def drawThreshMask(self, worm_img, worm_qimg, row_data, read_center=True):
#in very old versions of the tracker I didn't save the area in trajectories table,
#let's assign a default value to deal with this cases
if 'area' in row_data:
min_blob_area = row_data['area'] / 2
else:
min_blob_area = 10
c1, c2 = (row_data['coord_x'],
row_data['coord_y']) if read_center else (-1, -1)
worm_mask, worm_cnt, _ = getWormMask(
worm_img,
row_data['threshold'],
strel_size=self.strel_size,
roi_center_x=c1,
roi_center_y=c2,
min_blob_area=min_blob_area,
is_light_background=self.is_light_background)
worm_mask = QImage(worm_mask.data, worm_mask.shape[1],
worm_mask.shape[0], worm_mask.strides[0],
QImage.Format_Indexed8)
worm_mask = worm_mask.convertToFormat(QImage.Format_RGB32,
Qt.AutoColor)
worm_mask = QPixmap.fromImage(worm_mask)
worm_mask = worm_mask.createMaskFromColor(Qt.black)
p = QPainter(worm_qimg)
p.setPen(QColor(0, 204, 102))
p.drawPixmap(worm_qimg.rect(), worm_mask, worm_mask.rect())
if False:
#test skeletonization
skeleton, ske_len, cnt_side1, cnt_side2, cnt_widths, cnt_area = \
getSkeleton(worm_cnt, np.zeros(0), 49)
for cnt in skeleton, cnt_side1, cnt_side2:
p.setPen(Qt.black)
polyline = QPolygonF()
for point in cnt:
polyline.append(QPointF(*point))
p.drawPolyline(polyline)
p.end()
def get_skeleton_from_sides(side1, side2):
cnt_side1, cnt_side2 = side1, side2
ht_dist = np.sum((cnt_side2[0][None] - cnt_side1[[0, -1]])**2, axis=1)
if ht_dist[0] > ht_dist[1]:
cnt_side1 = cnt_side1[::-1]
head_coord = (cnt_side1[0] + cnt_side2[0]) / 2
tail_coord = (cnt_side1[-1] + cnt_side2[-1]) / 2
cnt_side1 = np.concatenate(
(head_coord[None], cnt_side1[1:-1], tail_coord[None]), axis=0)
cnt_side2 = np.concatenate(
(head_coord[None], cnt_side2[1:-1], tail_coord[None]), axis=0)
cnt_side1 = resample2pix(cnt_side1)
cnt_side2 = resample2pix(cnt_side2)
ii = 1
cc = np.concatenate((cnt_side1[ii:ii + 3], cnt_side1[ii][None]))
#contours must be in clockwise direction otherwise the angles algorithm will give a bad result
# make sure the contours are in the counter-clockwise direction
# x1y2 - x2y1(http://mathworld.wolfram.com/PolygonArea.html)
signed_area = np.sum(cc[:-1, 0] * cc[1:, 1] - cc[1:, 0] * cc[:-1, 1]) / 2
if signed_area > 0:
cnt_side1, cnt_side2 = cnt_side2, cnt_side1
contour = np.concatenate((cnt_side1, cnt_side2[::-1]), axis=0)
#contour = np.ascontiguousarray(contour[::-1])
skeleton, _, cnt_side1, cnt_side2, cnt_widths, _ = getSkeleton(contour)
# resample data
skeleton = smooth_curve(skeleton)
cnt_widths = smooth_curve(cnt_widths)
cnt_side1 = smooth_curve(cnt_side1)
cnt_side2 = smooth_curve(cnt_side2)
return skeleton, cnt_widths, cnt_side1, cnt_side2
def trajectories2Skeletons(skeletons_file,
masked_image_file,
resampling_N=49,
min_blob_area=50,
strel_size=5,
worm_midbody=(0.35, 0.65),
analysis_type="WORM",
skel_args = {'num_segments' : 24,
'head_angle_thresh' : 60}
):
#get the index number for the width limit
midbody_ind = (int(np.floor(
worm_midbody[0]*resampling_N)), int(np.ceil(worm_midbody[1]*resampling_N)))
#read trajectories data with pandas
with pd.HDFStore(skeletons_file, 'r') as ske_file_id:
trajectories_data = ske_file_id['/trajectories_data']
# extract the base name from the masked_image_file. This is used in the
# progress status.
base_name = masked_image_file.rpartition('.')[0].rpartition(os.sep)[-1]
progress_prefix = base_name + ' Calculating skeletons.'
# open skeleton file for append and #the compressed videos as read
with tables.File(skeletons_file, "r+") as ske_file_id:
#attribute useful to understand if we are dealing with dark or light worms
bgnd_param = ske_file_id.get_node('/trajectories_data')._v_attrs['bgnd_param']
bgnd_param = json.loads(bgnd_param.decode("utf-8"))
is_light_background = ske_file_id.get_node('/trajectories_data')._v_attrs['is_light_background']
if len(bgnd_param) > 0:
#invert (at least if is_light_background is true)
is_light_background = not is_light_background
#get generators to get the ROI for each frame
ROIs_generator = generateMoviesROI(masked_image_file,
trajectories_data,
bgnd_param = bgnd_param,
progress_prefix = progress_prefix)
# add data from the experiment info (currently only for singleworm)
with tables.File(skeletons_file, "r") as mask_fid:
if '/experiment_info' in ske_file_id:
ske_file_id.remove_node('/', 'experiment_info')
if '/experiment_info' in mask_fid:
dd = mask_fid.get_node('/experiment_info').read()
ske_file_id.create_array('/', 'experiment_info', obj=dd)
#initialize arrays to save the skeletons data
tot_rows = len(trajectories_data)
skel_arrays, has_skeleton = _initSkeletonsArrays(ske_file_id, tot_rows, resampling_N, worm_midbody)
# dictionary to store previous skeletons
prev_skeleton = {}
for worms_in_frame in ROIs_generator:
for ind, roi_dat in worms_in_frame.items():
row_data = trajectories_data.loc[ind]
worm_img, roi_corner = roi_dat
skeleton_id = int(row_data['skeleton_id'])
# get the previous worm skeletons to orient them
worm_index = row_data['worm_index_joined']
if worm_index not in prev_skeleton:
prev_skeleton[worm_index] = np.zeros(0)
if analysis_type == "ZEBRAFISH":
output = _zebra_func(worm_img, skel_args, resampling_N)
else:
_, worm_cnt, _ = getWormMask(worm_img,
row_data['threshold'],
strel_size,
min_blob_area=row_data['area'] / 2,
is_light_background = is_light_background)
# get skeletons
output = getSkeleton(worm_cnt, prev_skeleton[worm_index], resampling_N, **skel_args)
if output is not None and output[0].size > 0:
skeleton, ske_len, cnt_side1, cnt_side2, cnt_widths, cnt_area = output
prev_skeleton[worm_index] = skeleton.copy()
#mark row as a valid skeleton
has_skeleton[skeleton_id] = True
# save segwrom_results
skel_arrays['skeleton_length'][skeleton_id] = ske_len
skel_arrays['contour_width'][skeleton_id, :] = cnt_widths
mid_width = np.median(cnt_widths[midbody_ind[0]:midbody_ind[1]+1])
skel_arrays['width_midbody'][skeleton_id] = mid_width
# convert into the main image coordinates
skel_arrays['skeleton'][skeleton_id, :, :] = skeleton + roi_corner
skel_arrays['contour_side1'][skeleton_id, :, :] = cnt_side1 + roi_corner
skel_arrays['contour_side2'][skeleton_id, :, :] = cnt_side2 + roi_corner
skel_arrays['contour_area'][skeleton_id] = cnt_area