Fast Marching Method for Image Inpainting

.gitignore

@@ -24,3 +24,6 @@ doc/source/auto_examples/applications/
 doc/source/_static/random.js
 .idea/
 *.log
+*.html
+*.sh
+*.prof

bento.info

@@ -138,6 +138,9 @@ Library:
     Extension: skimage.filter.rank.bilateral_cy
         Sources:
             skimage/filter/rank/bilateral_cy.pyx
+    Extension: skimage.filter._inpaint_fmm
+        Sources:
+            skimage/filter/_inpaint_fmm.pyx
 
 Executable: skivi
     Module: skimage.scripts.skivi

doc/examples/plot_inpaint_fmm.py

@@ -0,0 +1,37 @@
+"""
+===========================
+Structural Image Inpainting
+===========================
+
+Image Inpainting is the process of reconstructing lost or deteriorated parts
+of an image.
+
+In this example we wll show Structural inpainting which uses geometric
+approaches for filling in the missing information in the region which should
+be inpainted. These algorithms focus on the consistency of the geometric
+structure.
+
+"""
+import numpy as np
+import matplotlib.pyplot as plt
+from skimage import data
+from skimage.filter import inpaint
+
+
+image = data.camera()
+paint_region = (slice(240, 260), slice(360, 420))
+paint_region1 = (slice(430, 450), slice(360, 410))
+
+mask = np.zeros_like(image, dtype=np.uint8)
+mask[paint_region] = 1
+mask[paint_region1] = 1
+image[mask == 1] = 0
+
+painted = inpaint.inpaint_fmm(image, mask)
+
+fig, (ax0, ax1) = plt.subplots(ncols=2)
+ax0.set_title("Input Image")
+ax0.imshow(image, cmap=plt.cm.gray)
+ax1.set_title("Inpainted Image")
+ax1.imshow(painted, cmap=plt.cm.gray)
+plt.show()

skimage/filter/__init__.py

@@ -2,14 +2,15 @@
 from .ctmf import median_filter
 from ._canny import canny
 from .edges import (sobel, hsobel, vsobel, scharr, hscharr, vscharr, prewitt,
-                    hprewitt, vprewitt, roberts , roberts_positive_diagonal,
+                    hprewitt, vprewitt, roberts, roberts_positive_diagonal,
                     roberts_negative_diagonal)
 from ._denoise import denoise_tv_chambolle, tv_denoise
 from ._denoise_cy import denoise_bilateral, denoise_tv_bregman
 from ._rank_order import rank_order
 from ._gabor import gabor_kernel, gabor_filter
 from .thresholding import threshold_otsu, threshold_adaptive
 from . import rank
+from .inpaint import inpaint_fmm
 
 
 __all__ = ['inverse',
@@ -38,4 +39,5 @@
            'gabor_filter',
            'threshold_otsu',
            'threshold_adaptive',
-           'rank']
+           'rank',
+           'inpaint_fmm']