def run(self, ips, imgs, para=None):
edges, nodes = [], []
ntitles = ['PartID', 'NodeID', 'Degree', 'X', 'Y', 'Z']
etitles = ['PartID', 'StartID', 'EndID', 'Length', 'Distance']
k, unit = ips.unit
comid = 0
for g in nx.connected_component_subgraphs(ips.data, False):
for idx in g.nodes():
o = g.node[idx]['o']
nodes.append([
comid, idx,
g.degree(idx),
round(o[1] * k, 2),
round(o[0] * k, 2),
round(o[2])
])
for (s, e) in g.edges():
eds = g[s][e]
for i in eds:
l = round(eds[i]['weight'] * k, 2)
dis = round(
np.linalg.norm(g.node[s]['o'] - g.node[e]['o']) * k, 2)
edges.append([comid, s, e, l, dis])
comid += 1
IPy.table(ips.title + '-nodes', nodes, ntitles)
IPy.table(ips.title + '-edges', edges, etitles)
def run(self, ips, imgs, para=None):
lab = WindowsManager.get(para['lab']).ips.get_img()
if lab.dtype != np.uint8 and lab.dtype != np.int16:
IPy.alert('Label image must be in type 8-bit or 16-bit')
return
index = list(range(1, lab.max() + 1))
titles = ['Max', 'Min', 'Mean', 'Variance', 'Standard', 'Sum']
key = {
'Max': 'max',
'Min': 'min',
'Mean': 'mean',
'Variance': 'var',
'Standard': 'std',
'Sum': 'sum'
}
titles = ['value'] + [i for i in titles if para[key[i]]]
data = [index]
img = ips.get_img()
if img is lab: img = img > 0
if para['max']: data.append(ndimage.maximum(img, lab, index))
if para['min']: data.append(ndimage.minimum(img, lab, index))
if para['mean']: data.append(ndimage.mean(img, lab, index).round(4))
if para['var']: data.append(ndimage.variance(img, lab, index).round(4))
if para['std']:
data.append(ndimage.standard_deviation(img, lab, index).round(4))
if para['sum']: data.append(ndimage.sum(img, lab, index).round(4))
data = list(zip(*data))
IPy.table(ips.title + '-segment', data, titles)
def run(self, ips, snap, img, para=None):
rst = builder.build_graph(img / 127)
data = []
for i in rst:
data.append([len(i[0]), len(i[1])])
print(data)
IPy.table('Graph Builder', data, ['nodes', 'arcs'])
def run(self, ips, imgs, para=None):
titles = ['P1', 'P2', 'P3', 'angle']
k, unit = ips.unit
graph = ips.data
datas = []
for s in graph.nodes():
o = graph.node[s]['o']
x = graph[s]
if len(x) <= 1: continue
rst = []
for e in x:
eds = x[e]
for ed in eds:
l = eds[ed]['pts']
if len(l) < 10: continue
if norm(l[0] - o) > norm(l[-1] - o): l = l[::-1]
p1, p2 = l[0], l[5]
rst.append((s, e, p2 - p1))
if len(rst) < 2: continue
com = combinations(range(len(rst)), 2)
for i1, i2 in com:
v1, v2 = rst[i1][2], rst[i2][2]
a = np.arccos(np.dot(v1, v2) / norm(v1) / norm(v2))
datas.append([rst[i1][1], rst[i1][0], rst[i2][1], round(a, 4)])
print(titles, datas)
IPy.table(ips.title + '-graph', datas, titles)
def report(self, title):
rst = []
titles = ['Area', 'OX', 'OY']
for pg in self.body:
plg = Polygon(pg)
area, xy = plg.area, plg.centroid
rst.append([round(i, 1) for i in [area, xy.x, xy.y]])
IPy.table(title, rst, titles)
def run(self, ips, imgs, para = None):
titles = ['Max','Min','Mean','Variance','Standard']
key = {'Max':'max','Min':'min','Mean':'mean','Variance':'var','Standard':'std'}
titles = [i for i in titles if para[key[i]]]
if self.para['stack']:
data = []
for n in range(ips.get_nslices()):
data.append(self.count(imgs[n], para))
self.progress(n, len(imgs))
else: data = [self.count(ips.img, para)]
IPy.table(ips.title+'-statistic', data, titles)
def run(self, ips, imgs, para=None):
data = []
sly, slx = ips.get_rect()
xs = np.linspace(0, slx.stop - slx.start - 1, para['num']).astype(int)
for img in imgs:
img = img[sly, slx]
ys = np.array([np.where(img[:, i] == 255)[0].max() for i in xs])
data.append(ys + sly.start)
xs += slx.start
ips.mark = Mark(xs, data)
k, unit = ips.unit
data = (np.array(data) * k).round(3)
IPy.table(ips.title + '-pts', data, ['%.3f' % i for i in xs * k])
def run(self, ips, imgs, para = None):
titles = ['PartID', 'Noeds', 'Edges', 'TotalLength', 'Density', 'AveConnect']
k, unit = ips.unit
gs = nx.connected_component_subgraphs(ips.data, False) if para['parts'] else [ips.data]
comid, datas = 0, []
for g in gs:
sl = 0
for (s, e) in g.edges():
sl += sum([i['weight'] for i in g[s][e].values()])
datas.append([comid, g.number_of_nodes(), g.number_of_edges(), round(sl*k, 2),
round(nx.density(g), 2), round(nx.average_node_connectivity(g),2)][1-para['parts']:])
comid += 1
print(titles, datas)
IPy.table(ips.title+'-graph', datas, titles[1-para['parts']:])
def run(self, ips, imgs, para=None):
if not para['slice']: imgs = [ips.get_img()]
data = []
for i in range(len(imgs)):
maxv = imgs[i].max()
if maxv == 0: continue
ct = np.histogram(imgs[i], maxv, [1, maxv + 1])[0]
titles = ['slice', 'value', 'count']
dt = [[i] * len(ct), list(range(maxv + 1)), ct]
if not para['slice']:
titles, dt = titles[1:], dt[1:]
if self.para['fre']:
fre = ct / float(ct.sum())
titles.append('frequence')
dt.append(fre.round(4))
dt = list(zip(*dt))
data.extend(dt)
IPy.table(ips.title + '-histogram', data, titles)
def report(self, title):
unit = 1 if self.unit is None else self.unit[0]
rst = [(x * unit, y * unit) for x, y in self.body]
titles = ['OX', 'OY']
IPy.table(title, rst, titles)
def report(self, title):
rst = self.body
titles = ['OX', 'OY']
IPy.table(title, rst, titles)
