def preproc_image(x, nlabels=None):
x_b = np.squeeze(x)
ims = x_b.shape[:2]
if nlabels:
x_b = np.uint8((x_b / (nlabels)) *
255) # not nlabels - 1 because I prefer gray over white
else:
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)
# ims_n = x_b.shape[:2]
# x_b = x_b[ims_n[0]//4:3*ims_n[0]//4, ims_n[1]//4: 3*ims_n[1]//4,...]
return x_b
def main(model_path, exp_config):
# Make and restore vagan model
segvae_model = segvae(exp_config=exp_config)
segvae_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 = segvae_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')
def main(model_path, exp_config):
data_loader = data_switch(exp_config.data_identifier)
data = data_loader(exp_config)
# 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
index = 165 #
x_b = data.test.images[index,
...].reshape([1] + list(exp_config.image_size))
s_b = data.test.labels[index, ...]
annot_index_gen = cycle(exp_config.annotator_range)
x_b_d = utils.convert_to_uint8(np.squeeze(x_b))
x_b_d = utils.resize_image(x_b_d, video_target_size)
if SAVE_VIDEO:
fourcc = cv2.VideoWriter_fourcc(*'XVID')
outfile = os.path.join(model_path, 'gt_samples_id%d.avi' % index)
out = cv2.VideoWriter(outfile, fourcc, 5.0,
(2 * video_target_size[1], video_target_size[0]))
for _ in range(50):
annot_index = next(annot_index_gen)
s_b_d = s_b[..., annot_index]
s_b_d = np.squeeze(np.uint8((s_b_d / exp_config.nlabels) * 255))
s_b_d = utils.resize_image(s_b_d,
video_target_size,
interp=cv2.INTER_NEAREST)
img = np.concatenate([x_b_d, s_b_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_b_d, 84, 86)
my = cv2.inRange(s_b_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':
# labels (0 85 170 255)
print(np.unique(s_b_d))
s1 = cv2.inRange(s_b_d, 84, 86)
s2 = cv2.inRange(s_b_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_b_d, 127, 255)
lesion, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE,
cv2.CHAIN_APPROX_SIMPLE)
cv2.drawContours(img, lesion, -1, (0, 255, 0), 1)
font = cv2.FONT_HERSHEY_SIMPLEX
cv2.putText(
img, 'Expert # %d/%d' %
(annot_index + 1, len(exp_config.annotator_range)), (30, 256 - 30),
font, 1, (255, 255, 255), 1, cv2.LINE_AA)
if SAVE_VIDEO:
out.write(img)
cv2.imshow('frame', img)
if cv2.waitKey(200) & 0xFF == ord('q'):
break
if SAVE_VIDEO:
out.release()
cv2.destroyAllWindows()
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()
def main(model_path, exp_config):
# Make and restore vagan model
segvae_model = segvae(exp_config=exp_config)
segvae_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
index = 165 #
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 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 = 50
for _ in range(samps):
# fix all below current level (the correct implementation)
feed_dict = {}
feed_dict[segvae_model.training_pl] = False
feed_dict[segvae_model.x_inp] = x_b
s_p, s_p_list = segvae_model.sess.run(
[segvae_model.s_out_eval, segvae_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)
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':
# labels (0 85 170 255)
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)
cv2.imshow('frame', img)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
if SAVE_VIDEO:
out.release()
cv2.destroyAllWindows()
def main(model_path, exp_config):
# Make and restore vagan model
segvae_model = segvae(exp_config=exp_config)
segvae_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
index = 165 #
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]))
#
# print(x_b.shape)
# print(s_b.shape)
# x_b[:,30:64+10,64:64+10,:] = np.mean(x_b)
#
# x_b = utils.add_motion_artefacts(np.squeeze(x_b), 15)
# x_b = x_b.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)
s_b_d = np.squeeze(np.uint8((s_b / exp_config.nlabels) * 255))
s_b_d = utils.resize_image(s_b_d,
video_target_size,
interp=cv2.INTER_NEAREST)
_, mu_list_init, _ = segvae_model.generate_prior_samples(
x_b, return_params=True)
if SAVE_VIDEO:
fourcc = cv2.VideoWriter_fourcc(*'XVID')
outfile = os.path.join(model_path, 'samplevid_id%d.avi' % index)
out = cv2.VideoWriter(outfile, fourcc, 10.0,
(3 * video_target_size[1], video_target_size[0]))
for lvl in reversed(range(lat_lvls)):
samps = 50 if lat_lvls > 1 else 200
for _ in range(samps):
# z_list, mu_list, sigma_list = segvae_model.generate_prior_samples(x_b, return_params=True)
print('doing level %d/%d' % (lvl, lat_lvls))
# fix all below current level
# for jj in range(lvl,lat_lvls-1):
# z_list[jj+1] = mu_list_init[jj+1] # fix jj's level to mu
# sample only current level
# z_list_new = z_list.copy()
# for jj in range(lat_lvls):
# z_list_new[jj] = mu_list_init[jj]
# z_list_new[lvl] = z_list[lvl]
# z_list = z_list_new
#
# print('z means')
# for jj, z in enumerate(z_list):
# print('lvl %d: %.3f' % (jj, np.mean(z)))
#
#
# feed_dict = {i: d for i, d in zip(segvae_model.prior_z_list_gen, z_list)}
# feed_dict[segvae_model.training_pl] = False
#
# fix all below current level (the correct implementation)
feed_dict = {}
for jj in range(lvl, lat_lvls - 1):
feed_dict[segvae_model.prior_z_list_gen[jj +
1]] = mu_list_init[jj +
1]
feed_dict[segvae_model.training_pl] = False
feed_dict[segvae_model.x_inp] = x_b
s_p, s_p_list = segvae_model.sess.run(
[segvae_model.s_out_eval, segvae_model.s_out_eval_list],
feed_dict=feed_dict)
s_p = np.argmax(s_p, axis=-1)
print(np.unique(s_p))
# print('mean logits for myo cardium per level')
# fig = plt.figure()
#
# cumsum = np.zeros((128,128))
# cumsum_all = np.zeros((128,128,4))
# for i, s in enumerate(reversed(s_p_list)):
#
# cumsum += s[0,:,:,2]
# cumsum_all += s[0,:,:,:]
#
# fig.add_subplot(4,4,i+1)
# plt.imshow(s[0,:,:,2])
#
# fig.add_subplot(4,4,i+1+4)
# plt.imshow(cumsum)
#
# fig.add_subplot(4,4,i+1+8)
# plt.imshow(1./(1+np.exp(-cumsum)))
#
# fig.add_subplot(4,4,i+1+12)
# plt.imshow(np.argmax(cumsum_all, axis=-1))
#
#
# plt.show()
# DEUBG
# cum_img = np.squeeze(s_p_list[lat_lvls-1])
# cum_img_disp = softmax(cum_img)
#
# indiv_img = np.squeeze(s_p_list[lat_lvls-1])
# indiv_img_disp = softmax(indiv_img)
#
# for ii in reversed(range(lat_lvls-1)):
# cum_img += np.squeeze(s_p_list[ii])
# indiv_img = np.squeeze(s_p_list[ii])
#
# cum_img_disp = np.concatenate([cum_img_disp, softmax(cum_img)], axis=1)
# indiv_img_disp = np.concatenate([indiv_img_disp, softmax(indiv_img)], axis=1)
#
#
# cum_img_disp = utils.convert_to_uint8(np.argmax(cum_img_disp, axis=-1))
# indiv_img_disp = utils.convert_to_uint8(indiv_img_disp[:,:,2])
#
# cum_img_disp = np.concatenate([cum_img_disp, indiv_img_disp], axis=0)
#
#
# print('cum img shape')
# print(cum_img_disp.shape)
# cv2.imshow('debug', cum_img_disp)
# END DEBUG
# 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)
img = np.concatenate([x_b_d, s_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':
# labels (0 85 170 255)
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)
font = cv2.FONT_HERSHEY_SIMPLEX
cv2.putText(img, 'Sampling level %d/%d' % (lvl + 1, lat_lvls),
(30, 256 - 30), font, 1, (255, 255, 255), 1,
cv2.LINE_AA)
print('actual size')
print(img.shape)
if SAVE_VIDEO:
out.write(img)
cv2.imshow('frame', img)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
if SAVE_VIDEO:
out.release()
cv2.destroyAllWindows()