current image bleach correction

Author: WeisongZhao

imagepy/menus/Image/Adjust/bleachCorrection_plg.py

@@ -6,83 +6,86 @@
 import numpy as np
 from imagepy import IPy
 from scipy.optimize import curve_fit
-# from .histogram_plg import like
 from skimage.exposure import histogram_matching
 import matplotlib.pyplot as plt 
 import pandas as pd
 
-def simple_ratio(imgs):
-    x,y,z = imgs.shape
-    ref = imgs[:,:,1].sum()/x/y
-    ratio=np.zeros([z,2])
-    ratio[0,0]=ref
-    ratio[0,1]=1
-    for i in range(z-1):
-        ratio[i+1,0]=(imgs[:,:,i+1].sum()/x/y)
-        ratio[i+1,1]=ref/(imgs[:,:,i+1].sum()/x/y)
-        imgs[:,:,i+1] *= ratio[i+1,1].astype(imgs.dtype)
-    return imgs, ratio
-
-def exponential_fit(imgs):
-    imgs_=imgs.astype('float32')
-    x,y,z = imgs.shape
-    t = np.linspace(0, z-1, z)
-    intensity = (imgs.sum(1).sum(0)/x/y)
-    popt, pcov = curve_fit(exponential_func, t, intensity)
-    for i in range(z-1):
-        ratio=exponential_func(0, popt[0], popt[1], popt[2])/ \
-        exponential_func(i+1, popt[0], popt[1], popt[2])
-        imgs_[:,:,i+1] *= ratio
-    return imgs_.astype(imgs.dtype), popt, intensity
-
-def histogram_match(imgs):    
-    x,y,z = imgs.shape
-    for i in range(z-1):
-        imgs[:,:,i+1] = histogram_matching.match_histograms(
-            imgs[:,:,i+1],imgs[:,:,0])
-    print(imgs.dtype)
-    return imgs 
+def copy(imgs):
+    if isinstance(imgs, list):
+        return [np.zeros_like(imgs[0])]
+    else: return np.zeros_like(imgs)
 
 def exponential_func(t, ref, k, offset):
     return ref * np.exp(- k * t) + offset
 
-class Plugin(Simple):
-    title = 'Bleach Correction'
-    asyn = False
-    note = ['8-bit','16-bit','stack']
-    para = {'Method':'Simple ratio', 'Background':0}
-    view = [(list, 'Method', 
-    	['Simple ratio','Exponential fit','Histogram match'], 
-    	str, 'Correction Method',''),
-    (int,'Background',(0,65535),0,'Background intensity','')]
+def simple_ratio(imgs, back=0, inplace=True, out=print):
+    if isinstance(back, int): back=imgs[back]
+    buf = imgs if inplace else copy(imgs)
+    z, (x, y) = len(imgs), imgs[0].shape 
+    values, k0 = np.zeros(z), back.sum()/x/y
+    lim = 255 if imgs[0].dtype.type==np.uint8 else 65535
+    for i in range(z):
+        values[i] = imgs[i].sum()/x/y 
+        np.clip(imgs[i], 0, lim/(k0/values[i]), out=buf[i])
+        np.multiply(buf[i], k0/values[i], out=buf[i], casting='unsafe')
+        out(i, z)
+    return buf, values, k0/values
 
-    def run(self, ips, imgs, para = None):
-        Bconstant = para['Background']
-        imgs_= np.array(imgs).transpose(1,2,0)
-        if Bconstant != 0:
-            for i in range (z):
-                imgs_[:,:,i] -= Bconstant
-            imgs_ = np.maximum(imgs_, 0)
-        print (para['Method'])
-        if para['Method'] == 'Simple ratio':
-            imgs_,ratio = simple_ratio(imgs_)
-            index = ['Frame%s'%i for i in range(1,imgs_.shape[2]+1)]
-            columns = ['Mean value', 'Ratio']
-            IPy.show_table(pd.DataFrame(ratio, index, columns), 'Log of simple ratio')
-        if para['Method'] == 'Exponential fit':
-            [imgs_, popt, intensity] = exponential_fit(imgs_)
-            t = np.linspace(0, imgs_.shape[2]-1, imgs_.shape[2])
-            fitresult = exponential_func(t, popt[0], popt[1], popt[2])
-            plt.plot(t,intensity,'r.',label='Experiment')
-            plt.plot(t,fitresult,'k',label=
-            'Exponential fitted curve\n y=a*exp(-bx)+c\n a=%f\n b=%f\n c=%f'
-                %(popt[0],popt[1],popt[2]))
-            plt.title('Exponential fitted result')
-            plt.legend()
-            plt.show()
-        if para['Method'] == 'Histogram match':
-            imgs_=histogram_match(imgs_)
-        IPy.show_img(imgs_.transpose(2,0,1),'Corrected %s'%ips.title)
+def exponential_fit(imgs, inplace=True, out=print):
+    buf = imgs if inplace else copy(imgs)
+    z, (x, y) = len(imgs), imgs[0].shape 
+    intensity = [i.sum()/x/y for i in imgs]
+    popt, pcov = curve_fit(exponential_func, np.arange(z), intensity)
+    k0 = exponential_func(0, popt[0], popt[1], popt[2])
+    rst = exponential_func(np.arange(z), popt[0], popt[1], popt[2])
+    lim = 255 if imgs[0].dtype.type==np.uint8 else 65535
+    for i in range(z):
+        np.clip(imgs[i], 0, lim/(rst[i]/k0), out=buf[i])
+        np.multiply(buf[i], rst[i]/k0, out=buf[i], casting='unsafe')
+        out(i, z)
+    return buf, popt, intensity, rst
 
+def histogram_match(imgs, back=0, inplace=True, out=print): 
+    if isinstance(back, int): back=imgs[back]   
+    buf = imgs if inplace else copy(imgs)
+    z, (x, y) = len(imgs), imgs[0].shape 
+    for i in range(z):
+        buf[i] = histogram_matching.match_histograms(imgs[i], back)
+        out(i, z)
+    return buf
 
+def plot(popt, intensity, fitresult):
+    t = np.arange(len(intensity))
+    plt.plot(t, intensity,'r.',label='Experiment')
+    plt.plot(t, fitresult,'k',label=
+        'Exponential fitted curve\n y=a*exp(-bx)+c\n a=%f\n b=%f\n c=%f'%tuple(popt))
+    plt.title('Exponential fitted result')
+    plt.legend()
+    plt.show()
 
+def plot_after(popt, intensity, fitresult):
+    import wx
+    wx.CallAfter(plot, popt, intensity, fitresult)
+
+class Plugin(Simple):
+    title = 'Bleach Correction'
+    note = ['8-bit','16-bit','stack']
+    para = {'method':'Simple Ratio', 'new':True}
+    view = [(list, 'method', ['Simple Ratio','Exponential Fit','Histogram Match'], 
+        str, 'Correction Method',''),
+            (bool, 'new', 'show new window'),
+            ('lab', 'lab', 'Correct intensity based on your current slice!')]
+            
+    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)

imagepy/menus/Image/Adjust/enhanceContrast_plg.py

@@ -10,8 +10,8 @@ class Plugin(Filter):
     title = 'Enhance contrast'
     note = ['all', 'auto_msk', 'auto_snap','preview']
     para = {'percentage': 0.3}
-    view = [(float, 'percentage', (0,100), 2, 'Saturated pixels', '%')]
+    view = [(float, 'percentage', (0,100), 4, 'Saturated pixels', '%')]
 
     def run(self, ips, snap, img, para = None):
-        up, down = np.percentile(snap, (0, 100 - para['percentage']/100))
+        up, down = np.percentile(snap, (0, 100 - para['percentage']))
         return exposure.rescale_intensity(snap, in_range=(up, down))

imagepy/menus/Image/Adjust/normalize_plg.py

@@ -15,7 +15,6 @@ class Plugin(Simple):
 
     def run(self, ips, imgs, para = None):
         imgs_= np.array(imgs).astype('float64').transpose(1,2,0)
-        x, y, z = imgs_.shape
         if para['if3d']:
             if para['Sb']:
                 imgs_ -= imgs_.min()

imagepy/menus/Plugins/temporal_plg.py

@@ -7,18 +7,6 @@
 from imagepy.core.manager import ColorManager
 from imagepy import IPy
 
-def color_code(imgs,cmap,start,end):
-    x,y,z = imgs.shape
-    z = end - start
-    imgLUT = np.zeros([x,y,3],'float32')
-    for i in range(start, end):
-        for rgb in range (2):
-            imgLUT[:,:,rgb] += imgs[:,:,i] * \
-            cmap[np.floor((i+1)*256/z).astype(np.int)-1,rgb]
-    for rgb in range(2):
-        imgLUT[:,:,rgb] = 255 * imgLUT[:,:,rgb] / imgLUT[:,:,rgb].max()
-    return imgLUT
-
 def color_code(img, lut):
     idx = np.linspace(0,255,len(img)).astype(int)
     cs = lut[idx].astype(np.uint32)