Demo about effect of the different stiching kernels¶

In [1]:

%matplotlib inline
import matplotlib.pyplot as plt

%matplotlib inline import matplotlib.pyplot as plt

In [2]:

import scipy.misc
import numpy
from nabu.testutils import get_data

raw_data_1 = get_data('brain_phantom.npz')['data']
raw_data_2 = numpy.random.rand(*raw_data_1.shape) *255.0

import scipy.misc import numpy from nabu.testutils import get_data raw_data_1 = get_data('brain_phantom.npz')['data'] raw_data_2 = numpy.random.rand(*raw_data_1.shape) *255.0

/tmp/ipykernel_3269051/1871216464.py:1: DeprecationWarning: scipy.misc is deprecated and will be removed in 2.0.0
  import scipy.misc

In [3]:

from nabu.stitching.overlap import OverlapStitchingStrategy, ZStichOverlapKernel

from nabu.stitching.overlap import OverlapStitchingStrategy, ZStichOverlapKernel

---------------------------------------------------------------------------
ImportError                               Traceback (most recent call last)
Cell In[3], line 1
----> 1 from nabu.stitching.overlap import OverlapStitchingStrategy, ZStichOverlapKernel

ImportError: cannot import name 'ZStichOverlapKernel' from 'nabu.stitching.overlap' (/home/pierre/.venv/py311/lib/python3.11/site-packages/nabu/stitching/overlap.py)

plot the two raw data side by side¶

In [4]:

fig,ax = plt.subplots(2)
ax[0].imshow(raw_data_1)
ax[1].imshow(raw_data_2)

fig,ax = plt.subplots(2) ax[0].imshow(raw_data_1) ax[1].imshow(raw_data_2)

Out[4]:

<matplotlib.image.AxesImage at 0x7efc66727850>

In [5]:

fig = plt.figure()
plt.title("raw data")
plt.subplot(121)
plt.imshow(raw_data_1)
plt.subplot(122)
plt.imshow(raw_data_2)

fig = plt.figure() plt.title("raw data") plt.subplot(121) plt.imshow(raw_data_1) plt.subplot(122) plt.imshow(raw_data_2)

Out[5]:

<matplotlib.image.AxesImage at 0x7efc663c4b10>

In [6]:

def plot_strategy_result(overlap_strategy, img_1, img_2, with_weights=True, extra_params=None):
    strategy = OverlapStitchingStrategy.from_value(overlap_strategy)
    kernel = ZStichOverlapKernel(
        frame_width=img_1.shape[1],
        stitching_strategy=strategy,
        overlap_size=img_1.shape[0],
        extra_params=extra_params,
    )
    stitch = kernel.stitch(img_1, img_2)[0]
    if with_weights:
        w_1 = kernel.weights_img_1
        w_2 = kernel.weights_img_2
    else:
        w_1 = w_2 = None
    
    fig = plt.figure(constrained_layout=True)
    if with_weights:
        axes = fig.subplot_mosaic(
            [
                ["00", "01"],
                ["10", "11"],
                ["2", "2"],
            ],
        )
    else:
        axes = fig.subplot_mosaic(
            [
                ["2", "2"],
            ],
        )
    if w_1 is not None and w_2 is not None:
        plt.title(f"{strategy.value} strategy")
        # plot weights curves
        axes["00"].set_title("image 1 weigths")
        axes["00"].plot(numpy.arange(len(w_1)), w_1, color="red")
        axes["01"].set_title("image 2 weigths")
        axes["01"].plot(numpy.arange(len(w_2)), w_2, color="blue")

        # plot weighted images
        axes["10"].set_title("image 1 with weights applied")
        axes["10"].imshow(img_1*w_1)
        axes["11"].set_title("image 2 with weights applied")
        axes["11"].imshow(img_2*w_2)
    
    # plot stitched images
    axes["2"].set_title("stitched image")
    axes["2"].imshow(stitch)

def plot_strategy_result(overlap_strategy, img_1, img_2, with_weights=True, extra_params=None): strategy = OverlapStitchingStrategy.from_value(overlap_strategy) kernel = ZStichOverlapKernel( frame_width=img_1.shape[1], stitching_strategy=strategy, overlap_size=img_1.shape[0], extra_params=extra_params, ) stitch = kernel.stitch(img_1, img_2)[0] if with_weights: w_1 = kernel.weights_img_1 w_2 = kernel.weights_img_2 else: w_1 = w_2 = None fig = plt.figure(constrained_layout=True) if with_weights: axes = fig.subplot_mosaic( [ ["00", "01"], ["10", "11"], ["2", "2"], ], ) else: axes = fig.subplot_mosaic( [ ["2", "2"], ], ) if w_1 is not None and w_2 is not None: plt.title(f"{strategy.value} strategy") # plot weights curves axes["00"].set_title("image 1 weigths") axes["00"].plot(numpy.arange(len(w_1)), w_1, color="red") axes["01"].set_title("image 2 weigths") axes["01"].plot(numpy.arange(len(w_2)), w_2, color="blue") # plot weighted images axes["10"].set_title("image 1 with weights applied") axes["10"].imshow(img_1*w_1) axes["11"].set_title("image 2 with weights applied") axes["11"].imshow(img_2*w_2) # plot stitched images axes["2"].set_title("stitched image") axes["2"].imshow(stitch)

mean strategy¶

In [7]:

plot_strategy_result(
    overlap_strategy="mean",
    img_1=raw_data_1,
    img_2=raw_data_2,
)

plot_strategy_result( overlap_strategy="mean", img_1=raw_data_1, img_2=raw_data_2, )

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
Cell In[7], line 1
----> 1 plot_strategy_result(
      2     overlap_strategy="mean",
      3     img_1=raw_data_1,
      4     img_2=raw_data_2,
      5 )

Cell In[6], line 2, in plot_strategy_result(overlap_strategy, img_1, img_2, with_weights, extra_params)
      1 def plot_strategy_result(overlap_strategy, img_1, img_2, with_weights=True, extra_params=None):
----> 2     strategy = OverlapStitchingStrategy.from_value(overlap_strategy)
      3     kernel = ZStichOverlapKernel(
      4         frame_width=img_1.shape[1],
      5         stitching_strategy=strategy,
      6         overlap_size=img_1.shape[0],
      7         extra_params=extra_params,
      8     )
      9     stitch = kernel.stitch(img_1, img_2)[0]

File /usr/lib/python3.11/enum.py:789, in EnumType.__getattr__(cls, name)
    787     return cls._member_map_[name]
    788 except KeyError:
--> 789     raise AttributeError(name) from None

AttributeError: from_value

closest strategy¶

In [8]:

plot_strategy_result(
    overlap_strategy="closest",
    img_1=raw_data_1,
    img_2=raw_data_2,
)

plot_strategy_result( overlap_strategy="closest", img_1=raw_data_1, img_2=raw_data_2, )

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
Cell In[8], line 1
----> 1 plot_strategy_result(
      2     overlap_strategy="closest",
      3     img_1=raw_data_1,
      4     img_2=raw_data_2,
      5 )

Cell In[6], line 2, in plot_strategy_result(overlap_strategy, img_1, img_2, with_weights, extra_params)
      1 def plot_strategy_result(overlap_strategy, img_1, img_2, with_weights=True, extra_params=None):
----> 2     strategy = OverlapStitchingStrategy.from_value(overlap_strategy)
      3     kernel = ZStichOverlapKernel(
      4         frame_width=img_1.shape[1],
      5         stitching_strategy=strategy,
      6         overlap_size=img_1.shape[0],
      7         extra_params=extra_params,
      8     )
      9     stitch = kernel.stitch(img_1, img_2)[0]

File /usr/lib/python3.11/enum.py:789, in EnumType.__getattr__(cls, name)
    787     return cls._member_map_[name]
    788 except KeyError:
--> 789     raise AttributeError(name) from None

AttributeError: from_value

linear weights¶

In [9]:

plot_strategy_result(
    overlap_strategy="linear weights",
    img_1=raw_data_1,
    img_2=raw_data_2,
)

plot_strategy_result( overlap_strategy="linear weights", img_1=raw_data_1, img_2=raw_data_2, )

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
Cell In[9], line 1
----> 1 plot_strategy_result(
      2     overlap_strategy="linear weights",
      3     img_1=raw_data_1,
      4     img_2=raw_data_2,
      5 )

Cell In[6], line 2, in plot_strategy_result(overlap_strategy, img_1, img_2, with_weights, extra_params)
      1 def plot_strategy_result(overlap_strategy, img_1, img_2, with_weights=True, extra_params=None):
----> 2     strategy = OverlapStitchingStrategy.from_value(overlap_strategy)
      3     kernel = ZStichOverlapKernel(
      4         frame_width=img_1.shape[1],
      5         stitching_strategy=strategy,
      6         overlap_size=img_1.shape[0],
      7         extra_params=extra_params,
      8     )
      9     stitch = kernel.stitch(img_1, img_2)[0]

File /usr/lib/python3.11/enum.py:789, in EnumType.__getattr__(cls, name)
    787     return cls._member_map_[name]
    788 except KeyError:
--> 789     raise AttributeError(name) from None

AttributeError: from_value

cosinus weights¶

In [10]:

plot_strategy_result(
    overlap_strategy="cosinus weights",
    img_1=raw_data_1,
    img_2=raw_data_2,
)

plot_strategy_result( overlap_strategy="cosinus weights", img_1=raw_data_1, img_2=raw_data_2, )

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
Cell In[10], line 1
----> 1 plot_strategy_result(
      2     overlap_strategy="cosinus weights",
      3     img_1=raw_data_1,
      4     img_2=raw_data_2,
      5 )

Cell In[6], line 2, in plot_strategy_result(overlap_strategy, img_1, img_2, with_weights, extra_params)
      1 def plot_strategy_result(overlap_strategy, img_1, img_2, with_weights=True, extra_params=None):
----> 2     strategy = OverlapStitchingStrategy.from_value(overlap_strategy)
      3     kernel = ZStichOverlapKernel(
      4         frame_width=img_1.shape[1],
      5         stitching_strategy=strategy,
      6         overlap_size=img_1.shape[0],
      7         extra_params=extra_params,
      8     )
      9     stitch = kernel.stitch(img_1, img_2)[0]

File /usr/lib/python3.11/enum.py:789, in EnumType.__getattr__(cls, name)
    787     return cls._member_map_[name]
    788 except KeyError:
--> 789     raise AttributeError(name) from None

AttributeError: from_value

image minimum divergence¶

This method will not applied a simple predetermined weights on the two images.

It split the two images into two parts: high frequency and low frequency.

The two low frequency parts will be stitched using a 'sinusoidal' / cosinus weights approach. When the two high frequency part will be stitched by taking the lower divergent pixels

In [11]:

plot_strategy_result(
    overlap_strategy="image minimum divergence",
    img_1=raw_data_1,
    img_2=raw_data_2,
    with_weights=False,
    extra_params={
        "frequency_threshold": 5,
    },
)
# uncomment to compare vs 'raw' sinusoidal
# plot_strategy_result(
#     overlap_strategy="cosinus weights",
#     img_1=raw_data_1,
#     img_2=raw_data_2,
#     with_weights=False,
# )

plot_strategy_result( overlap_strategy="image minimum divergence", img_1=raw_data_1, img_2=raw_data_2, with_weights=False, extra_params={ "frequency_threshold": 5, }, ) # uncomment to compare vs 'raw' sinusoidal # plot_strategy_result( # overlap_strategy="cosinus weights", # img_1=raw_data_1, # img_2=raw_data_2, # with_weights=False, # )

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
Cell In[11], line 1
----> 1 plot_strategy_result(
      2     overlap_strategy="image minimum divergence",
      3     img_1=raw_data_1,
      4     img_2=raw_data_2,
      5     with_weights=False,
      6     extra_params={
      7         "frequency_threshold": 5,
      8     },
      9 )
     10 # uncomment to compare vs 'raw' sinusoidal
     11 # plot_strategy_result(
     12 #     overlap_strategy="cosinus weights",
   (...)     15 #     with_weights=False,
     16 # )

Cell In[6], line 2, in plot_strategy_result(overlap_strategy, img_1, img_2, with_weights, extra_params)
      1 def plot_strategy_result(overlap_strategy, img_1, img_2, with_weights=True, extra_params=None):
----> 2     strategy = OverlapStitchingStrategy.from_value(overlap_strategy)
      3     kernel = ZStichOverlapKernel(
      4         frame_width=img_1.shape[1],
      5         stitching_strategy=strategy,
      6         overlap_size=img_1.shape[0],
      7         extra_params=extra_params,
      8     )
      9     stitch = kernel.stitch(img_1, img_2)[0]

File /usr/lib/python3.11/enum.py:789, in EnumType.__getattr__(cls, name)
    787     return cls._member_map_[name]
    788 except KeyError:
--> 789     raise AttributeError(name) from None

AttributeError: from_value

higher signal¶

This method will not applied predetermine weights on the two images.

It will pick pixel on the image having the higher signal.

A use case is that if there is some artefacts on images which creates stripes (from scintillator artefacts for example) it could be removed from this method

In [12]:

raw_data = get_data("brain_phantom.npz")["data"]
new_dataset_1 = raw_data_1.copy()
new_dataset_1[40:75] = 0.0
new_dataset_1[:, 210:245] = 0.0

new_dataset_2 = raw_data_1.copy()
new_dataset_2[:, 100:120] = 0.0

fig = plt.figure()
plt.title("raw data")
plt.subplot(121)
plt.imshow(new_dataset_1)
plt.subplot(122)
plt.imshow(new_dataset_2)

plot_strategy_result(
    overlap_strategy="higher signal",
    img_1=new_dataset_1,
    img_2=new_dataset_2,
    with_weights=False,
)

raw_data = get_data("brain_phantom.npz")["data"] new_dataset_1 = raw_data_1.copy() new_dataset_1[40:75] = 0.0 new_dataset_1[:, 210:245] = 0.0 new_dataset_2 = raw_data_1.copy() new_dataset_2[:, 100:120] = 0.0 fig = plt.figure() plt.title("raw data") plt.subplot(121) plt.imshow(new_dataset_1) plt.subplot(122) plt.imshow(new_dataset_2) plot_strategy_result( overlap_strategy="higher signal", img_1=new_dataset_1, img_2=new_dataset_2, with_weights=False, )

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
Cell In[12], line 16
     13 plt.subplot(122)
     14 plt.imshow(new_dataset_2)
---> 16 plot_strategy_result(
     17     overlap_strategy="higher signal",
     18     img_1=new_dataset_1,
     19     img_2=new_dataset_2,
     20     with_weights=False,
     21 )

Cell In[6], line 2, in plot_strategy_result(overlap_strategy, img_1, img_2, with_weights, extra_params)
      1 def plot_strategy_result(overlap_strategy, img_1, img_2, with_weights=True, extra_params=None):
----> 2     strategy = OverlapStitchingStrategy.from_value(overlap_strategy)
      3     kernel = ZStichOverlapKernel(
      4         frame_width=img_1.shape[1],
      5         stitching_strategy=strategy,
      6         overlap_size=img_1.shape[0],
      7         extra_params=extra_params,
      8     )
      9     stitch = kernel.stitch(img_1, img_2)[0]

File /usr/lib/python3.11/enum.py:789, in EnumType.__getattr__(cls, name)
    787     return cls._member_map_[name]
    788 except KeyError:
--> 789     raise AttributeError(name) from None

AttributeError: from_value

In [ ]: