:2]))
x_b_for_cnt = utils.convert_to_uint8(np.squeeze(x_b.copy()))
x_b_for_cnt = cv2.cvtColor(x_b_for_cnt, cv2.COLOR_GRAY2BGR)
x_b_for_cnt = utils.resize_image(x_b_for_cnt, (2*ims[0], 2*ims[1]), interp=cv2.INTER_NEAREST)
x_b_for_cnt = utils.histogram_equalization(x_b_for_cnt)
for ss in range(3):
print(ss)
s_p_list = phiseg_model.predict_segmentation_sample_levels(x_b, return_softmax=False)
accum_list = [None]*exp_config.latent_levels
accum_list[exp_config.latent_levels-1] = s_p_list[-1]
for lvl in reversed(range(exp_config.latent_levels-1)):
accum_list[lvl] = accum_list[lvl+1] + s_p_list[lvl]
print('Plotting accum_list')
for ii, img in enumerate(accum_list):
plt.figure()
img = utils.resize_image(np.squeeze(np.argmax(img, axis=-1)), (2*ims[0], 2*ims[1]), interp=cv2.INTER_NEAREST)
plt.imshow(img[2*30:2*192-2*30,2*30:2*192-2*30], cmap='gray')
plt.axis('off')
plt.savefig(os.path.join(outfolder, 'segm_lvl_%d_samp_%d.png' % (ii, ss)),bbox_inches='tight')
print('Plotting s_p_list')
for ii, img in enumerate(s_p_list):
img = utils.softmax(img)
plt.figure()
img = utils.resize_image(np.squeeze(img[...,1]), (2*ims[0], 2*ims[1]), interp=cv2.INTER_NEAREST)
plt.imshow(img[2*30:2*192-2*30,2*30:2*192-2*30], cmap='gray')
plt.axis('off')
plt.savefig(os.path.join(outfolder, 'residual_lvl_%d_samp_%d.png' % (ii, ss)),bbox_inches='tight')
s_p_d = np.uint8((np.squeeze(np.argmax(accum_list[0], axis=-1)) / (exp_config.nlabels-1)) * 255)
s_p_d = utils.resize_image(s_p_d, (2*ims[0], 2*ims[1]), interp=cv2.INTER_NEAREST)
print('Calculating contours')
print(np.unique(s_p_d))
rv = cv2.inRange(s_p_d, 84, 86)
my = cv2.inRange(s_p_d, 169, 171)
rv_cnt, hierarchy = cv2.findContours(rv, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
my_cnt, hierarchy = cv2.findContours(my, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
x_b_for_cnt = cv2.drawContours(x_b_for_cnt, rv_cnt, -1, (0, 255, 0), 1)
x_b_for_cnt = cv2.drawContours(x_b_for_cnt, my_cnt, -1, (0, 0, 255), 1)
x_b_for_cnt = cv2.cvtColor(x_b_for_cnt, cv2.COLOR_BGR2RGB)
print('Plotting final images...')
plt.figure()
plt.imshow(x_b_for_cnt[2*30:2*192-2*30,2*30:2*192-2*30,:], cmap='gray')
plt.axis('off')
plt.savefig(os.path.join(outfolder, 'input_img_cnts.png'),bbox_inches='tight')
plt.figure()
x_b = utils.convert_to_uint8(x_b)
x_b = cv2.cvtColor(np.squeeze(x_b), cv2.COLOR_GRAY2BGR)
x_b = utils.histogram_equalization(x_b)
x_b = utils.resize_image(x_b, (2*ims[0], 2*ims[1]), interp=cv2.INTER_NEAREST)
plt.imshow(x_b[2*30:2*192-2*30,2*30:2*192-2*30], cmap='gray')
plt.axis('off')
plt.savefig(os.path.join(outfolder, 'input_img.png'),bbox_inches='tight')
plt.figure()
s_b = utils.resize_image(np.squeeze(s_b), (2*ims[0], 2*ims[1]), interp=cv2.INTER_NEAREST)
plt.imshow(s_b[2*30:2*192-2*30,2*30:2*192-2*30], cmap='gray')
plt.axis('off')
plt.savefig(os.path.join(outfolder, 'gt_seg.png'),bbox_inches='tight')
# plt.show()
if __name__ == '__main__':
base_path = sys_config.project_root
# Code for selecting experiment from command line
# parser = argparse.ArgumentParser(
# description="Script for a simple test loop evaluating a network on the test dataset")
# parser.add_argument("EXP_PATH", type=str, help="Path to experiment folder (assuming you are in the working directory)")
# args = parser.parse_args()
# exp_path = args.EXP_PATH
#
exp_path = '/itet-stor/baumgach/net_scratch/logs/phiseg/uzh_prostate/phiseg_7_5'
model_path = os.path.join(base_path, exp_path)
config_file = glob.glob(model_path + '/*py')[0]
config_module = config_file.split('/')[-1].rstrip('.py')
exp_config = SourceFileLoader(config_module, os.path.join(config_file)).load_module()
main(model_path, exp_config=exp_config)