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phiseg_makegif_samples.py
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phiseg_makegif_samples.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
# import scipy.misc
from PIL import Image
logging.basicConfig(level=logging.INFO, format='%(asctime)s %(message)s')
def softmax(x):
"""Compute softmax values for each sets of scores in x."""
return np.exp(x) / np.sum(np.exp(x), axis=-1, keepdims=True)
SAVE_VIDEO = False
SAVE_GIF = True
DISPLAY_VIDEO = True
video_target_size = (256, 256)
def histogram_equalization(img):
lab = cv2.cvtColor(img, cv2.COLOR_BGR2LAB)
# -----Splitting the LAB image to different channels-------------------------
l, a, b = cv2.split(lab)
# -----Applying CLAHE to L-channel-------------------------------------------
clahe = cv2.createCLAHE(clipLimit=3.0, tileGridSize=(8, 8))
cl = clahe.apply(l)
# -----Merge the CLAHE enhanced L-channel with the a and b channel-----------
limg = cv2.merge((cl, a, b))
# -----Converting image from LAB Color model to RGB model--------------------
final = cv2.cvtColor(limg, cv2.COLOR_LAB2BGR)
return final
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_ged')
data_loader = data_switch(exp_config.data_identifier)
data = data_loader(exp_config)
lat_lvls = exp_config.latent_levels
# RANDOM IMAGE
# x_b, s_b = data.test.next_batch(1)
# FIXED IMAGE
# Cardiac: 100 normal image
# LIDC: 200 large lesion, 203, 1757 complicated lesion
# Prostate: 165 nice slice, 170 is a challenging and interesting slice
index = 165 # #
if SAVE_GIF:
outfolder_gif = os.path.join(model_path, 'model_samples_id%d_gif' % index)
utils.makefolder(outfolder_gif)
x_b = data.test.images[index,...].reshape([1]+list(exp_config.image_size))
x_b_d = utils.convert_to_uint8(np.squeeze(x_b))
x_b_d = utils.resize_image(x_b_d, video_target_size)
if exp_config.data_identifier == 'uzh_prostate':
# rotate
rows, cols = x_b_d.shape
M = cv2.getRotationMatrix2D((cols / 2, rows / 2), 270, 1)
x_b_d = cv2.warpAffine(x_b_d, M, (cols, rows))
if SAVE_VIDEO:
fourcc = cv2.VideoWriter_fourcc(*'XVID')
outfile = os.path.join(model_path, 'model_samples_id%d.avi' % index)
out = cv2.VideoWriter(outfile, fourcc, 5.0, (2*video_target_size[1], video_target_size[0]))
samps = 20
for ii in range(samps):
# fix all below current level (the correct implementation)
feed_dict = {}
feed_dict[phiseg_model.training_pl] = False
feed_dict[phiseg_model.x_inp] = x_b
s_p, s_p_list = phiseg_model.sess.run([phiseg_model.s_out_eval, phiseg_model.s_out_eval_list], feed_dict=feed_dict)
s_p = np.argmax(s_p, axis=-1)
# s_p_d = utils.convert_to_uint8(np.squeeze(s_p))
s_p_d = np.squeeze(np.uint8((s_p / exp_config.nlabels)*255))
s_p_d = utils.resize_image(s_p_d, video_target_size, interp=cv2.INTER_NEAREST)
if exp_config.data_identifier == 'uzh_prostate':
#rotate
s_p_d = cv2.warpAffine(s_p_d, M, (cols, rows))
img = np.concatenate([x_b_d, s_p_d], axis=1)
img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
img = histogram_equalization(img)
if exp_config.data_identifier == 'acdc':
# labels (0 85 170 255)
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)
cv2.drawContours(img, rv_cnt, -1, (0, 255, 0), 1)
cv2.drawContours(img, my_cnt, -1, (0, 0, 255), 1)
if exp_config.data_identifier == 'uzh_prostate':
print(np.unique(s_p_d))
s1 = cv2.inRange(s_p_d, 84, 86)
s2 = cv2.inRange(s_p_d, 169, 171)
# s3 = cv2.inRange(s_p_d, 190, 192)
s1_cnt, hierarchy = cv2.findContours(s1, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
s2_cnt, hierarchy = cv2.findContours(s2, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
# s3_cnt, hierarchy = cv2.findContours(s3, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
cv2.drawContours(img, s1_cnt, -1, (0, 255, 0), 1)
cv2.drawContours(img, s2_cnt, -1, (0, 0, 255), 1)
# cv2.drawContours(img, s3_cnt, -1, (255, 0, 255), 1)
elif exp_config.data_identifier == 'lidc':
thresh = cv2.inRange(s_p_d, 127, 255)
lesion, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
cv2.drawContours(img, lesion, -1, (0, 255, 0), 1)
if SAVE_VIDEO:
out.write(img)
if SAVE_GIF:
outfile_gif = os.path.join(outfolder_gif, 'frame_%s.png' % str(ii).zfill(3))
img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
# scipy.misc.imsave(outfile_gif, img_rgb)
im = Image.fromarray(img_rgb)
im = im.resize((im.size[0]*2, im.size[1]*2), Image.ANTIALIAS)
im.save(outfile_gif)
if DISPLAY_VIDEO:
cv2.imshow('frame', img)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
if SAVE_VIDEO:
out.release()
cv2.destroyAllWindows()
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/segvae/lidc/segvae_7_5'
# exp_path = '/itet-stor/baumgach/net_scratch/logs/segvae/lidc/probunet'
#
# exp_path = '/itet-stor/baumgach/net_scratch/logs/segvae/uzh_prostate_afterpaper/segvae_7_5_1annot'
# exp_path = '/itet-stor/baumgach/net_scratch/logs/segvae/uzh_prostate_afterpaper/segvae_7_5'
# exp_path = '/itet-stor/baumgach/net_scratch/logs/segvae/uzh_prostate_afterpaper/probUNET_1annotator_2'
exp_path = '/itet-stor/baumgach/net_scratch/logs/segvae/uzh_prostate_afterpaper/segvae_7_5_batchnorm_rerun'
# exp_path = '/itet-stor/baumgach/net_scratch/logs/segvae/uzh_prostate_afterpaper/segvae_7_5_batchnorm_schedule'
# exp_path = '/itet-stor/baumgach/net_scratch/logs/segvae/uzh_prostate_afterpaper/probUNET'
model_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)