def show_means_compared(name_frame, name_scene, id_exbl, N=1000):
"""
Plots the visualisation of impact of the second stage of the algorithm on
spectra, Fig.9
parameters:
name_frame: name of the source image (inductive scenario)
name_scene: name of the output image
id_exbl: id of the reference spectrum
N: no. vectors in the 2nd stage as int or percentage string e.g. '33p'
"""
data_frame, anno_frame = load_ds(name_frame, normalise=False)
data_scene, anno_scene = load_ds(name_scene, normalise=False)
blood_exbl = load_exbl(id_exbl)
blood_frame = np.mean(data_frame[anno_frame == 1], axis=0)
blood_scene = np.mean(data_scene[anno_scene == 1], axis=0)
assert len(blood_exbl) == len(blood_frame)
assert len(blood_exbl) == len(blood_scene)
markers = ['s', 'v', '<', '>', '1', '.', 'p']
n_blood = np.count_nonzero(anno_scene == 1)
N_v = decode_N(N, n_blood)
wav = get_wavelengths()
X_data = data_scene.reshape(-1, data_scene.shape[2]).copy()
mf = TwoStageMatchedFilter()
mf.fit(blood_exbl, X_data, N=N_v, N_supression=0)
plt.rcParams.update({'font.size': 14})
plt.plot(wav,
blood_scene,
label="Image mean",
marker=markers[0],
markevery=0.3)
plt.plot(wav,
blood_exbl,
label="Library",
marker=markers[1],
markevery=0.3)
plt.plot(wav,
mf.mf_2.mu_t,
label='Second stage',
marker=markers[2],
markevery=0.3)
plt.ylabel("Reflectance")
plt.xlabel("Wavelenghts")
plt.legend()
plt.tight_layout()
plt.show()
plt.close()
def show_days(absorbance=True):
"""
plots blood spectra sorted by days (Fig.2)
Parameters:
---------------------
absorbance: transform reflectance into (pseudo)absorbance i.e. log(1/R)
"""
wav = get_wavelengths()
days = ["Day 1(~1h)", "Day 1(~5h)", "Day 2", "Day 7"]
frames = ["F(1)", "F(1s)", "F(2)", "F(7)"]
plt.rcParams.update({'font.size': 14})
fig, ax = plt.subplots()
markers = ['s', 'v', '<', '>', '1', '.', 'p']
for i_d, d in enumerate(days):
data, anno = load_ds(frames[i_d], normalise=False)
s = np.mean(data[anno == 1], axis=0)
if absorbance:
s[s == 0] += 0.0001
s = np.log10(1.0 / s)
plt.plot(wav,
s,
label="{}".format(d),
marker=markers[i_d],
markevery=10)
plt.legend()
y0, y1 = ax.get_ylim()
plt.xlim(400, 1000)
if absorbance:
plt.ylim(0.2, y1)
plt.plot([542, 542], [0, y1],
lw=0.5,
linestyle='--',
alpha=0.7,
color='black')
plt.annotate('542', xy=(542, y1), xytext=(525, y1), fontsize=10)
plt.plot([577, 577], [0, y1],
lw=0.5,
linestyle='--',
alpha=0.7,
color='black')
plt.annotate('577', xy=(577, y1), xytext=(560, y1), fontsize=10)
if absorbance:
plt.ylabel("Log(1/R)")
else:
plt.ylabel("Reflectance")
plt.xlabel("Wavelenghts")
plt.tight_layout()
plt.show()
plt.close()
def show_classes(name='F(1)', absorbance=False):
"""
plots spectra of classes in the image (Fig.1)
Parameters:
---------------------
name: image name
absorbance: transform reflectance into (pseudo)absorbance i.e. log(1/R)
"""
data, anno = load_ds(name, normalise=False)
wav = get_wavelengths()
plt.rcParams.update({'font.size': 14})
plt.rcParams.update({'font.size': 12})
labels = [
'blood', 'ketchup', 'artificial blood', 'beetroot juice',
'poster paint', 'tomato concentrate', 'acrylic paint'
]
markers = ['s', 'v', '<', '>', '1', '.', 'p']
cmap = plt.get_cmap("Set1")
colors = [cmap(i / 7) for i in range(8)]
colors[-4] = cmap(1.0)
for c_label in range(1, 8):
if c_label in anno:
al = 1.0 if c_label == 1 else 0.7
pattern = data[anno == c_label]
s = np.median(pattern, axis=0)
if absorbance:
s[s == 0] += 0.0001
s = np.log10(1.0 / s)
plt.plot(wav,
s,
label="{} ({})".format(labels[c_label - 1], c_label),
color=colors[c_label - 1],
alpha=al,
marker=markers[c_label - 1],
markevery=10)
plt.legend()
if absorbance:
plt.ylabel("Log(1/R)")
else:
plt.ylabel("Reflectance")
plt.xlabel("Wavelenghts")
plt.tight_layout(pad=0)
plt.show()
plt.close()
def save_gt(data, anno_in, code):
"""
saves rgb with class annotation
fig.6-8
Parameters:
---------------------
data: data cube as nparray
annotation: 2d annotation array
"""
ax = plt.subplot()
colors = []
#listerd colormap
for i in [0, 6, 1, 2, 3, 4, 5, 10, 7, 8]:
colors.append(plt.get_cmap('tab20')(i / 20))
cmap = ListedColormap(colors, name='colors', N=len(colors))
anno = anno_in.copy()
anno[anno == 15] = 0
rgb = hsi2rgb(data, wavelengths=get_wavelengths(), gamma=0.7)
plt.imshow(rgb, aspect='auto')
X, y, pos = data2xy(data, anno)
for u in np.unique(y)[::-1]:
if u != 0:
where = y == u
pw = pos[where]
r = [t[0] for t in pw]
c = [t[1] for t in pw]
sc = plt.scatter(x=c,
y=r,
marker=',',
s=3,
lw=1,
alpha=0.8,
color=cmap(u))
ax.set_axis_off()
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
plt.tight_layout(pad=0)
plt.savefig('res/res_{}.png'.format(code),
bbox_inches='tight',
pad_inches=0)
plt.close()
def show_mixtures(absorbance=True):
"""
plots differences of blood spectra on different backgrounds (Fig.2)
Parameters:
---------------------
absorbance: transform reflectance into (pseudo)absorbance i.e. log(1/R)
"""
wav = get_wavelengths()
data, gt = load_ds('E(1)', normalise=False)
#lower range and material
backgrounds = [[44, 'metal'], [74, 'plastic'], [147, 'wood'],
[192, 'blue'], [271, 'red(t-shirt)'], [332, 'mixed'],
[430, 'mixed(green)'], [516, 'red(sweater)']]
plt.rcParams.update({'font.size': 14})
where = gt != 1
gt[where] = 0
markers = ['s', 'v', '<', '>', '1', '.', 'p', 'x']
for i_b, b in enumerate(backgrounds):
gta = gt.copy()
gta[b[0]:, :] = 0
if i_b != 0:
gta[:backgrounds[i_b - 1][0]:, :] = 0
X = data[gta == 1, :]
print(b, X.shape)
s = np.median(X, axis=0)
if absorbance:
s[s == 0] += 0.0001
s = np.log10(1.0 / s)
plt.plot(wav,
s,
label="{}".format(b[1]),
marker=markers[i_b],
markevery=10)
plt.legend()
if absorbance:
plt.ylabel("Log(1/R)")
else:
plt.ylabel("Reflectance")
plt.xlabel("Wavelenghts")
plt.show()
plt.close()