def run(self, ips, imgs, para = None):
if ips.isarray: imgs = imgs.copy()
else: imgs = [i.copy() for i in imgs]
back = Image(imgs)
back.cn, back.rg = ips.cn, ips.rg
ips.back = back
ips.mode = para['k'] if para['mode']=='ratial' else para['mode']
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.rect
labs = [i[slir, slic] for i in imgs]
ori = ips.back.imgs
oris = [i[slir, slic] for i in ori]
self.app.info('extract features...')
feat, lab, key = feature.get_feature(oris,
labs,
key,
callback=self.progress)
self.app.info('training data...')
# self.progress(None, 1)
model = self.classify(para)
model.fit(feat, lab)
self.app.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 = Image(outs, ips.title + 'rst')
nips.range, nips.lut = ips.range, ips.lut
nips.back, nips.mode = ips.back, 0.4
self.app.show_img(nips)
global model_para
model_para = model, key
def run(self, para=None):
first = generate(np.zeros((para['height'], para['width'])),
para['size'])
imgs = [first.copy() for i in range(para['slice'])]
ips = Image(imgs, para['name'])
ips.tool = Painter(para['size'])
self.app.show_img(ips)
def image_canvas_test():
obj = Image()
obj.img = camera()
obj.cn = 0
frame = wx.Frame(None, title='gray test')
canvas = ICanvas(frame, autofit=True)
canvas.set_img(obj)
frame.Show()
def image_mcanvas_test():
obj = Image()
obj.imgs = [astronaut(), 255 - astronaut()]
obj.cn = 0
frame = wx.Frame(None, title='gray test')
canvas = MCanvas(frame, autofit=True)
canvas.set_img(obj)
frame.Show()
def run(self, ips, imgs, para=None):
if not para['slice']: imgs = [ips.img]
shift = fftshift if para['shift'] else lambda x: x
rst = []
for i in range(len(imgs)):
rst.append(shift(fft2(imgs[i])))
self.progress(i, len(imgs))
ips = Image(rst, '%s-fft' % ips.title)
ips.log = True
self.app.show_img(ips)
def run(self, ips, imgs, para=None):
if '/' in para['predictor']: path = para['predictor']
else: path = self.root + '/' + para['predictor']
model, key = joblib.load(path)
if not para['slice']: imgs = [ips.img]
slir, slic = ips.rect
imgs = [i[slir, slic] for i in imgs]
rst = feature.get_predict(imgs, model, key, callback=self.progress)
if rst is None:
return self.app.alert('image channels dismatch this predictor!')
ips = Image(rst, ips.title + '-mark')
ips.range = ips.get_updown('all', 'one', step=512)
self.app.show_img(ips)
def _show_img(self, img, title=None):
print(img)
canvas = self.canvasnb.add_canvas()
if not isinstance(img, Image):
img = Image(img, title)
App.show_img(self, img, img.title)
canvas.set_img(img)
def run(self, ips, imgs, para=None):
shp = ips.img.shape[:2]
imgs = [
np.zeros(shp, dtype=np.uint8)
for i in range([1, len(imgs)][para['slice']])
]
newips = Image(imgs, ips.title + '-mark')
newips.back = ips
idx = [
'None', 'Max', 'Min', 'Mask', '2-8mix', '4-6mix', '5-5mix',
'6-4mix', '8-2mix'
]
modes = ['set', 'max', 'min', 'msk', 0.2, 0.4, 0.5, 0.6, 0.8]
newips.lut = ColorManager.get(para['cm'])
newips.mode = modes[idx.index(para['mode'])]
#newips.range = (0, para['n'])
self.app.show_img(newips)
def action_demo5():
app = App()
image = Image([camera()], 'camera')
app.show_img(image, 'camera')
Gaussian().start(app)
plt.subplot(121).imshow(camera())
plt.subplot(122).imshow(image.img)
plt.show()
def run(self, ips, imgs, para = None):
if not para['slice']: imgs = [ips.img]
labels = []
for i in range(len(imgs)):
self.progress(i, len(imgs))
con = 1 if para['con']=='4-Connect' else 2
idx = connect.connect_graph(imgs[i], con, para['back'])
idx = connect.mapidx(idx)
cmap = render.node_render(idx, para['colors'], 10)
lut = np.ones(imgs[i].max()+1, dtype=np.uint8)
lut[0] = 0
for j in cmap: lut[j] = cmap[j]
labels.append(lut[imgs[i]])
ips = Image(labels, ips.title+'-render')
ips.range = (0, para['colors'])
self.app.show_img(ips)
def _show_img(self, img, title=None):
cframe = CanvasFrame(self, True)
canvas = cframe.canvas
if not isinstance(img, Image):
img = Image(img, title)
App.show_img(self, img, img.title)
cframe.Bind(wx.EVT_ACTIVATE, self.on_active_img)
cframe.Bind(wx.EVT_CLOSE, self.on_close_img)
canvas.set_img(img)
cframe.SetIcon(self.GetIcon())
cframe.Show()
def run(self, para=None):
ips = self.app.get_img()
if ips == None:
img = np.arange(256 * 30, dtype=np.uint8).reshape((-1, 256))
ips = Image([img], self.title)
ips.lut = self.lut
return self.app.show_img(ips)
if ips.channels != 1:
return self.app.alert(
'only one channel image surport Lookup table!')
ips.lut = self.lut
ips.update()
def run(self, ips, imgs, para=None):
if not para['stack']: imgs = [ips.img]
sli = ips.rect
imgs = [i[sli].copy() for i in imgs]
if ips.isarray: imgs = np.array(imgs)
new = Image(imgs, ips.name + '-duplicate')
if not ips.roi is None:
new.roi = ROI(mark2shp(ips.roi.to_mark()))
offset(new.roi, new.roi.box[0] * -1, new.roi.box[1] * -1)
new.roi.dirty = True
if not ips.back is None and not ips.back.imgs is None:
back = [i[sli].copy() for i in ips.back.imgs]
if ips.isarray: back = np.array(back)
back = Image(back, ips.back.name + '-duplicate')
back.cn, back.rg, back.mode = ips.back.cn, ips.back.rg, ips.back.mode
new.back, new.mode = back, ips.mode
self.app.show_img(back)
self.app.show_img(new)
def object_test():
'''
there is a manager for every type of object.
such as img_manager, we can call app's:
show_img, close_img, active_img, get_img
'''
from sciapp.object import Image
from skimage.data import camera
app = App()
image = Image([camera()], 'camera')
app.show_img(image, 'camera')
# >>> UINT8 512x512 S:1/1 C:0/1 0.25M
print(app.get_img())
# >>> <sciapp.object.image.Image object at 0x000002076A025780>
print(app.img_names())
# >>> ['camera']
app.close_img('camera')
# >>> close image: camera
print(app.img_names())
def complex_test():
frame = wx.Frame(None, title='blend')
canvas = Canvas(frame, autofit=True)
canvas.set_img(fftshift(fft2(camera())))
canvas.set_rg((0, 31015306))
canvas.set_log(True)
frame.Show()
if __name__ == '__main__':
app = wx.App()
#gray_test()
#rgb_test()
#rgb_gray_blend()
#complex_test()
frame = wx.Frame(None, title='blend')
canvas = Canvas(frame, autofit=True)
image = Image()
image.img = camera()
image.pos = (0, 0)
canvas.images.append(image)
image = Image()
image.img = astronaut()
image.pos = (100, 200)
image.cn = (0, 1, 2)
canvas.images.append(image)
frame.Show()
app.MainLoop()