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CircleCI update of dev docs (68).
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‎_downloads/1b148e8c6f33b295852ee0a817ab9a7c/plot_otda_mapping_colors_images.ipynb‎

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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"\n# OT for image color adaptation with mapping estimation\n\n\nOT for domain adaptation with image color adaptation [6] with mapping\nestimation [8].\n\n[6] Ferradans, S., Papadakis, N., Peyre, G., & Aujol, J. F. (2014). Regularized\n discrete optimal transport. SIAM Journal on Imaging Sciences, 7(3),\n 1853-1882.\n[8] M. Perrot, N. Courty, R. Flamary, A. Habrard, \"Mapping estimation for\n discrete optimal transport\", Neural Information Processing Systems (NIPS),\n 2016.\n"
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"\n# OT for image color adaptation with mapping estimation\n\n\nOT for domain adaptation with image color adaptation [6] with mapping\nestimation [8].\n\n[6] Ferradans, S., Papadakis, N., Peyre, G., & Aujol, J. F. (2014). Regularized\ndiscrete optimal transport. SIAM Journal on Imaging Sciences, 7(3), 1853-1882.\n[8] M. Perrot, N. Courty, R. Flamary, A. Habrard, \"Mapping estimation for\ndiscrete optimal transport\", Neural Information Processing Systems (NIPS), 2016.\n"
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]
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},
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{

‎_downloads/210aa126ba182b7dc6e44edcda80f1cb/plot_gromov_barycenter.ipynb‎

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},
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"outputs": [],
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"source": [
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"def im2mat(I):\n \"\"\"Converts and image to matrix (one pixel per line)\"\"\"\n return I.reshape((I.shape[0] * I.shape[1], I.shape[2]))\n\n\nsquare = pl.imread('../data/square.png').astype(np.float64)[:, :, 2] / 256\ncross = pl.imread('../data/cross.png').astype(np.float64)[:, :, 2] / 256\ntriangle = pl.imread('../data/triangle.png').astype(np.float64)[:, :, 2] / 256\nstar = pl.imread('../data/star.png').astype(np.float64)[:, :, 2] / 256\n\nshapes = [square, cross, triangle, star]\n\nS = 4\nxs = [[] for i in range(S)]\n\n\nfor nb in range(4):\n for i in range(8):\n for j in range(8):\n if shapes[nb][i, j] < 0.95:\n xs[nb].append([j, 8 - i])\n\nxs = np.array([np.array(xs[0]), np.array(xs[1]),\n np.array(xs[2]), np.array(xs[3])])"
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"def im2mat(I):\n \"\"\"Converts and image to matrix (one pixel per line)\"\"\"\n return I.reshape((I.shape[0] * I.shape[1], I.shape[2]))\n\n\nsquare = pl.imread('../data/square.png').astype(np.float64)[:, :, 2]\ncross = pl.imread('../data/cross.png').astype(np.float64)[:, :, 2]\ntriangle = pl.imread('../data/triangle.png').astype(np.float64)[:, :, 2]\nstar = pl.imread('../data/star.png').astype(np.float64)[:, :, 2]\n\nshapes = [square, cross, triangle, star]\n\nS = 4\nxs = [[] for i in range(S)]\n\n\nfor nb in range(4):\n for i in range(8):\n for j in range(8):\n if shapes[nb][i, j] < 0.95:\n xs[nb].append([j, 8 - i])\n\nxs = np.array([np.array(xs[0]), np.array(xs[1]),\n np.array(xs[2]), np.array(xs[3])])"
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]
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{

‎_downloads/3c6c684eb6e01dc9a4319d2969f85cdd/plot_gromov_barycenter.py‎

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return I.reshape((I.shape[0] * I.shape[1], I.shape[2]))
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square = pl.imread('../data/square.png').astype(np.float64)[:, :, 2] / 256
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cross = pl.imread('../data/cross.png').astype(np.float64)[:, :, 2] / 256
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triangle = pl.imread('../data/triangle.png').astype(np.float64)[:, :, 2] / 256
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star = pl.imread('../data/star.png').astype(np.float64)[:, :, 2] / 256
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square = pl.imread('../data/square.png').astype(np.float64)[:, :, 2]
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cross = pl.imread('../data/cross.png').astype(np.float64)[:, :, 2]
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triangle = pl.imread('../data/triangle.png').astype(np.float64)[:, :, 2]
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star = pl.imread('../data/star.png').astype(np.float64)[:, :, 2]
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shapes = [square, cross, triangle, star]
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‎_downloads/95cfd09ecdd71f649d440fb889e6b1dd/auto_examples_python.zip‎

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‎_downloads/e52e48c884ac6254198b1566143ddadc/auto_examples_jupyter.zip‎

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‎_downloads/fac4a90e210a2a01da86c9309772fe37/plot_otda_mapping_colors_images.py‎

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estimation [8].
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[6] Ferradans, S., Papadakis, N., Peyre, G., & Aujol, J. F. (2014). Regularized
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discrete optimal transport. SIAM Journal on Imaging Sciences, 7(3),
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1853-1882.
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discrete optimal transport. SIAM Journal on Imaging Sciences, 7(3), 1853-1882.
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[8] M. Perrot, N. Courty, R. Flamary, A. Habrard, "Mapping estimation for
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discrete optimal transport", Neural Information Processing Systems (NIPS),
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2016.
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discrete optimal transport", Neural Information Processing Systems (NIPS), 2016.
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"""
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‎_images/sphx_glr_plot_OT_2D_samples_001.png‎

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‎_images/sphx_glr_plot_OT_2D_samples_002.png‎

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‎_images/sphx_glr_plot_OT_2D_samples_003.png‎

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‎_images/sphx_glr_plot_OT_2D_samples_004.png‎

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