Mosaic bug fix

Author: Michael Hughes

pyproject.toml

@@ -6,7 +6,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "PyFibreBundle"
-version = "1.3.4"
+version = "1.3.5"
 description = "Image processing of images acquired through fibre imaging bundle, including core removal, mosaicing and super-resolution.."
 readme = "README.md"
 authors = [{ name = "Michael Hughes", email = "m.r.hughes@kent.ac.uk" }]

src/pybundle/core_interpolation.py

@@ -413,7 +413,7 @@ def recon_tri_interp(img, calib, **kwargs):
      # Triangular linear interpolation
      if numba and numbaAvailable:
          if calib.mask is not None:
-             maskNumba = np.squeeze(np.reshape(calib.mask, (np.product(np.shape(calib.mask)),1)))
+             maskNumba = np.squeeze(np.reshape(calib.mask, (np.prod(np.shape(calib.mask)),1)))
          else:
              maskNumba = None
          pixelVal = grid_data_numba(calib.baryCoords, cVals, calib.coreIdx, calib.mapping, maskNumba)

src/pybundle/mosaic.py

@@ -107,9 +107,9 @@ def __init__(self, mosaicSize, **kwargs):
         self.resetSharpness = kwargs.get('resetSharpness', None)
 
         # These are created the first time they are needed
-        self.mosaic = []
-        self.mask = []
-        self.blendMask = []
+        self.mosaic = None
+        self.mask = None
+        self.blendMask = None
 
         # Initial values
         self.lastShift = [0,0]
@@ -266,7 +266,7 @@ def __initialise_mosaic(self, img):
         if self.imageType is None:
             self.imageType = img.dtype
 
-        if np.size(self.mask) == 0:
+        if self.mask is None:
             self.mask = pybundle.get_mask(np.zeros([self.imSize,self.imSize]),(self.imSize/2,self.imSize/2,self.cropSize / 2))
 
         if self.col:
@@ -302,6 +302,7 @@ def __insert_into_mosaic(mosaic, img, mask, position):
 
         oldRegion = mosaic[px:px + np.shape(img)[0] , py :py + np.shape(img)[1]]
 
+              
         oldRegion[np.array(mask)] = img[np.array(mask)]
         mosaic[px:px + np.shape(img)[0] , py :py + np.shape(img)[1]] = oldRegion
 
@@ -318,7 +319,7 @@ def __insert_into_mosaic_blended(mosaic, img, mask, blendMask, cropSize, blendDi
            img       : img to insert, 2D/3D numpy array
            mask      : 2D numpy array with values of 1 for pixels to be copied
                        and 0 for pixels not to be copied. Must be same size as img.           
-           blendMask : the cosine window blending mask with weighted pixel values. If passed empty []
+           blendMask : the cosine window blending mask with weighted pixel values. If None
                        this will be created
            cropSize  : size of input image.
            blendDist : number which controls the sptial extent of the blending
@@ -342,7 +343,7 @@ def __insert_into_mosaic_blended(mosaic, img, mask, blendMask, cropSize, blendDi
             blendingMask = np.max(blendingMask,2)
 
         # If first time, create blend mask giving weights to apply for each pixel
-        if blendMask == []:
+        if blendMask is None:
             maskRad = cropSize / 2
             blendImageMask = Mosaic.__cosine_window(np.shape(oldRegion)[0], maskRad, blendDist) 
 

src/pybundle/pybundle.py

@@ -14,6 +14,7 @@
 import numpy as np
 import math
 import time
+import pickle
 
 import cv2 as cv
 import pybundle
@@ -260,8 +261,6 @@ def __process_trilin(self, img):
             if self.srUseLut:
                 if self.srCalibrationLUT is not None and self.srParamValue is not None:
                     calibSR = self.srCalibrationLUT.calibrationSR(self.srParamValue)
-                    print("Taken calibration from LUT")
-                    print(calibSR)
                 else:
                     return None
             elif self.calibrationSR is not None:
@@ -787,7 +786,6 @@ def calibrate_sr(self):
         set of super-res shift images, coreSize and gridSize must have been 
         set prior to calling this.
         """
-        
         if self.srCalibImages is not None or self.srShifts is not None:
             self.calibrationSR = pybundle.SuperRes.calib_multi_tri_interp(
                 self.calibImage, self.srCalibImages,                                                                           
@@ -804,6 +802,23 @@ def calibrate_sr(self):
                 darkFrame = self.srDarkFrame,
                 filterSize = self.filterSize)
 
+    def save_calibration(self, filename):
+        
+        if self.calibration is not None:
+            with open(filename, 'wb') as pickleFile:               
+                pickle.dump(self.calibration, pickleFile)
+
+    def save_sr_calibration(self, filename):
+        if self.calibrationSR is not None:
+            with open(filename, 'wb') as pickleFile:               
+                pickle.dump(self.calibration, pickleFile)
+
+
+    def load_calibration(self, filename):
+        
+        with open(filename, 'rb') as pickleFile:
+            self.calibration = pickle.load(pickleFile)
+
 
     def get_pixel_scale(self):
         """ Returns the scaling factor between the pixel size in the raw image

src/pybundle/super_res.py

@@ -294,7 +294,7 @@ def recon_multi_tri_interp(imgs, calib, numba = True):
         # Triangular linear interpolation
         if numba and numbaAvailable:
             if calib.mask is not None:
-                maskNumba = np.squeeze(np.reshape(calib.mask, (np.product(np.shape(calib.mask)),1)))
+                maskNumba = np.squeeze(np.reshape(calib.mask, (np.prod(np.shape(calib.mask)),1)))
             else:
                 maskNumba = None
             pixelVal = pybundle.grid_data_numba(calib.baryCoords, cVals, calib.coreIdx, calib.mapping, maskNumba)

src/pybundle/utility.py

@@ -29,9 +29,11 @@ def extract_central(img, boxSize = None):
         
     Keyword Arguments:    
         boxSize : size of cropping square, default is largest possible
+        
+    Returns:
+        ndarray, cropped image
     """
 
-
     w = np.shape(img)[0]
     h = np.shape(img)[1]
 
@@ -46,71 +48,74 @@ def extract_central(img, boxSize = None):
     return imgOut
 
 
-def to8bit(img, **kwargs):
+def to8bit(img, minVal = None, maxVal = None):
     """ Returns an 8 bit representation of image. If min and max are specified,
     these pixel values in the original image are mapped to 0 and 255 
     respectively, otherwise the smallest and largest values in the 
     whole image are mapped to 0 and 255, respectively.
     
     Arguments:
-        img    : input image as 2D numpy array
+        img    : ndarray
+                 input image as 2D numpy array
         
    Keyword Arguments:    
-        minVal : optional, pixel value to scale to 0
-        maxVal : optional, pixel value to scale to 255
+        minVal : float
+                 optional, pixel value to scale to 0
+        maxVal : float
+                 optional, pixel value to scale to 255
     """
-    minV = kwargs.get("minVal", None)
-    maxV = kwargs.get("maxVal", None)
-        
-        
+ 
     img = img.astype('float64')
 
-    if minV is None:
-        minV = np.min(img)
+    if minVal is None:
+        minVal = np.min(img)
 
-    img = img - minV
+    img = img - minVal
 
-    if maxV is None:
-        maxV = np.max(img)
+    if maxVal is None:
+        maxVal = np.max(img)
     else:
-        maxV = maxV - minV
+        maxVal = maxVal - minVal
 
-    img = img / maxV * 255
+    img = img / maxVal * 255
     img = img.astype('uint8')
 
     return img
 
 
-def to16bit(img, **kwargs):
+def to16bit(img, minVal = None, maxVal = None):
     """ Returns an 16 bit representation of image. If min and max are specified,
     these pixel values in the original image are mapped to 0 and 2^16 
     respectively, otherwise the smallest and largest values in the 
     whole image are mapped to 0 and 2^16 - 1, respectively.
     
     Arguments:
-        img    : input image as 2D numpy array
+        img    : ndarray
+                 input image as 2D numpy array
         
     Keyword Arguments:    
-        minVal : optional, pixel value to scale to 0
-        maxVal : optional, pixel value to scale to 2^16 - 1
-    """
-    minV = kwargs.get("minVal", None)
-    maxV = kwargs.get("maxVal", None)
-        
+        minVal : float
+                 optional, pixel value to scale to 0
+        maxVal : float
+                 optional, pixel value to scale to 2^16 - 1
+                 
+    Returns:
+        ndarray, 16 bit image             
+    """   
 
     img = img.astype('float64')
 
-    if minV is None:
-        minV = np.min(img)
+    if minVal is None:
+        minVal = np.min(img)
 
-    img = img - minV
+    img = img - minVal
 
-    if maxV is None:
-        maxV = np.max(img)
+    if maxVal is None:
+        maxVal = np.max(img)
     else:
-        maxV = maxV - minV
+        maxVal = maxVal - minVal
 
-    img = img / maxV * (2^16 - 1)
+    img = img / maxVal * (2^16 - 1)
     img = img.astype('uint16')
 
     return img
@@ -123,9 +128,14 @@ def radial_profile(img, centre):
     Returns radial profile as 1D numpy array
 
     Arguments:
-        img    : input image as 2D numpy array
-        centre : centre point for radial profile, tuple of (x,y)        
+        img    : ndarray
+                 input image as 2D numpy array
+        centre : (int, int)
+                 centre point for radial profile, tuple of (x,y)  
+    Returns:
+        ndarray, 1D profile             
     """
+    
     y, x = np.indices((img.shape))
     r = np.sqrt((x - centre[1])**2 + (y - centre[0])**2)
     r = r.astype(int)
@@ -140,18 +150,30 @@ def radial_profile(img, centre):
 
 
 def save_image8(img, filename):
-    """ Saves image as 8 bit tif without scaling"""
+    """ Saves image as 8 bit tif without scaling.
+    
+    Arguments:
+         img      : ndarray, 
+                    input image as 2D numpy array
+                   
+         filename : str
+                    path to save to, folder must exist
+    """
+    
     im = Image.fromarray(img.astype('uint8'))
     im.save(filename)
 
 
 
 def save_image16(img, filename):
     """ Saves image as 16 bit tif without scaling.
-    
+        
     Arguments:
-        img: image as 2D/3D numpy array
-        filname : str, path to file. Folder must exist.
+         img      : ndarray, 
+                    input image as 2D numpy array
+                   
+         filename : str
+                    path to save to, folder must exist
     """
     im = Image.fromarray(img.astype('uint16'))
     im.save(filename)
@@ -160,10 +182,13 @@ def save_image16(img, filename):
 
 def save_image8_scaled(img, filename):
     """ Saves image as 8 bit tif with scaling to use full dynamic range.
-    
+            
     Arguments:
-        img: image as 2D/3D numpy array
-        filname : str, path to file. Folder must exist.        
+         img      : ndarray, 
+                    input image as 2D numpy array
+                   
+         filename : str
+                    path to save to, folder must exist
     """
 
     im = Image.fromarray(to8bit(img))
@@ -172,12 +197,15 @@ def save_image8_scaled(img, filename):
 
 def save_image16_scaled(img, filename):
     """ Saves image as 16 bit tif with scaling to use full dynamic range.
-    
+            
     Arguments:
-        img: image as 2D/3D numpy array
-        filname : str, path to file. Folder must exist.
+         img      : ndarray, 
+                    input image as 2D numpy array
+                   
+         filename : str
+                    path to save to, folder must exist
     """
-    
+        
     im = Image.fromarray(to16bit(img)[0])
     im.save(filename) 
 
@@ -188,7 +216,11 @@ def average_channels(img):
     
     
     Arguments:
-        img: image as 2D/3D numpy array
+        img:    ndarray
+                 image as 2D/3D numpy array
+            
+    Returns:
+        ndarray, averaged image        
     """     
 
     if img.ndim == 3:
@@ -203,16 +235,31 @@ def max_channels(img):
     
     
     Arguments:
-        img: image as 2D/3D numpy array
+        img:   ndarray
+               image as 2D/3D numpy array
+               
+    Returns:
+        ndarray, max value image           
     """     
 
     if img.ndim == 3:
         return np.max(img, 2)
     else:
         return img
 
+    
 def resample(img, factor):
+    """ Resizes an image by a factor.
     
+    Arguments:
+        img    : ndarray
+                 image as 2D numpy array
+        factor : float,
+                 resize factor        
+         
+    Returns:
+        ndarray, resmaple images
+    """    
     h,w = np.shape(img)
     img = cv.resize(img, ( int(w * factor), int(h * factor)))