diff --git a/bayes3d/neural/cosypose_baseline/cosypose_setup.sh b/bayes3d/neural/cosypose_baseline/cosypose_setup.sh index 0ada1972..b763a823 100644 --- a/bayes3d/neural/cosypose_baseline/cosypose_setup.sh +++ b/bayes3d/neural/cosypose_baseline/cosypose_setup.sh @@ -12,7 +12,7 @@ else fi unset __conda_setup -# # git clone --recurse-submodules https://github.com/Simple-Robotics/cosypose.git +git clone --recurse-submodules https://github.com/Simple-Robotics/cosypose.git cd cosypose # make sure to change numpy to version 1.19.2 sed -i 's/numpy=1.17.4/numpy=1.19.2/g' environment.yaml diff --git a/bayes3d/viz/meshcatviz.py b/bayes3d/viz/meshcatviz.py index 1be0fb42..04d318ca 100644 --- a/bayes3d/viz/meshcatviz.py +++ b/bayes3d/viz/meshcatviz.py @@ -16,6 +16,10 @@ def setup_visualizer(): VISUALIZER = meshcat.Visualizer() set_background_color([1, 1, 1]) +def get_visualizer(): + global VISUALIZER + return VISUALIZER + def set_background_color(color): VISUALIZER["/Background"].set_property("top_color", color) VISUALIZER["/Background"].set_property("bottom_color", color) diff --git a/scripts/experiments/deeplearning/feature_detection/feature_detector.ipynb b/scripts/experiments/deeplearning/feature_detection/feature_detector.ipynb index 3cd27967..62770175 100644 --- a/scripts/experiments/deeplearning/feature_detection/feature_detector.ipynb +++ b/scripts/experiments/deeplearning/feature_detection/feature_detector.ipynb @@ -31,7 +31,7 @@ "output_type": "stream", "text": [ "You can open the visualizer by visiting the following URL:\n", - "http://127.0.0.1:7003/static/\n" + "http://127.0.0.1:7000/static/\n" ] } ], @@ -125,7 +125,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "52f75ba5c5bc4d1287a276879814eefd", + "model_id": "7f6cf48ce1fc4f459ae5e704707a0be2", "version_major": 2, "version_minor": 0 }, @@ -214,7 +214,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -227,17 +227,17 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 13, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "19e321075ae445fdb420fa10dd0c1923", + "model_id": "e7d4db6b122847ffb80f78f30ee32479", "version_major": 2, "version_minor": 0 }, @@ -296,17 +296,22 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 50, "metadata": {}, "outputs": [], "source": [ "\n", "def get_error_between_patches(slice_centered, patch):\n", - " a = slice_centered.reshape(-1,3)\n", - " b = patch.reshape(-1,3)\n", - " distances = jnp.linalg.norm((a[None, :,] - b[:,None,:]),axis=-1)\n", - " return distances.min(1).sum()\n", + " # a = slice_centered.reshape(-1,3)\n", + " # b = patch.reshape(-1,3)\n", + " # distances = jnp.linalg.norm((a[None, :,] - b[:,None,:]),axis=-1)\n", + " # return distances.min(1).sum()\n", " # return distance\n", + "# def score_images(rendered, observed):\n", + " distances = jnp.linalg.norm(slice_centered - patch, axis=-1)\n", + " width = 0.005\n", + " probabilities_per_pixel = (distances > width/2)\n", + " return probabilities_per_pixel.mean()\n", " \n", "get_error_between_patches_parallel_patches = jax.vmap(get_error_between_patches, in_axes=(None, 0))\n", "\n", @@ -336,7 +341,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 51, "metadata": {}, "outputs": [], "source": [ @@ -349,7 +354,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 52, "metadata": {}, "outputs": [], "source": [ @@ -361,84 +366,16 @@ }, { "cell_type": "code", - "execution_count": 43, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(100, 100, 3)" - ] - }, - "execution_count": 43, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "observed_xyz.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(1059, 13, 13, 3)" - ] - }, - "execution_count": 44, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "patches_centered.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 3.14 ms, sys: 580 µs, total: 3.72 ms\n", - "Wall time: 1.77 ms\n" - ] - } - ], - "source": [ - "%%time\n", - "heatmaps = get_errors_vmap_jit(observed_xyz, patches_centered)[:, filter_size:-filter_size,filter_size:-filter_size]" - ] - }, - { - "cell_type": "code", - "execution_count": 39, + "execution_count": 65, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "(Array(756, dtype=int32), Array(21, dtype=int32), Array(47, dtype=int32))\n", - "1.3818688\n", - "1.3818688\n", - "63 48\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/var/tmp/ipykernel_287634/2716866411.py:17: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). Consider using `matplotlib.pyplot.close()`.\n", - " fig = plt.figure()\n" + "Best Index (Array(619, dtype=int32), Array(0, dtype=int32), Array(0, dtype=int32))\n", + "0.6331361\n", + "97 46\n" ] }, { @@ -447,25 +384,25 @@ "Text(0.5, 1.0, 'hello')" ] }, - "execution_count": 39, + "execution_count": 65, "metadata": {}, "output_type": "execute_result" }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d0fa13dcbd934870b8de1e04781daaa7", + "model_id": "3a6ef367c0984faab03bf08ffc7820c3", "version_major": 2, "version_minor": 0 }, - "image/png": 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", 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o608++URlZWX62c9+pv3796uoqEhLly7Vj3/849A5V199tc4//3z96le/CtvfkXvvvVcNDQ3atGmTzjzzTEnSrFmzNHr0aN1999168803Y3djiApNwHCcaJuAa2trwza/39/qPaqrqxUMBpWRkRG2PyMjQz6fr8Mybt26VTt27NAtt9xyynPq6+t1zz33aObMmUpNTY3wUwCAzvn+978f9vW0adP0ySefqLa2Vs8//7xM09R1112n6urq0JaZmamzzz5br7/+eqffJxgM6pVXXtH06dND4U+Shg4dqm9/+9vatGmTamtrY3ZfiA41gHAcS/ZG9FrN/83Ozg7bX1RUFPN5rMrKynTuuedqypQpbR5vaGjQddddJ8uy9Oijj8b0vQHgRCd3Lzn99NMlSZ999pn27Nkjy7J09tlnt3ltYmJip9/n8OHDOn78uEaPHt3q2NixY2WapiorKzV+/PgISo+uQgCE40TbBFxZWRlW45acnNzq3MGDB8vr9aqqqipsf1VVlTIzM9t9n7q6Oj3zzDP6yU9+0ubxlvC3f/9+/fGPf6T2D0CX8nq9be63LEumacowDP3hD39o87z+/ft3dfHQQwiAcJ3U1NQOQ1dSUpJyc3NVXl6u6dOnS5JM01R5ebnmz5/f7rXr1q2T3+/XjTfe2OpYS/jbs2ePXn/9dQ0aNMj2fQBAtM466yxZlqWRI0dq1KhRUb3WkCFD1LdvX+3atavVsZ07d8rj8bRqgUHPoQ8gHKe7poEpLCzUqlWrtGbNGr3//vuaO3eu6urqQqOCZ82aFTZIpEVZWZmmT5/eKtw1NDToW9/6lt5991399re/VTAYlM/nk8/nUyAQsP+BAIBNV199tbxer+6//35ZlhV2zLIsffLJJ51+La/Xq8svv1z//d//rQ8//DC0v6qqSk899ZQuuugiWjx6EWoA4TjdtRTcjBkzdPjwYS1evFg+n08TJ07Uxo0bQwNDKioq5PGE/w21a9cubdq0Sa+88kqr1ztw4IBeeuklSdLEiRPDjr3++uu6+OKLIyofAETrrLPO0tKlS7Vo0SJ9+OGHmj59uk477TTt27dPL7zwgr73ve/pzjvv7PTrLV26VK+++qouuugi3XbbbUpISNCvf/1r+f1+Pfjgg114J4gUARCO051rAc+fP/+UTb5vvPFGq32jR49u9Vd0i5ycnFMeA4CesnDhQo0aNUoPPfSQ7r//fklNg+Uuv/zyiCeqHz9+vN566y0tWrRIy5cvl2maysvL05NPPskcgL2MYfEbCQ5RW1urtLQ0Xfjf85XQr/XAjY401vn15288opqaGpohAACuRh9AAAAAl6EJGI5jyrA1D6CdawAAiEcEQDhOd/YBBAAgHhEA4TiWZciyEebsXAMAQDyiDyAAAM1KSkqUk5OjlJQU5eXlaevWrT1dJKBLEADhON01ETQAd1m7dq0KCwtVVFSk7du3a8KECSooKNChQ4d6umhAzNEEDMfpTU3Apmnq448/1mmnnSbDIGD2dpZl6ejRo8rKymo1iXc0eA6c5VTPwcqVK3XrrbeGVvspLS3V+vXrtXr1ai1cuLDd1+QZcJau+lngJARAOI5lszavvQBYUlKiFStWyOfzacKECXr44Yc1ZcqUDl/z448/Zm1LB6qsrNQZZ5wRts/uMyDxHDjVic9BIBDQtm3bwpZ39Hg8ys/P1+bNm1td6/f75ff7Q18fOHBA48aN6/pCI6ba+lngFgRAOI4lyc705ae6pKXZp7S0VHl5eSouLlZBQYF27dql9PT0dl/ztNNOkyRdpK8pQYmRFwrdqlEN2qQNoe9bi2ieAemE58C4SglG28+B4YlBrZDRQU1FJ97D8HbwGqbZ7mEr2ME/Pqv96zvj+X/8JerXaE/tMVMj/s+HYc9BdXW1gsFgaKnHFhkZGdq5c2er11i+fHlo1YwT7d+eo9T+7qxRcpK2ngG3IQDC9aJp9mlp6klQ4il/8aMXac4uJzfRRfMMnPh6Ccapn4OYNAt2FAA78R6G4e3ghA4CYAfHpegDYOpp3ROgovmeLFq0SIWFhaGva2trlZ2drdT+nm4rP6Ln5uZ6nlI4TstE0Ha2k7U0++Tn54f2tdfsg/jDMwBJGjx4sLxer6qqqsL2V1VVKTMzs9X5ycnJSk1NDdsAJyEAwnFaBoHY2U7WXrOPz+drdb7f71dtbW3YBmeL9BmQeA7iUVJSknJzc1VeXh7aZ5qmysvLNXXq1B4sGdA1CIBwnJ6cBmb58uVKS0sLbXT8dyeeg/hUWFioVatWac2aNXr//fc1d+5c1dXVhboGAPGEAAjHsSz728kibfZZtGiRampqQltlZWVX3Sa6SaTPgMRzEK9mzJihn//851q8eLEmTpyo9957Txs3bmxVOwzEA9cHQGZ9d7dIm33o9xN/7DT98RzEr/nz52v//v3y+/165513lJeX19NFArqEq0cBRzv1A3pGrCeCLiws1OzZszVp0iRNmTJFxcXFNPu4THc8A5bZ8dxFMZkqpgN/2Pt2l79HR/51+KR2jxeckdvu8Zc/2hbL4gCu5OoAGO3UD+gZsQ6AM2bM0OHDh7V48WL5fD5NnDiRZh+X4RkA4DauDYCRzvreFpb+6RodLdFjWoYMGwGwvUEg8+fP1/z58yN+TcQPngEAbuLaABjprO8SS/90t1Mt0XOqAR0dsXMNAADxyLUB0I5TLf1zst/85je69tpru6FE8allRn03L9EDAEBXcm0AtDv1Q1tL/5zsrLPOYlRgDJyqWb2pBtBOH8BoSwQAQHxw7TQwXTH1g2EYys7O1rRp07q07G4Xy5VAAABwI9fWAEqxnfqhpbaquLhYXm8Hi60jKlbzZuc6AADg8gAYy6kfzjjjDBUXF+vqq6/ugpLiRLGeBgYAALdxdQCUYjP1w+9//3v967/+KzV/ANAJGyvebfd4RxNBd3RcYrJooCOu7QMYS9OmTSP8dScrii1CLBUIAIhHBEA4j90BIBE2AbcsFVhUVKTt27drwoQJKigo0KFDh7roxgAA6B4EQDhOy0TQdrZInLhU4Lhx41RaWqq+fftq9erVXXNjAAB0EwIgXKe2tjZsO3F1lxYtSwXm5+eH9kW6VCAAAL0VARCOE+08gNnZ2UpLSwtty5cvb/Ue7S0V6PP5uuU+AQDoKq4fBQwHstGfL3SdmtYYPnES7+Tk5FiVDAAARyAAwnHs9OdruU5Sm6u4nMzOUoEAADgFARDO0w1LgZy4VOD06dMlfbFUYLTzRiKOWaYks+1jRsc9biyz/YfU8JzitUM6no7qirMvbPf4H/b8ucPXAOB8BEDgFDpaKnDWrFkaPHhwD5cSAIDIEQDhON21FFxHSwVWVFSosbEx4nIAANDTCIBwJjtNwDa0t1TgG2+8odraWj399NPdUxgAAGKEAAjH6a4aQAAA4hUBEM7TDYNAAACIZ0wEDQAA4DLUAMKBjObNznUAAIAACOehCRhoWwfzCEqyN4t6d7M6mu8QQLQIgHAeAiAAAFEhAMJ5olwLGAAAt2MQCAAAgMtQAwjHsSx73Zic0PUJACC9cjxR9VZip8//er/jXVia+EQAhPPQBxAA4s6+hmOh//+Py2+U+bFPkmR18Ne7YRg6e+cflWSY8kg6I6GPEg1vVxY1LhAA4Tz0AQSAuPJR4zHNm3ClrIbGpuYa8xTh78SR7h4jdM6C0ZfIMAzJ41Hu20d1/5C/ymvQy609fDoAAKDHbPMH9L3zrpQV7GD6n5OnOTKtNqc+2nZhqr6x58oYljA+UQMIxzGsps3OdQCAXsi0JNNsrv1rCoJhtX+dmeNSarrWQ91WZ/Ap9YDz567UhPkP9XQxnMuKYjvJkiVLZBhG2DZmzJjuuAv0Et32DFhmxxuA1joKf83HTwyM5oygrpz8NV05+Wsa//BtXVk6x6IGsBtMnfFzGaZkeQ2ZCZI30ZCRYOn8uStlJhgyEyUzUfr7zxb0dFGdIcZ9AMePH6/XXnst9HVCAv8s3IZnAOhZJ/f1C33d6Zo/K9QnUJKsY3VNf/Nblkas3qsrfne9ascN0J+LS2NT4DjAT7ku9pUrfqZEjyGrOehZhiGPYUmGIdOQDMOSx2h6aMff/ZD+/iAhsEMxHgWckJCgzMzMaEoEh+MZABzi5JryUwz02PWr0fru+W8raHkkHdFv38/Ul+bu0r8Ufl9vryQESjQBd6lLv7JMXr8pT4Mpo9GSEbTkCVoyTMkINm+mpOavPUFp/D0Paez/pXm4O+3Zs0dZWVk688wzdcMNN6iiouKU5/r9ftXW1oZtcL5IngGJ5wDoEif0/5PUuvavrW4SJ+2zml9j3IiDunfwThUN+YeKhvxDz13wmPaUjNCADf9Q7pK5XVB45yEAdpHL834ij79RngazOfRZ8jRaoaDnCVryBFuCX3go9DSKENieGPYBzMvL0xNPPKGNGzfq0Ucf1b59+zRt2jQdPXq0zbdevny50tLSQlt2dnbMbw/dK9JnQOI5AGJlS31Qs3/9w7B9bc77114f2ZZjJwTGQ0/k6LYDF4S+Pi8pRT+a+KrMz+uVsW5nNEWOGwTALlBw/mIZnzfIaDBlNARlNJjyNDSHv8YvgqARtORplIxGnbCPENihGAbAK664Qtdee63OO+88FRQUaMOGDTpy5IieffbZNt960aJFqqmpCW2VlZUxvz10r0ifAYnnAIiVv/uHafiv3w+v+TuZjQFSg9b9Ta/9c1Tbr8WAK0n0AYy5yycvkefzgNTcr08eyQh65Amasholw/DI8DT3VTWM5jlNTugPKEMtjybp/BS6cCLoAQMGaNSoUdq7d2+bx5OTk5WcnBz5e8MxOnoGJJ4DIFaC8rQdyFpq8zob1izzlP0BJck0LZnBltHCpszODi6JY2SMGPPUfi4j0CCjobFpazTlaWzqA+hpbOoD6Glo/m9jUw3gF//9olawZRt/D7WAJ2uZB9DO1pFjx47pn//8p4YOHdr1N4JeiWcA6GYRhDHLtMK2zryWaVr65FNTxz5rkGkFFQjW69PPqAUkAMbQJZf9VEZ9QPIHpIZGKRhsbgJu3hqbB4S0hMDGtkPgic3Bnsaevqv4duedd+rNN9/Uhx9+qLffflvf/OY35fV6NXPmzJ4uGrqJk56Bk3/5dfjLsC2m2f4Wh+7/+SfyDt0b2k4f9UHY8fr6es2bN0+DBg1S//79dc0116iqqqqHSutiphne/+/kAR5tPONt7rOspgElVstScZJpWTJNSwEFZMmS2cH6wm5AAIyRS18vVOKReqmhQVZDg9TQVAuohsYv+gG21AQ2WKH/thUCW/oKGs39A8+5k1rAMDHsA/jRRx9p5syZGj16tK677joNGjRIW7Zs0ZAhQ7rjTtAL8Ay4w/jRSTrw1xwd+GuOdv55eNixBQsW6He/+53WrVunN998Ux9//LGuvvrqHiqpS3Ww6kd7f+BYHTQXe72GTk/zhu0bkOpt81w3oQ9gDCz9+0x9+uylyvj8k6bwFwzKkmR4vTI8HlkejwyvITVIhmE0p26PTKl55QHJ03SF1Pxf05I8ltX8b8JGfzd0yjPPPNPTRUAP4xlwh4QEKTO96Vde3z5fBIWamhqVlZXpqaee0le/+lVJ0uOPP66xY8dqy5YtuuCCC9p8PXSfTtdun0IwaOmzmmDTF82vdaQ2GG2xHI8AGANP/3Wyhh5rrnIOBmUFGmSYlizDE1paqmWGco/RNMijJQSq+asv/vhpDoGWoZZabHuzHscvQzbXAo55SQA4xZ4PGnTGxH1KSTaUOyEltH/btm1qaGhQfn5+aN+YMWM0fPhwbd68mQDYDdqb9qWz4c8yLRknrARyYneGpl/BhiyPV4lKkun1hhZgcDMCYAwkHkpq6qvXvAC1FQxKpinDY8jyfBEADUlqrgE8OQRaJ45QtQzJsmRZzTWB3Xs7vV8XjgIGEH+mnJ+i1f+RodFnJepgVaOKVnwqSTp69Kh8Pp+SkpI0YMCAsGsyMjLk8/lO+Zp+v19+vz/0NZOBR69pEud2Al9bTbwnj/y1TMn0SN4vft7nbpup9Kv3SLKUlJOtl/70oo4e4zcrn0AMeOoNyZDMpISmh9G0ZDU2yqr3S36/rEBAhr+hqT9goKlPoOeEQSFhfQKb+wV6GyRPgyVv8/yB6BklJSXKyclRSkqK8vLytHXr1p4uEoAIXXFpP117VX+dNy5ZBZf007pVTcv+vfDCC7Zfk8nAu1BbtX+nmg7mhP0nnm8FTZ11y159bcyXlTmzoqkiZvI5euqtZ+T1GvJ4qBCI2wC4ZMmSUPNryzZmzJjQ8ZiO+rIk0yuZfRJkJCc17QoGm0JgoEEKNA8K6SAEnhgATxwcsu03hbH4SOJHDAeBtGft2rUqLCxUUVGRtm/frgkTJqigoECHDh2K1Z0A6AFpzQMAPvjgA2VmZioQCOjIkSNh51RVVbW7PjSTgcdQRyPQO5oL8BTHrUCDzM/rVTV7gn66588qfqZUaZ4+URQ0vsRtAJSk8ePH6+DBg6Ft06ZNoWOxHPVleSQz0VCwT4LUt4+MpMSm/Y2NsgIBWX6/LH+gdQgMNMoTaJSnIShPINgUBBsteQJNW1PtH/3/WummALhy5UrdeuutmjNnjsaNG6fS0lL17dtXq1evjtWdAOgBx+qaAkNmZqZyc3OVmJio8vLy0PFdu3apoqJCU6dOPeVrJCcnKzU1NWxD7Fim9cWqHaFpXU6xNV0Q/gIeQ7t/cb6u/98PVXzXo5qYnKyxSX27/0Z6sbjuA5iQkNDmX3CxHvVlJVoKJkuNfb0yU/vKc7x/U/jz+5v6AwYCJ5xsNfUFNMP7OnjU/BxbkuE1ZCY2DQwxvVRTn6yzkzq3dZ3Uuq9OW6s6BAIBbdu2TYsWLQrt83g8ys/P1+bNmyN/c6AzOqrpaGelg05dLynO/+5v0133V+vfLuunEdkJ+tjXqPt+2tQH8Fvf+pbS0tJ08803q7CwUAMHDlRqaqpuv/12TZ06lQEg3chqHkQpSZ60CML0Cb9HgzW1zc3AQRmeBHkH+3VTKi02pxLXPwn27NmjrKwsnXnmmbrhhhtUUVEhqeNRX5EyE6XGPoYa+nrUMCBFVtpp8vTvJyMhoWkwRzAoq6EpECrQICsQkFpWC/E3yvAHm7aWmsBGU56AKZnS2+vujNnnETeirAHMzs4O67uzfPnyVm9RXV2tYDCojIyMsP0ddQwH0Pt8dLBRN9zm09iL9uv6f/fp9AFNv/oGDx4sSXrooYf0b//2b7rmmmv05S9/WZmZmXr++ed7ssiuUHpkmFY9+A3JsuRJTpanfz95+veTEhJkJCZ2alNSYtMcPwkJ8vTr27T1aRrlfcZ/JuqC977Vw3fZe8VtDWBeXp6eeOIJjR49WgcPHtT999+vadOmaceOHTEf9WWmmGrsIwX6G0qoT5DH308JwaA8piXz+PGm8Nf8l40MQ4ZlNg1Zl2Q0V2EbliXL9MgyvU3zA3oM/Wn93TH+VCBJlZWVYc01rOkKxLenS8NbgmqPmnphwxergaSkpKikpEQlJSXdXTRX+5/akRr83I6mypLEBMnjDU2Z1hknTx9jJCeFpmOTpJQ3/leVl54vTYxlqeNH3AbAK664IvT/5513nvLy8jRixAg9++yz6tPHXifQ5cuX6/7772+13+oTVGN/Sw2fGwr4PfI0JMkI9pfXMOSpTZR1rE5WY/OabsFg08TQRmPTFDE6cfrnBFnNU8n8sXxRq/dBMxv9+ULXSZ3qrzN48GB5vd5WA4M66hgOAOicrwzYqZ/fep3OePz9psn6pKYmXY8hy7KaRu6eQqu5A0/q/mB4vTryb+fqS7kVsS523IjbAHiyAQMGaNSoUdq7d68uu+yy0KivE2sBOzPqq7DwixG5tbW1ys7OVmKfBjU2NgVAT4NHhilJKUpK8CghKVGelGRZx+ulhkDo4Q79lXNCJ1bLa8hK9so/kBqp9kTbB7AzkpKSlJubq/Lyck2fPl2SZJqmysvLNX/+/MjfHAAQZlZqtYbN+7Xu+vx7kiSrOfBlvfCBLBvrUh/65iiZCWrqS29JI2/co+fOei2WRY4rrgmAx44d0z//+U995zvfCRv1dc0110jq/KivtpoL+/YN6Kga1RBIlBE0JMvTPDI4WYkpCUrolyTP8QYZ/kDT+sBBsykAGoasxKaqb7NvkhpPS1JjH6/eeumuLvsc4kI3TQRdWFio2bNna9KkSZoyZYqKi4tVV1enOXPmSJJmzZoV6kMEAIjcpX2C2n7fo2H7/uXT78vT0Lm/2E/8w37D4p8r3dsvlsWLa3EbAO+8805dddVVGjFihD7++GMVFRXJ6/Vq5syZMR/1dXrf4wp4+8vf4JEsb1OwS/AomGSosY9HCad5lfB5sjyBpjn/ZDb3+fN6ZCZ61NjHq2AfjzwNlsxERv32FjNmzNDhw4e1ePFi+Xw+TZw4URs3bgwNDKmoqFBjS9M+ACAm3l5ZavNKwl8k4jYAfvTRR5o5c6Y++eQTDRkyRBdddJG2bNmiIUOGSGoa9eXxeHTNNdfI7/eroKBAv/rVr2y9V0pCowYl1+mwaajBkyzL65WZ5FEwxVDD54YS6g15/d6mFT/Mpr9YLEMyE6RgsqGG/oYa+0qn7w5KljR1xs+1eS2jf08pyj6AkZg/f/4pm3zfeOMN1dbW6umnn7ZRGAAAek7cBsBnnnmm3eOxHPVlWVJacr2MAdInXkv1iUkykxPU2Mcjr1/y+A15GySj0QhVV5sJUjBJauxnqWFgowYOO6LTr6jVZ8UjmvsQ4lS6ow8gYIvVzl8nLD7feSd38D8ZnyUQtbgNgN3p+Yse1PXv3asBKZ8r0RtUTXKKjvVJVsPxJDUGPDICRlP4MyVZhiyvJSvZktUnqH4DPte4QdUal3pQl6b+XUXeW3r6dnq/bqwBBAAgHhEAY2TDl3+pr/3pB0pLqle/xID8/RJUU5+i+oYENTZ61djokSxDHq+phARTp/WpV9/EBuX0/1SDk4/ppxP+S5J02bNNTcB5N/5C7zz5ox6+q17KZg0gARAAgCYEwBja8OVfavqm25TiCap/ol+nJx+X2Tzy1LQMJXhM9UsIaGBinQYl1undI8PlaSPJmAmGzASaOAAAQNcgAMbYixd9MZDk21tuVbKnUcneRvXxBNTH26DTvPVK8x7Xad569R3k1x+rxyjZ06j527+tR/7PU5KaBoaY3p66AwegCRgAgKgQALvQUxes6vCcQklff2u+hqQcC+1rTBHTwbSHAAgAQFQIgL3A0YZkBUyvvvTs/ydrfz+l9Df09wcX9HSxei1GAQMAEB1PTxcA0utf/YUqPjtdDTXJ8jSK8AcAALoUNYC9xD+mL+npIgAAAJcgAMJ56AMI2GZ1NMkyAFcgAMJx6AMIAEB0CIBwJsIcAAC2EQDhPDQBAwAQFUYBAwAAuAw1gHAc+gACABAdAiCchyZgAACiQgCE41ADCABAdAiAcJ4IagCPV/xTn2x5XfW+j9R4rLb1S1mWioqKtGrVKh05ckQXXnihHn30UZ199tmxLTN6zGfWYe3XbtXqMwVU3+p4tz0DnZl/z3D+GuAFw87v+KSOPosOPoeXD/wlghIBaAuDQBDXzIaAktOzlFFwdZvHH3zwQf3yl79UaWmp3nnnHfXr108FBQWqr28dFOBMQTWqv9I0Rm0HE54BAG5EDSCcJ4IawP5njlX/M8dKkg6c/DKWpeLiYt177736xje+IUn6z//8T2VkZOjFF1/U9ddfH7syo8cMNoZqsIY2fXHSc8MzAMCtqAGE47T0AYx0O9m+ffvk8/mUn58f2peWlqa8vDxt3ry5G+8IPYVnAIBbUQMI57E7CvgkPp9PkpSRkRG2PyMjI3TsZH6/X36/P/R1bW3rfoVwDjvPgMRzAMD5qAEEIrB8+XKlpaWFtuzs7J4uEnoAzwEApyMAwnksm9tJMjMzJUlVVVVh+6uqqkLHTrZo0SLV1NSEtsrKyljcEXqInWdA4jkA4HwEQDhOrPoAjhw5UpmZmSovLw/tq62t1TvvvKOpU6e2+d7JyclKTU0N2+Bcdp4BiecAgPPRBxDOE0EfwGDAr8CR6rB9f/vb3zR8+HANHz5cP/zhD7V06VKdffbZGjlypO677z5lZWVp+vTpkqSSkhKtWLFCPp9PEyZM0MMPP6wpU6bE9n7QpRqtRn2uY2H7InkGehXL7OBwJ/6mNxvbPdypefyixTx/QI8jAMJxIlkJpN5XqQ+f/VXYvmnTpmn27Nl64okndPfdd6uurk7f+973dOTIEV100UXauHGjUlJStHbtWhUWFqq0tFR5eXkqLi5WQUGBdu3apfT09C64M3SFWn2q7fpT2L7OPgMAEK8My+rM9PRoS21trdLS0lRTU0MTUAyd6nNt2T92/jJ5kyP/5Rz01+v9R37c6e9XXl6eJk+erEceeUSSZJqmsrOzdfvtt2vhwoVhZbpY31CCkRhxmdC9Gq0GvaH/jvm/2Zg9B9GuBGLEoFdPB7WM3aGrawBrj5o6fdQHMX0OWp6Bz3afqdTT6F3V23XFM+A0PKVwHruDQCL4UycQCGjbtm1h88N5PB7l5+czPxwAwPFoAobz2J0HsPmak+dsS05OVnJycti+6upqBYPBNueH27lzp403BwCg96AGEI5jRLFJUnZ2dtgcbsuXL+/mOwAAoGdRAxiFlu6TrAIQWy2f5ym7p0ZZA1hZWRnW5+Pk2j9JGjx4sLxeb8TzwwEA4AQEwCh88sknksQqAF3k6NGjSktLi/nrdmbetqSkJOXm5qq8vDw0HYhpmiovL9f8+fNjXiYAALoTATAKAwcOlCRVVFR0SVDpLrW1tcrOzm5VM9ZTLMvS0aNHlZWV1ebxSKaBOfm6SBQWFmr27NmaNGmSpkyZouLiYtXV1WnOnDmSpFmzZmnw4MGRFwQAgB5GAIyCx9PUhTItLa1XBKdo9aYVDdoN1FE2AXfWjBkzdPjwYS1evFg+n08TJ07Uxo0bQwNDKioq1NjY/qS6QEQ6mpUr2mliOqODqWQMT/tl2FjxbixLA6CLEADhTN00e+X8+fNP2eT7xhtvqLa2Vk8//XT3FAYAgBghAMJxuqsJGACAeMU0MFFITk5WUVFRm6NInSRe7gMAAHQONYBRSE5O1pIlS3q6GFFz3H10Ux9AAADiFQEQjkMTMAAA0SEAwnmoAQQAICoEQDhOb6oBbFmtpFENBEwHaFSDpHZWmbGp+56DjqaB6fpu3YbVfhlqj5pdXoZo1R5rKmMsn4PQylDHev/9o2ueAachAEahpKREK1askM/n04QJE/Twww9rypQpPV2sNi1ZskT3339/2L7Ro0dr586dkqT6+nr96Ec/0jPPPCO/36+CggL96le/Cs15h7YdPXpUkrRJG3q4JIhErFeZ6bbnoKPfVd3xu6yDfHP6qG4oQ4zE8jloeQZG/J8PY/J66B5dteKUExAAbVq7dq0KCwtVWlqqvLw8FRcXq6CgQLt27VJ6enpPF69N48eP12uvvRb6OiHhi2//ggULtH79eq1bt05paWmaP3++rr76av35z3/uiaK2rxc1AWdlZamyslKnnXaajOZJenvbyipO1RWfY0erzNh18nPAMxA7TnkOsrKy9I9//EPjxo2Lu+97PD7PXfWzwEkIgDatXLlSt956a2hZsNLSUq1fv16rV6/WwoULe7h0bUtISFBmZmar/TU1NSorK9NTTz2lr371q5Kkxx9/XGPHjtWWLVt0wQUXdHdR29eLAqDH49EZZ5zR5rHetLKKk8X6c+yKv/ZP9RzwDMROb38OPB6Phg0bJil+v+/xdl9urflrwTyANgQCAW3btk35+fmhfR6PR/n5+dq8eXMPlqx9e/bsUVZWls4880zdcMMNqqiokCRt27ZNDQ0NYfczZswYDR8+vFfeT0sfQDsbAAAgANpSXV2tYDDYqn9cRkaGfD5fD5WqfXl5eXriiSe0ceNGPfroo9q3b5+mTZumo0ePyufzKSkpSQMGDAi7pjffDwAAsI8mYJe44oorQv9/3nnnKS8vTyNGjNCzzz6rPn369GDJbOhFTcBtYWWV2HDy5+jksvc2TvosnVTWSMTrfbkdNYA2DB48WF6vV1VVVWH7q6qq2uxj1xsNGDBAo0aN0t69e5WZmalAIKAjR46EndNb78ewLNtbd2hZWYUfltFx8ufo5LL3Nk76LJ1U1kjE6325HQHQhqSkJOXm5qq8vDy0zzRNlZeXa+rUqT1Yss47duyY/vnPf2ro0KHKzc1VYmJi2P3s2rVLFRUVvfN+rCg2AABAE7BdhYWFmj17tiZNmqQpU6aouLhYdXV1oVHBvc2dd96pq666SiNGjNDHH3+soqIieb1ezZw5U2lpabr55ptVWFiogQMHKjU1VbfffrumTp3a+0YAq3dNBA0AgBMRAG2aMWOGDh8+rMWLF8vn82nixInauHFjr504+aOPPtLMmTP1ySefaMiQIbrooou0ZcsWDRkyRJL00EMPyePx6JprrgmbCBoAAMQfw3LzOihwlNraWqWlpen8bz8gb1JKxNcHA/X6y1P/VzU1NXE1lxUAAJGiDyAcpzfPA1hSUqKcnBylpKQoLy9PW7du7fo3dbg//elPuuqqq5SVlSXDMPTiiy+GHbcsS4sXL9bQoUPVp08f5efna8+ePT1T2E7gGbDH6c+B07/vS5YskWEYYduYMWNCx+vr6zVv3jwNGjRI/fv31zXXXNNqICSchQAI5+mlg0BalgcsKirS9u3bNWHCBBUUFOjQoUNd+8YOV1dXpwkTJqikpKTN4w8++KB++ctfqrS0VO+884769eungoIC1dfXd3NJO8YzYJ+Tn4N4+b6PHz9eBw8eDG2bNm0KHVuwYIF+97vfad26dXrzzTf18ccf6+qrr+7B0iJaNAHDMVqagHNn2G8C3ra265qA8/LyNHnyZD3yyCOSmkaGZ2dn6/bbb++1ywP2NoZh6IUXXtD06dMlNdX6ZGVl6Uc/+pHuvPNOSU1LF2ZkZOiJJ57Q9ddf34OlbY1nIDac9hzEw/d9yZIlevHFF/Xee++1OlZTU6MhQ4boqaee0re+9S1J0s6dOzV27Fht3ry5Vw4WRMeoAQRiwKnLA/Z2+/btk8/nC/tc09LSlJeX1+s+V56BrtObn4N4+r7Hy3Kh6BwCIJynFzYBO3F5QCdo+eyc8LnyDHSd3vwcxMv3neVC3YdpYOBIzOkHALETV8uFolOoAYTzWJb9rYvEw/KAvVHLZ+eEz5VnoOv05ucgXr/vTl4uFJ1DAITj9MZpYOJhecDeaOTIkcrMzAz7XGtra/XOO+/0us+VZ6Dr9ObnIF6/745eLhSdQhMwECNOWx6wtzh27Jj27t0b+nrfvn167733NHDgQA0fPlw//OEPtXTpUp199tkaOXKk7rvvPmVlZYVGiPYmPAP2Ofk5iIfvezwtF4rOIQDCeewO6LBxTUlJiVasWCGfz6cJEybo4Ycf1pQpU9o8d8aMGdq/f7/mz5+vY8eOyTAMnXHGGdq2bZu+9rWvSZKCwaCWLFmiJ598Uj6fT1lZWbrpppt07733yjAMGzflfO+++64uueSS0NeFhYWSpNmzZ+uJJ57Q3Xffrbq6On3ve9/TkSNHdNFFF2njxo1KSYl8KqCu5rQlInsTJz8H8fB9Z7lQ92EeQDhGyzyAk7+5VAmJkf/Qb2yo1/+8cG+n5wFcu3atZs2apdLSUuXl5am4uFjr1q3Trl27lJ6e3ur8QCCgCy+8UOnp6frxj3+sYcOGaf/+/RowYIAmTJggSVq2bJlWrlypNWvWaPz48Xr33Xc1Z84cPfDAA/rBD34Q8T0BAGAHNYBwnm6qAVy5cqVuvfXWUDNOaWmp1q9fr9WrV7c5uevq1av16aef6u2331ZiYqIkKScnJ+yct99+W9/4xjd05ZVXho4//fTTjls2CgDgbAwCgeNEOwiktrY2bPP7/a3ew87kri+99JKmTp2qefPmKSMjQ+ecc46WLVumYDAYOudf/uVfVF5ert27d0uS/vrXv2rTpk1hUzAAANDVqAGE62RnZ4d9XVRUpCVLloTta29y1507d7b5uh988IH++Mc/6oYbbtCGDRu0d+9e3XbbbWpoaFBRUZEkaeHChaqtrdWYMWPk9XoVDAb1wAMP6IYbbojdDQIA0AECIJzH7px+zddUVlaG9QFMTk6OSbFM01R6eroee+wxeb1e5ebm6sCBA1qxYkUoAD777LP67W9/q6eeekrjx4/Xe++9px/+8IfKysrS7NmzY1IOAAA6QgCE49id06/lmtTU1A4HgdiZ3HXo0KFKTEyU1+sN7Rs7dqx8Pp8CgYCSkpJ01113aeHChaHF688991zt379fy5cvJwACALoNfQDhPN2wFrCdyV0vvPBC7d27V6Zphvbt3r1bQ4cOVVJSkiTp+PHj8njC/9l5vd6wawAA6GoEQOAUCgsLtWrVKq1Zs0bvv/++5s6dGza566xZs7Ro0aLQ+XPnztWnn36qO+64Q7t379b69eu1bNkyzZs3L3TOVVddpQceeEDr16/Xhx9+qBdeeEErV67UN7/5zW6/PwCAe9EEDMeJtgm4szqa3LWioiKsNi87O1svv/yyFixYoPPOO0/Dhg3THXfcoXvuuSd0zsMPP6z77rtPt912mw4dOqSsrCz9+7//uxYvXhz5DQEAYBMTQcMxWiaCvuBrP7E9EfSWDYs7PRE0AADxihpAOE531QACABCvCIBwnm5cCxgAgHjEIBAAAACXoQYQjkMTMAAA0SEAwnlMq2mzcx0AACAAwoHoAwgAQFQIgHAcQzabgGNeEgAAnIlBIAAAAC5DDSCcx7KaNjvXAQAAAiCch1HAAABEhwAI52EQCAAAUSEAwnEMy5JhoznXzjUAAMQjBoEAAAC4DDWAcB6zebNzHQAAIADCeWgCBgAgOgRAOA+DQAAAiAoBEM7DPIAAAESFQSAAAAAuQw0gHIeJoAEAiA4BEM5DEzAAAFEhAMJxDLNps3MdAACgDyAAAIDrUAMI56EJGACAqBAA4TzMAwgAQFQIgHAcVgIBACA6BEA4D03AAABEhUEgQDtKSkqUk5OjlJQU5eXlaevWre2ef+TIEc2bN09Dhw5VcnKyRo0apQ0bNoSO5+TkyDCMVtu8efO6+lYAAAihBhDOY0myM6VLhBWAa9euVWFhoUpLS5WXl6fi4mIVFBRo165dSk9Pb3V+IBDQZZddpvT0dD333HMaNmyY9u/frwEDBoTO+Z//+R8Fg8HQ1zt27NBll12ma6+91sYNAQBgDwEQjtNdfQBXrlypW2+9VXPmzJEklZaWav369Vq9erUWLlzY6vzVq1fr008/1dtvv63ExERJTTV+JxoyZEjY1z/96U911lln6Stf+UpEZQMAIBo0AcN5LH3RDzCireny2trasM3v97d6i0AgoG3btik/Pz+0z+PxKD8/X5s3b26zWC+99JKmTp2qefPmKSMjQ+ecc46WLVsWVuN38ns8+eST+u53vyvDMKL+WAAA6CwCIJzHVvj7YuBIdna20tLSQtvy5ctbvUV1dbWCwaAyMjLC9mdkZMjn87VZrA8++EDPPfecgsGgNmzYoPvuu0+/+MUvtHTp0jbPf/HFF3XkyBHddNNN0X0eAABEiCZguE5lZaVSU1NDXycnJ8fkdU3TVHp6uh577DF5vV7l5ubqwIEDWrFihYqKilqdX1ZWpiuuuEJZWVkxeX8AADqLAAjnMSXZaTFtHjiSmpoaFgDbMnjwYHm9XlVVVYXtr6qqUmZmZpvXDB06VImJifJ6vaF9Y8eOlc/nUyAQUFJSUmj//v379dprr+n555+3cSMAAESHJmA4TssgEDtbZyUlJSk3N1fl5eWhfaZpqry8XFOnTm3zmgsvvFB79+6VaX4xRHn37t0aOnRoWPiTpMcff1zp6em68sorI7x7AACiRwCE80TZB7CzCgsLtWrVKq1Zs0bvv/++5s6dq7q6utCo4FmzZmnRokWh8+fOnatPP/1Ud9xxh3bv3q3169dr2bJlreb4M01Tjz/+uGbPnq2EBCrhAQDdj98+cJ5uWglkxowZOnz4sBYvXiyfz6eJEydq48aNoYEhFRUV8ni++BsqOztbL7/8shYsWKDzzjtPw4YN0x133KF77rkn7HVfe+01VVRU6Lvf/W7k9wAAQAwYlsX6WHCG2tpapaWl6dJxdyrBG/nAjcagX+X/+Llqamo67AMIAEA8owYQzsNawAAARIUACOeJchQwAABuRwCE43TXUnAAAMQrAiCchyZgAACiwjQwAAAALkMNIJzHtCTDRm2eSQ0gAAASARBORBMwAABRIQDCgWwGQBEAAQCQ6AMIAADgOtQAwnloAgYAICoEQDiPaclWcy6DQAAAkEQAhBNZZtNm5zoAAEAAhAPRBAwAQFQYBAIAAOAy1ADCeegDCABAVAiAcB6agAEAiAoBEM5jyWYAjHlJAABwJAIgnIcaQAAAosIgEAAAAJehBhDOY5qSbMzpZzIPIAAAEgEQTkQTMAAAUSEAwnkIgAAARIUACOdhHkAAAKLCIBAAAACXoQYQjmNZpiwr8gEddq4BACAeUQMI57GspubcSDcbfQBLSkqUk5OjlJQU5eXlaevWre2ef+TIEc2bN09Dhw5VcnKyRo0apQ0bNoSdc+DAAd14440aNGiQ+vTpo3PPPVfvvvtuxGUDAMAuagDhPJbNPoARBsC1a9eqsLBQpaWlysvLU3FxsQoKCrRr1y6lp6e3Oj8QCOiyyy5Tenq6nnvuOQ0bNkz79+/XgAEDQud89tlnuvDCC3XJJZfoD3/4g4YMGaI9e/bo9NNPj/x+AACwybAshkbCGWpra5WWlqZL076jBCMp4usbrYDKa/5/1dTUKDU1tcPz8/LyNHnyZD3yyCOSJNM0lZ2drdtvv10LFy5sdX5paalWrFihnTt3KjExsc3XXLhwof785z/rrbfeirj8AADECk3AcB7TtL91UiAQ0LZt25Sfnx/a5/F4lJ+fr82bN7d5zUsvvaSpU6dq3rx5ysjI0DnnnKNly5YpGAyGnTNp0iRde+21Sk9P1/nnn69Vq1bZ/ywAALCBAAjnaZkH0M6mpprEEze/39/qLaqrqxUMBpWRkRG2PyMjQz6fr81iffDBB3ruuecUDAa1YcMG3XffffrFL36hpUuXhp3z6KOP6uyzz9bLL7+suXPn6gc/+IHWrFkTww8IAID20QcQjmOZpizD/ijg7OzssP1FRUVasmRJ1OUyTVPp6el67LHH5PV6lZubqwMHDmjFihUqKioKnTNp0iQtW7ZMknT++edrx44dKi0t1ezZs6MuAwAAnUEAhPNEOQiksrIyrA9gcnJyq1MHDx4sr9erqqqqsP1VVVXKzMxs8+WHDh2qxMREeb3e0L6xY8fK5/MpEAgoKSlJQ4cO1bhx48KuGzt2rP7rv/4r8vsBAMAmmoDhOqmpqWFbWwEwKSlJubm5Ki8vD+0zTVPl5eWaOnVqm6974YUXau/evTJP6Gu4e/duDR06VElJSaFzdu3aFXbd7t27NWLEiFjcGgAAnUIAhPPYmQOwZYtAYWGhVq1apTVr1uj999/X3LlzVVdXpzlz5kiSZs2apUWLFoXOnzt3rj799FPdcccd2r17t9avX69ly5Zp3rx5oXMWLFigLVu2aNmyZdq7d6+eeuopPfbYY2HnAADQ1WgChvNYliQbq3pEOOPRjBkzdPjwYS1evFg+n08TJ07Uxo0bQwNDKioq5PF88TdUdna2Xn75ZS1YsEDnnXeehg0bpjvuuEP33HNP6JzJkyfrhRde0KJFi/STn/xEI0eOVHFxsW644YbI7wcAAJuYBxCO0TIP4CUJ31KC0fY8e+1ptBr0euNznZ4HEACAeEUNIJzHMmWvBpC1gAEAkOgDCAAA4DrUAMJxLNOSZUTec4HeDgAANCEAwnEaLb+t5txGNXRBaQAAcB4CIBwjKSlJmZmZ2uTbYPs1MjMzQ3PyAQDgVowChqPU19crEAjYvj4pKUkpKSkxLBEAAM5DAAQAAHAZRgEDAAC4DAEQAADAZQiAAAAALkMABAAAcBkCIAAAgMsQAAEAAFyGAAgAAOAyBEAAAACXIQACAAC4DAEQAADAZQiAAAAALkMABAAAcBkCIAAAgMsQAAEAAFyGAAgAAOAyBEAAAACXIQACAAC4DAEQAADAZQiAAAAALkMABAAAcBkCIAAAgMsQAAEAAFyGAAgAAOAyBEAAAACXIQACAAC4DAEQAADAZQiAAAAALkMABAAAcBkCIAAAgMsQAAEAAFyGAAgAAOAyBEAAAACXIQACAAC4DAEQAADAZQiAAAAALkMABAAAcBkCIAAAgMsQAAEAAFyGAAgAAOAyBEAAAACXIQACAAC4DAEQAADAZQiAAAAALkMABAAAcBkCIAAAgMsQAAEAAFyGAAgAAOAyBEAAAACXIQACAAC4DAEQAADAZQiAAAAALkMABAAAcBkCIAAAgMsQAAEAAFyGAAgAAOAyBEAAAACXIQACAAC4DAEQAADAZQiAAAAALkMABAAAcBkCIAAAgMsQAAEAAFyGAAgAAOAyBEAAAACXIQACAAC4DAEQAADAZQiAAAAALkMABAAAcBkCIAAAgMsQAAEAAFyGAAgAAOAyBEAAAACXIQACAAC4zP8D8EsfSiFMiw8AAAAASUVORK5CYII=", "text/html": [ "\n", "
\n", "
\n", " Figure\n", "
\n", - " \n", + " \n", "
\n", " " ], @@ -480,7 +417,7 @@ "source": [ "\n", "# observed_xyz = images_subset[5,...,:3]\n", - "i = 90\n", + "i = 4\n", "observed_xyz = test_images[i,...,:3]\n", "heatmaps = get_errors_vmap_jit(observed_xyz, patches_centered)[:, filter_size:-filter_size,filter_size:-filter_size]\n", "# heatmaps = heatmaps.at[heatmaps < 1e-5].set(jnp.inf)\n", @@ -493,8 +430,7 @@ "observed_patch_centered = observed_patch - observed_patch[filter_size, filter_size,:]\n", "\n", "\n", - "print(best_idx)\n", - "print(heatmaps.min())\n", + "print(\"Best Index \", best_idx)\n", "fig = plt.figure()\n", "ax = fig.add_subplot(1, 4, 1)\n", "d = ax.imshow(b.preprocess_for_viz( observed_xyz[filter_size:-filter_size,filter_size:-filter_size][...,2]))\n", @@ -513,7 +449,8 @@ "ax = fig.add_subplot(1, 4, 4)\n", "ax.imshow(images_subset[best_idx[0]][...,2])\n", "print(pixel_coordinates_subset[best_idx[0],0], pixel_coordinates_subset[best_idx[0],1])\n", - "ax.scatter(pixel_coordinates_subset[best_idx[0],0], pixel_coordinates_subset[best_idx[0],1], c='black', alpha=0.3,s=10, marker='x')\n", + "ax.scatter(pixel_coordinates_subset[best_idx[0],0], pixel_coordinates_subset[best_idx[0],1], \n", + " c='black', alpha=0.1,s=10, marker='x')\n", "ax.set_title(\"hello\")\n" ] }, @@ -547,7 +484,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 34, "metadata": {}, "outputs": [ { @@ -556,24 +493,24 @@ "text": [ "(13, 13, 3)\n", "(1059, 13, 13, 3)\n", - "2.668858\n" + "0.8402367\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ca44704586b74d49bede9f191380187a", + "model_id": "e9ca523a4eee4925a390d7fba30138e9", "version_major": 2, "version_minor": 0 }, - "image/png": 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", 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Vv1Zl9eVJkyaZX375xWRnZ5s33njDHHvssUaSef75540x5fumMcaMGjXK1KlTx5SUlESn9e/f37Rp06Zc23/9619Gkpk+fXq5x8r+Xspqady4sdmxY0f08f/85z8VLrOqUPZ3vOfN7/ebefPmxbR9++23jSTz1FNPxUxfunRpzPQXXnjBSDIffPDBXl/3l19+iWt9lVmxYoWRZBYuXGhefvllY1mW+f77740xxtx4443RvnTqqaeazp07xzy3onXZr1+/mP5njDGdO3cu9xlljDG33367kWQWLVpU7rGydVlW3xFHHGECgUD08QceeMBIMp999llc79MYY0aPHm12jwfxLndjjGnTpo2RZN57773otLK/wbS0NPPdd99Fpz/yyCNGklmxYkV02ogRI4wkc+2118a8x/79+xufzxfzebHn+qtoOa9cudJIMk888UR02sKFC8u9rjHG5Ofnm4yMDPPnP/85Znp2drZJT08vN72mYxNwJcvPz5ck1a9f/3fblT2el5cXM73s12CZa6+9VpL03//+V1LpZhGpdPOQ4zgVznvhwoU64ogj1LFjR23fvj16K9vEtGLFipj2p556qjp16hQzrXfv3mrSpImeeeaZ6LSdO3dq2bJluvDCCxN+rbL6r7vuupjXqYwRsZEjR8qyrOj9bt26yRijkSNHRqd5PB4df/zx+vbbb2Oem5aWFvP+cnNz1atXL61duzY6fenSpZIU/dVapmzdlDHG6Pnnn9fAgQNljIlZHv369VNubm7MfKs7+nLV9uUJEyaoadOmysrK0mmnnaaNGzdq6tSp0dGo3ftmfn6+tm/frl69eqmoqEhff/31Puf//PPPq0mTJuX6qaSYvxdJuvDCC9WwYcPo/bIR3z3/XqrSzJkztWzZMi1btkz//ve/dfrpp+vKK6+MjtpJpesoPT1df/zjH2PWUdeuXVWvXr3oOirrWy+//LJCoVCl13rGGWeoUaNGWrBggYwxWrBggYYNG7bX9ruvy7KRzlNPPVXffvutcnNz9/l6zz//vLp06VLhwVZ7rsvLL788ZqS7MtZlvMu9TKdOndSjR4/o/W7dukkq/Vts3bp1uekV1TZmzJjo/8u2qASDQb322mt7rXP35RwKhfTrr7/qsMMOU0ZGRlyfvcuWLVNOTo6GDRsW8z49Ho+6detW7n3WdGwCrmRlX4ZlX557s7cv1w4dOsTcb9++vWzbju7DcOGFF+qf//ynrrzySt18883q06ePzj33XJ133nnRIfT169frq6++KrfPVplt27bF3G/Xrl25Nl6vV0OGDNH8+fMVCATk9/u1aNEihUKhmC/NeF/ru+++k23bMZudJOkPf/hDhc9LxO4fKJKUnp4uSWrVqlW56Xvur/Lyyy/rzjvv1Mcffxyzn9fuH6plte+5nA477LCY+7/88otycnI0Z84czZkzp8Ja91z21Rl9ueLXqqy+fNVVV+n888+XbdvKyMiI7rdY5osvvtDf/vY3vf766+XCdTyhYePGjfrDH/5Qbh+6iuz5N1QWBg/mwQwnnnhizEEgw4YN07HHHqsxY8ZowIAB8vl8Wr9+vXJzc9WsWbMK51G2jk499VQNGTJEkyZN0v3336/TTjtNZ599ti666KKYZby/UlJSdP7552v+/Pk68cQTtWXLFl100UV7bf/uu+9qwoQJWrlypYqKimIey83NjX5m7c3GjRs1ZMiQuGqrinUZ73LfWw2/95lcUW22bZc7qOzwww+XpN89dUtxcbGmTJmiuXPn6scff4zZZzCev5n169dL+m1/3D01aNBgn/OoSQiAlSw9PV3NmzevcD+Q3X366ac65JBD9tmh9vx1l5aWprfeeksrVqzQK6+8oqVLl+qZZ55R79699b///U8ej0eO4+ioo47S9OnTK5znnn+Eu/9q2t3QoUP1yCOPaMmSJTr77LP17LPPqmPHjurSpUu0TaKvVRUq2ul6b9N3/0B4++23NWjQIJ1yyil6+OGH1bx5c6WkpGju3LmaP39+wnWUjWJdfPHFGjFiRIVt4tnHqLqgL//+ax2oDh06qG/fvhU+lpOTo1NPPVUNGjTQ5MmT1b59e6Wmpmrt2rX6v//7v72OmO6vvf0NmX3sdF+VbNvW6aefrgceeEDr169X586d5TiOmjVrpqeeeqrC55SF97ITIq9atUqLFy/Wq6++qiuuuEJ///vftWrVquh+kgfioosu0uzZszVx4kR16dKl3MhzmY0bN6pPnz7q2LGjpk+frlatWsnn8+m///2v7r///hqxLuNd7vuqoar72bXXXqu5c+fqhhtuUI8ePZSeni7LsjR06NC4lnNZmyeffFJZWVnlHo/nx1RNUrveTTUxYMAAPfroo3rnnXd08sknl3v87bff1ubNmzVq1Khyj61fvz5mFGPDhg1yHCdmx1PbttWnTx/16dNH06dP1913361bb71VK1asUN++fdW+fXt98skn6tOnT7kv3USccsopat68uZ555hmdfPLJev3116M7J5eJ97XatGkjx3GioxJl1q1bt9/1Hajnn39eqampevXVV2NGBebOnRvTrqz2TZs2xYxq7XlkXdkRoZFIZK9f7DUNfbm8g9GX33jjDf36669atGiRTjnllOj0siOEd7e3Wtu3b6/Vq1crFAodtAM5Kls4HJZUehCEVPqeXnvtNfXs2XOvYX933bt3V/fu3XXXXXdp/vz5Gj58uBYsWKArr7zygPqTJJ188slq3bq13njjDU2dOnWv7RYvXqxAIKCXXnopZmSsos2Jv7cuP//88wOq90AkutwPlOM4+vbbb6OjfpL0zTffSNLvHoTx3HPPacSIEfr73/8enVZSUqKcnJyYdr+3nCWpWbNmteYz/PewD2AVuPHGG5WWlqZRo0bp119/jXlsx44duvrqq1WnTh3deOON5Z47c+bMmPsPPfSQpNJzZJU9f09lJ40t24R5wQUX6Mcff9Sjjz5arm1xcbEKCwvjeh+2beu8887T4sWL9eSTTyocDsdsMkvktcrqf/DBB2PaJPMKBh6PR5ZlxZxWY/PmzeWOLO3Xr5+k0pO07q5s3ew+vyFDhuj555+v8MP6l19+qaTKDx76cvnXOhh9uWykZPeRkWAwWK4PSlLdunUr3Lw1ZMgQbd++Xf/4xz/KPZbMkb14hUIh/e9//5PP54seoXnBBRcoEonojjvuKNc+HA5Hv+h37txZ7j3u2bfq1KkjSeXCQbwsy9KDDz6oCRMm6JJLLtlru4rWZW5ubrkfmlLpuqyoniFDhuiTTz7RCy+8UO6xg7Eu413ulWn3fmuM0T/+8Q+lpKSoT58+e32Ox+MptzweeuihmM94qXQ5S+XXfb9+/dSgQQPdfffdFe47WhM/w38PI4BVoEOHDnr88cc1fPhwHXXUUeWunrB9+3Y9/fTT5fYhkkp/4Q8aNEh/+tOftHLlyuipJso2VU2ePFlvvfWW+vfvrzZt2mjbtm16+OGH1bJly+gIzSWXXKJnn31WV199tVasWKGePXsqEono66+/1rPPPqtXX301Zl+b33PhhRfqoYce0oQJE3TUUUeVOxN8vK91zDHHaNiwYXr44YeVm5urk046ScuXL6+U81Ptr/79+2v69On605/+pIsuukjbtm3TzJkzddhhh8Vs9uzatauGDBmiGTNm6Ndff42eBqbsF+nuvybvuecerVixQt26ddOf//xnderUSTt27NDatWv12muvVRh6qjP6cnL68kknnaSGDRtqxIgRuu6662RZlp588skKv+y7du2qZ555RuPGjdMJJ5ygevXqaeDAgbr00kv1xBNPaNy4cXr//ffVq1cvFRYW6rXXXtM111yjwYMHV1q9lWHJkiXRg1u2bdum+fPna/369br55pujuxeceuqpGjVqlKZMmaKPP/5YZ5xxhlJSUrR+/XotXLhQDzzwgM477zw9/vjjevjhh3XOOeeoffv2ys/P16OPPqoGDRrorLPOklS6u0CnTp30zDPP6PDDD1ejRo105JFHJnQZt8GDB+9zOZ5xxhny+XwaOHCgRo0apYKCAj366KNq1qyZfv7555i2Xbt21axZs3TnnXfqsMMOU7NmzdS7d2/deOONeu6553T++efriiuuUNeuXbVjxw699NJLmj17dsyuDFUh3uVeWVJTU7V06VKNGDFC3bp105IlS/TKK6/olltu2es+ulLpFosnn3xS6enp6tSpk1auXKnXXnstemqlMsccc4w8Ho+mTp2q3Nxc+f3+6HkaZ82apUsuuUTHHXechg4dqqZNm+r777/XK6+8op49e1b4g6rGOujHHbvIp59+aoYNG2aaN29uUlJSTFZWlhk2bFiFh+OXnTrjyy+/NOedd56pX7++adiwoRkzZowpLi6Otlu+fLkZPHiwadGihfH5fKZFixZm2LBh5ptvvomZXzAYNFOnTjWdO3c2fr/fNGzY0HTt2tVMmjTJ5ObmRttJMqNHj97re3Acx7Rq1cpIMnfeeWeFbeJ9reLiYnPdddeZxo0bm7p165qBAweaLVu2HPBpYPY81UPZstzz9CIjRowwdevWjZn22GOPmQ4dOhi/3286duxo5s6dG33+7goLC83o0aNNo0aNTL169czZZ59t1q1bZySZe+65J6bt1q1bzejRo02rVq2i671Pnz5mzpw5v/seqzP6ctX25Yq8++67pnv37iYtLc20aNHC3HTTTdHTaex++oqCggJz0UUXmYyMDCMp5pQwRUVF5tZbbzXt2rWLrrfzzjvPbNy4cZ+1xPNeKkNFp4FJTU01xxxzjJk1a1bMKZ7KzJkzx3Tt2tWkpaWZ+vXrm6OOOsrcdNNN5qeffjLGGLN27VozbNgw07p1a+P3+02zZs3MgAEDzIcffhgzn/fee8907drV+Hy+fb7f3U8D83sqOg3MSy+9ZI4++miTmppq2rZta6ZOnRo9Tc+mTZui7bKzs03//v1N/fr1jaSYU8L8+uuvZsyYMeaQQw4xPp/PtGzZ0owYMSJ6yqm91Ve2jufOnfu7de9uz9PAlNnXcjem9DQw/fv3L/fciv4+K+p/ZZ/TGzduNGeccYapU6eOyczMNBMmTDCRSKTcPHdfZzt37jSXX365adKkialXr57p16+f+frrr02bNm3MiBEjYp776KOPmkMPPdR4PJ5yf1MrVqww/fr1M+np6SY1NdW0b9/eXHbZZeX6T01nGVMDtgUA1dDHH3+sY489Vv/+9781fPjwZJcDADXeZZddpueeey663yeqDvsAAnEoLi4uN23GjBmybTtmJ30AAGoC9gEE4jBt2jStWbNGp59+urxer5YsWaIlS5boqquuOiinugEAoDIRAIE4nHTSSVq2bJnuuOMOFRQUqHXr1po4cWK5U4kAAFATsA8gAACAy7h+H8CZM2eqbdu2Sk1NVbdu3fT+++8nuyQAAIAq5eoAWHburAkTJmjt2rXq0qWL+vXrV6Ou1woAAJAoV28C7tatm0444YToiR0dx1GrVq107bXX6uabb97n8x3H0U8//aT69esf8GWF4F7GGOXn56tFixay7YPzm4y+i8pA30VNlYy+W9249iCQYDCoNWvWaPz48dFptm2rb9++WrlyZYXPCQQC0csISdKPP/641wuAA4nasmWLWrZseVBe66effuLoZVQa+i5qqoPZd6sb1wbA7du3KxKJKDMzM2Z6ZmZm9HJEe5oyZYomTZpUbvqWLVuilyoCEpWXl6dWrVqpfv36B+01y16LvosDkcy+23nYbfL4UvfZPlQv/lHCQEZiG8SCmeG427ZqvT3utte3fS3utp8Ut467rSQ9+eFJcbf1ZccfEexg/Mt5zd9Gx922qiSj71Y3rg2A+2P8+PEaN25c9H5ZB2rQoAFfojhgiW7Omjlzpu69915lZ2erS5cueuihh3TiiScm9Fr0XVSG/dkUu7/9t+y1PL7UuAKg44+/Nk9qYgHQTos/AHrr+uNuW6e+J+62qZ6UuNtKkp2272VWxpOaQABMoA9Up88cN+9G4M4N35KaNGkij8ejrVu3xkzfunWrsrKyKnyO3++PfmHyxYlk4gAm1GT0XyD5XBsAfT6funbtquXLl0enOY6j5cuXq0ePHkmsDNi36dOn689//rMuv/xyderUSbNnz1adOnX0r3/9K9mlAftE/wWSz7UBUJLGjRunRx99VI8//ri++uor/eUvf1FhYaEuv/zyZJcG7FXZAUx9+/aNTovnAKa8vLyYG5AMifZf+i5QNVwdAC+88ELdd999uv3223XMMcfo448/1tKlS8sdGAJUJ793AFN2dnaFz5kyZYrS09OjN46iRLIk2n/pu0DVcHUAlKQxY8bou+++UyAQ0OrVq9WtW7dklwRUuvHjxys3Nzd627JlS7JLAuJC3wWqBkcBAzXM/h7A5PfHfxQiUFUS7b/0XaBquH4EEKhpOIAJNRn9F6geGAEEaqBx48ZpxIgROv7443XiiSdqxowZHMCEGoP+CyQfARCogS688EL98ssvuv3225Wdna1jjjmGA5hQY1RG//UEJE8c5212UuI/ubOnJLGTAluB+DeihZz42w489LP428bdstTtRyb4hDgdfcP9cbc9ZvT0hOb98cxx+26EhBEAgRpqzJgxGjNmTLLLAPYL/RdILvYBBAAAcBkCIAAAgMsQAAEAAFyGAAgAAOAyBEAAAACXIQACAAC4DAEQAADAZQiAAAAALkMABAAAcBmuBAIAqHE8AUdex9lnOycl/nEOTyCxGuyS+OddUOJPbOY1zKczxsbdttvFf09o3l2ui/8yc588GH8dbscIIAAAgMsQAAEAAFyGAAgAAOAytTYATpkyRSeccILq16+vZs2a6eyzz9a6deti2pSUlGj06NFq3Lix6tWrpyFDhmjr1q1JqhgAAODgqLUB8M0339To0aO1atUqLVu2TKFQSGeccYYKCwujbcaOHavFixdr4cKFevPNN/XTTz/p3HPPTWLVAAAAVa/WHgW8dOnSmPvz5s1Ts2bNtGbNGp1yyinKzc3VY489pvnz56t3796SpLlz5+qII47QqlWr1L1792SUDQAAUOVq7QjgnnJzcyVJjRo1kiStWbNGoVBIffv2jbbp2LGjWrdurZUrV1Y4j0AgoLy8vJgbAABATeOKAOg4jm644Qb17NlTRx55pCQpOztbPp9PGRkZMW0zMzOVnZ1d4XymTJmi9PT06K1Vq1ZVXToAAEClc0UAHD16tD7//HMtWLDggOYzfvx45ebmRm9btmyppAoBAAAOnlq7D2CZMWPG6OWXX9Zbb72lli1bRqdnZWUpGAwqJycnZhRw69atysrKqnBefr9ffn/tPps7AACo/WrtCKAxRmPGjNELL7yg119/Xe3atYt5vGvXrkpJSdHy5cuj09atW6fvv/9ePXr0ONjlAgAAHDS1dgRw9OjRmj9/vv7zn/+ofv360f360tPTlZaWpvT0dI0cOVLjxo1To0aN1KBBA1177bXq0aMHRwADQDXnLTHyRsw+2zkp+25TJlxiJVRDItcOLi5Jibutk90h7rZ21vr4i6gmvCXxrxNJqr8lXEWVuFutDYCzZs2SJJ122mkx0+fOnavLLrtMknT//ffLtm0NGTJEgUBA/fr108MPP3yQKwUAADi4am0ANGbfvzBSU1M1c+ZMzZw58yBUBAAAUD3U2n0AAQAAUDECIAAAgMsQAAEAAFyGAAgAAOAyBEAAAACXIQACAAC4DAEQAADAZQiAAAAALlNrTwQNwN2O+/P0uNvWy07sUlMpBfG39xQlMO+wE3fTSANf/POVtHzFLQm1r+48QUceZ9/Ly+OL//JunmBilyjzJHDpuJJg/F+3awLBuNueEHfL6sMTiL+fS1JKbqiKKnE3RgABAABchgAIAADgMgRAAAAAlyEAAgAAuAwBEAAAwGUIgAAAAC5DAAQAAHAZAiAAAIDLEAABAABcxhUB8J577pFlWbrhhhui00pKSjR69Gg1btxY9erV05AhQ7R169bkFQkAAHCQ1PpLwX3wwQd65JFHdPTRR8dMHzt2rF555RUtXLhQ6enpGjNmjM4991y9++67SaoUwL4cN6pqLu/m/6UkoTo8OUVxt7WK4p+3KYm/rW0Su2zZma1viLvtku9nJDTvZPAUR+TxRvbZzpuSwKXgShIbE/EE4m9rij1xt81x0hKqo6axE7wUnKeQS8FVhVo9AlhQUKDhw4fr0UcfVcOGDaPTc3Nz9dhjj2n69Onq3bu3unbtqrlz5+q9997TqlWrklgxAABA1avVAXD06NHq37+/+vbtGzN9zZo1CoVCMdM7duyo1q1ba+XKlQe7TCBhEydOlGVZMbeOHTsmuyxgn+i7QPVQazcBL1iwQGvXrtUHH3xQ7rHs7Gz5fD5lZGTETM/MzFR2dvZe5xkIBBQI/Dbmn5eXV2n11jaRSERvv/22fv75ZzVv3ly9evWSxxP/JhDsW+fOnfXaa69F73u9tfbPGbUMfRdIvlr5V7dlyxZdf/31WrZsmVJTUyttvlOmTNGkSZMqbX611aJFi3T99dfrhx9+iE5r2bKlHnjgAZ177rlJrKx28Xq9ysrKSnYZQMLou0Dy1cpNwGvWrNG2bdt03HHHyev1yuv16s0339SDDz4or9erzMxMBYNB5eTkxDxv69atv/uhNH78eOXm5kZvW7ZsqeJ3UvMsWrRI5513Xkz4k6QffvhBQ4YM0VGdh6vP6XcnqbraZf369WrRooUOPfRQDR8+XN9///1e2wYCAeXl5cXcgGSh7wLJVysDYJ8+ffTZZ5/p448/jt6OP/54DR8+PPr/lJQULV++PPqcdevW6fvvv1ePHj32Ol+/368GDRrE3PCbSCSi66+/XuZ3jk5cv+FlWUVB/bH7ZPU9+c6DWF3t0q1bN82bN09Lly7VrFmztGnTJvXq1Uv5+fkVtp8yZYrS09Ojt1atWh3kioFS9F2geqiVm4Dr16+vI488MmZa3bp11bhx4+j0kSNHaty4cWrUqJEaNGiga6+9Vj169FD37t2TUXKt8Pbbb5cb+dtTIJin3B2b1KheG5lQrfz9cVCceeaZ0f8fffTR6tatm9q0aaNnn31WI0eOLNd+/PjxGjduXPR+Xl4eX6RICvouUD249hv4/vvv14ABAzRkyBCdcsopysrK0qJFi5JdVo32888/x9Vu3OQ/ScbIchydcSL7VFaGjIwMHX744dqwYUOFjzN6jeqKvgskh2sC4BtvvKEZM2ZE76empmrmzJnasWOHCgsLtWjRInZKPkDNmzePu13+4emld4xR355sCj5QBQUF2rhxY9zrAKgu6LtActTKTcBIjl69esnTMF2Rnbl7beNrWl83Fi7Rtv4N1fGB0jP0W5F9nxX+5HPuleO1FKprK+KXIj5L4brSF/eMrbT6a5K//vWvGjhwoNq0aaOffvpJEyZMkMfj0bBhw5JdGvC76LtA9UAARKXxeDxqfNlZ2nb/03tt0+D8wdr2YxOl7PDIpAQlScaz74Fox2spWM9WqL4lx6vSm4tPK/jDDz9o2LBh+vXXX9W0aVOdfPLJWrVqlZo2bZrs0hJ22NT4L+/mzYz/sl5rH7lpf8qJSyKj1t5fdsTd1hQXx93WCSRwHTJJlq8w7rZnthgTV7uwE0yoBqny+q4nEJYnvO/L/Tm++Dd0eQOJXV7PUxJ/f7QTuBTcZyXx7+P4x7hbVh+eBC8FZwe4FFxVIACiUtXvfoT8N5+nnx/9n8K//na6Bk/DdDU6b7Dqdjhadq4lb7Gl4qw0+fJCskKOeveeonBdj8JptoL1bPkKHAUa2Cra9YXvdPbI8RkZr5EcyTKSjHTYPdO14eZxe6mm9lqwYEGySwD2C30XqB4IgKgUf1wxVj/kpMuyPPIfd7QOmdFFxZ9/r8ivBfLWSVdaq0PlcTyy8y3ZQckOSzntvWq43iglNyRZkhzJW+zIU2KUtrVYO/vW1+AL3tH3xY2UE0xTXiBVO4vSVFLsUyjgkVXklRW01G7G32U80uZr/1+yFwMAADUCARCVYltBPYVCHkXCHjlBjxSwldbycNnNLHkCluzC0uDnCao0AIaMvCWlz3V8HsmSPEFHnuKwvL8Waut9tga0/EyHp2WrYUqhdobq6tdQXW1NbaCcQJoKgz7lF/kVCnhLX49TygAAEDe+NXHAjn3lVpUEUxQJ7Qp/QVt2wJanxJYdtOQpseQJlIY/T4nkLSkNf94SI+3a5caKGKXsKFEww6fNFzRTt6zvdXhqthp5CpTpzVUzX56y/HlqmlqgJmkFapRWpAZ1S5RaJ6iUOiFZqRG1feKe5C4IAABqCEYAccCKAz6Fgl45AY8UtmUFbNklluywSgNgoGzUT/KUmNIgGDLyBIwsR7KMkR2MyLM9V8VHNtCF572hRt5C1bUDauwpUKoVUqodUooVUcTYSrEj8tkRSZLHdlTk8alYkuPYOnTBXfp26K3JXSAAAFRzBEAcsGAgRZESj6yAR1bIkh3Ytdk3KHlCkqdY8gSN7FDpqJ8nYOQJGnkCjjzFEdmhiOzikBQOS5aU7i1SfU+x6tgBpVphZXgDqusElGqF5JGjVDukNDso2zJK8USU6g0r13YUCHnlOAxqAwCwLwRAHJBDF9wlJ+SRwpYUtkoDYLh0tC/6b8js+rf0/6Vh0MgOOqXhrygoe0e+vhtxqILHFKiOHVSqFZLPKh3lq2NFFLGDiqhI+U6aQsariLFV7PgUNqWBLxQpPcVCmAAIAMA+EQBxQJywLROxpMiu4BeRrLAlK1IaAK3wriAYluywkSdkZIWN7JAjOxiRFYjIKgnJ2bFTjU736co2b8tjGaVYEdlyFJGlFEuqq7BCVlD17WKFPB6FjEcFHr8CHq8cYymQ4lXYsWVFEjuPFwAAbkQAxAExYbt05C96026jfbuP/u0a+QsaeYKO7EBEdiAsOxCSFQzJOI7SvCE19hYoYmz5rIg8liPH2AqY0qOVMuygCj1FcmQrYmwVeX1yjCXbMgo7thxjqSRMlwYAYF/4tsSBcSQ5lqyIVXpAx65/7YhKRwEjkh0x0X+tiJEddmRFjKywIyu3QKaoWJbPJ783rLpWUEF55LGc0lFAq3QU0JZRiqXSA0KskOraAdWxgyqyffLbYfk8EaXYETme+M/MDwCAWxEAsV+OeGGiSop8kuOV5ViSUWkAdFR6apeym1M23ciKlJ7uRRFTev3fiJEpLJS8XgWOPVSHpH6tFOu3Szt59NvlgjyWlCIp1Yoo1So9IjjFishvh+W3Q/LtCoERY+uk//2f0lJCWn56/JcYAwDATQiA2C/GWDLGigY9y9FuYW+30LfH/bJRQCviyIpEZCKOinseqscevl+FxquQseWRUUSWPDLRW2n4s5UiRylWWKl2UH679PQwAdsrvx2Rzw4rbNsK2aUjiKgZQs3iv87nqUd+VYWVxO+1d/+W7BL0R/v8hNqbBK4d7OQXxNfOJH4t4MpiFYdlefbddzy++K/B603ger2S5AkkcC3gYPxtNxRlJlRHTePNT+w61qgaHDKJqmFKr9drRQPirmv4OkaWMbtGAh3JKQ1qfkvy7DortL3b5l+PjPyWlGJZsi1LHuu3UOizwrItR7Zlov96bUcem/AHAMDvIQCiylm7H5jrSHKMZHa77YVHRrZllGpZSrF+66r2bqN7Hhl5LEceq7Rt6eO//R8AAJTHJmDsF9s2smwjY0na7Wb2tpXD/BYEo9nMGMkqfULASKFd5/RzjB0d/UuRo/q2T5JUYsLRx/dalwz7/gEAsA+MAGK/pHgism0jeYyMx8jYkrEllf27KwxGb7ZVGg7tPUKiXdoFC41XQdkKGY8iKm1gW0Y+y5FXpfvlhIyjkLEVkVV6K/u/sRXZa/IEAAB7qtUB8Mcff9TFF1+sxo0bKy0tTUcddZQ+/PDD6OPGGN1+++1q3ry50tLS1LdvX61fvz6JFdccnwy8QxuH3irZpUN70fBXNhK4KwSW+79lSbZVOvJn/RbaSoxHJSZFQXkUkR0zyufIKGKMHEkRWdErgURkK+SUngjaMaXnAXREEAQAYF9qbQDcuXOnevbsqZSUFC1ZskRffvml/v73v6thw4bRNtOmTdODDz6o2bNna/Xq1apbt6769eunkpKSJFZes9heR/IYybtrFLBsNNAjGY9V+q9ddrOiIdDsCoCWVXokcb6TqhInRSVOSnQU0Nk1qhcyEQVMWCXGqMR4FNwVFgO72oaMZ1cI/O05AABg72rtPoBTp05Vq1atNHfu3Oi0du3aRf9vjNGMGTP0t7/9TYMHD5YkPfHEE8rMzNSLL76ooUOHHvSaayJPSun1ep2IJcdXur3XikhOWLvO+1caBB2PZKVITootK+RIXlvy2JKndPNuTqRu6fwsRykKyyOjoBVR0NgqMiGVGKNCx1aR41e+k6Yix68ix6eA41VxxKfiSIqCjpdrAQMAEIda+2350ksv6fjjj9f555+vZs2a6dhjj9Wjjz4afXzTpk3Kzs5W3759o9PS09PVrVs3rVy5Mhkl10g+X0QeryOlGBlv6c1JMXI8kuOVHK8lx1s2IrhrFNBjyXis0v3/PKVdMN9JLd0EbDzRffscY8uRpRJjVGQsFRmvCo1PJSYlOloYcLwKOF4FHY+CkdLnAgCA31drvy2//fZbzZo1Sx06dNCrr76qv/zlL7ruuuv0+OOPS5Kys7MlSZmZsSfczMzMjD62p0AgoLy8vJib23159kSl+MKyfRGZFEeOz8h4JZMiOSmS8e4WBHdtEna8tozHlknxSJZdugk4kqZCx79rE7D3twM8ZCnfsZXvpCjfSY22KxsBLHZ8CjgeBZ3S53AEMAAA+1ZrNwE7jqPjjz9ed999tyTp2GOP1eeff67Zs2drxIgR+zXPKVOmaNKkSZVZZq3QsG6xcm2jIkmO16OIJcmx5dl1kIcVsSQZGcuSHfntN4cV8crrS5Ek7QjXVbq3SM6ukz3XV7Gk0lPD5Buf8p005Tmp+jVST7nhOiqI+JUTSlN+KFVFYZ/yg36FIomdxR8AALeqtQGwefPm6tSpU8y0I444Qs8//7wkKSsrS5K0detWNW/ePNpm69atOuaYYyqc5/jx4zVu3Ljo/by8PLVq1aqSK6953jtjqk763//JGEsldooikpzwrvBnLEXCKt030BhFQpasiC05kh30SF6PUrcV6an/nqqep3+u4xtslkeOfFZEEdkqNL7oyF9OpI5yw3WUG0lTYdiv/FCqCsJ+BcJeBSMeDgCpoRo0Loy77eF1Kx6dr8jDX58Wd9urM36Mu60k/RCO71JpkrQ14ou77Y/hjLjbDvu6XtxtJenpTgl8Vv3OCdr3q10VsAJBWZ59/83bgfi/5jwJtC1tH/+PTk8CxxbuCNZJqI6axiooTqi9SfVXUSXuVms3Affs2VPr1q2LmfbNN9+oTZs2kkoPCMnKytLy5cujj+fl5Wn16tXq0aNHhfP0+/1q0KBBzA2l3jtjqr48e6Lq1gnImxaWkxZRJM0onGoU8UuRVCmcainstxTxW4qk2or4bRlfijzZO3XYY9n6akemHGNHLwPnGFv5Tqp+CTfQL+EG2hGup53hOsoPpyov7FdB2K/CkE+FIZ+CYa8iHAACAEBcau0I4NixY3XSSSfp7rvv1gUXXKD3339fc+bM0Zw5cyRJlmXphhtu0J133qkOHTqoXbt2uu2229SiRQudffbZyS2+Bjuq2c/6uaiBsv31VVLsU7jQK+PxylMieXxW6QmhvZInxZIsr9Sukbz5IaX8vFOO+a07bgk13rWvn087w3VVFPGpMOJXTjBNRWGfSiJeFQT9Kg55Fd41+pfm2/eF4QEAQC0OgCeccIJeeOEFjR8/XpMnT1a7du00Y8YMDR8+PNrmpptuUmFhoa666irl5OTo5JNP1tKlS5WamprEymu2+d0fjbnfZfFtyvHWk1PkkVNsS5Yl47XkSZEku/TE0Y5Rim3LGEtFjk8/hRoqP5KqgPGqIOxXXjhNxZEUFYVTVBjyqzicomDEo+JgisKR0ufZttGHZ96djLcMAECNU2sDoCQNGDBAAwYM2OvjlmVp8uTJmjx58kGsyl0+GXhHhdOPuPV+SZaMbctyvPLXS9XOvFR9ln+ImqfmRs/tVxxJie7nF4h4VRRKUSjsUSjiUSjkkXEsbbjwbwf3TQEAUMPV6gCI6qvo0JBCv3qVkmspkOFRIL2h2s4J6OsOR+jHC35SKOJR2LEVcWwFwh6Fwx5FIrYiYY+ciCUT2XXSaY+T7LcCAECNQwBEUnw38qYKp58ycJp2vHyIImlSMN0o1DgsObuO9HNKjyqWJFmll5/bfNn4g1QxAAC1BwEQ1cpbi2/SaWfco2CGV0VNbRVGvDIelV5DWJJ2XW/427F/TXKlAADUXARAVDuO35YVkVJ3GHmLLIXqWaWnkvFLkTSjiI/z/QEAcCAIgKh23lr82+bh40ZNl7Glz+8bm8SKAACoXQiAqNbWPjJu340AAEBCCIAAkqp5g7y42zbyxH/ZOI8V/xHiE37pHHdbSfq+uFHcbX8ozIi77faCunG3zS9Ii7utJB1eb2P8je34drOwjJGKEiqj0lglAVlxXPzH3nW98Xh4AvG3lSRvApeOswPxX6loa3H9hOqoaaxIYmdvCNfnUnBVgWtnAQAAuAwBEAAAwGUIgAAAAC5DAAQAAHAZAiAAAIDLEAABAABchgAIAADgMgRAAAAAlyEAAgAAuAwBEAAAwGW4FByASrX2u9YJte+Y3jPutp8Utoq7bXZJg7jbbsnPiLutJO3Ii/+SbcG8+C9jZRd44m6bkpfY7/ctVx0Zd9vWT30bVzvLib/eymZKAjK22Wc7K4FLwdnFvoRq8JYkcpm5+NfXzqLELvNXVfr2uivutt7tBfHPuE5qQnW89u7fEmqP+NTaEcBIJKLbbrtN7dq1U1pamtq3b6877rhDxvz2gWGM0e23367mzZsrLS1Nffv21fr165NYNQAAQNWrtQFw6tSpmjVrlv7xj3/oq6++0tSpUzVt2jQ99NBD0TbTpk3Tgw8+qNmzZ2v16tWqW7eu+vXrp5KSkiRWDrd76623NHDgQLVo0UKWZenFF1+MeZwfLqiu6LtAzVFrA+B7772nwYMHq3///mrbtq3OO+88nXHGGXr//fcllX4QzZgxQ3/72980ePBgHX300XriiSf0008/lfvQAg6mwsJCdenSRTNnzqzwcX64oLqi7wI1R60NgCeddJKWL1+ub775RpL0ySef6J133tGZZ54pSdq0aZOys7PVt2/f6HPS09PVrVs3rVy5Mik1A5J05pln6s4779Q555xT7jF+uKA6o+8CNUetPQjk5ptvVl5enjp27CiPx6NIJKK77rpLw4cPlyRlZ2dLkjIzM2Oel5mZGX1sT4FAQIFAIHo/Ly+viqoHKravHy5Dhw6t8Hn0XSQbfReoXmrtCOCzzz6rp556SvPnz9fatWv1+OOP67777tPjjz++3/OcMmWK0tPTo7dWreI/IhGoDPvzw0Wi7yL56LtA9VJrA+CNN96om2++WUOHDtVRRx2lSy65RGPHjtWUKVMkSVlZWZKkrVu3xjxv69at0cf2NH78eOXm5kZvW7Zsqdo3AVQS+i5qKvouUDVqbQAsKiqSbce+PY/HI8dxJEnt2rVTVlaWli9fHn08Ly9Pq1evVo8ePSqcp9/vV4MGDWJuwMG0Pz9cJPouko++C1QvtTYADhw4UHfddZdeeeUVbd68WS+88IKmT58e3TnZsizdcMMNuvPOO/XSSy/ps88+06WXXqoWLVro7LPPTm7xwF7szw8XoDqg7wLVS609COShhx7SbbfdpmuuuUbbtm1TixYtNGrUKN1+++3RNjfddJMKCwt11VVXKScnRyeffLKWLl2q1NTEzlIOVKaCggJt2LAhen/Tpk36+OOP1ahRI7Vu3Tr6w6VDhw5q166dbrvtNn64oFqg7wI1R60NgPXr19eMGTM0Y8aMvbaxLEuTJ0/W5MmTD15hwD58+OGHOv3006P3x40bJ0kaMWKE5s2bxw8XVFv0XaDmsMzu10ZDQvLy8pSenq7c3Fz2S8F+S0Y/SvQ1V33XNu55ryjolFAt3xRm7rvRLpsLGsXddmte/bjbFuUlGEDy4v/tnJIb/542vjwrgbaJfXT7c+Jvn/HRL3G1C0cCWr5hRlL6bu86Q+W19n3tXjs9/rqcphkJ1VLSIv4+VpgVf5/xBOJfV758J+62kuQtiiRQR/xt5cRfc3W4ti/f37V4H0AAAABUjAAIAADgMgRAAAAAlyEAAgAAuAwBEAAAwGUIgAAAAC5DAAQAAHAZAiAAAIDLEAABAABchgAIAADgMrX2WsAAKs9r+UfG3fbrBC7tJkmb8xrH3faXvHpxty3Jif/ybp5cT9xtJcmXyOXdchOZb/yX00rNTeAyXZJ8OeH4G3vifX/JG0NwiorlWPt+T5YvJe55WsV1EqrBU5LAOjDxf92WNI5/uZY0TGwdBNPjr+Oru8YmNG/ULIwAAgAAuAwBEAAAwGUIgAAAAC5DAAQAAHAZAiAAAIDLEAABAABchgAIAADgMgRAAAAAlyEAAgAAuAxXAjkAxpSetT8vLy/JlaAmK+s/Zf3pYEi075YUhOKed6gomFAt4cJA3G0jRfF/ZDnF8ddglSR2JZBIIP7fzpH4354iwfj7QDiU2JVA7HD8VwKx4yw6vKtdMvpuWCEpjpc1JoH+mMjKkhQOl8Q/6wTWbSRoxV+EE39TKbG3WJu/25LxuVvdEAAPQH5+viSpVatWSa4EtUF+fr7S09MP2mtJ9F1UjmT03Xf03/iekJPAzBNpK0kbE2xfw6T//ZZkl1DlDmbfrW4s4+b4e4Acx9G6devUqVMnbdmyRQ0aNEh2SdVSXl6eWrVqxTLaC2OM8vPz1aJFC9n2wdkrw3Ec/fTTT6pfv74s67fRhtq+rnh/lYu+e/Dw/ipXMvpudcMI4AGwbVuHHHKIJKlBgwa18o+yMrGM9u5g/wK1bVstW7bc6+O1fV3x/ioPfffg4v1VHreO/JVxZ+wFAABwMQIgAACAyxAAD5Df79eECRPk9/uTXUq1xTKqOWr7uuL91V61/b3z/lDZOAgEAADAZRgBBAAAcBkCIAAAgMsQAAEAAFyGAAgAAOAyBMADMHPmTLVt21apqanq1q2b3n///WSXlDQTJ06UZVkxt44dO0YfLykp0ejRo9W4cWPVq1dPQ4YM0datW5NYMfZUW/vzvvpmTfTWW29p4MCBatGihSzL0osvvhjzuDFGt99+u5o3b660tDT17dtX69evT06xB0Ft7btS7eu/9N3qgwC4n5555hmNGzdOEyZM0Nq1a9WlSxf169dP27ZtS3ZpSdO5c2f9/PPP0ds777wTfWzs2LFavHixFi5cqDfffFM//fSTzj333CRWi93V9v78e32zJiosLFSXLl00c+bMCh+fNm2aHnzwQc2ePVurV69W3bp11a9fP5WUlBzkSqtebe+7Uu3qv/TdasRgv5x44olm9OjR0fuRSMS0aNHCTJkyJYlVJc+ECRNMly5dKnwsJyfHpKSkmIULF0anffXVV0aSWbly5UGqEL+nNvfn3+ubtYEk88ILL0TvO45jsrKyzL333hudlpOTY/x+v3n66aeTUGHVqs1915ja3X/d3neTjRHA/RAMBrVmzRr17ds3Os22bfXt21crV65MYmXJtX79erVo0UKHHnqohg8fru+//16StGbNGoVCoZjl1bFjR7Vu3drVy6u6cEN/3lvfrI02bdqk7OzsmPWZnp6ubt261Zr1WcYNfVdyT/91U9+tDgiA+2H79u2KRCLKzMyMmZ6Zmans7OwkVZVc3bp107x587R06VLNmjVLmzZtUq9evZSfn6/s7Gz5fD5lZGTEPMfNy6s6qe39+ff6Zm1Uts5q6/rcXW3vu5K7+q+b+m514E12AagdzjzzzOj/jz76aHXr1k1t2rTRs88+q7S0tCRWBrf7vb45cuTIJFYG7Bv9F1WFEcD90KRJE3k8nnJHsW7dulVZWVlJqqp6ycjI0OGHH64NGzYoKytLwWBQOTk5MW1YXtWD2/rz7n2zNipbZ25Yn27ru1Lt7r9u6rvVAQFwP/h8PnXt2lXLly+PTnMcR8uXL1ePHj2SWFn1UVBQoI0bN6p58+bq2rWrUlJSYpbXunXr9P3337O8qgG39efd+2Zt1K5dO2VlZcWsz7y8PK1evbrWrU+39V2pdvdfN/XdaiHZR6HUVAsWLDB+v9/MmzfPfPnll+aqq64yGRkZJjs7O9mlJcX/+3//z7zxxhtm06ZN5t133zV9+/Y1TZo0Mdu2bTPGGHP11Veb1q1bm9dff918+OGHpkePHqZHjx5JrhplanN/3lffrIny8/PNRx99ZD766CMjyUyfPt189NFH5rvvvjPGGHPPPfeYjIwM85///Md8+umnZvDgwaZdu3amuLg4yZVXvtrcd42pff2Xvlt9EAAPwEMPPWRat25tfD6fOfHEE82qVauSXVLSXHjhhaZ58+bG5/OZQw45xFx44YVmw4YN0ceLi4vNNddcYxo2bGjq1KljzjnnHPPzzz8nsWLsqbb25331zZpoxYoVRlK524gRI4wxpafTuO2220xmZqbx+/2mT58+Zt26dcktugrV1r5rTO3rv/Td6sMyxpjkjD0CAAAgGdgHEAAAwGUIgAAAAC5DAAQAAHAZAiAAAIDLEAABAABchgAIAADgMgRAAAAAlyEAAgAAuAwBEAAAwGUIgAAAAC5DAAQAAHAZAiAAAIDLEAABAABchgAIAADgMgRAAAAAlyEAAgAAuAwBEAAAwGUIgAAAAC5DAAQAAHAZAiAAAIDLEAABAABchgAIAADgMgRAAAAAlyEAAgAAuAwBEAAAwGUIgAAAAC5DAAQAAHAZAiAAAIDLEAABAABchgAIAADgMgRAAAAAlyEAAgAAuAwBEAAAwGUIgAAAAC5DAAQAAHAZAiAAAIDLEAABAABchgAIAADgMgRAAAAAlyEAAgAAuAwBEAAAwGUIgAAAAC5DAAQAAHAZAiAAAIDLEAABAABchgAIAADgMgRAAAAAlyEAAgAAuAwBEAAAwGUIgAAAAC5DAAQAAHAZAiAAAIDLEAABAABchgAIAADgMgRAAAAAlyEAAgAAuAwBEAAAwGUIgAAAAC5DAAQAAHAZAiAAAIDLEAABAABchgAIAADgMgRAAAAAlyEAAgAAuAwBEAAAwGUIgAAAAC5DAAQAAHAZAiAAAIDLEAABAABchgAIAADgMgRAAAAAlyEAAgAAuAwBEAAAwGUIgAAAAC5DAAQAAHAZAiAAAIDLEAABAABchgAIAADgMgRAAAAAlyEAAgAAuAwBEAAAwGUIgAAAAC5DAAQAAHAZAiAAAIDLEAABAABchgAIAADgMgRAAAAAlyEAAgAAuAwBEAAAwGUIgAAAAC5DAAQAAHAZAiAAAIDLEAABAABchgAIAADgMgRAAAAAlyEAAgAAuAwBEAAAwGUIgAAAAC5DAAQAAHAZAiAAAIDLEAABAABchgAIAADgMgRAAAAAlyEAAgAAuAwBEAAAwGUIgAAAAC5DAAQAAHAZAiAAAIDLEAABAABchgAIAADgMgRAAAAAlyEAAgAAuAwBEAAAwGUIgAAAAC5DAAQAAHAZAiAAAIDLEAABAABchgAIAADgMgRAAAAAlyEAAgAAuAwBEAAAwGUIgAAAAC5DAAQAAHAZAiAAAIDLEAABAABchgAIAADgMgRAAAAAlyEAAgAAuAwBEAAAwGUIgAAAAC5DAAQAAHAZAiAAAIDLEAABAABchgAIAADgMgRAAAAAlyEAAgAAuAwBEAAAwGUIgAAAAC5DAAQAAHAZAiAAAIDLEAABAABchgAIAADgMgRAAAAAl/n/+XMpXwQu3QgAAAAASUVORK5CYII=", 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\n", " " ], @@ -586,6 +523,7 @@ } ], "source": [ + "i = 512\n", "observed_xyz = test_images[i,...,:3]\n", "x,y = pixel_coordinates[i]\n", "\n", diff --git a/test/test_cosypose.py b/test/test_cosypose.py new file mode 100644 index 00000000..7a0a1be1 --- /dev/null +++ b/test/test_cosypose.py @@ -0,0 +1,12 @@ +import os +import bayes3d as b +import jax +import jax.numpy as jnp +import numpy as np +import subprocess +from bayes3d.neural.cosypose_baseline import cosypose_utils + +bop_ycb_dir = os.path.join(b.utils.get_assets_dir(), "bop/ycbv") +rgbd, gt_ids, gt_poses, masks = b.utils.ycb_loader.get_test_img('55', '1592', bop_ycb_dir) + +pred = cosypose_utils.cosypose_interface(np.array(rgbd.rgb), b.K_from_intrinsics(rgbd.intrinsics)) \ No newline at end of file