Plano de Estudo

Analise de imagens de microfósseis

82 cursos 

!pip install graphviz==0.9
!pip install pydot
!pip install seaborn==0.9.0
!conda install scikit-image

import pandas as pd 
import numpy as np 
import matplotlib.pyplot as plt
import matplotlib.cm as cm 
import seaborn as sns 
import statsmodels.api as sm 
import statsmodels.formula.api as smf
from skimage.io import imread
import matplotlib.pyplot as plt
from skimage.util import montage as montage2d

fossil_path = "Gut-PhilElvCropped.tif"
fossil_data = imread(fossil_path)
print('Loading Fossil Data sized {}'.format(fossil_data.shape))


%matplotlib inline
slice_idx = int(fossil_data.shape[0]/2)
fig, (ax1, ax2) = plt.subplots(1,2, figsize = (10, 5))

ax1.imshow(fossil_data[slice_idx], cmap = 'bone')
ax1.set_title('Axial Slices')
_ = ax2.hist(fossil_data[slice_idx].ravel(), 20)
ax2.set_title('Slice Histogram')

%matplotlib inline
from scipy.ndimage.filters import median_filter
# filter the data
filter_fossil_data = median_filter(fossil_data, (3,3,3))

# setup the plot
slice_idx = int(fossil_data.shape[0]/2)
test_slice = fossil_data[slice_idx]
test_filt_slice = filter_fossil_data[slice_idx]
# setup the default image arguments
im_args = dict(cmap = 'bone', vmin = 50, vmax = 70)

fig, (ax1, ax2) = plt.subplots(1,2, figsize = (10, 5))
ax1.imshow(test_slice, **im_args)
ax1.set_title('Unfilt')
_ = ax2.imshow(test_filt_slice, **im_args)
ax2.set_title('Filtrado')

%matplotlib inline
skip_border = 50
skip_middle = 4
fig, (ax1, ax2, ax3) = plt.subplots(1,3, figsize = (14, 5))
ax1.imshow(montage2d(filter_fossil_data[skip_border:-skip_border:skip_middle]),**im_args)
ax1.set_title('Axial Slices')
ax1.axis('off')

ax2.imshow(montage2d(filter_fossil_data.transpose(1,2,0)[skip_border:-skip_border:skip_middle]), **im_args)
ax2.set_title('vertigal Slices')
ax2.axis('off')

ax3.imshow(montage2d(filter_fossil_data.transpose(2,0,1)[skip_border:-skip_border:skip_middle]), **im_args)
ax3.set_title('horizontal Slices')
ax3.axis('off')

%matplotlib inline
fig, (ax1, ax2) = plt.subplots(1,2, figsize = (10, 5))
thresh_fossil_data = filter_fossil_data > 65
thresh_slice = thresh_fossil_data[slice_idx]
ax1.imshow(test_filt_slice, cmap = 'bone')
ax1.set_title('Filtrado')
_ = ax2.imshow(thresh_slice)
ax2.set_title('axial')

%matplotlib inline
from skimage.morphology import binary_closing, ball
closed_fossil_data = binary_closing(thresh_fossil_data, ball(5))
close_slice = closed_fossil_data[slice_idx]
fig, (ax1, ax2) = plt.subplots(1,2, figsize = (10, 5))

ax1.imshow(test_filt_slice, cmap = 'bone')
ax1.set_title('Filtered Slices')
_ = ax2.imshow(close_slice)
ax2.set_title('Slice After Closing')

from mpl_toolkits.mplot3d.art3d import Poly3DCollection
from skimage import measure
def show_3d_mesh(p, threshold):
    verts, faces, _, _ = measure.marching_cubes_lewiner(p, threshold)

    fig = plt.figure(figsize=(10, 10))
    ax = fig.add_subplot(111, projection='3d')
    mesh = Poly3DCollection(verts[faces], alpha=0.9, edgecolor='none', linewidth = 0.1)
    mesh.set_facecolor([.1, 1, .1])
    mesh.set_edgecolor([1, 0, 0])

    ax.add_collection3d(mesh)

    ax.set_xlim(0, p.shape[0])
    ax.set_ylim(0, p.shape[1])
    ax.set_zlim(0, p.shape[2])

    ax.view_init(45, 45)
    return fig

from scipy.ndimage import zoom

fossil_downscale = zoom(closed_fossil_data.astype(np.float32), 0.25)
_ = show_3d_mesh(fossil_downscale, 0.5)
Criado por Afonso de Jesus

529.1k xp

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