def run(self, ips, imgs, para = None):
k = para['diag']/np.sqrt((np.array(ips.img.shape)**2).sum())
size = tuple((np.array(ips.img.shape)*k).astype(np.int16))
IPy.set_info('down sample...')
news = []
for img in imgs:
if k!=0: img = tf.resize(img, size)
if para['sigma']!=0:
img = ndimg.gaussian_filter(img, para['sigma'])
news.append(img)
IPy.set_info('register...')
sr = StackReg(eval('StackReg.%s'%para['trans']))
sr.register_stack(np.array(news), reference=para['ref'])
mats = sr._tmats.reshape((sr._tmats.shape[0],-1))
if k!=0: mats[:,[0,1,3,4,6,7]] *= k
if k!=0: mats[:,[0,1,2,3,4,5]] /= k
if para['tab']: IPy.show_table(pd.DataFrame(
mats, columns=['A%d'%(i+1) for i in range(mats.shape[1])]), title='%s-Tmats'%ips.title)
if para['new'] == 'None': return
IPy.set_info('transform...')
for i in range(sr._tmats.shape[0]):
tform = tf.ProjectiveTransform(matrix=sr._tmats[i])
img = tf.warp(imgs[i], tform)
img -= imgs[i].min(); img *= imgs[i].max() - imgs[i].min()
if para['new'] == 'Inplace': imgs[i][:] = img
if para['new'] == 'New': news[i] = img.astype(ips.img.dtype)
self.progress(i, len(imgs))
if para['new'] == 'New': IPy.show_img(news, '%s-reg'%ips.title)
def run(self, para=None):
path = "/Volumes/Macintosh/curioData/screening/20181022_wt_mad1/FLUO_80_1_FluoAnalysis/mad1/croppedImgs"
# TODO this getDir and wx.DirDialog will both crash
# tmp_dir = IPy.getdir("Choose a directory", "", "")
list_file = os.listdir(path)
data = generate_empty_annotation(len(list_file), 70)
IPy.show_table(data, 'Annotations[%s,%s]' % data.shape)
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.show_table(pd.DataFrame(nodes, columns=ntitles),
ips.title + '-nodes')
IPy.show_table(pd.DataFrame(edges, columns=etitles),
ips.title + '-edges')
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 para['nozero']: imgs = imgs[imgs!=0]
data = self.count(imgs, para)
IPy.show_table(pd.DataFrame(data, columns=titles), ips.title+'-statistic')
def run(self, ips, imgs, para=None):
sc, sr = ips.get_rect()
print(sc, sr)
c, r, w, h = sr.start, sc.start, sr.stop - sr.start, sc.stop - sc.start
roi = imgs[0][r:r + h, c:c + w].copy()
track_window = (c, r, w, h)
hsv_roi = cv2.cvtColor(roi, cv2.COLOR_BGR2HSV)
mask = cv2.inRange(hsv_roi, np.array((0., 60., 32.)),
np.array((180., 255., 255.)))
roi_hist = cv2.calcHist([hsv_roi], [0], mask, [180], [0, 180])
cv2.normalize(roi_hist, roi_hist, 0, 255, cv2.NORM_MINMAX)
# Setup the termination criteria, either 10 iteration or move by atleast 1 pt
term_crit = (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 1)
n = len(imgs)
locs, mark = [], {'type': 'layers', 'body': {}}
for i in range(n):
prgs = (i, n)
frame = imgs[i].copy()
hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
dst = cv2.calcBackProject([hsv], [0], roi_hist, [0, 180], 1)
ret, track_window = cv2.meanShift(dst, track_window, term_crit)
x, y, w, h = track_window
img2 = cv2.rectangle(frame, (x, y), (x + w, y + h), 255, 2)
locs.append([x, y, w, h])
layer = {'type': 'layer', 'body': []}
layer['body'].append({
'type': 'rectangle',
'body': (x + w / 2, y + h / 2, w, h)
})
mark['body'][i] = layer
ips.mark = GeometryMark(mark)
IPy.show_table(pd.DataFrame(locs, columns=['X', 'Y', 'W', 'H']),
ips.title + '-region')
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.show_table(pd.DataFrame(datas, columns=titles),
ips.title + '-graph')
def run(self, ips, imgs, para=None):
self.modedict = {
'SQDIFF': cv2.TM_SQDIFF,
'SQDIFF_NORMED': cv2.TM_SQDIFF_NORMED,
'CCORR': cv2.TM_CCORR,
'CCORR_NORMED': cv2.TM_CCORR_NORMED,
'CCOEFF': cv2.TM_CCOEFF
}
data = []
sly, slx = ips.get_rect()
#将矩形中的线进行等分，x是固定的
print(sly, slx)
img_moudle = imgs[0][sly, slx.start + 60:slx.stop - 60]
n = len(imgs)
locs = []
for i in range(n):
prgs = (i, n)
self.progress(i, len(imgs))
x, y = self.mathc_img(imgs[i][sly, slx.start:slx.stop], img_moudle,
self.modedict[self.para['mode']])
temp = imgs[i][self.para['amp_x'] +
(x - self.para['amp_x']):-self.para['amp_x'] +
(x - self.para['amp_x']), self.para['amp_y'] +
(y - self.para['amp_y']):-self.para['amp_y'] +
(y - self.para['amp_y'])]
imgs[i][self.para['amp_x']:-self.para['amp_x'],
self.para['amp_y']:-self.para['amp_y']] = temp
locs.append((x, y))
# print(locs)
# IPy.show_table(pd.DataFrame(locs, ['X','Y']),'locations')
IPy.show_table(pd.DataFrame(locs, columns=['X', 'Y']), 'locations')
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.show_table(pd.DataFrame(datas, columns=titles[1 - para['parts']:]),
ips.title + '-graph')
def run(self, ips, imgs, para = None):
inten = ImageManager.get(para['inten'])
if not para['slice']:
imgs = [inten.img]
msks = [ips.img]
else:
msks = ips.imgs
imgs = inten.imgs
if len(msks)==1:
msks *= len(imgs)
buf = imgs[0].astype(np.uint16)
strc = ndimage.generate_binary_structure(2, 1 if para['con']=='4-connect' else 2)
idct = ['Max','Min','Mean','Variance','Standard','Sum']
key = {'Max':'max','Min':'min','Mean':'mean',
'Variance':'var','Standard':'std','Sum':'sum'}
idct = [i for i in idct if para[key[i]]]
titles = ['Slice', 'ID'][0 if para['slice'] else 1:]
if para['center']: titles.extend(['Center-X','Center-Y'])
if para['extent']: titles.extend(['Min-Y','Min-X','Max-Y','Max-X'])
titles.extend(idct)
k = ips.unit[0]
data, mark = [],{'type':'layers', 'body':{}}
# data,mark=[],[]
for i in range(len(imgs)):
n = ndimage.label(msks[i], strc, output=buf)
index = range(1, n+1)
dt = []
if para['slice']:dt.append([i]*n)
dt.append(range(n))
xy = ndimage.center_of_mass(imgs[i], buf, index)
xy = np.array(xy).round(2).T
if para['center']:dt.extend([xy[1]*k, xy[0]*k])
boxs = [None] * n
if para['extent']:
boxs = ndimage.find_objects(buf)
boxs = [( i[1].start+(i[1].stop-i[1].start)/2, i[0].start+(i[0].stop-i[0].start)/2, i[1].stop-i[1].start,i[0].stop-i[0].start) for i in boxs]
for j in (0,1,2,3):
dt.append([i[j]*k for i in boxs])
if para['max']:dt.append(ndimage.maximum(imgs[i], buf, index).round(2))
if para['min']:dt.append(ndimage.minimum(imgs[i], buf, index).round(2))
if para['mean']:dt.append(ndimage.mean(imgs[i], buf, index).round(2))
if para['var']:dt.append(ndimage.variance(imgs[i], buf, index).round(2))
if para['std']:dt.append(ndimage.standard_deviation(imgs[i], buf, index).round(2))
if para['sum']:dt.append(ndimage.sum(imgs[i], buf, index).round(2))
layer = {'type':'layer', 'body':[]}
xy=np.int0(xy).T
texts = [(i[1],i[0])+('id=%d'%n,) for i,n in zip(xy,range(len(xy)))]
layer['body'].append({'type':'texts', 'body':texts})
if para['extent']: layer['body'].append({'type':'rectangles', 'body':boxs})
mark['body'][i] = layer
data.extend(list(zip(*dt)))
IPy.show_table(pd.DataFrame(data, columns=titles), inten.title+'-region statistic')
inten.mark = GeometryMark(mark)
inten.update = True
def run(self, ips, imgs, para = None):
if not para['slice']:imgs = [ips.img]
k = ips.unit[0]
titles = ['Slice', 'ID'][0 if para['slice'] else 1:]
if para['center']:titles.extend(['Center-X','Center-Y'])
if para['area']:titles.append('Area')
if para['l']:titles.append('Perimeter')
if para['extent']:titles.extend(['Min-Y','Min-X','Max-Y','Max-X'])
if para['ed']:titles.extend(['Diameter'])
if para['ca']:titles.extend(['ConvexArea'])
if para['holes']:titles.extend(['Holes'])
if para['fa']:titles.extend(['FilledArea'])
if para['solid']:titles.extend(['Solidity'])
if para['cov']:titles.extend(['Major','Minor','Ori'])
buf = imgs[0].astype(np.uint16)
data, mark = [], []
strc = generate_binary_structure(2, 1 if para['con']=='4-connect' else 2)
for i in range(len(imgs)):
label(imgs[i], strc, output=buf)
ls = regionprops(buf)
dt = [[i]*len(ls), list(range(len(ls)))]
if not para['slice']:dt = dt[1:]
if not para['cov']: cvs = [None] * len(ls)
else: cvs = [(i.major_axis_length, i.minor_axis_length, i.orientation) for i in ls]
centroids = [i.centroid for i in ls]
mark.append([(center, cov) for center,cov in zip(centroids, cvs)])
if para['center']:
dt.append([round(i.centroid[1]*k,1) for i in ls])
dt.append([round(i.centroid[0]*k,1) for i in ls])
if para['area']:
dt.append([i.area*k**2 for i in ls])
if para['l']:
dt.append([round(i.perimeter*k,1) for i in ls])
if para['extent']:
for j in (0,1,2,3):
dt.append([i.bbox[j]*k for i in ls])
if para['ed']:
dt.append([round(i.equivalent_diameter*k, 1) for i in ls])
if para['ca']:
dt.append([i.convex_area*k**2 for i in ls])
if para['holes']:
dt.append([1-i.euler_number for i in ls])
if para['fa']:
dt.append([i.filled_area*k**2 for i in ls])
if para['solid']:
dt.append([round(i.solidity, 2) for i in ls])
if para['cov']:
dt.append([round(i.major_axis_length*k, 1) for i in ls])
dt.append([round(i.minor_axis_length*k, 1) for i in ls])
dt.append([round(i.orientation*k, 1) for i in ls])
data.extend(list(zip(*dt)))
ips.mark = Mark(mark)
IPy.show_table(pd.DataFrame(data, columns=titles), ips.title+'-region')
def run(self, ips, imgs, para=None):
sc, sr = ips.get_rect()
print(sc, sr)
c, r, w, h = sr.start, sc.start, sr.stop - sr.start, sc.stop - sc.start
roi = imgs[0][r:r + h, c:c + w].copy()
track_window = (c, r, w, h)
hsv_roi = cv2.cvtColor(roi, cv2.COLOR_BGR2HSV)
mask = cv2.inRange(hsv_roi, np.array((0., 60., 32.)),
np.array((180., 255., 255.)))
roi_hist = cv2.calcHist([hsv_roi], [0], mask, [180], [0, 180])
cv2.normalize(roi_hist, roi_hist, 0, 255, cv2.NORM_MINMAX)
# Setup the termination criteria, either 10 iteration or move by atleast 1 pt
term_crit = (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 1)
n = len(imgs)
locs = []
locs, mark = [], {'type': 'layers', 'body': {}}
for i in range(n):
prgs = (i, n)
frame = imgs[i].copy()
hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
dst = cv2.calcBackProject([hsv], [0], roi_hist, [0, 180], 1)
ret, track_window = cv2.CamShift(dst, track_window, term_crit)
pts = cv2.boxPoints(ret)
pts = np.int0(pts)
layer = {'type': 'layer', 'body': []}
temp = [tuple(i) for i in pts]
layer['body'].append({
'type':
'line',
'body': [(pts[0][0], pts[0][1]), (pts[1][0], pts[1][1]),
(pts[2][0], pts[2][1]), (pts[3][0], pts[3][1])]
})
layer['body'].append({
'type':
'line',
'body': [(pts[0][0], pts[0][1]), (pts[3][0], pts[3][1])]
})
mark['body'][i] = layer
locs.append([
pts[0][0], pts[0][1], pts[1][0], pts[1][1], pts[2][0],
pts[2][1], pts[3][0], pts[3][1]
])
ips.mark = GeometryMark(mark)
IPy.show_table(
pd.DataFrame(locs,
columns=[
'P1_X',
'P1_y',
'P2_X',
'P2_y',
'P3_X',
'P3_y',
'P4_X',
'P4_y',
]), ips.title + '-region')
def run(self, para=None):
p = subprocess.Popen('%s -m pip list' % sys.executable,
stdin=subprocess.PIPE,
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
shell=True)
lst = str(p.stdout.read(), encoding='utf-8').replace('\r\n',
'\n').split('\n')
IPy.show_table(pd.DataFrame([[i] for i in lst], columns=['Packages']),
'Packages')
def report(self, title):
rst = []
titles = ['Area', 'OX', 'OY']
for pg in self.body:
plg = Polygon(pg)
area, xy = plg.area, plg.centroid
unit = 1 if self.unit==None else self.unit[0]
axy = [area*unit**2, xy.x*unit, xy.y*unit]
rst.append([round(i,1) for i in axy])
IPy.show_table(pd.DataFrame(rst, columns=titles), title)
def run(self, tps, snap, data, para=None):
rst, axis = {}, (0, 1)[para['axis'] == 'Row']
if para['sum']: rst['sum'] = snap.sum(axis=axis)
if para['mean']: rst['mean'] = snap.mean(axis=axis)
if para['max']: rst['max'] = snap.max(axis=axis)
if para['min']: rst['min'] = snap.min(axis=axis)
if para['var']: rst['var'] = snap.var(axis=axis)
if para['std']: rst['std'] = snap.std(axis=axis)
if para['skew']: rst['skew'] = snap.skew(axis=axis)
if para['kurt']: rst['kurt'] = snap.kurt(axis=axis)
IPy.show_table(pd.DataFrame(rst), tps.title + '-statistic')
def report(self, title):
rst, titles = [], ['K']
for line in self.body:
pts = np.array(line)
mid = (pts[:-1]+pts[1:])/2
dxy = (pts[:-1]-pts[1:])
dxy[:,1][dxy[:,1]==0] = 1
l = norm(dxy, axis=1)*-np.sign(dxy[:,1])
rst.append(np.round(np.arccos(dxy[:,0]/l)/np.pi*180,1))
IPy.show_table(pd.DataFrame(rst, columns=titles), title)
def run(self, ips, imgs, para = None):
if para['nozero']: imgs = imgs[imgs!=0]
minv, maxv = imgs.min(), imgs.max()
bins = para['bins']
if bins==0:bins = int(maxv - minv)+1
ct, bins = np.histogram(imgs, bins, [minv, maxv+1])
titles = ['value','count','frequence']
dt = [bins[:-1].round(2), ct, (ct/ct.sum()).round(4)]
dt = list(zip(*dt))
IPy.show_table(pd.DataFrame(dt, columns=titles), ips.title+'-histogram')
def run(self, para=None):
import calendar
ls = calendar.month(para['year'], para['month']).split('\n')
title = ls[0].strip()
titles = ls[1].split(' ')
table = []
for i in ls[2:-1]:
a = i.replace(' ', ' None ').strip()
table.append(a.replace(' ', ' ').split(' '))
dataframe = pd.DataFrame(table, columns=titles)
IPy.show_table(dataframe, ls[0].strip())
def run(self, ips, imgs, para=None):
k = ips.unit[0]
titles = ['ID']
if para['center']: titles.extend(['Center-X', 'Center-Y', 'Center-Z'])
if para['surf']: titles.append('Surface')
if para['vol']: titles.append('Volume')
if para['extent']:
titles.extend(
['Min-Z', 'Min-Y', 'Min-X', 'Max-Z', 'Max-Y', 'Max-X'])
if para['ed']: titles.extend(['Diameter'])
if para['fa']: titles.extend(['FilledArea'])
if para['cov']: titles.extend(['Axis1', 'Axis2', 'Axis3'])
strc = generate_binary_structure(
3, 1 if para['con'] == '4-connect' else 2)
buf, n = label(imgs, strc, output=np.uint32)
ls = regionprops(buf)
dt = [range(len(ls))]
centroids = [i.centroid for i in ls]
if para['center']:
dt.append([round(i.centroid[1] * k, 1) for i in ls])
dt.append([round(i.centroid[0] * k, 1) for i in ls])
dt.append([round(i.centroid[2] * k, 1) for i in ls])
if para['surf']:
buf[find_boundaries(buf, mode='outer')] = 0
vts, fs, ns, cs = marching_cubes_lewiner(buf, level=0)
lst = [[] for i in range(n + 1)]
for i in fs:
lst[int(cs[i[0]])].append(i)
dt.append([
0 if len(i) == 0 else mesh_surface_area(vts, np.array(i)) *
k**2 for i in lst
][1:])
if para['vol']:
dt.append([i.area * k**3 for i in ls])
if para['extent']:
for j in (0, 1, 2, 3, 4, 5):
dt.append([i.bbox[j] * k for i in ls])
if para['ed']:
dt.append([round(i.equivalent_diameter * k, 1) for i in ls])
if para['fa']:
dt.append([i.filled_area * k**3 for i in ls])
if para['cov']:
ites = np.array([i.inertia_tensor_eigvals for i in ls])
rst = np.sqrt(np.clip(ites.sum(axis=1) // 2 - ites.T, 0, 1e10)) * 4
for i in rst[::-1]:
dt.append(np.abs(i))
IPy.show_table(pd.DataFrame(list(zip(*dt)), columns=titles),
ips.title + '-region')
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 not self.para['slice']:imgs = [ips.img]
data = []
msk = ips.get_msk('in')
for n in range(len(imgs)):
img = imgs[n] if msk is None else imgs[n][msk]
data.append(self.count(img, para))
self.progress(n, len(imgs))
IPy.show_table(pd.DataFrame(data, columns=titles), ips.title+'-statistic')
def run(self, ips, snap, img, para = None):
ips.lut = self.buflut
k, unit = ips.unit
lev, ds, step = para['thr'], para['ds'], para['step']
scube = np.cumprod(ips.imgs.shape)[-1] * k**3
sfront = (ips.imgs[::ds,::ds,::ds]>lev).sum() * ds ** 3 * k**3
sback = scube - sfront
print(scube, sfront, sback)
vts, fs, ns, cs = marching_cubes_lewiner(ips.imgs[::ds,::ds,::ds], lev, step_size=step)
area = mesh_surface_area(vts, fs) * (ds**2 * k **2)
rst = [round(i,3) for i in [scube, sfront, sback, sfront/scube, area, area/sfront]]
titles = ['Cube Volume', 'Volume', 'Blank', 'Volume/Cube', 'Surface', 'Volume/Surface']
IPy.show_table(pd.DataFrame([rst], columns=titles), ips.title+'-Volume Measure')
def report(self, title):
rst = []
for line in self.body:
pts = np.array(line)
dis = norm((pts[:-1] - pts[1:]), axis=1)
dis *= 1 if self.unit is None else self.unit[0]
rst.append(list(dis.round(2)))
lens = [len(i) for i in rst]
maxlen = max(lens)
fill = [[0] * (maxlen - i) for i in lens]
rst = [i + j for i, j in zip(rst, fill)]
titles = ['L{}'.format(i + 1) for i in range(maxlen)]
IPy.show_table(pd.DataFrame(rst, columns=titles), title)
def lineregress(self, ips):
thick = self.body[1]['z']
gray = [
self.get_gray(ips.imgs[0].copy(), i) for i in self.body[0]['body']
]
print(thick, gray)
slope, intercept = lineregress(thick, gray)
print(slope, intercept)
a = np.arange(256)
IPy.show_table(pd.DataFrame(np.array([a, slope * a + intercept])),
title='temp')
wx.CallAfter(pub.sendMessage,
'lineregress_plot',
para=([thick, gray], slope, intercept))
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])
IPy.show_table(pd.DataFrame(data, columns=['%.3f'%i for i in xs*k]), ips.title+'-pts')
def run(self, tps, snap, data, para=None):
rst, axis = {}, (0, 1)[para['axis'] == 'Row']
if para['sum']: rst['sum'] = snap.sum(axis=axis)
if para['mean']: rst['mean'] = snap.mean(axis=axis)
if para['max']: rst['max'] = snap.max(axis=axis)
if para['min']: rst['min'] = snap.min(axis=axis)
if para['var']: rst['var'] = snap.var(axis=axis)
if para['std']: rst['std'] = snap.std(axis=axis)
if para['skew']: rst['skew'] = snap.skew(axis=axis)
if para['kurt']: rst['kurt'] = snap.kurt(axis=axis)
cols = ['sum', 'mean', 'min', 'max', 'var', 'std', 'skew', 'kurt']
cols = [i for i in cols if i in rst]
IPy.show_table(
pd.DataFrame(rst, columns=cols).T, tps.title + '-statistic')
def run(self, para):
fs = glob(para['path']+'/*.tif')
boxes, csr = [], {}
for i in fs:
print('build', i, '...')
rs = gdal.Open(i)
csr = rs.GetProjection()
m = rs.GetGeoTransform()
m = np.array(m).reshape((2,3))
shp = (rs.RasterYSize, rs.RasterXSize)
boxes.append([raster_box(shp, csr, m), i])
gdf = gpd.GeoDataFrame(boxes, columns=['geometry', 'path'])
print(csr)
gdf.crs = makeprj(csr).ExportToProj4()
IPy.show_table(gdf, 'geoindex')
def run(self, para = None):
path = para['path']
date = para['time']
check = para['check']
line = para['line']
point = para['type'] == '点图'
text = [para['t%d'%i] for i in (1,2,3,4,5,6,7,8)]
text = [i for i in text if len(i)>1]
try:
rst = draw(path, date, text, point, line, check)
if check: IPy.show_table(rst, '新增地名列表')
else: IPy.show_img([rst], '草地贪夜蛾分布示意图')
except:
IPy.alert('请检查数据格式！')
def run(self, ips, imgs, para=None):
if para['method'] == 'Simple Ratio':
rst, value, ratio = simple_ratio(imgs, ips.img, not para['new'],
self.progress)
body = pd.DataFrame({'Mean value': value, 'Ratio': ratio})
IPy.show_table(body, '%s-simple ratio' % ips.title)
if para['method'] == 'Exponential Fit':
rst, popt, intensity, fitrst = exponential_fit(
imgs, not para['new'], self.progress)
plot_after(popt, intensity, fitrst)
body = {'Intensity': intensity, 'Fit': fitrst}
IPy.show_table(pd.DataFrame(body), '%s-exp fit' % ips.title)
if para['method'] == 'Histogram Match':
rst = histogram_match(imgs, ips.img, not para['new'],
self.progress)
if para['new']: IPy.show_img(rst, '%s-corrected' % ips.title)
def report(self, title):
rst = []
for line in self.body:
pts = np.array(line)
v1 = pts[:-2]-pts[1:-1]
v2 = pts[2:]-pts[1:-1]
a = np.sum(v1*v2, axis=1)*1.0
a/=norm(v1,axis=1)*norm(v2,axis=1)
ang = np.arccos(a)/np.pi*180
rst.append(list(ang.round(1)))
lens = [len(i) for i in rst]
maxlen = max(lens)
fill = [[0]*(maxlen-i) for i in lens]
rst = [i+j for i,j in zip(rst, fill)]
titles = ["A{}".format(i+1) for i in range(maxlen)]
IPy.show_table(pd.DataFrame(rst, columns=titles), title)
def run(self, tps, snap, data, para=None):
rg = None if para['auto'] else (para['min'], para['max'])
for i in para['cn']:
hist, bins = np.histogram(data[i], para['bins'], rg)
vs = {'bins': bins[:-1]}
if para['count']: vs['count'] = hist
if para['fre']: vs['frequency'] = hist / hist.sum()
if para['weight']:
vs['weight'] = np.histogram(data[i], bins, rg,
weights=data[i])[0]
df = pd.DataFrame(
vs,
columns=[
i for i in ['bins', 'count', 'frequency', 'weight']
if i in vs
])
IPy.show_table(df, '%s-frequency' % i)
def run(self, ips, snap, img, para=None):
icemsk = ips.get_msk()
grid, lines, row, col = self.grid(ips, para)
from time import time
a = time()
mjd = []
for pts in grid:
msk = polygon(*pts.T[::-1], shape=img.shape[:2])
inice = icemsk[msk] > 0
if inice.sum() <= len(msk[0]) // 2: mjd.append(-3)
else:
value = img[msk[0][inice], msk[1][inice]].mean()
mjd.append(value if value > para['thr'] else 0)
#ips.img[msk[0], msk[1]] = 0
data = np.array(mjd).reshape(row, col)
print('value', time() - a)
IPy.show_table(pd.DataFrame(data), title=ips.title + '-Field')
