def export_figure(idx_row, df_slices_info, path_out):
""" load image, segmentation and csv with centres
1) draw figure with image, segmentation and csv
2) draw expety annotation
3) expert figure
:param idx_row:
:param df_slices_info:
:param path_out:
"""
_, row = idx_row
img_name = os.path.splitext(os.path.basename(row['path_image']))[0]
try:
if img_name not in df_slices_info.index:
logging.debug('missing image in annotation - "%s"', img_name)
return
img = tl_data.io_imread(row['path_image'])
segm = tl_data.io_imread(row['path_segm'])
df = pd.read_csv(os.path.join(row['path_centers']), index_col=0)
centres = df[['X', 'Y']].values
fig = figure_draw_img_centre_segm(None, img, centres, segm)
row_slice = df_slices_info.loc[img_name]
fig = figure_draw_annot_csv(fig, img, row_slice)
tl_visu.figure_image_adjustment(fig, img.shape)
fig.savefig(os.path.join(path_out, img_name + '.png'))
plt.close(fig)
except Exception:
logging.exception('failed for: %s', img_name)
def export_draw_image_segm(path_fig, img, segm=None, segm_obj=None, centers=None):
""" draw and export visualisation of image and segmentation
:param str path_fig: path to the exported figure
:param ndarray img:
:param ndarray segm:
:param ndarray segm_obj:
:param ndarray centers:
"""
size = np.array(img.shape[:2][::-1], dtype=float)
fig, ax = plt.subplots(figsize=(size / size.max() * MAX_FIGURE_SIZE))
ax.imshow(img, alpha=1., cmap=plt.cm.Greys)
if segm is not None:
ax.contour(segm)
if segm_obj is not None:
ax.imshow(segm_obj, alpha=0.1)
assert len(np.unique(segm_obj)) < 1e2, \
'too many labeled objects - %i' % len(np.unique(segm_obj))
ax.contour(segm_obj, levels=np.unique(segm_obj).tolist(),
cmap=plt.cm.jet_r, linewidths=(10, ))
if centers is not None:
ax.plot(np.array(centers)[:, 1], np.array(centers)[:, 0], 'o', color='r')
fig = tl_visu.figure_image_adjustment(fig, img.shape)
fig.savefig(path_fig)
plt.close(fig)
def expert_visual(row, method_name, path_out, max_fig_size=10):
""" export several visualisation segmentation and annotation
:param dict row:
:param str method_name:
:param str path_out:
:param int max_fig_size:
"""
im_name = os.path.splitext(os.path.basename(row['path_image']))[0]
img, _ = tl_data.load_image_2d(row['path_image'])
# annot = tl_data.load_image(row['path_annot'])
egg_segm, _ = tl_data.load_image_2d(row['path_egg-segm'])
in_segm, _ = tl_data.load_image_2d(row['path_in-segm'])
centers = tl_data.load_landmarks_csv(row['path_centers'])
centers = np.array(tl_data.swap_coord_x_y(centers))
fig_size = max_fig_size * np.array(img.shape[:2]) / float(np.max(
img.shape))
fig_name = '%s_%s.jpg' % (im_name, method_name)
fig, ax = plt.subplots(figsize=fig_size[::-1])
ax.imshow(img[:, :, 0], cmap=plt.cm.gray)
ax.imshow(egg_segm, alpha=0.15)
ax.contour(egg_segm, levels=np.unique(egg_segm), linewidths=(3, ))
ax.plot(centers[:, 1], centers[:, 0], 'ob')
tl_visu.figure_image_adjustment(fig, img.shape)
path_fig = os.path.join(path_out, NAME_DIR_VISUAL_1, fig_name)
fig.savefig(path_fig, bbox_inches='tight', pad_inches=0)
plt.close(fig)
fig, ax = plt.subplots(figsize=fig_size[::-1])
# ax.imshow(np.max(in_segm) - in_segm, cmap=plt.cm.gray)
ax.imshow(LUT_COLOR[in_segm], vmin=0., vmax=1., alpha=0.5)
ax.contour(in_segm, levels=np.unique(in_segm), colors='k')
ax.imshow(egg_segm, alpha=0.3)
ax.contour(egg_segm, levels=np.unique(egg_segm), linewidths=(5, ))
ax.plot(centers[:, 1], centers[:, 0], 'or')
tl_visu.figure_image_adjustment(fig, img.shape)
path_fig = os.path.join(path_out, NAME_DIR_VISUAL_2, fig_name)
fig.savefig(path_fig, bbox_inches='tight', pad_inches=0)
plt.close(fig)
fig, ax = plt.subplots(figsize=fig_size[::-1])
ax.imshow(img[:, :, 0], cmap=plt.cm.gray, alpha=1.)
ax.contour(in_segm, levels=np.unique(in_segm), colors='w')
ax.imshow(egg_segm, alpha=0.3)
ax.contour(egg_segm, levels=np.unique(egg_segm), linewidths=(5, ))
ax.plot(centers[:, 1], centers[:, 0], 'og')
tl_visu.figure_image_adjustment(fig, img.shape)
path_fig = os.path.join(path_out, NAME_DIR_VISUAL_3, fig_name)
fig.savefig(path_fig, bbox_inches='tight', pad_inches=0)
plt.close(fig)
