#
# # calculate variables from acf of each set of image chunks and append to the df
# vars_far = []
# for im in far_chunks_df['far_chunks']:
# acf = xy_autocorr(im)
# middle = im.shape[0]//2+1
# acf_x = x_profile(acf)[middle:]
# acf_y = y_profile(acf)[middle:]
# vars_far.append(acf_variables(acf_x, acf_y))
# vars_far_df = pd.concat(vars_far, axis=1).transpose()
# vars_far_df['label'] = ['f']*vars_far_df.shape[0]
# dat_far = pd.concat([far_chunks_df, vars_far_df], axis=1)
#
vars = []
for im in dat['chunk_im']:
acf = xy_autocorr(im)
middle = im.shape[0] // 2 + 1
acf_x = x_profile(acf)[middle:]
acf_y = y_profile(acf)[middle:]
vars.append(acf_variables(acf_x, acf_y))
vars = pd.concat(vars, axis=1).transpose()
dat = pd.concat([dat, vars], axis=1)
dat = dat.replace([np.inf, -np.inf], np.nan).dropna()
# define which of the original images are near and which are unknown
dat_near = dat[dat['distance'] <= chunk_size]
dat_near['label'] = ['n'] * dat_near.shape[0]
dat_unknown = dat[dat['distance'] > chunk_size]
# add the next part if I use the far data from the original images
dist_90pc = np.percentile(dat['distance'], 90)
dat_unknown = dat_unknown[dat_unknown['distance'] < dist_90pc]
plot_seg = False
f = open(dat_file, 'r')
for line in f.readlines():
if line.startswith('Image') and not line.endswith('x\n') and not line.endswith('x'):
vals = line.split(',')
seg_file = os.path.join(seg_folder, vals[1])
# read in the segmented images
seg_im = io.imread(seg_file)
hole_bool = (seg_im != 2).ravel()
b_val = 2
# print(np.unique(seg_im))
seg_im[seg_im != 2] = 0
seg_acf = xy_autocorr(seg_im)
ang_acf = orientation(seg_acf)['angle'][0]
# ang_acf = get_angle(orientation(seg_acf))
if plot_seg:
if ang_acf < 0:
interc = 300
elif ang_acf > math.pi/2 and ang_acf < math.pi:
interc = 300
else:
interc = 200
pl.imshow(seg_im, origin='lower')
abline(ang_acf, interc)
# pl.title(vals[1].replace('_', ', '))
save_file = vals[1].rstrip('.tif') + '_angle_acf_onimage.png'
pl.savefig(os.path.join(save_folder, save_file))
im_file = '/Users/s1101153/OneDrive - University of Edinburgh/Files/bubbles/confocal_data/tunnels/Image73.lsm'
seg_file = '/Users/s1101153/OneDrive - University of Edinburgh/Files/bubbles/confocal_data/tunnels/region_growing/Classified_image73.tif'
im = io.imread(im_file)[0]
seg_im = io.imread(seg_file)
hole_bool = (seg_im != 2).ravel()
chunks = split_image(im=im,
seg_im=seg_im,
bijel_val=2,
chunk_x=chunk_size,
chunk_y=chunk_size,
shift=chunk_size)
seg_angle = seg_orientation(seg_im, hole_bool)
seg_acf = xy_autocorr(seg_im)
seg_angle = orientation(seg_acf)['angle'][0]
seg_angle
seg_save = 'Image73'
pl.imshow(seg_im)
abline(seg_angle, 300)
pl.xlim(0, 512)
pl.savefig(seg_save + '_image.png')
pl.show()
pl.imshow(seg_acf)
abline(seg_angle, 300)
pl.xlim(0, 512)
pl.savefig(seg_save + '_acf.png')