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February 18, 2019 23:47
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| { | |
| "cells": [ | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "# Unit tests and observations for Binary mask for `maskrcnn_benchmark` PR [#150](https://github.com/facebookresearch/maskrcnn-benchmark/pull/150)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 89, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "import torch\n", | |
| "import numpy as np\n", | |
| "from maskrcnn_benchmark.structures.segmentation_mask import Mask, Polygons, SegmentationMask\n", | |
| "\n", | |
| "import matplotlib.pyplot as plt" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "## Define a simple polygon\n", | |
| "\n", | |
| "A simple rectangle will do, 100 pixels away from each border" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 137, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "width = 640\n", | |
| "height = 480\n", | |
| "poly = [[100, 100, width-100, 100, width-100, height-100, 100, height-100]]\n", | |
| "size = (width, height)\n", | |
| "box = [poly[0][0]-50, poly[0][1]-50, poly[0][4]-50, poly[0][5]-50]\n", | |
| "\n", | |
| "polygon = Polygons(poly, size, 'polygon')\n", | |
| "mask = Mask(poly, size, 'mask')" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 138, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "<matplotlib.image.AxesImage at 0x7f7942876da0>" | |
| ] | |
| }, | |
| "execution_count": 138, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| }, | |
| { | |
| "data": { | |
| "image/png": 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\n", | |
| "text/plain": [ | |
| "<Figure size 1440x1440 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| }, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "plt.figure(figsize=(20, 20))\n", | |
| "plt.title('Mask')\n", | |
| "plt.imshow(mask.convert('mask'))" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "## TEST: Crop [PASS]" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 139, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "mask_from_poly_crop = polygon.crop(box).convert('mask')\n", | |
| "mask_crop = mask.crop(box).convert('mask')\n", | |
| "diff = torch.abs(mask_from_poly_crop.float() - mask_crop.float())" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 140, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "<matplotlib.image.AxesImage at 0x7f7943304400>" | |
| ] | |
| }, | |
| "execution_count": 140, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| }, | |
| { | |
| "data": { | |
| "image/png": 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\n", | |
| "text/plain": [ | |
| "<Figure size 1440x1440 with 3 Axes>" | |
| ] | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| }, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "plt.figure(figsize=(20, 20))\n", | |
| "\n", | |
| "plt.subplot(1, 3, 1)\n", | |
| "plt.title('Mask from Crop(Poly)')\n", | |
| "plt.imshow(mask_from_poly_crop)\n", | |
| "\n", | |
| "plt.subplot(1, 3, 2)\n", | |
| "plt.title('Mask from Crop(Mask)')\n", | |
| "plt.imshow(mask_crop)\n", | |
| "\n", | |
| "plt.subplot(1, 3, 3)\n", | |
| "plt.title('abs( Crop(Poly) - Crop(Mask) ) = %f'%diff.sum())\n", | |
| "plt.imshow(diff)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "## TEST: Resize [FAIL]" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 100, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| "/home/csbotos/anaconda3/envs/debugmask/lib/python3.7/site-packages/torch/nn/functional.py:2423: UserWarning: Default upsampling behavior when mode=bilinear is changed to align_corners=False since 0.4.0. Please specify align_corners=True if the old behavior is desired. See the documentation of nn.Upsample for details.\n", | |
| " \"See the documentation of nn.Upsample for details.\".format(mode))\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "new_size = (600, 500)\n", | |
| "mask_from_poly_resize = polygon.resize(new_size).convert('mask')\n", | |
| "mask_resize = mask.resize(new_size).convert('mask')\n", | |
| "diff = torch.abs(mask_from_poly_resize.float() - mask_resize.float())" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 101, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "<matplotlib.image.AxesImage at 0x7f794318ec88>" | |
| ] | |
| }, | |
| "execution_count": 101, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| }, | |
| { | |
| "data": { | |
| "image/png": 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| |
| "text/plain": [ | |
| "<Figure size 2880x1440 with 3 Axes>" | |
| ] | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| }, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "plt.figure(figsize=(40, 20))\n", | |
| "\n", | |
| "plt.subplot(1, 3, 1)\n", | |
| "plt.title('Mask from Resize(Poly)')\n", | |
| "plt.imshow(mask_from_poly_resize)\n", | |
| "\n", | |
| "plt.subplot(1, 3, 2)\n", | |
| "plt.title('Mask from Resize(Mask)')\n", | |
| "plt.imshow(mask_resize)\n", | |
| "\n", | |
| "plt.subplot(1, 3, 3)\n", | |
| "plt.title('abs( Resize(Poly) - Resize(Mask) ) = %f'%diff.sum())\n", | |
| "plt.imshow(diff)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "### Observations\n", | |
| "- Probably the good old [Detectron inconsistency legacy](https://github.com/facebookresearch/maskrcnn-benchmark/pull/150#discussion_r233075909), otherwise seems OK\n", | |
| "- the UserWarning is there, maybe it should be resolved" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "## TEST: Transpose [FAIL]" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 102, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "FLIP_LEFT_RIGHT = 0\n", | |
| "FLIP_TOP_BOTTOM = 1\n", | |
| "methods = (FLIP_LEFT_RIGHT, FLIP_TOP_BOTTOM)\n", | |
| "results = []\n", | |
| "for method in methods:\n", | |
| " mask_from_poly_flip = polygon.transpose(method).convert('mask')\n", | |
| " mask_flip = mask.transpose(method).convert('mask')\n", | |
| " diff = torch.abs(mask_from_poly_flip.float() - mask_flip.float())\n", | |
| " results.append([mask_from_poly_flip, mask_flip, diff])" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 103, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "Horizontal\n" | |
| ] | |
| }, | |
| { | |
| "data": { | |
| "image/png": 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| |
| "text/plain": [ | |
| "<Figure size 2880x1440 with 3 Axes>" | |
| ] | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| }, | |
| "output_type": "display_data" | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "Vertical\n" | |
| ] | |
| }, | |
| { | |
| "data": { | |
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| "text/plain": [ | |
| "<Figure size 2880x1440 with 3 Axes>" | |
| ] | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| }, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "for result, method in zip(results, ['Horizontal', 'Vertical']):\n", | |
| " \n", | |
| " mask_from_poly_flip, mask_flip, diff = result\n", | |
| " \n", | |
| " print(method)\n", | |
| " plt.figure(figsize=(40, 20))\n", | |
| "\n", | |
| " plt.subplot(1, 3, 1)\n", | |
| " plt.title('Mask from flip(Poly)')\n", | |
| " plt.imshow(mask_from_poly_flip)\n", | |
| "\n", | |
| " plt.subplot(1, 3, 2)\n", | |
| " plt.title('Mask from flip(Mask)')\n", | |
| " plt.imshow(mask_flip)\n", | |
| "\n", | |
| " plt.subplot(1, 3, 3)\n", | |
| " plt.title('abs( flip(Poly) - flip(Mask) ) = %f'%diff.sum())\n", | |
| " plt.imshow(diff)\n", | |
| " plt.show()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "### Observations\n", | |
| "- Same as with the Resize: probably the good old [Detectron inconsistency legacy](https://github.com/facebookresearch/maskrcnn-benchmark/pull/150#discussion_r233075909), otherwise seems OK\n", | |
| "- the vertical and the horizontal transformation takes effect differently\n", | |
| "<pre>\n", | |
| "\n", | |
| "\n", | |
| "\n", | |
| "\n", | |
| "\n", | |
| "\n", | |
| "\n", | |
| "\n", | |
| "\n", | |
| "</pre>" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "## Define a disjoint polygon\n", | |
| "\n", | |
| "Two rectangles, one on the top-left and one on the bottom-right" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 116, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "width = 640\n", | |
| "height = 480\n", | |
| "poly = [[100, 100, 200, 100, 200, 200, 100, 200],\n", | |
| " [width-200, height-200, width-100, height-200, width-100, height-100, width-200, height-100]]\n", | |
| "size = (width, height)\n", | |
| "box = [110, 110, 210, 210]\n", | |
| "\n", | |
| "polygon = Polygons(poly, size, 'polygon')\n", | |
| "mask = Mask(poly, size, 'mask')" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 117, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "<matplotlib.image.AxesImage at 0x7f7942ac1a58>" | |
| ] | |
| }, | |
| "execution_count": 117, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| }, | |
| { | |
| "data": { | |
| "image/png": 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\n", | |
| "text/plain": [ | |
| "<Figure size 1440x1440 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| }, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "plt.figure(figsize=(20, 20))\n", | |
| "plt.title('Mask')\n", | |
| "plt.imshow(mask.convert('mask'))" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "## TEST: Crop [PASS]" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 141, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "mask_from_poly_crop = polygon.crop(box).convert('mask')\n", | |
| "mask_crop = mask.crop(box).convert('mask')\n", | |
| "diff = torch.abs(mask_from_poly_crop.float() - mask_crop.float())" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 142, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "<matplotlib.image.AxesImage at 0x7f79425f5940>" | |
| ] | |
| }, | |
| "execution_count": 142, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| }, | |
| { | |
| "data": { | |
| "image/png": 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\n", | |
| "text/plain": [ | |
| "<Figure size 1440x1440 with 3 Axes>" | |
| ] | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| }, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "plt.figure(figsize=(20, 20))\n", | |
| "\n", | |
| "plt.subplot(1, 3, 1)\n", | |
| "plt.title('Mask from Crop(Poly)')\n", | |
| "plt.imshow(mask_from_poly_crop)\n", | |
| "\n", | |
| "plt.subplot(1, 3, 2)\n", | |
| "plt.title('Mask from Crop(Mask)')\n", | |
| "plt.imshow(mask_crop)\n", | |
| "\n", | |
| "plt.subplot(1, 3, 3)\n", | |
| "plt.title('abs( Crop(Poly) - Crop(Mask) ) = %f'%diff.sum())\n", | |
| "plt.imshow(diff)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "## TEST: Resize [FAIL]" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 120, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "new_size = (600, 500)\n", | |
| "mask_from_poly_resize = polygon.resize(new_size).convert('mask')\n", | |
| "mask_resize = mask.resize(new_size).convert('mask')\n", | |
| "diff = torch.abs(mask_from_poly_resize.float() - mask_resize.float())" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 121, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "<matplotlib.image.AxesImage at 0x7f794296dc88>" | |
| ] | |
| }, | |
| "execution_count": 121, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| }, | |
| { | |
| "data": { | |
| "image/png": 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| |
| "text/plain": [ | |
| "<Figure size 2880x1440 with 3 Axes>" | |
| ] | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| }, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "plt.figure(figsize=(40, 20))\n", | |
| "\n", | |
| "plt.subplot(1, 3, 1)\n", | |
| "plt.title('Mask from Resize(Poly)')\n", | |
| "plt.imshow(mask_from_poly_resize)\n", | |
| "\n", | |
| "plt.subplot(1, 3, 2)\n", | |
| "plt.title('Mask from Resize(Mask)')\n", | |
| "plt.imshow(mask_resize)\n", | |
| "\n", | |
| "plt.subplot(1, 3, 3)\n", | |
| "plt.title('abs( Resize(Poly) - Resize(Mask) ) = %f'%diff.sum())\n", | |
| "plt.imshow(diff)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "### Observations\n", | |
| "- Different result on the different rectangles. Aliasing? Magic?\n", | |
| "- On real cases diff will be definitely >0.\n", | |
| "- However it seems OK, not sure what should be the acceptance threshold" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "## TEST: Transpose [FAIL]" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 122, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "FLIP_LEFT_RIGHT = 0\n", | |
| "FLIP_TOP_BOTTOM = 1\n", | |
| "methods = (FLIP_LEFT_RIGHT, FLIP_TOP_BOTTOM)\n", | |
| "results = []\n", | |
| "for method in methods:\n", | |
| " mask_from_poly_flip = polygon.transpose(method).convert('mask')\n", | |
| " mask_flip = mask.transpose(method).convert('mask')\n", | |
| " diff = torch.abs(mask_from_poly_flip.float() - mask_flip.float())\n", | |
| " results.append([mask_from_poly_flip, mask_flip, diff])" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 123, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "Horizontal\n" | |
| ] | |
| }, | |
| { | |
| "data": { | |
| "image/png": 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| "text/plain": [ | |
| "<Figure size 2880x1440 with 3 Axes>" | |
| ] | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| }, | |
| "output_type": "display_data" | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "Vertical\n" | |
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| |
| "text/plain": [ | |
| "<Figure size 2880x1440 with 3 Axes>" | |
| ] | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| }, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "for result, method in zip(results, ['Horizontal', 'Vertical']):\n", | |
| " \n", | |
| " mask_from_poly_flip, mask_flip, diff = result\n", | |
| " \n", | |
| " print(method)\n", | |
| " plt.figure(figsize=(40, 20))\n", | |
| "\n", | |
| " plt.subplot(1, 3, 1)\n", | |
| " plt.title('Mask from flip(Poly)')\n", | |
| " plt.imshow(mask_from_poly_flip)\n", | |
| "\n", | |
| " plt.subplot(1, 3, 2)\n", | |
| " plt.title('Mask from flip(Mask)')\n", | |
| " plt.imshow(mask_flip)\n", | |
| "\n", | |
| " plt.subplot(1, 3, 3)\n", | |
| " plt.title('abs( flip(Poly) - flip(Mask) ) = %f'%diff.sum())\n", | |
| " plt.imshow(diff)\n", | |
| " plt.show()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "### Observations\n", | |
| "- Same as with the Resize: probably the good old [Detectron inconsistency legacy](https://github.com/facebookresearch/maskrcnn-benchmark/pull/150#discussion_r233075909), otherwise seems OK\n", | |
| "- the vertical and the horizontal transformation takes effect differently **but consistently**\n", | |
| "<pre>\n", | |
| "\n", | |
| "\n", | |
| "\n", | |
| "\n", | |
| "\n", | |
| "\n", | |
| "\n", | |
| "\n", | |
| "\n", | |
| "</pre>" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "## Define a single concave polygon\n", | |
| "\n", | |
| "Modified shape from [unit test of](https://github.com/facebookresearch/maskrcnn-benchmark/blob/b8c5bcafa02e4a7489b5a79bf4609933f98c9599/tests/test_segmentation_mask.py) PR #150" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 145, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "''' ORIGINAL SHAPE\n", | |
| "poly = [[423.0, 306.5, 406.5, 277.0, 400.0, 271.5, 389.5, 277.0, 387.5, 292.0,\n", | |
| " 384.5, 295.0, 374.5, 220.0, 378.5, 210.0, 391.0, 200.5, 404.0, 199.5,\n", | |
| " 414.0, 203.5, 425.5, 221.0, 438.5, 297.0, 423.0, 306.5],\n", | |
| " [385.5, 240.0, 404.0, 234.5, 419.5, 234.0, 416.5, 219.0, 409.0, 209.5,\n", | |
| " 394.0, 207.5, 385.5, 213.0, 382.5, 221.0, 385.5, 240.0]] \n", | |
| "'''\n", | |
| "\n", | |
| "poly = [[423.0, 306.5, 406.5, 277.0, 400.0, 271.5, 389.5, 277.0, 387.5, 292.0,\n", | |
| " 384.5, 295.0, 374.5, 220.0, 378.5, 210.0, 391.0, 200.5, 404.0, 199.5,\n", | |
| " 414.0, 203.5, 425.5, 221.0, 438.5, 297.0, 423.0, 306.5]]\n", | |
| "width = 640\n", | |
| "height = 480\n", | |
| "size = (width, height)\n", | |
| "box = [350, 200, 400, 300]\n", | |
| "\n", | |
| "polygon = Polygons(poly, size, 'polygon')\n", | |
| "mask = Mask(poly, size, 'mask')" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 146, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "<matplotlib.image.AxesImage at 0x7f794243a048>" | |
| ] | |
| }, | |
| "execution_count": 146, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| }, | |
| { | |
| "data": { | |
| "image/png": 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AAAAAxcQhAAAAgGLiEAAAAEAxcQgAAACgmDgEAAAAUEwcAgAAACh2xTg0M3fPzHtm5iMz8+GZ+Ylt/9Nm5p0z8zvb72/b9s/M/NzMXJyZD83Mdx/3hwAAAADg2hxkcujLSf7RWus5Sb4nyStn5jlJXp3kXWutZyd517adJD+Q5Nnbz/1JXnfkqwYAAADgSNx0pRPWWp9I8ont8Rdm5qNJ7kzykiTft532xiTvTfKT2/43rbVWkvfNzG0zc8f2OgAAcOrs7O0e6+tfOHf+WF8fAL6Zq7rn0Mw8I8l3JXl/ktv3BZ/fS3L79vjOJA/ve9oj277Hv9b9M/PQzDz0WL50lcsGAAAA4ChccXLospn5c0n+c5J/uNb6/Mx89dhaa83Mupo3Xms9kOSBJPnWedpVPRcAAI7TcU8KHeT9TBMBcL0caHJoZm7OpTD0n9Zav7rt/v2ZuWM7fkeST277H01y976n37XtAwAAAOAGc8XJobk0IvT6JB9da/2bfYceTHJvktdsv9++b/+Pz8xbkjwvyefcbwgAgBvd9Z4WupLHr8ckEQDH5SCXlX1vkr+T5Ldn5vJ/of5ZLkWht87MfUk+nuSHt2PvSPKiJBeT/HGSHzvSFQMAwBG60aLQN+LSMwCOy0G+rew3k8w3OPyCJzh/JXnlIdcFAAAAwHVw4BtSAwDAWXJaJoa+mcufwQQRAIdxVV9lDwAAAMDZYnIIAIAaZ2FaCACOmskhAAAAgGLiEAAAZ97O3u6Znho6y58NgOPnsjIAAM6spmji5tQAXCuTQwAAAADFTA4BAHCmNE0LAcBRMDkEAAAAUMzkEAAAZ4KJoUvcewiAq2VyCAAAAKCYySEAAE41E0NPzAQRAAdlcggAAACgmDgEAAAAUEwcAgCAM8xldwBciTgEAAAAUMwNqQEAOJVMxBycm1MD8M2YHAIAAAAoJg4BAAAAFBOHAAA4dVxSdm129nb97QD4OuIQAAAAQDFxCAAAypggAmA/cQgAAACgmDgEAAAAUEwcAgAAACgmDgEAcGq4V87R8rcEIBGHAACgmuAGgDgEAAAAUEwcAgAAACgmDgEAAAAUE4cAAAD3HgIoJg4BAAAAFBOHAAAAAIqJQwAAAADFxCEAAACAYuIQAAAAQDFxCAAAAKCYOAQAAABQTBwCAAAAKCYOAQAAX7Wzt5udvd2TXgYA15E4BADAqXHh3PlcOHf+pJcBAGeKOAQAAABQTBwCAAAAKCYOAQAAABQThwAAAACKiUMAAAAAxcQhAAAAgGLiEAAAAEAxcQgAAACgmDgEAAB8nZ293ZNeAgDXiTgEAAAAUEwcAgAAACgmDgEAAAAUE4cAADh1Lpw7f9JLAIAzQxwCAAAAKCYOAQAAABQThwAAAACKiUMAAAAAxcQhAAAAgGLiEAAAAEAxcQgAAHhCO3u72dnbPellAHDMxCEAAACAYuIQAAAAQDFxCAAAAKCYOAQAAABQTBwCAAAAKCYOAQBwKl04dz4Xzp0/6WUAwKknDgEAAAAUE4cAAAAAiolDAAAAAMXEIQAA4Jva2dvNzt7uSS8DgGMiDgEAAAAUE4cAAAAAiolDAAAAAMXEIQAAAIBi4hAAAABAMXEIAAAAoJg4BAAAAFBMHAIAAAAoJg4BAHCqXTh3PhfOnT/pZQDAqSUOAQAAB7Kzt5udvd2TXgYAR0wcAgAAACgmDgEAAAAUE4cAAAAAiolDAAAAAMXEIQAAAIBi4hAAAABAMXEIAAAAoJg4BAAAAFBMHAIAAK7Kzt7uSS8BgCMkDgEAAAAUE4cAAAAAiolDAAAAAMXEIQAAzoQL587nwrnzJ70MADh1xCEAAACAYuIQAAAAQDFxCAAAAKCYOAQAAABQTBwCAAAAKCYOAQAAABQThwAAgKu2s7ebnb3dk14GAEdAHAIAAAAoJg4BAAAAFBOHAAAAAIqJQwAAAADFxCEAAM6UC+fOn/QSAOBUEYcAAAAAiolDAAAAAMXEIQAAAIBi4hAAAABAMXEIAAC4Zjt7u9nZ2z3pZQBwCOIQAAAAQDFxCAAAAKCYOAQAAABQTBwCAAAAKCYOAQAAABQThwAAAACKiUMAAAAAxcQhAAAAgGI3nfQCAADgqF04dz5JsrO3eyLvu9/1XgMAXC2TQwAAAADFTA4BAMAhPNG00BMdP+sTRJc/35X+HgDceMQhAAC4CtcaP/Y/76yHIgBOF5eVAQAAABQzOQQAAFdw1JdKtVxqBsDpYHIIAAAAoJjJIQAAeJzrdVNlE0QA3AhMDgEAAAAUE4cAAOCEXTh33lfAA3BiXFYGAAAbgQaARiaHAAAAAIqJQwAAnFmn7XKt07TWb8TNtQFOH3EIAAAAoJg4BAAAN5DTNu0EwOknDgEAAAAUE4cAAAAAiolDAABwA3J5GQDXizgEAAAAUEwcAgAAACgmDgEAAAAUE4cAAOAG5t5DABw3cQgAAACgmDgEAACJ6ZwjtLO3m5293ZNeBgAHJA4BAMApIF4BcFzEIQAAAIBi4hAAAGeemzoDwDcmDgEAAAAUE4cAAOCUMAEFwHEQhwAAAACKiUMAAHDKmCAC4CiJQwAAwLHY2dvNzt7uSS8DgCsQhwAAAACKiUMAAHBKubwMgKMgDgEAAAAUu+mkFwAAACfJ5A0A7UwOAQAAABQThwAAqHSW7tdzVj4HACfDZWUAANQQUQDg65kcAgAAACgmDgEAwBlwli6TA+D6EocAAAAAiolDAADAsdrZ283O3u5JLwOAb0AcAgAAACjm28oAAOAMuXzfoRtpUse9kABubOIQAACcQScZicQggNPlipeVzcytM/PfZ+Z/zMyHZ+Zntv3PnJn3z8zFmfnlmXnytv+WbfvidvwZx/sRAAAAALhWB5kc+lKS56+1/nBmbk7ymzPzX5O8Kslr11pvmZmfT3Jfktdtvz+z1nrWzLw8yc8m+VvHtH4AAOAGYFoI4PS64uTQuuQPt82bt5+V5PlJ3rbtf2OSl26PX7JtZzv+gpmZI1sxAAAAAEfmQN9WNjNPmpndJJ9M8s4kv5vks2utL2+nPJLkzu3xnUkeTpLt+OeSPP0oFw0AABzMcU/0XDh33tQQwCl3oBtSr7X+NMn5mbktyX9J8hcP+8Yzc3+S+5Pk1jzlsC8HAABcJ2IQwNlyoMmhy9Zan03yniR/JcltM3M5Lt2V5NHt8aNJ7k6S7fhTk3zqCV7rgbXWPWute27OLde4fAAAAAAO4yDfVvYd28RQZubPJnlhko/mUiT6oe20e5O8fXv84Lad7fi711rrKBcNAAAc3FFd+uUSMoCz6SCXld2R5I0z86RciklvXWv92sx8JMlbZuZfJflgktdv578+yZtn5mKSTyd5+TGsGwAAAIAjcMU4tNb6UJLveoL9H0vy3CfY/8UkLzuS1QEAACfGlBBAh6u65xAAAAAAZ8uBvq0MAAA4/S5PAu3s7V7xHAB6iEMAAIAoBFDMZWUAAAAAxUwOAQBAGVNCAOxncggAAACgmDgEAAAAUEwcAgAAACgmDgEAAAAUE4cAAAAAiolDAAAAAMXEIQAAAIBi4hAAAABAMXEIAAAAoJg4BAAAAFBMHAIAAAAoJg4BAAAAFBOHAAAAAIqJQwAAAADFxCEAAACAYuIQAAAAQDFxCAAAAKCYOAQAAABQTBwCAAAAKCYOAQAAABQThwAAAACKiUMAAAAAxcQhAAAAgGLiEAAAAEAxcQgAAACgmDgEAAAAUEwcAgAAACgmDgEAAAAUE4cAAAAAiolDAAAAAMXEIQAAAIBi4hAAAABAMXEIAAAAoJg4BAAAAFBMHAIAAAAoJg4BAAAAFBOHAAAAAIqJQwAAAADFxCEAAACAYuIQAAAAQDFxCAAAAKCYOAQAAABQTBwCAAAAKCYOAQAAABQThwAAAACKiUMAAAAAxcQhAAAAgGLiEAAAAEAxcQgAAACgmDgEAAAAUEwcAgAAACgmDgEAAAAUE4cAAAAAiolDAAAAAMXEIQAAAIBi4hAAAABAMXEIAAAAoJg4BAAAAFBMHAIAAAAoJg4BAAAAFBOHAAAAAIqJQwAAAADFxCEAAACAYuIQAAAAQDFxCAAAAKCYOAQAAABQTBwCAAAAKCYOAQAAABQThwAAAACKiUMAAAAAxcQhAAAAgGLiEAAAAEAxcQgAAACgmDgEAAAAUEwcAgAAACgmDgEAAAAUE4cAAAAAiolDAAAAAMXEIQAAAIBi4hAAAABAMXEIAAAAoJg4BAAAAFBMHAIAAAAoJg4BAAAAFBOHAAAAAIqJQwAAAADFxCEAAACAYuIQAAAAQDFxCAAAAKCYOAQAAABQTBwCAAAAKCYOAQAAABQThwAAAACKiUMAAAAAxcQhAAAAgGLiEAAAAEAxcQgAAACgmDgEAAAAUEwcAgAAACgmDgEAAAAUE4cAAAAAiolDAAAAAMXEIQAAAIBi4hAAAABAMXEIAAAAoJg4BAAAAFBMHAIAAAAoJg4BAAAAFBOHAAAAAIqJQwAAAADFxCEAAACAYuIQAAAAQDFxCAAAAKCYOAQAAABQTBwCAAAAKCYOAQAAABQThwAAAACKiUMAAAAAxcQhAAAAgGLiEAAAAEAxcQgAAACgmDgEAAAAUEwcAgAAACgmDgEAAAAUE4cAAAAAiolDAAAAAMXEIQAAAIBi4hAAAABAMXEIAAAAoJg4BAAAAFBMHAIAAAAoJg4BAAAAFBOHAAAAAIqJQwAAAADFxCEAAACAYuIQAAAAQDFxCAAAAKCYOAQAAABQTBwCAAAAKCYOAQAAABQThwAAAACKiUMAAAAAxcQhAAAAgGLiEAAAAEAxcQgAAACgmDgEAAAAUEwcAgAAACgmDgEAAAAUE4cAAAAAiolDAAAAAMXEIQAAAIBi4hAAAABAMXEIAAAAoJg4BAAAAFBMHAIAAAAoJg4BAAAAFBOHAAAAAIqJQwAAAADFxCEAAACAYuIQAAAAQDFxCAAAAKDYgePQzDxpZj44M7+2bT9zZt4/Mxdn5pdn5snb/lu27Yvb8Wccz9IBAAAAOKyrmRz6iSQf3bf9s0leu9Z6VpLPJLlv239fks9s+1+7nQcAAADADehAcWhm7kry4iT/YdueJM9P8rbtlDcmeen2+CXbdrbjL9jOBwAAAOAGc9DJoX+b5J8k+cq2/fQkn11rfXnbfiTJndvjO5M8nCTb8c9t53+Nmbl/Zh6amYcey5eucfkAAAAAHMYV49DM/GCST661fuso33it9cBa65611j0355ajfGkAAAAADuimA5zzvUn+xsy8KMmtSb41yb9LctvM3LRNB92V5NHt/EeT3J3kkZm5KclTk3zqyFcOAAAAwKFdcXJorfVP11p3rbWekeTlSd691vqRJO9J8kPbafcmefv2+MFtO9vxd6+11pGuGgAAAIAjcTXfVvZ4P5nkVTNzMZfuKfT6bf/rkzx92/+qJK8+3BIBAAAAOC4Huazsq9Za703y3u3xx5I89wnO+WKSlx3B2gAAAAA4ZoeZHAIAAADglBOHAAAAAIqJQwAAAADFxCEAAACAYuIQAAAAQDFxCAAAAKCYOAQAAABQTBwCAAAAKCYOAQAAABQThwAAAACKiUMAAAAAxcQhAAAAgGLiEAAAAEAxcQgAAACgmDgEAAAAUEwcAgAAACgmDgEAAAAUE4cAAAAAiolDAAAAAMXEIQAAAIBi4hAAAABAMXEIAAAAoJg4BAAAAFBMHAIAAAAoJg4BAAAAFBOHAAAAAIqJQwAAAADFxCEAAACAYuIQAAAAQDFxCAAAAKCYOAQAAABQTBwCAAAAKCYOAQAAABQThwAAAACKiUMAAAAAxcQhAAAAgGLiEAAAAEAxcQgAAACgmDgEAAAAUEwcAgAAACgmDgEAAAAUE4cAAAAAiolDAAAAAMXEIQAAAIBi4hAAAABAMXEIAAAAoJg4BAAAAFBMHAIAAAAoJg4BAAAAFBOHAAAAAIqJQwD/v727CbWtrMMA/vy5aoaF5gcSXksjSRzkVSKUREwprCQbSBhFIoITBwZFWJMocNAkKwoh1LLoS25Z0iASFWqSpWlpWmRiqKjX8qMyUKx/g73udXcbiPfCWWvz/n5w2Ot91xq8g4dz1nn2+gAAABiYcggAAABgYMohAAC31CPKAAAHb0lEQVQAgIEphwAAAAAGphwCAAAAGJhyCAAAAGBgyiEAAACAgSmHAAAAAAamHAIAAAAYmHIIAAAAYGDKIQAAAICBKYcAAAAABqYcAgAAABiYcggAAABgYMohAAAAgIEphwAAAAAGphwCAAAAGJhyCAAAAGBgyiEAAACAgSmHAAAAAAamHAIAAAAYmHIIAAAAYGDKIQAAAICBKYcAAAAABqYcAgAAABiYcggAAABgYMohAAAAgIEphwAAAAAGphwCAAAAGJhyCAAAAGBgyiEAAACAgSmHAAAAAAamHAIAAAAYmHIIAAAAYGDKIQAAAICBKYcAAAAABqYcAgAAABiYcggAAABgYMohAAAAgIEphwAAAAAGphwCAAAAGJhyCAAAAGBgyiEAAACAgSmHAAAAAAamHAIAAAAYmHIIAAAAYGDKIQAAAICBKYcAAAAABqYcAgAAABiYcggAAABgYMohAAAAgIEphwAAAAAGphwCAAAAGJhyCAAAAGBgyiEAAACAgSmHAAAAAAamHAIAAAAYmHIIAAAAYGDKIQAAAICBKYcAAAAABqYcAgAAABiYcggAAABgYMohAAAAgIEphwAAAAAGphwCAAAAGJhyCAAAAGBgyiEAAACAgSmHAAAAAAamHAIAAAAYmHIIAAAAYGDKIQAAAICBKYcAAAAABqYcAgAAABiYcggAAABgYMohAAAAgIEphwAAAAAGphwCAAAAGJhyCAAAAGBgyiEAAACAgSmHAAAAAAamHAIAAAAYmHIIAAAAYGDKIQAAAICBVXfPvYZU1ZNJnkvy17nXAq/QkZFbNpPssonklk0kt2wq2WUTye3/e2N3H/VyBy2iHEqSqrqju9829zrglZBbNpXssonklk0kt2wq2WUTye2+c1sZAAAAwMCUQwAAAAADW1I59LW5FwD7QG7ZVLLLJpJbNpHcsqlkl00kt/toMc8cAgAAAGDrLenKIQAAAAC22OzlUFWdW1V/rKoHquqKudcD66rquqraVVX3rs0dXlU3V9Wfps/XTfNVVV+esvy7qjp1vpUzsqo6tqpuq6r7qur3VXX5NC+7LFZVHVxVv6qq3065/ew0f3xV3T7l8/tVddA0/6pp/MC0/7g51w9Vta2q7qqqn0xj2WXRquqhqrqnqu6uqjumOecKLF5VHVZVO6vqD1V1f1WdLrv7b9ZyqKq2JflqkvckOSnJh6rqpDnXBHv5RpJz95q7Iskt3X1CklumcbLK8QnTz6VJrt6iNcLeXkzy8e4+KclpSS6bfrfKLkv2fJKzu/vkJDuSnFtVpyX5fJKruvvNSZ5Ocsl0/CVJnp7mr5qOgzldnuT+tbHssgne2d071l797VyBTfClJD/t7hOTnJzV717Z3U9zXzn09iQPdPeD3f1Cku8lOX/mNcEe3f3zJE/tNX1+kuun7euTfGBt/pu98sskh1XV67dmpfCS7n6su38zbf8jqz+Yx0R2WbApf/+chgdOP53k7CQ7p/m9c7s7zzuTnFNVtUXLhf9RVduTvC/JNdO4IrtsJucKLFpVHZrkzCTXJkl3v9Ddz0R299vc5dAxSR5eGz8yzcGSHd3dj03bjyc5etqWZxZnul3hlCS3R3ZZuOm2nLuT7Epyc5I/J3mmu1+cDlnP5p7cTvufTXLE1q4Y9vhikk8m+c80PiKyy/J1kp9V1Z1Vdek051yBpTs+yZNJvj7dyntNVR0S2d1vc5dDsNF69bo/r/xjkarqNUl+kORj3f339X2yyxJ197+7e0eS7VldXXzizEuCl1VV5yXZ1d13zr0WeIXO6O5Ts7rt5rKqOnN9p3MFFuqAJKcmubq7T0nyXF66hSyJ7O6rucuhR5McuzbePs3Bkj2x+1LE6XPXNC/PLEZVHZhVMfTt7v7hNC27bITp8vDbkpye1eXfB0y71rO5J7fT/kOT/G2LlwpJ8o4k76+qh7J6RMLZWT0PQ3ZZtO5+dPrcleTGrEp55wos3SNJHunu26fxzqzKItndT3OXQ79OcsL0NoeDklyY5KaZ1wQv56YkF03bFyX58dr8R6cn4p+W5Nm1Sxthy0zPrrg2yf3d/YW1XbLLYlXVUVV12LT96iTvyup5WbcluWA6bO/c7s7zBUlunb4phC3V3Z/q7u3dfVxW57K3dveHI7ssWFUdUlWv3b2d5N1J7o1zBRauux9P8nBVvWWaOifJfZHd/VZz/y2qqvdmdZ/2tiTXdfeVsy4I1lTVd5OcleTIJE8k+UySHyW5IckbkvwlyQe7+6npH/KvZPV2s38lubi775hj3Yytqs5I8osk9+Sl5198OqvnDskui1RVb83qAZLbsvry6obu/lxVvSmrqzEOT3JXko909/NVdXCSb2X1TK2nklzY3Q/Os3pYqaqzknyiu8+TXZZsyueN0/CAJN/p7iur6og4V2DhqmpHVi8AOCjJg0kuznTuENndZ7OXQwAAAADMZ+7bygAAAACYkXIIAAAAYGDKIQAAAICBKYcAAAAABqYcAgAAABiYcggAAABgYMohAAAAgIEphwAAAAAG9l89AbUPv91WHQAAAABJRU5ErkJggg==\n", | |
| "text/plain": [ | |
| "<Figure size 1440x1440 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| }, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "plt.figure(figsize=(20, 20))\n", | |
| "plt.title('Mask')\n", | |
| "plt.imshow(mask.convert('mask'))" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "## TEST: Crop [PASS]" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 147, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "mask_from_poly_crop = polygon.crop(box).convert('mask')\n", | |
| "mask_crop = mask.crop(box).convert('mask')\n", | |
| "diff = torch.abs(mask_from_poly_crop.float() - mask_crop.float())" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 148, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "<matplotlib.image.AxesImage at 0x7f7942369668>" | |
| ] | |
| }, | |
| "execution_count": 148, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| }, | |
| { | |
| "data": { | |
| "image/png": 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qHjL3KcdV1QOr6tcWtPclSZ5SVV9a012RnpbD+8xnVtUt5i/2P5DpLolJ8tocemrSIhcm+UymOykdV1XflelUqpdusPyrMt0d94k5PBw5K1OAsXZkzlY9K9PojfPnOpGqOr2m29lvdNv012a6xMIjq2pfVf3LTKfO/vGqZR5dVfesqhMzXaT75fMpt9dnCp42eq5elumacH+wzryTMo1Qvbqm6xI9fWXGJn8vSZL5tLWfTfIXVbX6zmcL+/K57vxfmerQYcYYf7U6bFvn319tsOkLkjxhfp5OznT9qd/bYNk/zHSq2w/O7XlakreP6YLjK9t6Sk0Xz/6aTHeh+71dXndPEgKRJBlj/P0Y46INZv9wkp+rqmszvSlWd1hfnmn4+DWZ0uc3ZU3wMHeE/zzTUPgX3NQP+5kuKPfpTBfgfHOmo54vuInbOsQY4+WZhlf+q0zJ/4eT/EKSVy9Y7QWZfte/zHR09nNzG1d7U6ajAa9L8htjuv7EyqkIr88Gt/BcxyOS3Hlu2x9mum7DXyxY/oJMF+9bW9QfmeT8McZ1W9wv0JjasFy14Wi/HjXdDvpTY7pVPLCOMca7Mh14e0umPuLrM53atNqFmS4W/LFM13h56DwS4maZwuaDST6R6Yv6v5m3e32mL5qPzgbGGP9uXv8pmUKoyzONSHrVgib/QqZbYL89yTuSvHWetuJDmU5nOpjplKx/vfJleIzx1iSfrKr7Ltj+StuuzxT6PHD+vX87yWNXfbFeu/xnk7wiyV1y+GjHR2Ua3XiTjDE+kWkU5A1JLpz7yddlurbLpRus8/FMAdhPZjqd7KeS/MAY42OrFnthptfoQ5ku0v3jq+b9lxx+raWVbX92jPEX8++81rMz3cr+Y5kO2v7Zqnkb/r2s2f75mUKp11fVnefJj5jbtJEHJXnjfGrethlj/FmSX8t03bkPZDptbnWwdXHNdzgb0zWqfjDTe+SqTBfpfviqzT090wWbL8tUx3593v6urbtX1aGnpwLbYe5w35fkuLHBBftquoPC+UnOGtv8RqyqxyY5b4zxbaumHZ9piOd3jDE+sp37A2Bzu10bbqyqekWmW/u+djfbAV1V1ZdmukvifTYIDLZ7f9+V5EVjjDsuWOZ7kvzwGGOz09Buyv6fluTuY4zVd7r9skxfvO8zpmtfLoWqemOm5+p5C5b560x3uHzbRsscDVX1oCSPGWM8bMEyFyZ5whjjnUevZewWIRDsgK180N/BfZ+Y6Ujyb48x1jvXGIBdsJu1AWAzWwmBdnDfpyR5W6aw4i+P9v5vrK2EQLCsnA4Gx5CqOifTcOAPZzolAgAAllZV/VCmU9n+dC8EQLDXGQkEAAAA0ICRQAAAAAAN7DuSlavqe5P8VpKbJ3neGONXFi1/izp+nJBbHskuAZIkd/+Gz+x2E7bV+y+/IR/7xD/Ubrdju6kTwG5RJ/YGdQJge3wun87147pN68RNDoGq6uZJ/mOSByS5IsnfVtVr5tskruuE3DL3rbNv6i4Bvmj//gO73YRtddY5l+92E7adOgHsJnVi+akTANvnwvG6LS13JKeDnZXk0jHGe8cY1yd5aZIHH8H2ADi2qBMALKJOABxlRxICnZ7pKu4rrpinAUCiTgCwmDoBcJQd0TWBtqKqzktyXpKckBN3encA7DHqBACLqBMA2+dIRgJ9MMmdVj2+4zztEGOM544xzhxjnHlcjj+C3QGwx6gTACyiTgAcZUcSAv1tkrtV1V2q6hZJHp7kNdvTLACOAeoEAIuoEwBH2U0+HWyM8fmq+tEk+zPd0vEFY4yLt61lQBv7Dx5bd3Bhok4A20WdODapEwBH3xFdE2iM8dokr92mtgBwjFEnAFhEnQA4uo7kdDAAAAAA9gghEAAAAEADQiAAAACABoRAAAAAAA0c0YWhAdZyBxcAFlEnAGD3GAkEAAAA0IAQCAAAAKABIRAAAABAA0IgAAAAgAaEQAAAAAANCIEAAAAAGnCLeOBGc3tfABZRJwBgORkJBAAAANCAEAgAAACgASEQAAAAQANCIAAAAIAGhEAAAAAADbg7GLAhd3cBYBF1AgD2FiOBAAAAABoQAgEAAAA0IAQCAAAAaEAIBAAAANCAEAgAAACgASEQAAAAQANuEQ9NuI0vAIuoEwBw7DMSCAAAAKABIRAAAABAA0IgAAAAgAaEQAAAAAANCIEAAAAAGnB3MDiGuLMLAIuoEwDQm5FAAAAAAA0IgQAAAAAaEAIBAAAANCAEAgAAAGhACAQAAADQgBAIAAAAoAG3iIc9yC1+AVhEnQAA1mMkEAAAAEADQiAAAACABoRAAAAAAA0IgQAAAAAaEAIBAAAANCAEAgAAAGjALeJhF7mFLwCLqBMAwHYyEggAAACgASEQAAAAQANCIAAAAIAGhEAAAAAADQiBAAAAABpwdzA4CtzdBYBF1AkA4GgwEggAAACgASEQAAAAQANCIAAAAIAGhEAAAAAADQiBAAAAABoQAgEAAAA04BbxsE3c3heARdQJAGC3GQkEAAAA0IAQCAAAAKABIRAAAABAA0IgAAAAgAaEQAAAAAANCIEAAAAAGhACAQAAADQgBAIAAABoQAgEAAAA0IAQCAAAAKABIRAAAABAA0IgAAAAgAaEQAAAAAANCIEAAAAAGhACAQAAADQgBAIAAABoQAgEAAAA0IAQCAAAAKCBfbvdANhr9h88sNtNAGCJqRMAwLIyEggAAACgASEQAAAAQANCIAAAAIAGhEAAAAAADQiBAAAAABoQAgEAAAA0IAQCAAAAaEAIBAAAANCAEAgAAACgASEQAAAAQANCIAAAAIAGhEAAAAAADQiBAAAAABrYt9sNgGW0/+CB3W4CAEtMnQAA9iIjgQAAAAAaEAIBAAAANCAEAgAAAGhACAQAAADQgBAIAAAAoAEhEAAAAEADQiAAAACABoRAAAAAAA0IgQAAAAAaEAIBAAAANCAEAgAAAGhACAQAAADQgBAIAAAAoAEhEAAAAEADQiAAAACABoRAAAAAAA0IgQAAAAAaEAIBAAAANLBvtxsAu2X/wQO73QQAlpg6AQAca4wEAgAAAGhACAQAAADQgBAIAAAAoAEhEAAAAEADQiAAAACABoRAAAAAAA0IgQAAAAAaEAIBAAAANCAEAgAAAGhACAQAAADQgBAIAAAAoAEhEAAAAEADQiAAAACABjYNgarqTlX1hqp6V1VdXFVPnKefUlV/XlXvmf+/7c43F4Blo04AsIg6AbA8tjIS6PNJfnKMcc8k35TkR6rqnkmenOR1Y4y7JXnd/BiAftQJABZRJwCWxKYh0BjjyjHGW+efr01ySZLTkzw4yfnzYucnechONRKA5aVOALCIOgGwPPbdmIWr6s5J7pPkwiSnjjGunGd9KMmpG6xzXpLzkuSEnHhT2wnAHqBOALCIOgGwu7Z8YeiqulWSVyT5f8cY16yeN8YYScZ6640xnjvGOHOMceZxOf6IGgvA8lInAFhEnQDYfVsKgarquEwd9ovHGK+cJ3+4qu4wz79Dko/sTBMBWHbqBACLqBMAy2ErdwerJM9PcskY41mrZr0mybnzz+cmefX2Nw+AZadOALCIOgGwPLZyTaBvTfKYJO+oqgPztJ9N8itJ/qCqnpDksiQP25kmArDk1AkAFlEnAJbEpiHQGOPNSWqD2Wdvb3MA2GvUCQAWUScAlseWLwwNAAAAwN4lBAIAAABoQAgEAAAA0IAQCAAAAKABIRAAAABAA0IgAAAAgAaEQAAAAAANCIEAAAAAGhACAQAAADQgBAIAAABoQAgEAAAA0IAQCAAAAKABIRAAAABAA0IgAAAAgAaEQAAAAAANCIEAAAAAGhACAQAAADQgBAIAAABoQAgEAAAA0IAQCAAAAKABIRAAAABAA0IgAAAAgAaEQAAAAAANCIEAAAAAGhACAQAAADQgBAIAAABoQAgEAAAA0IAQCAAAAKABIRAAAABAA0IgAAAAgAaEQAAAAAANCIEAAAAAGhACAQAAADQgBAIAAABoQAgEAAAA0IAQCAAAAKCBfbvdANgt55x2xobz9h88cBRbAsAyUicAgGONkUAAAAAADQiBAAAAABoQAgEAAAA0IAQCAAAAaEAIBAAAANCAEAgAAACgASEQAAAAQANCIAAAAIAGhEAAAAAADQiBAAAAABoQAgEAAAA0IAQCAAAAaEAIBAAAANCAEAgAAACgASEQAAAAQANCIAAAAIAGhEAAAAAADQiBAAAAABoQAgEAAAA0IAQCAAAAaEAIBAAAANCAEAgAAACgASEQAAAAQANCIAAAAIAGhEAAAAAADQiBAAAAABoQAgEAAAA0IAQCAAAAaEAIBAAAANCAEAgAAACgASEQAAAAQANCIAAAAIAGhEAAAAAADQiBAAAAABoQAgEAAAA0IAQCAAAAaEAIBAAAANCAEAgAAACgASEQAAAAQANCIAAAAIAGhEAAAAAADQiBAAAAABoQAgEAAAA0IAQCAAAAaEAIBAAAANDAvt1uACyjc047Y8N5+w8eOIotAWAZqRMAwF5kJBAAAABAA0IgAAAAgAaEQAAAAAANCIEAAAAAGhACAQAAADQgBAIAAABoQAgEAAAA0IAQCAAAAKABIRAAAABAA0IgAAAAgAaEQAAAAAANCIEAAAAAGhACAQAAADQgBAIAAABoQAgEAAAA0IAQCAAAAKABIRAAAABAA0IgAAAAgAaEQAAAAAANCIEAAAAAGhACAQAAADQgBAIAAABoQAgEAAAA0IAQCAAAAKABIRAAAABAA0IgAAAAgAaEQAAAAAANCIEAAAAAGhACAQAAADQgBAIAAABoQAgEAAAA0IAQCAAAAKABIRAAAABAA0IgAAAAgAaEQAAAAAANCIEAAAAAGhACAQAAADQgBAIAAABoQAgEAAAA0MCWQ6CqunlVva2q/nh+fJequrCqLq2ql1XVLXaumQAsO3UCgEXUCYDdd2NGAj0xySWrHv9qkt8cY9w1yVVJnrCdDQNgz1EnAFhEnQDYZVsKgarqjkm+P8nz5seV5P5JXj4vcn6Sh+xEAwFYfuoEAIuoEwDLYasjgZ6d5KeSfGF+fLskV48xPj8/viLJ6eutWFXnVdVFVXXRDbnuiBoLwNJSJwBYRJ0AWAKbhkBV9QNJPjLG+LubsoMxxnPHGGeOMc48LsfflE0AsMTUCQAWUScAlse+LSzzrUn+aVV9X5ITktw6yW8lObmq9s3p/R2TfHDnmgnAElMnAFhEnQBYEpuOBBpj/MwY445jjDsneXiS148xHpXkDUkeOi92bpJX71grAVha6gQAi6gTAMvjxtwdbK2fTvKkqro00zm9z9+eJgFwjFAnAFhEnQA4yrZyOtgXjTHemOSN88/vTXLW9jcJgL1KnQBgEXUCYHcdyUggAAAAAPYIIRAAAABAA0IgAAAAgAaEQAAAAAAN3KgLQwPJOaedse70/QcPHOWWALCM1AkAYFkZCQQAAADQgBAIAAAAoAEhEAAAAEADQiAAAACABoRAAAAAAA0IgQAAAAAaEAIBAAAANCAEAgAAAGhACAQAAADQgBAIAAAAoAEhEAAAAEADQiAAAACABoRAAAAAAA0IgQAAAAAaEAIBAAAANCAEAgAAAGhACAQAAADQgBAIAAAAoAEhEAAAAEADQiAAAACABoRAAAAAAA0IgQAAAAAaEAIBAAAANCAEAgAAAGhACAQAAADQgBAIAAAAoAEhEAAAAEADQiAAAACABoRAAAAAAA0IgQAAAAAaEAIBAAAANCAEAgAAAGhACAQAAADQgBAIAAAAoAEhEAAAAEADQiAAAACABoRAAAAAAA0IgQAAAAAaEAIBAAAANCAEAgAAAGhACAQAAADQgBAIAAAAoAEhEAAAAEADQiAAAACABoRAAAAAAA3s2+0GwLHinNPO2HDe/oMHjmJLAFhG6gQAsNuMBAIAAABoQAgEAAAA0IAQCAAAAKABIRAAAABAA0IgAAAAgAaEQAAAAAANCIEAAAAAGhACAQAAADQgBAIAAABoQAgEAAAA0IAQCAAAAKABIRAAAABAA0IgAAAAgAaEQAAAAAANCIEAAAAAGhACAQAAADQgBAIAAABoQAgEAAAA0IAQCAAAAKABIRAIpW/CAAARNElEQVQAAABAA0IgAAAAgAaEQAAAAAANCIEAAAAAGhACAQAAADQgBAIAAABoQAgEAAAA0IAQCAAAAKABIRAAAABAA0IgAAAAgAaEQAAAAAANCIEAAAAAGhACAQAAADQgBAIAAABoQAgEAAAA0IAQCAAAAKABIRAAAABAA0IgAAAAgAaEQAAAAAANCIEAAAAAGhACAQAAADQgBAIAAABoQAgEAAAA0IAQCAAAAKABIRAAAABAA0IgAAAAgAaEQAAAAAANCIEAAAAAGhACAQAAADQgBAIAAABoQAgEAAAA0IAQCAAAAKCBfbvdAOjgnNPOWHf6/oMHjnJLAFhG6gQAcDQYCQQAAADQgBAIAAAAoAEhEAAAAEADQiAAAACABoRAAAAAAA0IgQAAAAAaEAIBAAAANCAEAgAAAGhACAQAAADQgBAIAAAAoAEhEAAAAEADQiAAAACABoRAAAAAAA0IgQAAAAAaEAIBAAAANCAEAgAAAGhACAQAAADQgBAIAAAAoAEhEAAAAEADWwqBqurkqnp5Vb27qi6pqm+uqlOq6s+r6j3z/7fd6cYCsJzUCQAWUScAlsNWRwL9VpI/G2N8TZJ7J7kkyZOTvG6Mcbckr5sfA9CTOgHAIuoEwBLYNASqqtsk+Y4kz0+SMcb1Y4yrkzw4yfnzYucnechONRKA5aVOALCIOgGwPLYyEuguST6a5Her6m1V9byqumWSU8cYV87LfCjJqeutXFXnVdVFVXXRDblue1oNwDJRJwBYRJ0AWBJbCYH2JfnGJP9pjHGfJJ/OmqGaY4yRZKy38hjjuWOMM8cYZx6X44+0vQAsH3UCgEXUCYAlsZUQ6IokV4wxLpwfvzxTJ/7hqrpDksz/f2RnmgjAklMnAFhEnQBYEpuGQGOMDyW5vKruMU86O8m7krwmybnztHOTvHpHWgjAUlMnAFhEnQBYHvu2uNyPJXlxVd0iyXuTPD5TgPQHVfWEJJcledjONBGAPUCdAGARdQJgCWwpBBpjHEhy5jqzzt7e5gCwF6kTACyiTgAsh61cEwgAAACAPU4IBAAAANCAEAgAAACgASEQAAAAQANCIAAAAIAGhEAAAAAADQiBAAAAABoQAgEAAAA0IAQCAAAAaEAIBAAAANCAEAgAAACgASEQAAAAQANCIAAAAIAGhEAAAAAADQiBAAAAABoQAgEAAAA0IAQCAAAAaGDfbjcAOjvntDM2nLf/4IGj2BIAlpE6AQBsJyOBAAAAABoQAgEAAAA0IAQCAAAAaEAIBAAAANCAEAgAAACgASEQAAAAQANCIAAAAIAGhEAAAAAADQiBAAAAABoQAgEAAAA0IAQCAAAAaEAIBAAAANCAEAgAAACgASEQAAAAQANCIAAAAIAGhEAAAAAADQiBAAAAABoQAgEAAAA0IAQCAAAAaEAIBAAAANCAEAgAAACgASEQAAAAQANCIAAAAIAGhEAAAAAADQiBAAAAABoQAgEAAAA0IAQCAAAAaEAIBAAAANCAEAgAAACgASEQAAAAQANCIAAAAIAGhEAAAAAADQiBAAAAABoQAgEAAAA0IAQCAAAAaEAIBAAAANCAEAgAAACgASEQAAAAQANCIAAAAIAGhEAAAAAADQiBAAAAABoQAgEAAAA0IAQCAAAAaEAIBAAAANCAEAgAAACgASEQAAAAQANCIAAAAIAGhEAAAAAADQiBAAAAABoQAgEAAAA0IAQCAAAAaEAIBAAAANDAvt1uALC+c047Y8N5+w8eOIot4aZY9Pqt53+Pj+9QS4BjlTqxt6kTAOwGI4EAAAAAGhACAQAAADQgBAIAAABoQAgEAAAA0IAQCAAAAKABIRAAAABAA24RD7CJG3sbXwB6UScA2CuMBAIAAABoQAgEAAAA0IAQCAAAAKABIRAAAABAA0IgAAAAgAbcHQxoxR1cAFhEnQDgWGYkEAAAAEADQiAAAACABoRAAAAAAA0IgQAAAAAaEAIBAAAANCAEAgAAAGjALeKBPcktfAFYRJ0AgMMZCQQAAADQgBAIAAAAoAEhEAAAAEADQiAAAACABoRAAAAAAA24Oxiw69zBBYBF1AkA2B5GAgEAAAA0IAQCAAAAaEAIBAAAANCAEAgAAACgASEQAAAAQANCIAAAAIAGhEAAAAAADQiBAAAAABoQAgEAAAA0IAQCAAAAaEAIBAAAANCAEAgAAACggX273QCgj3NOO2O3mwDAElMnAGBnGQkEAAAA0IAQCAAAAKABIRAAAABAA0IgAAAAgAaEQAAAAAANCIEAAAAAGnCLeNiDNrqF7v6DB45ySwBYRuoEALAeI4EAAAAAGhACAQAAADQgBAIAAABoQAgEAAAA0IAQCAAAAKABIRAAAABAA1sKgarqJ6rq4qp6Z1W9pKpOqKq7VNWFVXVpVb2sqm6x040FYDmpEwAsok4ALIdNQ6CqOj3Jjyc5c4zxdUlunuThSX41yW+OMe6a5KokT9jJhgKwnNQJABZRJwCWx1ZPB9uX5Euqal+SE5NcmeT+SV4+zz8/yUO2v3kA7BHqBACLqBMAS2DTEGiM8cEkv5HkA5k6608m+bskV48xPj8vdkWS09dbv6rOq6qLquqiG3Ld9rQagKWhTgCwiDoBsDy2cjrYbZM8OMldkpyW5JZJvnerOxhjPHeMceYY48zjcvxNbigAy0mdAGARdQJgeWzldLDvTvK+McZHxxg3JHllkm9NcvI8nDNJ7pjkgzvURgCWmzoBwCLqBMCS2EoI9IEk31RVJ1ZVJTk7ybuSvCHJQ+dlzk3y6p1pIgBLTp0AYBF1AmBJbOWaQBdmumDbW5O8Y17nuUl+OsmTqurSJLdL8vwdbCcAS0qdAGARdQJgeezbfJFkjPH0JE9fM/m9Sc7a9hYBsOeoEwAsok4ALIet3iIeAAAAgD1MCAQAAADQgBAIAAAAoAEhEAAAAEADQiAAAACABoRAAAAAAA0IgQAAAAAaEAIBAAAANCAEAgAAAGhACAQAAADQgBAIAAAAoAEhEAAAAEADQiAAAACABoRAAAAAAA0IgQAAAAAaEAIBAAAANCAEAgAAAGhACAQAAADQgBAIAAAAoAEhEAAAAEADQiAAAACABoRAAAAAAA0IgQAAAAAa2LfbDQCOLeecdsZuNwGAJaZOAMDuMRIIAAAAoAEhEAAAAEADQiAAAACABoRAAAAAAA0IgQAAAAAaEAIBAAAANCAEAgAAAGhACAQAAADQgBAIAAAAoAEhEAAAAEADQiAAAACABoRAAAAAAA0IgQAAAAAa2LfbDQC2zzmnnbHhvP0HDxzFlgCwjNQJAOjNSCAAAACABoRAAAAAAA0IgQAAAAAaEAIBAAAANCAEAgAAAGhACAQAAADQgBAIAAAAoAEhEAAAAEADQiAAAACABoRAAAAAAA0IgQAAAAAaEAIBAAAANCAEAgAAAGhACAQAAADQgBAIAAAAoAEhEAAAAEADQiAAAACABoRAAAAAAA0IgQAAAAAaEAIBAAAANCAEAgAAAGhACAQAAADQgBAIAAAAoAEhEAAAAEADQiAAAACABoRAAAAAAA0Igf5Pe/cTqlldxgH8+zCjGESYKWKOZZEQsyiDCKMWIkRWki0iigIXLVsYFGFtoqBFm7JFmyjJRVRSUdJOTKiV/bPoj0QWSMXkFCXVxrCeFu95nXuvo80Ivud6n88HhnnPuRfOj2fe93zhO+ecFwAAAGAAJRAAAADAAEogAAAAgAGUQAAAAAADKIEAAAAABji+9gKA5543v/jatZcAwCEmJwDgcHIlEAAAAMAASiAAAACAAZRAAAAAAAMogQAAAAAGUAIBAAAADKAEAgAAABjAV8QDT8lX/ALwdOQEADy3uBIIAAAAYAAlEAAAAMAASiAAAACAAZRAAAAAAAMogQAAAAAGUAIBAAAADOAr4mEIX+MLwNOREwBw9LkSCAAAAGAAJRAAAADAAEogAAAAgAGUQAAAAAADKIEAAAAABlACAQAAAAygBAIAAAAYQAkEAAAAMIASCAAAAGAAJRAAAADAAEogAAAAgAGUQAAAAAADKIEAAAAABlACAQAAAAygBAIAAAAYQAkEAAAAMIASCAAAAGAAJRAAAADAAEogAAAAgAGUQAAAAAADKIEAAAAABlACAQAAAAygBAIAAAAYQAkEAAAAMIASCAAAAGAAJRAAAADAAEogAAAAgAGUQAAAAAADKIEAAAAABlACAQAAAAygBAIAAAAYQAkEAAAAMIASCAAAAGAAJRAAAADAAEogAAAAgAGUQAAAAAADKIEAAAAABlACAQAAAAxQ3b27g1X9JcnDy+alSf66s4MfXuZgBlvmsDF1Di/t7svWXsTa5MRZmYMZbJnDxtQ5yInIiadgDmawZQ6zZ3BOObHTEmjfgat+3N2vXeXgh4g5mMGWOWyYA1veCxvmYAZb5rBhDmx5L2yYgxlsmYMZnAu3gwEAAAAMoAQCAAAAGGDNEugLKx77MDEHM9gyhw1zYMt7YcMczGDLHDbMgS3vhQ1zMIMtczCD/2u1ZwIBAAAAsDtuBwMAAAAYQAkEAAAAMMAqJVBV3VhVv6mqh6rqtjXWsIaquqOqTlfVL/fsu6Sq7qmq3y5/v3DNNT7bquqqqrqvqn5dVb+qqluX/dPmcFFV/bCqfr7M4RPL/pdV1f3LZ+PrVXXh2mt9tlXVsap6oKq+u2yPmwH7yYi5GZHIiURGHCQnOEhOyAk5ISf2khPnZ+clUFUdS/L5JG9JcjLJe6rq5K7XsZIvJ7nxwL7bktzb3dckuXfZPsoeT/Kh7j6Z5LokH1j+/afN4bEkN3T3q5Ncm+TGqrouyaeTfLa7X5Hk70nev+Iad+XWJA/u2Z44AxYyYnxGJHIikREHyQmeICfkROREIicOkhPnYY0rgV6X5KHu/n13/zvJ15LcvMI6dq67v5/kbwd235zkzuX1nUnesdNF7Vh3n+runy6v/5nNh/XKzJtDd/e/ls0Llj+d5IYk31j2H/k5VNWJJG9L8sVluzJsBjyJjNhv1LkxkROJjNhLTnAWcmK/MefGLTkhJ/aSE+dvjRLoyiR/2LP9x2XfVJd396nl9Z+TXL7mYnapqq5O8pok92fgHJbLFn+W5HSSe5L8Lsmj3f348isTPhu3J/lIkv8u2y/KvBmwn4zYb9y5ca/JOSEjniAnOEhO7Dfq3HiQnJATkRPnzYOhD5Hu7mwa3COvqp6f5JtJPtjd/9j7sylz6O7/dPe1SU5k879ar1x5STtVVTclOd3dP1l7LfBcMOXcuDU9J6ZnRCIn4HxNODfuJSfkhJx4Zo6vcMw/Jblqz/aJZd9Uj1TVFd19qqquyKbJPdKq6oJsTthf6e5vLbvHzWGrux+tqvuSvD7JxVV1fGmuj/pn4w1J3l5Vb01yUZIXJPlcZs2AJ5MR+408N8qJMwZnRCInODs5sd/Ic6OcOENOyInztcaVQD9Kcs3yxO4Lk7w7yd0rrOOwuDvJLcvrW5J8Z8W1POuWezS/lOTB7v7Mnh9Nm8NlVXXx8vp5Sd6Uzf3M9yV55/JrR3oO3f3R7j7R3Vdncx74Xne/N4NmwFnJiP1GnRsTOZHIiC05wVOQE/uNOTduyQk5sSUnnpnaXCm344NumrrbkxxLckd3f2rni1hBVX01yfVJLk3ySJKPJ/l2kruSvCTJw0ne1d0HH/h2ZFTVG5P8IMkvcua+zY9lcx/vpDm8KpuHlB3Lpoy9q7s/WVUvz+YBh5ckeSDJ+7r7sfVWuhtVdX2SD3f3TVNnwBkyYm5GJHIikRFnIyfYS07IicgJOXGAnDh3q5RAAAAAAOyWB0MDAAAADKAEAgAAABhACQQAAAAwgBIIAAAAYAAlEAAAAMAASiAAAACAAZRAAAAAAAP8D+C/aq7fjSltAAAAAElFTkSuQmCC\n", | |
| "text/plain": [ | |
| "<Figure size 1440x1440 with 3 Axes>" | |
| ] | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| }, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "plt.figure(figsize=(20, 20))\n", | |
| "\n", | |
| "plt.subplot(1, 3, 1)\n", | |
| "plt.title('Mask from Crop(Poly)')\n", | |
| "plt.imshow(mask_from_poly_crop)\n", | |
| "\n", | |
| "plt.subplot(1, 3, 2)\n", | |
| "plt.title('Mask from Crop(Mask)')\n", | |
| "plt.imshow(mask_crop)\n", | |
| "\n", | |
| "plt.subplot(1, 3, 3)\n", | |
| "plt.title('abs( Crop(Poly) - Crop(Mask) ) = %f'%diff.sum())\n", | |
| "plt.imshow(diff)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "### Observation\n", | |
| "\n", | |
| "- Surprisingly looks like a seducing lady showing her ankles from behind the door\n", | |
| "- Otherwise OK" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "## TEST: Resize [FAIL]" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 149, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "new_size = (600, 500)\n", | |
| "mask_from_poly_resize = polygon.resize(new_size).convert('mask')\n", | |
| "mask_resize = mask.resize(new_size).convert('mask')\n", | |
| "diff = torch.abs(mask_from_poly_resize.float() - mask_resize.float())" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 150, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "<matplotlib.image.AxesImage at 0x7f794225d278>" | |
| ] | |
| }, | |
| "execution_count": 150, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| }, | |
| { | |
| "data": { | |
| "image/png": 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| |
| "text/plain": [ | |
| "<Figure size 2880x1440 with 3 Axes>" | |
| ] | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| }, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "plt.figure(figsize=(40, 20))\n", | |
| "\n", | |
| "plt.subplot(1, 3, 1)\n", | |
| "plt.title('Mask from Resize(Poly)')\n", | |
| "plt.imshow(mask_from_poly_resize)\n", | |
| "\n", | |
| "plt.subplot(1, 3, 2)\n", | |
| "plt.title('Mask from Resize(Mask)')\n", | |
| "plt.imshow(mask_resize)\n", | |
| "\n", | |
| "plt.subplot(1, 3, 3)\n", | |
| "plt.title('abs( Resize(Poly) - Resize(Mask) ) = %f'%diff.sum())\n", | |
| "plt.imshow(diff)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "### Observations\n", | |
| "- This kind of misalignment will be frequent if resized\n", | |
| "- Still it seems it could be minimized" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "## TEST: Transpose [FAIL]" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 152, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "FLIP_LEFT_RIGHT = 0\n", | |
| "FLIP_TOP_BOTTOM = 1\n", | |
| "methods = (FLIP_LEFT_RIGHT, FLIP_TOP_BOTTOM)\n", | |
| "results = []\n", | |
| "for method in methods:\n", | |
| " mask_from_poly_flip = polygon.transpose(method).convert('mask')\n", | |
| " mask_flip = mask.transpose(method).convert('mask')\n", | |
| " diff = torch.abs(mask_from_poly_flip.float() - mask_flip.float())\n", | |
| " results.append([mask_from_poly_flip, mask_flip, diff])" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 153, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "Horizontal\n" | |
| ] | |
| }, | |
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| |
| "text/plain": [ | |
| "<Figure size 2880x1440 with 3 Axes>" | |
| ] | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| }, | |
| "output_type": "display_data" | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "Vertical\n" | |
| ] | |
| }, | |
| { | |
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| |
| "text/plain": [ | |
| "<Figure size 2880x1440 with 3 Axes>" | |
| ] | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| }, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "for result, method in zip(results, ['Horizontal', 'Vertical']):\n", | |
| " \n", | |
| " mask_from_poly_flip, mask_flip, diff = result\n", | |
| " \n", | |
| " print(method)\n", | |
| " plt.figure(figsize=(40, 20))\n", | |
| "\n", | |
| " plt.subplot(1, 3, 1)\n", | |
| " plt.title('Mask from flip(Poly)')\n", | |
| " plt.imshow(mask_from_poly_flip)\n", | |
| "\n", | |
| " plt.subplot(1, 3, 2)\n", | |
| " plt.title('Mask from flip(Mask)')\n", | |
| " plt.imshow(mask_flip)\n", | |
| "\n", | |
| " plt.subplot(1, 3, 3)\n", | |
| " plt.title('abs( flip(Poly) - flip(Mask) ) = %f'%diff.sum())\n", | |
| " plt.imshow(diff)\n", | |
| " plt.show()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "### Observations\n", | |
| "- Same as with the Resize: probably the good old [Detectron inconsistency legacy](https://github.com/facebookresearch/maskrcnn-benchmark/pull/150#discussion_r233075909), otherwise seems OK\n", | |
| "- the vertical and the horizontal transformation takes effect differently\n", | |
| "<pre>\n", | |
| "\n", | |
| "\n", | |
| "\n", | |
| "\n", | |
| "\n", | |
| "\n", | |
| "\n", | |
| "\n", | |
| "\n", | |
| "</pre>" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "## Define multiple general polygons\n", | |
| "\n", | |
| "Modified shape from [unit test of](https://github.com/facebookresearch/maskrcnn-benchmark/blob/b8c5bcafa02e4a7489b5a79bf4609933f98c9599/tests/test_segmentation_mask.py) PR #150" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 166, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "poly = [[423.0, 306.5, 406.5, 277.0, 400.0, 271.5, 389.5, 277.0, 387.5, 292.0,\n", | |
| " 384.5, 295.0, 374.5, 220.0, 378.5, 210.0, 391.0, 200.5, 404.0, 199.5,\n", | |
| " 414.0, 203.5, 425.5, 221.0, 438.5, 297.0, 423.0, 306.5],\n", | |
| " [100, 100, 200, 100, 200, 200, 100, 200],\n", | |
| " [width-200, height-200, width-100, height-200, width-100, height-100, width-200, height-100]\n", | |
| " ] \n", | |
| "\n", | |
| "width = 640\n", | |
| "height = 480\n", | |
| "size = (width, height)\n", | |
| "box = [400, 250, 500, 300]\n", | |
| "\n", | |
| "polygon = Polygons(poly, size, 'polygon')\n", | |
| "mask = Mask(poly, size, 'mask')" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 167, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "<matplotlib.image.AxesImage at 0x7f79420022b0>" | |
| ] | |
| }, | |
| "execution_count": 167, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| }, | |
| { | |
| "data": { | |
| "image/png": 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\n", | |
| "text/plain": [ | |
| "<Figure size 1440x1440 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| }, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "plt.figure(figsize=(20, 20))\n", | |
| "plt.title('Mask')\n", | |
| "plt.imshow(mask.convert('mask'))" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "## TEST: Crop [FAIL]" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 168, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "mask_from_poly_crop = polygon.crop(box).convert('mask')\n", | |
| "mask_crop = mask.crop(box).convert('mask')\n", | |
| "diff = torch.abs(mask_from_poly_crop.float() - mask_crop.float())" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 169, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "<matplotlib.image.AxesImage at 0x7f7942028160>" | |
| ] | |
| }, | |
| "execution_count": 169, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| }, | |
| { | |
| "data": { | |
| "image/png": 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\n", | |
| "text/plain": [ | |
| "<Figure size 1440x1440 with 3 Axes>" | |
| ] | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| }, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "plt.figure(figsize=(20, 20))\n", | |
| "\n", | |
| "plt.subplot(1, 3, 1)\n", | |
| "plt.title('Mask from Crop(Poly)')\n", | |
| "plt.imshow(mask_from_poly_crop)\n", | |
| "\n", | |
| "plt.subplot(1, 3, 2)\n", | |
| "plt.title('Mask from Crop(Mask)')\n", | |
| "plt.imshow(mask_crop)\n", | |
| "\n", | |
| "plt.subplot(1, 3, 3)\n", | |
| "plt.title('abs( Crop(Poly) - Crop(Mask) ) = %f'%diff.sum())\n", | |
| "plt.imshow(diff)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "### Observation\n", | |
| "\n", | |
| "- Random pixel error. No idea why" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "## TEST: Resize [FAIL]" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 170, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "new_size = (600, 500)\n", | |
| "mask_from_poly_resize = polygon.resize(new_size).convert('mask')\n", | |
| "mask_resize = mask.resize(new_size).convert('mask')\n", | |
| "diff = torch.abs(mask_from_poly_resize.float() - mask_resize.float())" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 171, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "<matplotlib.image.AxesImage at 0x7f7942166cc0>" | |
| ] | |
| }, | |
| "execution_count": 171, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| }, | |
| { | |
| "data": { | |
| "image/png": 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| |
| "text/plain": [ | |
| "<Figure size 2880x1440 with 3 Axes>" | |
| ] | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| }, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "plt.figure(figsize=(40, 20))\n", | |
| "\n", | |
| "plt.subplot(1, 3, 1)\n", | |
| "plt.title('Mask from Resize(Poly)')\n", | |
| "plt.imshow(mask_from_poly_resize)\n", | |
| "\n", | |
| "plt.subplot(1, 3, 2)\n", | |
| "plt.title('Mask from Resize(Mask)')\n", | |
| "plt.imshow(mask_resize)\n", | |
| "\n", | |
| "plt.subplot(1, 3, 3)\n", | |
| "plt.title('abs( Resize(Poly) - Resize(Mask) ) = %f'%diff.sum())\n", | |
| "plt.imshow(diff)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "### Observations\n", | |
| "- consistent error" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "## TEST: Transpose [FAIL]" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 174, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "FLIP_LEFT_RIGHT = 0\n", | |
| "FLIP_TOP_BOTTOM = 1\n", | |
| "methods = (FLIP_LEFT_RIGHT, FLIP_TOP_BOTTOM)\n", | |
| "results = []\n", | |
| "for method in methods:\n", | |
| " mask_from_poly_flip = polygon.transpose(method).convert('mask')\n", | |
| " mask_flip = mask.transpose(method).convert('mask')\n", | |
| " diff = torch.abs(mask_from_poly_flip.float() - mask_flip.float())\n", | |
| " results.append([mask_from_poly_flip, mask_flip, diff])" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 175, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "Horizontal\n" | |
| ] | |
| }, | |
| { | |
| "data": { | |
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| |
| "text/plain": [ | |
| "<Figure size 2880x1440 with 3 Axes>" | |
| ] | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| }, | |
| "output_type": "display_data" | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "Vertical\n" | |
| ] | |
| }, | |
| { | |
| "data": { | |
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| |
| "text/plain": [ | |
| "<Figure size 2880x1440 with 3 Axes>" | |
| ] | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| }, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "for result, method in zip(results, ['Horizontal', 'Vertical']):\n", | |
| " \n", | |
| " mask_from_poly_flip, mask_flip, diff = result\n", | |
| " \n", | |
| " print(method)\n", | |
| " plt.figure(figsize=(40, 20))\n", | |
| "\n", | |
| " plt.subplot(1, 3, 1)\n", | |
| " plt.title('Mask from flip(Poly)')\n", | |
| " plt.imshow(mask_from_poly_flip)\n", | |
| "\n", | |
| " plt.subplot(1, 3, 2)\n", | |
| " plt.title('Mask from flip(Mask)')\n", | |
| " plt.imshow(mask_flip)\n", | |
| "\n", | |
| " plt.subplot(1, 3, 3)\n", | |
| " plt.title('abs( flip(Poly) - flip(Mask) ) = %f'%diff.sum())\n", | |
| " plt.imshow(diff)\n", | |
| " plt.show()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "### Observations\n", | |
| "- consistent error\n", | |
| "<pre>\n", | |
| "\n", | |
| "\n", | |
| "\n", | |
| "\n", | |
| "\n", | |
| "\n", | |
| "\n", | |
| "\n", | |
| "\n", | |
| "</pre>" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "## IMPORTANT: error checking and handling is missing\n", | |
| "\n", | |
| "I will try to cover some usual cases, however I think there are infinite number of ways to define a **Polygon** incorrectly, or the **cropping box**" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "## Case 1: Overflow" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 181, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "<matplotlib.image.AxesImage at 0x7f7941893be0>" | |
| ] | |
| }, | |
| "execution_count": 181, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| }, | |
| { | |
| "data": { | |
| "image/png": 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\n", | |
| "text/plain": [ | |
| "<Figure size 1440x1440 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| }, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "width = 640\n", | |
| "height = 480\n", | |
| "poly = [[1000, 100, width-100, 100, width-100, height-100, 100, height-100]]\n", | |
| "size = (width, height)\n", | |
| "box = [poly[0][0]-50, poly[0][1]-50, poly[0][4]-50, poly[0][5]-50]\n", | |
| "\n", | |
| "polygon = Polygons(poly, size, 'polygon')\n", | |
| "mask = Mask(poly, size, 'mask')\n", | |
| "\n", | |
| "plt.figure(figsize=(20, 20))\n", | |
| "plt.title('Mask')\n", | |
| "plt.imshow(mask.convert('mask'))" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "## Case 2: Underflow" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 182, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "<matplotlib.image.AxesImage at 0x7f7941870898>" | |
| ] | |
| }, | |
| "execution_count": 182, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| }, | |
| { | |
| "data": { | |
| "image/png": 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\n", | |
| "text/plain": [ | |
| "<Figure size 1440x1440 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| }, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "width = 640\n", | |
| "height = 480\n", | |
| "poly = [[100, 100, width-100, 100, width-100, height-100, 100, height-1000]]\n", | |
| "size = (width, height)\n", | |
| "box = [poly[0][0]-50, poly[0][1]-50, poly[0][4]-50, poly[0][5]-50]\n", | |
| "\n", | |
| "polygon = Polygons(poly, size, 'polygon')\n", | |
| "mask = Mask(poly, size, 'mask')\n", | |
| "\n", | |
| "plt.figure(figsize=(20, 20))\n", | |
| "plt.title('Mask')\n", | |
| "plt.imshow(mask.convert('mask'))" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "## Case 2: zero measure" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 187, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "<matplotlib.image.AxesImage at 0x7f79416b2710>" | |
| ] | |
| }, | |
| "execution_count": 187, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| }, | |
| { | |
| "data": { | |
| "image/png": 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\n", | |
| "text/plain": [ | |
| "<Figure size 1440x1440 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| }, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "width = 640\n", | |
| "height = 480\n", | |
| "poly = [[100, 100, 100, 100, 100, height-100, 100, height-100]]\n", | |
| "size = (width, height)\n", | |
| "box = [poly[0][0]-50, poly[0][1]-50, poly[0][4]-50, poly[0][5]-50]\n", | |
| "\n", | |
| "polygon = Polygons(poly, size, 'polygon')\n", | |
| "mask = Mask(poly, size, 'mask')\n", | |
| "\n", | |
| "plt.figure(figsize=(20, 20))\n", | |
| "plt.title('Mask')\n", | |
| "plt.imshow(mask.convert('mask'))" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "## Case 3: Wrong ordering (maybe not a problem?)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 186, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "<matplotlib.image.AxesImage at 0x7f79416d8b38>" | |
| ] | |
| }, | |
| "execution_count": 186, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| }, | |
| { | |
| "data": { | |
| "image/png": 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\n", | |
| "text/plain": [ | |
| "<Figure size 1440x1440 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| }, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "width = 640\n", | |
| "height = 480\n", | |
| "poly = [[100, 100, width-100, height-100, width-100, 100, 100, height-100]]\n", | |
| "size = (width, height)\n", | |
| "box = [poly[0][0]-50, poly[0][1]-50, poly[0][4]-50, poly[0][5]-50]\n", | |
| "\n", | |
| "polygon = Polygons(poly, size, 'polygon')\n", | |
| "mask = Mask(poly, size, 'mask')\n", | |
| "\n", | |
| "plt.figure(figsize=(20, 20))\n", | |
| "plt.title('Mask')\n", | |
| "plt.imshow(mask.convert('mask'))" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [] | |
| } | |
| ], | |
| "metadata": { | |
| "kernelspec": { | |
| "display_name": "Python 3", | |
| "language": "python", | |
| "name": "python3" | |
| }, | |
| "language_info": { | |
| "codemirror_mode": { | |
| "name": "ipython", | |
| "version": 3 | |
| }, | |
| "file_extension": ".py", | |
| "mimetype": "text/x-python", | |
| "name": "python", | |
| "nbconvert_exporter": "python", | |
| "pygments_lexer": "ipython3", | |
| "version": "3.7.2" | |
| } | |
| }, | |
| "nbformat": 4, | |
| "nbformat_minor": 2 | |
| } |
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