def estimate_eggs_from_info(row_slice, mask_shape):
""" finds all eggs for particular slice and mask them by ellipse annotated
by ant, post and lat in the all info table
:param str path_img:
:return ndarray: ndarray
"""
pos_ant, pos_lat, pos_post = tl_visu.parse_annot_rectangles(row_slice)
list_masks = tl_visu.draw_eggs_rectangle(mask_shape, pos_ant, pos_lat,
pos_post)
mask_eggs = tl_visu.merge_object_masks(list_masks, thr_overlap=0.5)
return mask_eggs
def select_optimal_ellipse(idx_row,
path_dir_csv,
overlap_thr=OVERLAP_THRESHOLD):
""" reconstruct the user annotation and compare it with estimated ellipses
:param (int, row) idx_row: index and row with user annotation
:param str path_dir_csv: path to list of ellipse parameters
:param float overlap_thr: skip all annot. which Jaccard lower then threshold
:return {}:
"""
_, row = idx_row
dict_row = dict(row)
path_csv = os.path.join(path_dir_csv, row['image_name'] + '.csv')
df_ellipses = pd.read_csv(path_csv, index_col=0)
pos = row[tl_visu.COLUMNS_POSITION_EGG_ANNOT]
max_size = 2 * max(pos) + min(pos)
pos_ant = [[row['ant_x'], row['ant_y']]]
pos_lat = [[row['lat_x'], row['lat_y']]]
pos_post = [[row['post_x'], row['post_y']]]
mask_ref = tl_visu.draw_eggs_rectangle((max_size, max_size), pos_ant,
pos_lat, pos_post)[0]
list_jaccard = []
for _, ell_row in df_ellipses.iterrows():
mask_ell = ell_fit.add_overlap_ellipse(np.zeros(mask_ref.shape),
ell_row.values.tolist(), 1)
union = np.sum(np.logical_and(mask_ref, mask_ell))
intersect = np.sum(np.logical_or(mask_ref, mask_ell))
list_jaccard.append(union / float(intersect))
# if no match with annotation
if max(list_jaccard) < overlap_thr:
dict_row['ellipse_Jaccard'] = max(list_jaccard)
return dict_row
idx_best = np.argmax(list_jaccard)
ell_best = dict(df_ellipses.iloc[idx_best])
# optional swap main axis and rotate by 90 deg
if ell_best['b'] > ell_best['a']:
ell_best['a'], ell_best['b'] = ell_best['b'], ell_best['a']
ell_best['theta'] += np.deg2rad(90)
ell_best['Jaccard'] = max(list_jaccard)
# add to each name ellipse prefix
dict_row.update({'ellipse_' + n: ell_best[n] for n in ell_best})
return dict_row
def figure_draw_annot_csv(fig, img, row_slice, subfig_size=FIGURE_SIZE):
""" draw from expert annotation stored in info file
:param obj fig:
:param ndarray img: backround image
:param row_slice: line from info file containing annotation
:param int subfig_size:
:return obj:
"""
if fig is None:
norm_size = np.array(img.shape[:2]) / float(np.max(img.shape))
fig, ax = plt.subplots(figsize=(norm_size[::-1] * subfig_size))
ax.imshow(img[:, :, 0], cmap=plt.cm.Greys_r)
else:
ax = fig.gca()
pos_ant, pos_lat, pos_post = tl_visu.parse_annot_rectangles(row_slice)
list_masks = tl_visu.draw_eggs_rectangle(img.shape[:2],
pos_ant, pos_lat, pos_post)
for mask in list_masks:
ax.contour(mask, colors=COLOR_ANNOT, linewidths=(3,))
return fig