Example #1
0
    def pre_process(self):
        in_pData = self.get_plugin_in_datasets()[0]
        data = self.get_in_datasets()[0]

        dict_entry = data.get_name() + '_preview_starts'
        shift = self.exp.meta_data.get(dict_entry)

        det_y = data.get_data_dimension_by_axis_label('detector_y')
        det_x = data.get_data_dimension_by_axis_label('detector_x')

        # If the data is cropped then the centre of distortion must be shifted
        # accordingly, e.g if preview is [:, a:b, c:d] then shift is (a, c)
        centre = np.array(
            [self.parameters['centre_x'], self.parameters['centre_y']])
        centre[0] -= shift[det_y]
        centre[1] -= shift[det_x]
        # flipping the values
        centre = centre[::-1]

        # pass two empty arrays of frame chunk size
        unwarp.setcoeff(*self.parameters['polynomial_coeffs'])
        unwarp.setctr(*centre)
        plugin_data_shape = in_pData.get_shape()
        temp_array = np.empty(plugin_data_shape, dtype=np.float32)
        unwarp.setup(temp_array, temp_array)

        self.new_slice = [slice(None)] * 3
        orig_shape = self.get_in_datasets()[0].get_shape()
        for ddir in self.core_dims:
            self.new_slice[ddir] = \
                slice(self.crop, orig_shape[ddir]-self.crop)
Example #2
0
    def pre_process(self):
        in_pData = self.get_plugin_in_datasets()[0]
        data = self.get_in_datasets()[0]

        name = data.get_name()
        shift = self.exp.meta_data.get(name + '_preview_starts')
        step = self.exp.meta_data.get(name + '_preview_steps')

        det_y = data.get_data_dimension_by_axis_label('detector_y')
        det_x = data.get_data_dimension_by_axis_label('detector_x')

        # check stepping as should not be > 1
        self.step_check = \
            True if max([step[i] for i in [det_y, det_x]]) > 1 else False

        # If the data is cropped then the centre of distortion must be shifted
        # accordingly, e.g if preview is [:, a:b, c:d] then shift is (a, c)
        centre = np.array([self.parameters['centre_from_left'],
                           self.parameters['centre_from_top']])

        centre[0] -= shift[det_x]
        centre[1] -= shift[det_y]

        # pass two empty arrays of frame chunk size
        unwarp.setcoeff(*self.parameters['polynomial_coeffs'])
        unwarp.setctr(*centre)
        plugin_data_shape = in_pData.get_shape()
        temp_array = np.empty(plugin_data_shape, dtype=np.float32)
        unwarp.setup(temp_array, temp_array)

        self.new_slice = [slice(None)]*3
        orig_shape = self.get_in_datasets()[0].get_shape()
        for ddir in self.core_dims:
            self.new_slice[ddir] = \
                slice(self.crop, orig_shape[ddir]-self.crop)
Example #3
0
    def pre_process(self):
        centre = np.array(self.parameters['centre'])
        centre[0] -= self.parameters['shift'][0]
        centre[1] -= self.parameters['shift'][1]

        # flipping the values
        centre = centre[::-1]

        #pass two empty arrays of frame chunk size
        unwarp.setcoeff(*self.parameters['polynomial_coeffs'])
        unwarp.setctr(*centre)
        plugin_data_shape = self.get_plugin_in_datasets()[0].get_shape()
        temp_array = np.empty(plugin_data_shape, dtype=np.float32)
        unwarp.setup(temp_array, temp_array)

        self.new_slice = [slice(None)]*3
        orig_shape = self.get_in_datasets()[0].get_shape()
        for ddir in self.core_dir:
            self.new_slice[ddir] = \
                slice(self.crop, orig_shape[ddir]-self.crop)
    def pre_process(self):
        centre = np.array(self.parameters['centre'])
        centre[0] -= self.parameters['shift'][0]
        centre[1] -= self.parameters['shift'][1]

        # flipping the values
        centre = centre[::-1]

        #pass two empty arrays of frame chunk size
        unwarp.setcoeff(*self.parameters['polynomial_coeffs'])
        unwarp.setctr(*centre)
        plugin_data_shape = self.get_plugin_in_datasets()[0].get_shape()
        temp_array = np.empty(plugin_data_shape, dtype=np.float32)
        unwarp.setup(temp_array, temp_array)

        self.slice_list = [slice(None)]*3
        orig_shape = self.get_in_datasets()[0].get_shape()
        for ddir in self.core_dir:
            self.slice_list[ddir] = \
                slice(self.crop, orig_shape[ddir]-self.crop)
    def pre_process(self):
        in_pData = self.get_plugin_in_datasets()[0]
        data = self.get_in_datasets()[0]

        name = data.get_name()
        shift = self.exp.meta_data.get(name + '_preview_starts')
        step = self.exp.meta_data.get(name + '_preview_steps')

        det_y = data.get_data_dimension_by_axis_label('detector_y')
        det_x = data.get_data_dimension_by_axis_label('detector_x')

        # check stepping as should not be > 1
        self.step_check = \
            True if max([step[i] for i in [det_y, det_x]]) > 1 else False

        # If the data is cropped then the centre of distortion must be shifted
        # accordingly, e.g if preview is [:, a:b, c:d] then shift is (a, c)

        centre = np.array([self.parameters['centre_from_left'],
                           self.parameters['centre_from_top']])

        centre[0] -= shift[det_x]
        centre[1] -= shift[det_y]

        # pass two empty arrays of frame chunk size
        unwarp.setcoeff(*self.parameters['polynomial_coeffs'])
        unwarp.setctr(*centre)
        plugin_data_shape = in_pData.get_shape()
        temp_array = np.empty(plugin_data_shape, dtype=np.float32)
        unwarp.setup(temp_array, temp_array)

        self.new_slice = [slice(None)] * 3
        orig_shape = self.get_in_datasets()[0].get_shape()
        for ddir in self.core_dims:
            self.new_slice[ddir] = \
                slice(self.crop, orig_shape[ddir] - self.crop)
Example #6
0
def get_image_array():
   global nbatch
   #inim=np.expand_dims(tifffile.imread("index-1.tif"),0)
   #inim=np.expand_dims(tifffile.imread("p_00600.tif"),0)
   inim=np.expand_dims(tifffile.imread("i32.tif"),0)
   inimt=np.tile(inim,(nbatch,1,1))
   inim3=np.ascontiguousarray(inimt.astype(np.float32))
   print "shape: ",inim3.shape
   outim=np.empty_like(inim3)
   return(inim3,outim)
#end of get_image_array

(inarray,outarray)=get_image_array()

#set the coefficients -- this coudl be rolled into setup with additional args?
unwarp.setcoeff(1,-1e-3,-6e-7,5e-10,-4e-13)
unwarp.setctr(1000,1000)

#call setup routine 
unwarp.setup(inarray,outarray)

unwarp.run(inarray,outarray)
#multiple calls so long as centre and parameters are fixed 
#could be different data solong as it is same type/size/etc
unwarp.run(inarray,outarray)


unwarp.cleanup()

print("Writing the files to /dls/i12/data/2015/cm12163-5/tmp/unwarptest/cython_out")
try: