Commit 127d9ffa authored by jlaura's avatar jlaura Committed by GitHub
Browse files

Merge pull request #147 from Kelvinrr/ciratefi_integration 

Ciratefi integration
parents e4795b00 00758e5d

autocnet/matcher/ciratefi.py

+16 −8
Changes for autocnet/matcher/ciratefi.py: 16 added lines, 8 removed lines.
Original line number Diff line number Diff line
@@ -481,8 +481,8 @@ def tefi(template, search_image, candidate_pixels, best_scales, best_angles, 


    # check for upsampling

    if upsampling > 1:

        template = zoom(template, upsampling, order=3)

        search_image = zoom(search_image, upsampling, order=3)

        u_template = zoom(template, upsampling, order=3)

        u_search_image = zoom(search_image, upsampling, order=3)



    alpha_list = np.arange(0, 2*math.pi, alpha)

    candidate_pixels *= int(upsampling)
@@ -505,13 +505,13 @@ def tefi(template, search_image, candidate_pixels, best_scales, best_angles, 


        max_coeff = -math.inf

        for j in range(scalesxalphas.shape[0]):

            transformed_template = imresize(template, scalesxalphas[j][0])

            transformed_template = imresize(u_template, scalesxalphas[j][0])

            transformed_template = rotate(transformed_template, scalesxalphas[j][1])



            y_window, x_window = (math.floor(transformed_template.shape[0]/2),

                                  math.floor(transformed_template.shape[1]/2))



            cropped_search = search_image[y-y_window:y+y_window+1, x-x_window:x+x_window+1]

            cropped_search = u_search_image[y-y_window:y+y_window+1, x-x_window:x+x_window+1]



            if(y < y_window or x < x_window or cropped_search.shape < transformed_template.shape or

               cropped_search.shape != transformed_template.shape):
@@ -531,16 +531,24 @@ def tefi(template, search_image, candidate_pixels, best_scales, best_angles, 
    if use_percentile:

        thresh = np.percentile(tefi_coeffs, int(thresh))



    candidate_pixels = candidate_pixels/upsampling

    result_points = candidate_pixels[np.where(tefi_coeffs >= thresh)]

    result_coeffs = tefi_coeffs[np.where(tefi_coeffs >= thresh)]



    results = candidate_pixels[np.where(tefi_coeffs >= thresh)]

    x = result_points[0][1]

    y = result_points[0][0]



    ideal_y = u_search_image.shape[0] / 2

    ideal_x = u_search_image.shape[1] / 2



    if verbose:  # pragma: no cover

        plt.imshow(image_pixels, interpolation='none')

        plt.scatter(y=results[:, 0], x=results[:, 1], c='w', s=80)

        plt.scatter(y=y/upsampling, x=x/upsampling, c='w', s=80)

        plt.show()



    return results

    x = (ideal_x - x)/upsampling

    y = (ideal_y - y)/upsampling



    return x, y, result_coeffs[0]





def ciratefi(template, search_image, upsampling=1, cifi_thresh=95, rafi_thresh=95, tefi_thresh=100,

autocnet/matcher/subpixel.py

+7 −2
Changes for autocnet/matcher/subpixel.py: 7 added lines, 2 removed lines.
Original line number Diff line number Diff line 
import numpy as np



from autocnet.matcher import naive_template

from autocnet.matcher import ciratefi





# TODO: look into KeyPoint.size and perhaps use to determine an appropriately-sized search/template.
@@ -49,7 +51,7 @@ def clip_roi(img, center, img_size): 
    return clipped_img





def subpixel_offset(template, search, **kwargs):

def subpixel_offset(template, search, method='naive', **kwargs):

    """

    Uses a pattern-matcher on subsets of two images determined from the passed-in keypoints and optional sizes to

    compute an x and y offset from the search keypoint to the template keypoint and an associated strength.
@@ -74,7 +76,10 @@ def subpixel_offset(template, search, **kwargs): 
               Strength of the correspondence in the range [-1, 1]

    """



    x_offset, y_offset, strength = naive_template.pattern_match(template, search, **kwargs)

    functions = { 'naive' : naive_template.pattern_match,

                  'ciratefi' : ciratefi.ciratefi}



    x_offset, y_offset, strength = functions[method](template, search, **kwargs)

    return x_offset, y_offset, strength



'''

autocnet/matcher/tests/test_ciratefi.py

+5 −6
Changes for autocnet/matcher/tests/test_ciratefi.py: 5 added lines, 6 removed lines.
Original line number Diff line number Diff line
@@ -142,24 +142,23 @@ class TestCiratefi(unittest.TestCase): 
                print(warn)



            self.assertEqual(len(w), 0)

            self.assertIn((np.floor(self.search.shape[0]/2), np.floor(self.search.shape[1]/2)), pixel)

            self.assertTrue(pixel[0][0] == self.search_center[0] and pixel[0][1] == self.search_center[1])

            print(pixel)

            self.assertTrue(np.equal((.5, .5), (pixel[1], pixel[0])).all())



    def test_ciratefi(self):

        results = ciratefi.ciratefi(self.template, self.search, upsampling=10, cifi_thresh=self.cifi_thresh,

                                    rafi_thresh=self.rafi_thresh, tefi_thresh=self.tefi_thresh,

                                    use_percentile=self.use_percentile, alpha=self.alpha, radii=self.radii)



        self.assertEqual(len(results), 1)

        self.assertTrue(np.equal(results[0], self.search_center).all())

        self.assertEqual(len(results), 3)

        self.assertTrue((np.array(results[1], results[0]) < 1).all())



        results = ciratefi.ciratefi(self.offset_template, self.search, upsampling=self.upsampling,

                                    cifi_thresh=self.cifi_thresh, rafi_thresh=self.rafi_thresh,

                                    tefi_thresh=self.tefi_thresh,

                                    use_percentile=self.use_percentile, alpha=self.alpha, radii=self.radii)



        print(results)

        self.assertTrue(np.equal(results[0], np.add(self.search_center, list(self.offset))).all())





    def tearDown(self):

        pass

notebooks/.ipynb_checkpoints/Ciratefi-checkpoint.ipynb

0 → 100644
+124 −0
Changes for notebooks/.ipynb_checkpoints/Ciratefi-checkpoint.ipynb: 124 added lines, 0 removed lines.
Original line number Diff line number Diff line 
%% Cell type:code id: tags:



``` python

import os

import sys

sys.path.insert(0, os.path.abspath('..'))



from autocnet.examples import get_path

from autocnet.graph.network import CandidateGraph

from autocnet.graph.edge import Edge

from autocnet.matcher.feature import FlannMatcher

from autocnet.matcher import ciratefi





from autocnet.matcher import subpixel as sp

from scipy.misc import imresize

import math

import warnings

import cv2



from bisect import bisect_left



from scipy.ndimage.interpolation import rotate



from IPython.display import display

warnings.filterwarnings('ignore')



%matplotlib inline

%pylab inline

```



%% Cell type:markdown id: tags:



# Create Basic Structures



%% Cell type:code id: tags:



``` python

#Point to the adjacency Graph

adjacency = get_path('three_image_adjacency.json')

basepath = get_path('Apollo15')

cg = CandidateGraph.from_adjacency(adjacency, basepath=basepath)



#Apply SIFT to extract features

cg.extract_features(method='sift', extractor_parameters={'nfeatures':300})



#Match

cg.match_features()



# Perform the symmetry check

cg.symmetry_checks()

# Perform the ratio check

cg.ratio_checks(clean_keys = ['symmetry'])

# Create fundamental matrix

cg.compute_fundamental_matrices(clean_keys = ['symmetry', 'ratio'])



# Step: Compute the homographies and apply RANSAC

cg.compute_homographies(clean_keys=['symmetry', 'ratio'])



# Step: Compute subpixel offsets for candidate points

cg.subpixel_register(clean_keys=['ransac'])



cg.edge[0][2].plot(clean_keys=['symmetry', 'ratio'])

```



%% Cell type:markdown id: tags:



# Do Stuff



%% Cell type:code id: tags:



``` python

from scipy.ndimage.interpolation import zoom

from scipy.stats.stats import pearsonr



figsize(10,10)

e = cg.edge[0][2]

matches = e.matches

clean_keys = ['ratio', 'symmetry']



full_offsets = np.zeros((len(matches), 3))



if clean_keys:

    matches, mask = e.clean(clean_keys)



# Preallocate the numpy array to avoid appending and type conversion

edge_offsets = np.empty((len(matches),3))



# for each edge, calculate this for each keypoint pair

for i, (idx, row) in enumerate(matches.iterrows()):

    s_idx = int(row['source_idx'])

    d_idx = int(row['destination_idx'])

    s_kps = e.source.get_keypoints().iloc[s_idx]

    d_kps = e.destination.get_keypoints().iloc[d_idx]



    s_keypoint = e.source.get_keypoints().iloc[s_idx][['x', 'y']].values

    d_keypoint = e.destination.get_keypoints().iloc[d_idx][['x', 'y']].values



    # Get the template and search windows

    s_template = sp.clip_roi(e.source.geodata, s_keypoint, 9)

    s_template = rotate(s_template, 0)

    s_template = imresize(s_template, 1.)



    d_search = sp.clip_roi(e.destination.geodata, d_keypoint, 21)

    d_search = rotate(d_search, 0)

    d_search = imresize(d_search, 1.)



    print(sp.subpixel_offset(s_template, d_search, method='ciratefi', upsampling=16, alpha=math.pi/4,

                     cifi_thresh=70, rafi_thresh=70, tefi_thresh=100,

                     use_percentile=True, radii=list(range(1,5))))



    break

```



%% Cell type:code id: tags:



``` python



```



%% Cell type:code id: tags:



``` python

```

notebooks/Ciratefi.ipynb

+13 −13
Changes for notebooks/Ciratefi.ipynb: 13 added lines, 13 removed lines.
Original line number Diff line number Diff line 
%% Cell type:code id: tags:



``` python

import os

import sys

sys.path.insert(0, os.path.abspath('..'))



from autocnet.examples import get_path

from autocnet.graph.network import CandidateGraph

from autocnet.graph.edge import Edge

from autocnet.matcher.feature import FlannMatcher

from autocnet.matcher import ciratefi





from autocnet.matcher import subpixel as sp

from scipy.misc import imresize

import math

import warnings

import cv2



from bisect import bisect_left



from scipy.ndimage.interpolation import rotate



from IPython.display import display

warnings.filterwarnings('ignore')



%matplotlib inline

%pylab inline

```



%% Cell type:markdown id: tags:



# Create Basic Structures



%% Cell type:code id: tags:



``` python

#Point to the adjacency Graph

adjacency = get_path('three_image_adjacency.json')

basepath = get_path('Apollo15')

cg = CandidateGraph.from_adjacency(adjacency, basepath=basepath)



#Apply SIFT to extract features

cg.extract_features(method='sift', extractor_parameters={'nfeatures':300})



#Match

cg.match_features()



# Perform the symmetry check

cg.symmetry_checks()

# Perform the ratio check

cg.ratio_checks(clean_keys = ['symmetry'])

# Create fundamental matrix

cg.compute_fundamental_matrices(clean_keys = ['symmetry', 'ratio'])





# Step: Compute the homographies and apply RANSAC

cg.compute_homographies(clean_keys=['symmetry', 'ratio'])



# Step: Compute subpixel offsets for candidate points

cg.subpixel_register(clean_keys=['ransac'])



cg.suppress(clean_keys=['symmetry', 'ratio', 'subpixel'])

cg.edge[0][2].plot(clean_keys=['symmetry', 'ratio'])

```



%% Cell type:markdown id: tags:



# Do Stuff



%% Cell type:code id: tags:



``` python

from scipy.ndimage.interpolation import zoom

from scipy.stats.stats import pearsonr



figsize(10,10)

e = cg.edge[0][2]

matches = e.matches

clean_keys = ['subpixel']

clean_keys = ['ratio', 'symmetry']



full_offsets = np.zeros((len(matches), 3))



if clean_keys:

    matches, mask = e.clean(clean_keys)



# Preallocate the numpy array to avoid appending and type conversion

edge_offsets = np.empty((len(matches),3))



# for each edge, calculate this for each keypoint pair

for i, (idx, row) in enumerate(matches.iterrows()):

    s_idx = int(row['source_idx'])

    d_idx = int(row['destination_idx'])

    s_kps = e.source.get_keypoints().iloc[s_idx]

    d_kps = e.destination.get_keypoints().iloc[d_idx]



    s_keypoint = e.source.get_keypoints().iloc[s_idx][['x', 'y']].values

    d_keypoint = e.destination.get_keypoints().iloc[d_idx][['x', 'y']].values



    # Get the template and search windows

    s_template = sp.clip_roi(e.source.geodata, s_keypoint, 5)

    s_template = sp.clip_roi(e.source.geodata, s_keypoint, 9)

    s_template = rotate(s_template, 0)

    s_template = imresize(s_template, 1.)



    d_search = sp.clip_roi(e.destination.geodata, d_keypoint, 11)

    d_search = sp.clip_roi(e.destination.geodata, d_keypoint, 21)

    d_search = rotate(d_search, 0)

    d_search = imresize(d_search, 1.)



    imshow(s_template, cmap='Greys')

    show()

    imshow(d_search, cmap='Greys')

    show()



    result = ciratefi.ciratefi(s_template, d_search, upsampling=10., alpha=math.pi/4,

    print(sp.subpixel_offset(s_template, d_search, method='ciratefi', upsampling=16, alpha=math.pi/4,

                     cifi_thresh=70, rafi_thresh=70, tefi_thresh=100,

                     use_percentile=True, radii=list(range(1,3)), verbose=True)

    print(result)

                     use_percentile=True, radii=list(range(1,5))))



    break

```



%% Cell type:code id: tags:



``` python



```



%% Cell type:code id: tags:



``` python

```