def run(self, para=None):
xs = [0, 30, 117, 255]
ys = [0, 0, 255, 255]
data = np.array([30, 57, 93, 105, 110, 117, 108, 87, 52, 39])
l1 = plt.plot(xs, ys, 'r', lw=3)
plt.grid()
plt.title('y = 2.931 * x - 87.931')
plt.gca().set_ylim(0, 256)
plt.gca().set_xlim(0, 256)
plt.figure()
plt.grid()
xs = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
ys = np.round(data * 2.931 - 87.931)
l1 = plt.plot(xs, ys, '-o')
plt.show()
data = np.array([30, 57, 93, 105, 110, 117, 108, 87, 52, 39])
ori = np.zeros((10, 30, 30), dtype=np.uint8)
ori.T[:] = np.round(data * 2.931 - 87.931)
#area = (area-5) * 8
IPy.show_img([np.hstack(ori)], 'Extend')
def run(self, ips, imgs, para=None):
if not para['slice']: imgs = [ips.img]
data = []
msk = ips.get_msk('in')
for i in range(len(imgs)):
img = imgs[i] if msk is None else imgs[i][msk]
maxv = img.max()
if maxv == 0: continue
ct = np.histogram(img, 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.show_table(pd.DataFrame(data, columns=titles),
ips.title + '-histogram')
def on_minmax(self, event):
ips = IPy.get_ips()
if ips is None: return
minv, maxv = ips.get_updown()
self.range = ips.range = (minv, maxv)
hist = ips.histogram()
self.histpan.SetValue(hist)
self.sli_low.set_para(self.range, 10)
self.sli_high.set_para(self.range, 10)
self.sli_low.SetValue(minv)
self.sli_high.SetValue(maxv)
self.histpan.set_lim(0, 255)
ips.update = 'pix'
def zoom(self, k, x, y):
# print 'scale', k
k1 = self.oldscale * 1.0
self.oldscale = k2 = k
self.imgbox[0] = int(self.imgbox[0] + (k1-k2)*x+0.5)
self.imgbox[1] = int(self.imgbox[1] + (k1-k2)*y+0.5)
self.imgbox[2] = int(self.ips.size[1] * k2+0.5)
self.imgbox[3] = int(self.ips.size[0] * k2+0.5)
lay(self.box, self.imgbox)
if self.imgbox[2]<=self.scrsize[0]*0.9 and\
self.imgbox[3]<=self.scrsize[1]*0.9 and IPy.uimode()=='ij':
self.SetInitialSize((self.imgbox[2], self.imgbox[3]))
if not self.handle is None: self.handle(self.ips, True)
def on_mouseevent(self, me):
tool = self.ips.tool
if tool == None : tool = ToolsManager.curtool
x,y = self.to_data_coor(me.GetX(), me.GetY())
if me.Moving() and not me.LeftIsDown() and not me.RightIsDown() and not me.MiddleIsDown():
xx,yy = int(round(x)), int(round(y))
k, unit = self.ips.unit
if xx>=0 and xx<self.ips.img.shape[1] and yy>=0 and yy<self.ips.img.shape[0]:
IPy.set_info('Location:%.1f %.1f Value:%s'%(x*k, y*k, self.ips.img[yy,xx]))
if tool==None:return
sta = [me.AltDown(), me.ControlDown(), me.ShiftDown()]
if me.ButtonDown():tool.mouse_down(self.ips, x, y, me.GetButton(), alt=sta[0], ctrl=sta[1], shift=sta[2], canvas=self)
if me.ButtonUp():tool.mouse_up(self.ips, x, y, me.GetButton(), alt=sta[0], ctrl=sta[1], shift=sta[2], canvas=self)
if me.Moving():tool.mouse_move(self.ips, x, y, None, alt=sta[0], ctrl=sta[1], shift=sta[2], canvas=self)
btn = [me.LeftIsDown(), me.MiddleIsDown(), me.RightIsDown(),True].index(True)
if me.Dragging():tool.mouse_move(self.ips, x, y, 0 if btn==3 else btn+1, alt=sta[0], ctrl=sta[1], shift=sta[2], canvas=self)
wheel = np.sign(me.GetWheelRotation())
if wheel!=0:tool.mouse_wheel(self.ips, x, y, wheel, alt=sta[0], ctrl=sta[1], shift=sta[2], canvas=self)
if hasattr(tool, 'cursor'):
self.SetCursor(wx.Cursor(tool.cursor))
else : self.SetCursor(wx.Cursor(wx.CURSOR_ARROW))
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 run(self, ips, imgs, para=None):
ls = []
for x, y, z in ips.roi.body:
x, y, z = int(x), int(y), int(z)
r, g, b = imgs[z][y, x]
ls.append([x, y, z, r, g, b])
data = pd.DataFrame(ls, columns=[*'XYZRGB'])
rst = {}
if para['sum']: rst['sum'] = data[[*'RGB']].sum()
if para['mean']: rst['mean'] = data[[*'RGB']].mean()
if para['max']: rst['max'] = data[[*'RGB']].max()
if para['min']: rst['min'] = data[[*'RGB']].min()
if para['var']: rst['var'] = data[[*'RGB']].var()
if para['std']: rst['std'] = data[[*'RGB']].std()
if para['skew']: rst['skew'] = data[[*'RGB']].skew()
if para['kurt']: rst['kurt'] = data[[*'RGB']].kurt()
if para['lst']:
IPy.show_table(data, ips.title + '-pts color')
IPy.show_table(pd.DataFrame(rst), ips.title + '-pts color statistic')
def run(self, ips, snap, img, para=None):
# 人脸检测
# 获取当前工作路径
path = os.getcwd()
face_cascade = cv2.CascadeClassifier(
path + '\menus\Opencv\Filters\haarcascade_frontalface_default.xml')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(gray, 1.3, 5)
for (x, y, w, h) in faces:
ips.mark = GeometryMark({
'type': 'rectangles',
'color': (255, 0, 0),
'lw': 2,
'fcolor': (0, 0, 0),
'fill': False,
'body': [(x, y, w, h)]
})
# 用下面这句会影响到原图像，导致不能再编辑，可以用上面的标记
# cv2.rectangle(img, (x, y), (x + w, y + h), (255, 0, 0), 2)
IPy.show_table(
pd.DataFrame([[x, y, w, h]],
index=['矩形'],
columns=['x', 'y', 'w', 'h']), '人脸参数')
def on_8bit( self, event ): ips = IPy.get_ips() if ips is None: return self.range = ips.range = (0,255) hist = ips.histogram() self.histpan.set_hist(hist) self.sli_low.SetMax(255) self.sli_low.SetMin(0) self.sli_high.SetMax(255) self.sli_high.SetMin(0) self.sli_low.SetValue(0) self.sli_high.SetValue(255) self.histpan.set_lim(0,255) ips.update = 'pix'
def start(self):
#if not WidgetsManager.getref(self.title) is None: return
pan = self.pan(IPy.curapp)
WidgetsManager.addref(pan)
IPy.curapp.auimgr.AddPane(
pan,
aui.AuiPaneInfo().Caption(self.title).Left().Layer(15).PinButton(
True).Float().Resizable().FloatingSize(
wx.DefaultSize).Dockable(
IPy.uimode() == 'ipy').DestroyOnClose())
IPy.curapp.Layout()
IPy.curapp.auimgr.Update()
'''
def run(self, para=None):
xs = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]
ys = [10, 12, 16, 22, 26, 28, 30, 30, 25, 21, 16, 11]
lbs = [
'Jan', 'Feb', 'Mar', 'Apr', 'May', 'June', 'Jul', 'Aug', 'Sep',
'Oct', 'Nov', 'Dec'
]
plt.xticks(xs, lbs)
plt.plot(xs, ys, '-o')
plt.grid()
plt.title('Temperature in ChengDu')
plt.xlabel('Month')
plt.ylabel('Temperature (C)')
plt.show()
area = np.zeros((12, 30, 30), dtype=np.uint8)
temp = np.array([10, 12, 16, 22, 26, 28, 30, 30, 25, 21, 16, 11])
area.T[:] = temp
area = (area - 5) * 8
IPy.show_img([np.hstack(area)], 'Chengdu Temperature')
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:] + [
'Center-X', 'Center-Y', 'N', 'Neighbors'
]
buf = imgs[0].astype(np.uint32)
data, mark = [], {'type': 'layers', 'body': {}}
for i in range(len(imgs)):
if para['labled']: buf = imgs[i]
else: label(imgs[i], generate_binary_structure(2, 1), output=buf)
conarr = connect(buf, 1 if para['con'] == '4-connect' else 2,
not self.para['nozero'])
conmap = mapidx(conarr)
ls = regionprops(buf)
dt = [[i] * len(ls), list(range(1, 1 + len(ls)))]
if not para['slice']: dt = dt[1:]
layer = {'type': 'layer', 'body': []}
texts = [(i.centroid[::-1]) + ('id=%d' % i.label, ) for i in ls]
lines = [(ls[i - 1].centroid[::-1], ls[j - 1].centroid[::-1])
for i, j in conarr]
layer['body'].append({'type': 'texts', 'body': texts})
layer['body'].append({'type': 'lines', 'body': lines})
mark['body'][i] = layer
dt.append([round(i.centroid[1] * k, 1) for i in ls])
dt.append([round(i.centroid[0] * k, 1) for i in ls])
neibs = [conmap[i.label] if i.label in conmap else [] for i in ls]
dt.extend([[len(i) for i in neibs], neibs])
data.extend(list(zip(*dt)))
ips.mark = GeometryMark(mark)
IPy.show_table(pd.DataFrame(data, columns=titles),
ips.title + '-region')
def on_8bit(self, event):
ips = IPy.get_ips()
if ips is None: return
rg = (0, 255)
ips.chan_range[self.active] = rg
if (rg[0] == rg[1]): rg = (rg[0] - 1e-4, rg[1] + 1e-4)
slis = 'all' if self.chk_stack.GetValue() else ips.cur
hist = ips.histogram(rg, slis, self.active, 512)
self.histpan.SetValue(hist)
self.sli_low.set_para(rg, 10)
self.sli_high.set_para(rg, 10)
self.sli_low.SetValue(rg[0])
self.sli_high.SetValue(rg[1])
self.histpan.set_lim(0, 255)
ips.update()
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
IPy.show_table(pd.DataFrame(datas, columns=titles[1 - para['parts']:]),
ips.title + '-graph')
def run(self, para=None):
xs = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12])
ys = np.array([1, 2, 1, 2, 2, 3, 8, 9, 8, 10, 9, 10], dtype=np.float32)
ds = ndimg.convolve1d(ys, [0, 1, -1])
lbs = [
'Jan', 'Feb', 'Mar', 'Apr', 'May', 'June', 'Jul', 'Aug', 'Sep',
'Oct', 'Nov', 'Dec'
]
plt.xticks(xs, lbs)
plt.plot(xs, ys, '-o', label='Temperature')
plt.plot(xs, ds, '-o', label='Difference')
plt.grid()
plt.gca().legend()
plt.title('Temperature in XX')
plt.xlabel('Month')
plt.ylabel('Temperature (C)')
plt.show()
IPy.show_img([block((ys - ys.min()) * (180 / ys.max() - ys.min()))],
'Temperature')
IPy.show_img([block((ds - ds.min()) * (180 / ds.max() - ds.min()))],
'Difference')
def run(self, ips, imgs, para=None):
name = self.para['name']
if ips.get_nslices() == 1 or self.para['stack'] == False:
if ips.roi == None:
img = ips.img.copy()
ipsd = ImagePlus([img], name)
else:
img = ips.get_subimg().copy()
ipsd = ImagePlus([img], name)
box = ips.roi.get_box()
ipsd.roi = ips.roi.affine(np.eye(2), (-box[0], -box[1]))
elif ips.get_nslices() > 1 and self.para['stack']:
if ips.roi == None:
if ips.is3d: imgs = imgs.copy()
else: imgs = [i.copy() for i in imgs]
else:
sc, sr = ips.get_rect()
if ips.is3d: imgs = imgs[:, sc, sr].copy()
else: imgs = [i[sc, sr].copy() for i in imgs]
ipsd = ImagePlus(imgs, name)
if ips.roi != None:
ipsd.roi = ips.roi.affine(np.eye(2), (-sr.start, -sc.start))
IPy.show_ips(ipsd)
def run(self, ips, imgs, para=None):
name = para['name']
print('name------------------', name)
if ips.get_nslices() == 1 or self.para['stack'] == False:
if ips.roi == None:
img = ips.img.copy()
ipsd = ImagePlus([img], name)
ipsd.back = ips.back
else:
img = ips.get_subimg().copy()
ipsd = ImagePlus([img], name)
box = ips.roi.get_box()
ipsd.roi = ips.roi.affine(np.eye(2), (-box[0], -box[1]))
'''
if not ips.backimg is None:
sr, sc = ips.get_rect()
ipsd.backimg = ips.backimg[sr, sc]
'''
elif ips.get_nslices() > 1 and self.para['stack']:
imgs = imgs[para['start'] - 1:para['end']]
if ips.roi == None:
if ips.is3d: imgs = imgs.copy()
else: imgs = [i.copy() for i in imgs]
#backimg = ips.backimg
else:
sc, sr = ips.get_rect()
if ips.is3d: imgs = imgs[:, sc, sr].copy()
else: imgs = [i[sc, sr].copy() for i in imgs]
#if not ips.backimg is None:
# backimg = None #ips.backimg[sr, sr]
ipsd = ImagePlus(imgs, name)
if ips.roi != None:
ipsd.roi = ips.roi.affine(np.eye(2), (-sr.start, -sc.start))
#if not ips.backimg is None: ipsd.backimg = backimg
ipsd.chan_mode = ips.chan_mode
IPy.show_ips(ipsd)
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['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'])
buf = imgs.astype(np.uint32)
strc = generate_binary_structure(
3, 1 if para['con'] == '4-connect' else 2)
label(imgs, strc, output=buf)
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['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])
IPy.show_table(pd.DataFrame(list(zip(*dt)), columns=titles),
ips.title + '-region')
def start(self, para=None, callafter=None):
wb = pyxl.load_workbook(self.cont)
xlreport.repair(wb)
info, key = xlreport.parse(wb)
if para is not None:
return self.runasyn(wb, info, para, callafter)
dialog = GridDialog(IPy.curapp, self.title, info, key)
rst = dialog.ShowModal()
para = dialog.GetValue()
dialog.Destroy()
if rst != 5100: return
filt = '|'.join(['%s files (*.%s)|*.%s' % ('XLSX', 'xlsx', 'xlsx')])
if not IPy.getpath('Save..', filt, 'save', para): return
win = WidgetsManager.getref('Macros Recorder')
if win != None: win.write('{}>{}'.format(self.title, para))
self.runasyn(wb, info, key, para, callafter)
def load(self):
dirdialog = wx.DirDialog(IPy.get_window(),
message=wx.DirSelectorPromptStr,
defaultPath="",
style=wx.DD_DEFAULT_STYLE,
pos=wx.DefaultPosition,
size=wx.DefaultSize,
name=wx.DirDialogNameStr)
if dirdialog.ShowModal() == wx.ID_OK:
self.pathAPI = dirdialog.GetPath()
self.pathAPI = self.pathAPI + os.path.sep
else:
return False
if platform.system() == 'Windows':
self.python = 'python'
else:
self.python = 'python3'
subprocess.check_output(
[self.python, self.pathAPI + 'listFrameworks.py'])
data = json.load(open('data.json'))
frameworks = data['frameworks']
subprocess.check_output(
[self.python, self.pathAPI + 'listModels.py', '-f', 'Keras'])
data = json.load(open('data.json'))
models = data['models']
Para = {'f': 'Keras', 'm': 'VGG16'}
View = [('lab', 'Select the framework and the model'),
(list, frameworks, str, 'Framework', 'f', ''),
(list, models, str, 'Model', 'm', '')]
md = MyDialog(None, 'DeepClas4BioPy', self.pathAPI, self.python, View,
Para)
md.initView()
if md.ShowModal() == wx.ID_OK:
self.framework = md.para['f']
self.model = md.para['m']
md.Destroy()
return True
else:
md.Destroy()
return False
def on_gray(self, event):
ips = IPy.get_ips()
if ips is None: return
if not isinstance(ips.chan, int): ips.chan = 0
chanrg = ips.chan_range[ips.chan]
rg = ips.get_updown('all', ips.chan, 512)
if (rg[0] == rg[1]): rg = (rg[0] - 1e-4, rg[1] + 1e-4)
slis = 'all' if self.chk_stack.GetValue() else ips.cur
hist = ips.histogram(rg, slis, ips.chan, 512)
self.histpan.SetValue(hist)
self.sli_low.set_para(rg, 10)
self.sli_high.set_para(rg, 10)
self.sli_low.SetValue(chanrg[0])
self.sli_high.SetValue(chanrg[1])
self.active = ips.chan
lim1 = (chanrg[0] - rg[0]) / (rg[1] - rg[0])
lim2 = (chanrg[1] - rg[0]) / (rg[1] - rg[0])
self.histpan.set_lim(lim1 * 255, lim2 * 255)
ips.update()
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 on_p(self, event, k):
ips = IPy.get_ips()
if ips is None: return
rg = ips.get_updown('all', self.active, 512)
if (rg[0] == rg[1]): rg = (rg[0] - 1e-4, rg[1] + 1e-4)
slis = 'all' if self.chk_stack.GetValue() else ips.cur
hist = ips.histogram(rg, slis, self.active, 512)
msk = np.abs(np.cumsum(hist) / hist.sum() - 0.5) < k / 2
idx = np.where(msk)[0]
vs = np.array([idx.min(), idx.max()])
vs = vs * (rg[1] - rg[0]) / 255 + rg[0]
ips.chan_range[self.active] = tuple(vs)
self.histpan.SetValue(hist)
self.sli_low.set_para(rg, 10)
self.sli_high.set_para(rg, 10)
print(rg, vs)
self.sli_low.SetValue(vs[0])
self.sli_high.SetValue(vs[1])
lim1 = (vs[0] - rg[0]) / (rg[1] - rg[0])
lim2 = (vs[1] - rg[0]) / (rg[1] - rg[0])
self.histpan.set_lim(lim1 * 255, lim2 * 255)
ips.update()
def run(self, ips, imgs, para=None, preview=False):
if len(ips.imgs) == 1: ips.img[:] = ips.snap
key = {'chans': None, 'grade': para['grade'], 'w': para['w']}
key['items'] = [i for i in ['ori', 'blr', 'sob', 'eig'] if para[i]]
slir, slic = ips.get_rect()
labs = [i[slir, slic] for i in imgs]
ori = ImageManager.get(para['img']).imgs
if len(imgs) == 1: ori = [ImageManager.get(para['img']).img]
oris = [i[slir, slic] for i in ori]
IPy.set_info('extract features...')
feat, lab, key = feature.get_feature(oris,
labs,
key,
callback=self.progress)
IPy.set_info('training data...')
self.progress(None, 1)
model = self.classify(para)
model.fit(feat, lab)
IPy.set_info('predict data...')
if preview:
return feature.get_predict(oris,
model,
key,
labs,
callback=self.progress)
if len(imgs) == 1: ips.swap()
outs = feature.get_predict(oris, model, key, callback=self.progress)
nips = ImagePlus(outs, ips.title + 'rst')
nips.range, nips.lut = ips.range, ips.lut
nips.back, nips.chan_mode = ips.back, 0.4
IPy.show_ips(nips)
global model_para
model_para = model, key
def profile(self, body, img):
(x1, y1), (x2, y2) = body[0]
dx, dy = x2-x1, y2-y1
n = max(abs(dx), abs(dy)) + 1
xs = np.linspace(x1, x2, n).round().astype(np.int16)
ys = np.linspace(y1, y2, n).round().astype(np.int16)
msk = (xs>=0) * (xs<img.shape[1])
msk*= (ys>=0) * (ys<img.shape[0])
ix = np.arange(len(xs))
frame = IPy.plot('Profile', 'Profile - Line', 'pixels coordinate', 'value of pixcels')
frame.clear()
if len(img.shape) == 3:
vs = np.zeros((3,len(xs)), dtype=np.int16)
vs[:,msk] = img[ys[msk], xs[msk]].T
frame.add_data(ix, vs[0], (255,0,0), 1)
frame.add_data(ix, vs[1], (0,255,0), 1)
frame.add_data(ix, vs[2], (0,0,255), 1)
else:
vs = np.zeros(len(xs), dtype=np.int16)
vs[msk] = img[ys[msk], xs[msk]]
frame.add_data(ix, vs, (0,0,0), 1)
frame.draw()
def on_run(self, event):
if self.active != event.GetIndex():
return IPy.alert('please double click to activate an item')
code = event.GetKeyCode()
title = self.buf[event.GetIndex()][0]
txt = self.buf[event.GetIndex()][1]
if code == wx.WXK_DELETE:
txt = ''
elif code == wx.WXK_CONTROL:
txt = self.ist(txt, 'Ctrl')
elif code == wx.WXK_ALT:
txt = self.ist(txt, 'Alt')
elif code == wx.WXK_SHIFT:
txt = self.ist(txt, 'Shift')
elif code in range(340, 352):
fs = ['F' + str(i) for i in range(1, 13)]
txt = self.ist(txt, fs[code - 340])
elif code < 100:
txt = self.ist(txt, chr(event.GetKeyCode()))
if len(txt) > 0 and txt[-1] == '-': txt = txt[:-1]
self.buf[event.GetIndex()][1] = txt
self.lst_plgs.RefreshItem(event.GetIndex())
if txt == '': ShotcutManager.rm(title)
ShotcutManager.set(title, txt)
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))
IPy.set_progress(round((n + 1) * 100.0 / len(imgs)))
IPy.set_progress(0)
else:
data = [self.count(ips.get_img(), para)]
IPy.table(ips.title + '-statistic', data, titles)
def run(self, para=None):
xs = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
ys = np.array([30, 57, 93, 105, 110, 117, 108, 87, 52, 39])
l1 = plt.plot(xs, ys, '-o', label='orignal')
l2 = plt.plot(xs, ys + 100, '-o', label='add')
l3 = plt.plot(xs, ys * 2, '-o', label='multiply')
plt.grid()
plt.title('Math Operator')
plt.gca().set_ylim(0, 255)
plt.gca().legend()
plt.show()
ori = np.zeros((10, 30, 30), dtype=np.uint8)
add = np.zeros((10, 30, 30), dtype=np.uint8)
mul = np.zeros((10, 30, 30), dtype=np.uint8)
ori.T[:], add.T[:], mul.T[:] = ys, ys + 100, ys * 2
#area = (area-5) * 8
IPy.show_img([np.hstack(ori)], 'Orignal Line')
IPy.show_img([np.hstack(add)], 'Add 100')
IPy.show_img([np.hstack(mul)], 'Multiply 2')
def run(self, ips, imgs, para=None):
ips1 = ImageManager.get(para['img1'])
ips2 = ImageManager.get(para['img2'])
ips1.snapshot()
sl1, sl2 = ips1.get_nslices(), ips2.get_nslices()
cn1, cn2 = ips1.get_nchannels(), ips2.get_nchannels()
if ips1.dtype != ips2.dtype:
IPy.alert('Two stack must be equal dtype!')
return
elif sl1 > 1 and sl2 > 1 and sl1 != sl2:
IPy.alert('Two stack must have equal slices!')
return
elif cn1 > 1 and cn2 > 1 and cn1 != cn2:
IPy.alert('Two stack must have equal channels!')
return
w, h = ips1.size, ips2.size
w, h = min(w[0], h[0]), min(w[1], h[1])
if sl1 == 1:
bliter.blit(ips1.get_subimg(), ips2.get_subimg(), mode=para['op'])
elif sl1 > 1 and sl2 == 1:
for i in range(sl1):
self.progress(i, sl1)
ss1, se1 = ips1.get_rect()
bliter.blit(ips1.imgs[i][ss1, se1],
ips2.get_subimg(),
mode=para['op'])
elif sl1 > 1 and sl2 > 1:
for i in range(sl1):
self.progress(i, sl1)
ss1, se1 = ips1.get_rect()
ss2, se2 = ips2.get_rect()
bliter.blit(ips1.imgs[i][ss1, se1],
ips2.imgs[i][ss2, se2],
mode=para['op'])
ips1.update = 'pix'
