update docs

Author: MikeHughesKent

benchmark/benchmark.py

@@ -60,7 +60,7 @@
 
 """ Gaussian Filter """
 
-pyb = PyBundle(coreMethod = PyBundle.FILTER, calibImage = calibImg, 
+pyb = PyBundle(coreMethod = PyBundle.FILTER, calibImage = calibImg, crop = True,
                filterSize = 2)
 pyb.calibrate()
 

docs/source/index.rst

@@ -1,15 +1,11 @@
 PyFibreBundle
 ====================================
 PyFibreBundle is a Python package for processing of images captured through optical fibre bundles. 
-It is developed mainly by `Mike Hughes <https://research.kent.ac.uk/applied-optics/hughes/>`_'s lab in the `Applied Optics Group <https://research.kent.ac.uk/applied-optics>`_, School of Physics and Astronomy, University of Kent. Bug reports, contributions and pull requests are welcome. 
 
 The project is hosted on `github <https://github.com/MikeHughesKent/PyFibreBundle/>`_. The latest stable release can be installed via pip::
 
     pip install PyFibreBundle
 
-The package was originally developed for applications in endoscopic microscopy, including fluorescence endomicroscopy and 
-holographic endomicroscopy, but there are also potential applications in endoscopy. 
-
 The package supports fibre core pattern removal by filtering and triangular linear interpolation, background correction and 
 flat fielding, as well as automatic bundle location, cropping and masking. Both monochrome and colour images can be processed.
 The :doc:`PyBundle<pybundle_class>` class is the preferred way to access this functionality, 
@@ -22,6 +18,8 @@ frame rates of over 100 fps can be achieved on mid-level hardware, including cor
 The Numba just-in-time compiler is used to accelerate key portions of code (particularly triangular linear interpolation) and 
 OpenCV is used for fast mosaicing. If the Numba package is not installed then PyFibreBundle falls back on Python interpreted code.
 
+PyFibreBundle is developed mainly by `Mike Hughes <https://research.kent.ac.uk/applied-optics/hughes/>`_'s lab in the `Applied Optics Group <https://research.kent.ac.uk/applied-optics>`_, School of Physics and Astronomy, University of Kent. Bug reports, contributions and pull requests are welcome. 
+
 ^^^^^^^^
 Contents
 ^^^^^^^^

docs/source/pybundle_class.rst

@@ -56,6 +56,7 @@ for a detailed description of each option's meaning.
 * coreSize = 3 (``set_core_size``)
 * gridSize  = 512 (``set_grid_size``)
 * useNumba = True (``set_use_numba``)
+* whiteBalance = False (``set_white_balance``)
 
 **SUPER RESOLUTION Settings:**
 
@@ -69,6 +70,7 @@ for a detailed description of each option's meaning.
 * srDarkFrame = None (``set_sr_dark_frame``)
 * srUseLut = False (``set_sr_use_lut``)
 * srParamValue = None (``set_sr_param_value``)
+
 
 ^^^^^^^^^^^^^^^
 Methods

pyproject.toml

@@ -6,7 +6,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "PyFibreBundle"
-version = "1.3.3"
+version = "1.3.4"
 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/pybundle.py

@@ -588,6 +588,15 @@ def set_edge_filter_shape(self, edgePos, edgeSlope):
 
         self.edgeFilterShape  = (edgePos, edgeSlope)
 
+    def set_white_balance(self, whiteBalance):
+        """ Sets whether each colour channel should be normalised independently
+        when using linear interpolation method.
+        
+        Arguments:
+            whiteBalance   : boolean, each channel normalised independently if True (default is False).
+        """
+        
+        self.whiteBalance = whiteBalance
 
     def set_use_numba(self, useNumba):
         """ Sets whether Numba should be used for JIT compiler acceleration for 

src/pybundle/super_res.py

@@ -147,12 +147,11 @@ def calib_multi_tri_interp(calibImg, imgs, coreSize, gridSize, **kwargs):
         imgsProc = np.zeros((gridSize, gridSize, nImages))
 
         if shifts is None:
-
+            t1 = time.perf_counter()
             for idx in range(nImages):
 
                 imgsProc[:, :, idx] = pybundle.recon_tri_interp(
                     imgs[:, :, idx], singleCalib)
-
             shifts = SuperRes.get_shifts(imgsProc, **kwargs)
 
             # Since we have done the shift estimation on a different sized grid
@@ -170,9 +169,10 @@ def calib_multi_tri_interp(calibImg, imgs, coreSize, gridSize, **kwargs):
             coreYList = np.append(
                 coreYList, singleCalib.coreY + shifts[idx + 1][1])
         #breakpoint()    
-
+        #t1 = time.perf_counter()
         calib = pybundle.init_tri_interp(calibImg, coreXList, coreYList, centreX, centreY,
                                          radius, gridSize, filterSize=filterSize, background=None, normalise=None)
+        #print(time.perf_counter() - t1)    
 
         # We store the number of cores in a single image
         calib.nCores = np.shape(singleCalib.coreX)[0]

test/test_super_res_oop.py

@@ -78,7 +78,8 @@
                     [ -4.60631216, -16.69788157],
                     [ -9.78841333, -30.51681804],
                     [-17.27367059, -27.06208392] ])
-    
+
+
 pyb = PyBundle(coreMethod = PyBundle.TRILIN, superRes = True, gridSize = gridSize, autoContrast = False, 
                calibImage = calibImg, normaliseImage = calibImg)