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jeanpat/smallsample_125_displaycoco_train-predict_with_lightning-flash.ipynb

Created September 1, 2022 06:51
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SmallSample_125_displayCOCO_Train-Predict_with_lightning-flash.ipynb
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smallsample_125_displaycoco_train-predict_with_lightning-flash.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/jeanpat/5ba746d6cffb9abeb7ac451ee3ff8797/smallsample_125_displaycoco_train-predict_with_lightning-flash.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "raw",
"metadata": {
"id": "0aDR_z6fgaMS"
},
"source": [
"# Check and display a dataset of overlapping pairs of chromosomes in COCO-format:\n",
"\n",
"The dataset was prepared from DAPI conterstained human chromosomes from human lymphocytes labelled with Cy3-PNA telomeric probes. DAPI and CY3 images were combined in a single chanel grayscaled image.\n",
"Pairs of single chromosomes where chosen an systematically overlapped. A small subset of these overlapping chromosomes were segmented by hand aka annotated with and online tool, makesens.ai and saved in COCO format.\n",
"\n",
"The aim of this dataset is to check a protocol to load and train an instance segmentation algorithm possibly based on pytorch + flightning-flash\n",
"\n",
"This notebook is intended to run in google colab environnement using collaboratory."
]
},
{
"cell_type": "markdown",
"source": [
"# check modules before install"
],
"metadata": {
"id": "pmENCPwvMun3"
}
},
{
"cell_type": "code",
"source": [
"import torch\n",
"import fastai"
],
"metadata": {
"id": "m6UYQCCGMzsv"
},
"execution_count": 1,
"outputs": []
},
{
"cell_type": "code",
"source": [
"print(torch.__version__)\n",
"print(fastai.__version__)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "XJ9nKRe_M6ix",
"outputId": "598703cd-d52f-4fb3-aff6-89778897cac1"
},
"execution_count": 2,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"1.12.1+cu113\n",
"2.7.9\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"# install modules"
],
"metadata": {
"id": "vLjp5OQLJocw"
}
},
{
"cell_type": "markdown",
"source": [
"According to https://github.com/Lightning-AI/lightning-flash/issues/803"
],
"metadata": {
"id": "QNTJwBhpQLSC"
}
},
{
"cell_type": "code",
"source": [
"!pip uninstall -y torchtext fastai\n",
"!pip install lightning-flash[image] icevision"
],
"metadata": {
"id": "lPkrVecMQLnL",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"outputId": "8a71b808-e7b6-4e65-975c-38df05bbc2f4"
},
"execution_count": 3,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Found existing installation: torchtext 0.13.1\n",
"Uninstalling torchtext-0.13.1:\n",
" Successfully uninstalled torchtext-0.13.1\n",
"Found existing installation: fastai 2.7.9\n",
"Uninstalling fastai-2.7.9:\n",
" Successfully uninstalled fastai-2.7.9\n",
"Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
"Collecting lightning-flash[image]\n",
" Downloading lightning_flash-0.7.5-py3-none-any.whl (1.1 MB)\n",
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"\u001b[?25hCollecting torchmetrics!=0.5.1,>=0.5.0\n",
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"\u001b[?25hCollecting pytorch-lightning>=1.3.6\n",
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"\u001b[K |████████████████████████████████| 706 kB 48.8 MB/s \n",
"\u001b[?25hCollecting kornia>=0.5.1\n",
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"\u001b[?25hCollecting pystiche==1.*\n",
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"\u001b[?25hCollecting lightning-bolts>=0.3.3\n",
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"\u001b[?25hCollecting docstring-parser>=0.7.3\n",
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"Collecting tensorboard>=2.2\n",
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"Collecting timm>=0.3.2\n",
" Downloading timm-0.4.12-py3-none-any.whl (376 kB)\n",
"\u001b[K |████████████████████████████████| 376 kB 56.1 MB/s \n",
"\u001b[?25hCollecting efficientnet-pytorch==0.7.1\n",
" Downloading efficientnet_pytorch-0.7.1.tar.gz (21 kB)\n",
"Collecting pretrainedmodels==0.7.4\n",
" Downloading pretrainedmodels-0.7.4.tar.gz (58 kB)\n",
"\u001b[K |████████████████████████████████| 58 kB 6.6 MB/s \n",
"\u001b[?25hCollecting munch\n",
" Downloading munch-2.5.0-py2.py3-none-any.whl (10 kB)\n",
"Building wheels for collected packages: antlr4-python3-runtime, fire, fvcore, iopath, efficientnet-pytorch, pretrainedmodels\n",
" Building wheel for antlr4-python3-runtime (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
" Created wheel for antlr4-python3-runtime: filename=antlr4_python3_runtime-4.9.3-py3-none-any.whl size=144575 sha256=0f1e2a815014c5f67bbfb62f7417bc367b2e7b134455f6d48fea9d505e243b00\n",
" Stored in directory: /root/.cache/pip/wheels/8b/8d/53/2af8772d9aec614e3fc65e53d4a993ad73c61daa8bbd85a873\n",
" Building wheel for fire (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
" Created wheel for fire: filename=fire-0.4.0-py2.py3-none-any.whl size=115942 sha256=69319cff24a0d8f341bc5fd122912f73c75d14f967927ea285afbcfe53ca2980\n",
" Stored in directory: /root/.cache/pip/wheels/8a/67/fb/2e8a12fa16661b9d5af1f654bd199366799740a85c64981226\n",
" Building wheel for fvcore (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
" Created wheel for fvcore: filename=fvcore-0.1.5.post20220512-py3-none-any.whl size=61288 sha256=5b344ff2a295961bc94380c9267eb9fca068974eb65b2527a331490eb12ed012\n",
" Stored in directory: /root/.cache/pip/wheels/68/20/f9/a11a0dd63f4c13678b2a5ec488e48078756505c7777b75b29e\n",
" Building wheel for iopath (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
" Created wheel for iopath: filename=iopath-0.1.10-py3-none-any.whl size=31549 sha256=054f2df00e191e320a6401c6d40b71415aa1b2188f040b200a2d4f7e57b5da12\n",
" Stored in directory: /root/.cache/pip/wheels/aa/cc/ed/ca4e88beef656b01c84b9185196513ef2faf74a5a379b043a7\n",
" Building wheel for efficientnet-pytorch (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
" Created wheel for efficientnet-pytorch: filename=efficientnet_pytorch-0.7.1-py3-none-any.whl size=16446 sha256=c310dea54d1529a264a0b25442f0e5d49ab3376174c62f93f28c177be52e0cdd\n",
" Stored in directory: /root/.cache/pip/wheels/0e/cc/b2/49e74588263573ff778da58cc99b9c6349b496636a7e165be6\n",
" Building wheel for pretrainedmodels (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
" Created wheel for pretrainedmodels: filename=pretrainedmodels-0.7.4-py3-none-any.whl size=60965 sha256=6d823df34922aeaea83bcea72bfb3bdc9a680a38284c9b5449325bae4cb74e27\n",
" Stored in directory: /root/.cache/pip/wheels/ed/27/e8/9543d42de2740d3544db96aefef63bda3f2c1761b3334f4873\n",
"Successfully built antlr4-python3-runtime fire fvcore iopath efficientnet-pytorch pretrainedmodels\n",
"Installing collected packages: torch, portalocker, pillow, yacs, torchvision, torchmetrics, tensorboard, pyDeprecate, munch, jsonargparse, jedi, iopath, docstring-parser, antlr4-python3-runtime, timm, terminaltables, pytorch-lightning, pybboxes, pretrainedmodels, omegaconf, nose, fvcore, fire, efficientnet-pytorch, click, yolov5-icevision, segmentation-models-pytorch, sahi, resnest, pystiche, loguru, lightning-flash, lightning-bolts, kornia, fastcore, effdet, dataclasses, albumentations, icevision\n",
" Attempting uninstall: torch\n",
" Found existing installation: torch 1.12.1+cu113\n",
" Uninstalling torch-1.12.1+cu113:\n",
" Successfully uninstalled torch-1.12.1+cu113\n",
" Attempting uninstall: pillow\n",
" Found existing installation: Pillow 7.1.2\n",
" Uninstalling Pillow-7.1.2:\n",
" Successfully uninstalled Pillow-7.1.2\n",
" Attempting uninstall: torchvision\n",
" Found existing installation: torchvision 0.13.1+cu113\n",
" Uninstalling torchvision-0.13.1+cu113:\n",
" Successfully uninstalled torchvision-0.13.1+cu113\n",
" Attempting uninstall: tensorboard\n",
" Found existing installation: tensorboard 2.8.0\n",
" Uninstalling tensorboard-2.8.0:\n",
" Successfully uninstalled tensorboard-2.8.0\n",
" Attempting uninstall: click\n",
" Found existing installation: click 7.1.2\n",
" Uninstalling click-7.1.2:\n",
" Successfully uninstalled click-7.1.2\n",
" Attempting uninstall: fastcore\n",
" Found existing installation: fastcore 1.5.22\n",
" Uninstalling fastcore-1.5.22:\n",
" Successfully uninstalled fastcore-1.5.22\n",
" Attempting uninstall: albumentations\n",
" Found existing installation: albumentations 1.2.1\n",
" Uninstalling albumentations-1.2.1:\n",
" Successfully uninstalled albumentations-1.2.1\n",
"\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
"torchaudio 0.12.1+cu113 requires torch==1.12.1, but you have torch 1.10.2 which is incompatible.\n",
"tensorflow 2.8.2+zzzcolab20220719082949 requires tensorboard<2.9,>=2.8, but you have tensorboard 2.10.0 which is incompatible.\n",
"flask 1.1.4 requires click<8.0,>=5.1, but you have click 8.0.4 which is incompatible.\u001b[0m\n",
"Successfully installed albumentations-1.0.3 antlr4-python3-runtime-4.9.3 click-8.0.4 dataclasses-0.6 docstring-parser-0.14.1 effdet-0.2.4 efficientnet-pytorch-0.7.1 fastcore-1.3.29 fire-0.4.0 fvcore-0.1.5.post20220512 icevision-0.12.0 iopath-0.1.10 jedi-0.18.1 jsonargparse-4.13.2 kornia-0.6.7 lightning-bolts-0.5.0 lightning-flash-0.7.5 loguru-0.6.0 munch-2.5.0 nose-1.3.7 omegaconf-2.2.3 pillow-8.4.0 portalocker-2.5.1 pretrainedmodels-0.7.4 pyDeprecate-0.3.2 pybboxes-0.1.4 pystiche-1.0.1 pytorch-lightning-1.7.4 resnest-0.0.6b20220831 sahi-0.10.4 segmentation-models-pytorch-0.3.0 tensorboard-2.10.0 terminaltables-3.1.10 timm-0.4.12 torch-1.10.2 torchmetrics-0.9.3 torchvision-0.11.3 yacs-0.1.8 yolov5-icevision-6.0.0\n"
]
},
{
"output_type": "display_data",
"data": {
"application/vnd.colab-display-data+json": {
"pip_warning": {
"packages": [
"PIL",
"dataclasses",
"pydevd_plugins",
"torch"
]
}
}
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"#import torchvision\n",
"torchvision.__version__"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 35
},
"id": "dm5vgzLMNaAF",
"outputId": "9fa11288-28ee-48a5-ed4b-13fbeb86e090"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"'0.11.3+cu102'"
],
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "string"
}
},
"metadata": {},
"execution_count": 3
}
]
},
{
"cell_type": "code",
"source": [
"#!pip3 uninstall -y torch"
],
"metadata": {
"id": "CaXyGAEKVn0K"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"#!pip3 uninstall -y lightning-flash"
],
"metadata": {
"id": "iVfzMMZeXbv0"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"#!pip install -U torch torchvision"
],
"metadata": {
"id": "K5dz1toBXYO6"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"#!pip install 'git+https://github.com/PyTorchLightning/lightning-flash.git'\n",
"#!pip install 'git+https://github.com/PyTorchLightning/lightning-flash.git#egg=lightning-flash[image]'"
],
"metadata": {
"id": "4r7NbYuSX0VL"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"#! pip install --quiet \"torch>=1.8\" \"pytorch-lightning>=1.4\" \"ipython[notebook]\" #\"setuptools==59.5.0\" \"matplotlib\"\n",
"#! pip3 install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu116"
],
"metadata": {
"id": "RN4jBIq0KsUV"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"#!pip install 'git+https://github.com/PyTorchLightning/lightning-flash.git#egg=lightning-flash[image]'\n",
"#!pip install 'icevision[all]'\n",
"#!pip install 'lightning-flash[image]'"
],
"metadata": {
"id": "Hf0hAZXp4ACW"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"#!pip install 'icevision' #'lightning-flash[image]'"
],
"metadata": {
"id": "d2oMedWgEs3i"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"the method above fails to install flash correctly"
],
"metadata": {
"id": "ItF-DLffMlsq"
}
},
{
"cell_type": "code",
"source": [
"#!pip install torch==1.8.1+cu102 -f https://download.pytorch.org/whl/torch_stable.html\n",
"#!pip install icevision #==0.9.0a1\n",
"#!pip install effdet \n",
"#!pip install lightning-flash[image]\n",
"#!pip install git+https://github.com/PyTorchLightning/lightning-flash.git\n",
"#!pip install torchtext==0.9.1\n",
"#!pip uninstall fastai -y\n",
"#There is a bug in the latest release of icevision. Manually apply the fix.\n",
"#!curl https://raw.githubusercontent.com/airctic/icevision/944b47c5694243ba3f3c8c11a6ef56f05fb111eb/icevision/core/record_components.py --output /usr/local/lib/python3.7/dist-packages/icevision/core/record_components.py\n",
"#Restart the kernel"
],
"metadata": {
"id": "rPCik7gQIqmu"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"## import modules\n",
"Don't forget to restart the runtime "
],
"metadata": {
"id": "0TaFXg61JwXc"
}
},
{
"cell_type": "code",
"source": [
"import flash"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "zhWL4Gq2NF4x",
"outputId": "f4a23806-3eab-429f-c937-6722d1cc274b"
},
"execution_count": 1,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"\u001b[1m\u001b[1mINFO \u001b[0m\u001b[1m\u001b[0m - \u001b[1mDownloading default `.ttf` font file - SpaceGrotesk-Medium.ttf from https://raw.githubusercontent.com/airctic/storage/master/SpaceGrotesk-Medium.ttf to /root/.icevision/fonts/SpaceGrotesk-Medium.ttf\u001b[0m | \u001b[36micevision.visualize.utils\u001b[0m:\u001b[36mget_default_font\u001b[0m:\u001b[36m70\u001b[0m\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Downloading https://ultralytics.com/assets/Arial.ttf to /root/.config/Ultralytics/Arial.ttf...\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"import icevision"
],
"metadata": {
"id": "LkxhaCVYNOyU"
},
"execution_count": 2,
"outputs": []
},
{
"cell_type": "code",
"source": [
"flash.__version__"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 35
},
"id": "-SSGUDKhMQS6",
"outputId": "f8c7467b-32ed-4607-deb3-b79aface5c1c"
},
"execution_count": 3,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"'0.7.5'"
],
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "string"
}
},
"metadata": {},
"execution_count": 3
}
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"id": "WoU1cigtgaMV"
},
"outputs": [],
"source": [
"#from fastai.vision import *\n",
"import os, sys\n",
"import numpy as np\n",
"#from scipy import ndimage as nd\n",
"#from skimage import morphology as mo\n",
"#from scipy.ndimage import distance_transform_bf as distance\n",
"from matplotlib import pyplot as plt"
]
},
{
"cell_type": "code",
"source": [
"#!apt-get install python3-dev\n",
"#!pip install cython\n",
"#!pip install git+git://github.com/waspinator/[email protected]"
],
"metadata": {
"id": "8zianJbJhLxZ"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"id": "Izs9flyGgaMZ"
},
"outputs": [],
"source": [
"import pycocotools\n",
"#import pycococreatortools\n",
"from pycocotools.coco import COCO\n",
"import skimage.io as io"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "HHBhQxzbgaMb",
"outputId": "be6ff84b-cf36-4313-8f89-b4ad7214959e"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"bin\t dev lib32 NGC-DL-CONTAINER-LICENSE\troot sys var\n",
"boot\t etc lib64 opt\t\t\trun tmp\n",
"content home media proc\t\t\tsbin tools\n",
"datalab lib mnt python-apt\t\tsrv usr\n"
]
}
],
"source": [
"!ls .."
]
},
{
"cell_type": "markdown",
"source": [
"# Possibly import the dataset available on github\n",
"https://github.com/jeanpat/DeepFISH/tree/master/dataset/SmallDataset/train\n",
"\n",
"save it somwhere on your google drive and adapt the path to the files (png images and segmentation in the json file)\n",
" "
],
"metadata": {
"id": "FxGlwjwLtPTc"
}
},
{
"cell_type": "code",
"source": [],
"metadata": {
"id": "JdDIaBO2tOGW"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"##google snippet:import data"
],
"metadata": {
"id": "4wXvT09HiQlk"
}
},
{
"cell_type": "code",
"source": [
"from google.colab import drive\n",
"drive.mount('/content/gdrive/', force_remount=True)"
],
"metadata": {
"id": "sJqYr-hohNAc",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "d58959aa-67bb-441a-8fe6-6940f451d541"
},
"execution_count": 7,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Mounted at /content/gdrive/\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"%ls gdrive/MyDrive/Data\\ Science/SmallCOCODataSet/UltraSmall-COCO-Dataset_125"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "IB61H0usl6sZ",
"outputId": "01a4069b-10e1-4a71-8fca-016edada24d8"
},
"execution_count": 8,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"grey0012100.png grey0075766.png\n",
"grey0013300.png grey0076001.png\n",
"grey0013500.png grey0077000.png\n",
"grey0020000.png grey0077100.png\n",
"grey0021500.png grey0077300.png\n",
"grey0026500.png grey0077400.png\n",
"grey0027200.png grey0077601.png\n",
"grey0028200.png grey0077701.png\n",
"grey0035223.png grey0077801.png\n",
"grey0059995.png grey0077901.png\n",
"grey0059998.png grey0077902.png\n",
"grey0060101.png grey0077905.png\n",
"grey0060107.png grey0078000.png\n",
"grey0060110.png grey0078300.png\n",
"grey0060112.png grey0078702.png\n",
"grey0060118.png grey0078852.png\n",
"grey0060119.png grey0078900.png\n",
"grey0060120.png grey0079001.png\n",
"grey0060126.png grey0079101.png\n",
"grey0060129.png grey0079652.png\n",
"grey0060130.png grey0079952.png\n",
"grey0060223.png grey0080000.png\n",
"grey0060232.png grey0080105.png\n",
"grey0060250.png grey0080106.png\n",
"grey0060260.png grey0080107.png\n",
"grey0060263.png grey0080650.png\n",
"grey0060268.png grey0080651.png\n",
"grey0060365.png grey0080652.png\n",
"grey0060566.png grey0081663.png\n",
"grey0060666.png grey0081668.png\n",
"grey0060700.png grey0081770.png\n",
"grey0060730.png grey0081970.png\n",
"grey0060740.png grey0081980.png\n",
"grey0060772.png grey0082250.png\n",
"grey0061587.png grey0082350.png\n",
"grey0065587.png grey0082400.png\n",
"grey0070586.png grey0083400.png\n",
"grey0070886.png grey0083500.png\n",
"grey0070902.png grey0083550.png\n",
"grey0070955.png grey0083600.png\n",
"grey0070958.png grey0083651.png\n",
"grey0070966.png grey0083755.png\n",
"grey0071100.png grey0083855.png\n",
"grey0071362.png grey0083856.png\n",
"grey0071600.png grey0083900.png\n",
"grey0071700.png grey0096560.png\n",
"grey0071800.png grey0096760.png\n",
"grey0071852.png grey0096800.png\n",
"grey0071952.png grey0096812.png\n",
"grey0072100.png grey0096902.png\n",
"grey0072150.png grey0096906.png\n",
"grey0072251.png grey0097001.png\n",
"grey0073251.png grey0097006.png\n",
"grey0073355.png grey0097101.png\n",
"grey0073655.png grey0097201.png\n",
"grey0073658.png grey0097352.png\n",
"grey0073700.png grey0097452.png\n",
"grey0075000.png grey0097630.png\n",
"grey0075101.png grey0097750.png\n",
"grey0075201.png grey0097801.png\n",
"grey0075302.png grey0097918.png\n",
"grey0075502.png instance_segmentation_model_10epoch.pt\n",
"grey0075602.png labels_overlappchromosomes_2021-07-05-09-18-52.json\n",
"grey0075756.png \u001b[0m\u001b[01;34mTest\u001b[0m/\n"
]
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "5Ou_ZlQ5gaMc"
},
"source": [
"# Acces to small dataset images and annotations with pycocotools\n",
"**Google colab version**\n",
" * Annotation files was generated with online annotator tool [https://www.makesense.ai/](https://www.makesense.ai/)\n"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "U1XgyTuZgaMe",
"outputId": "daaba984-13f5-4347-89aa-89c9217e7bf4"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"loading annotations into memory...\n",
"Done (t=0.20s)\n",
"creating index...\n",
"index created!\n"
]
}
],
"source": [
"#IMAGE_DIR = './UltraSmall-COCO-Dataset_125'\n",
"IMAGE_DIR = 'gdrive/MyDrive/Data Science/SmallCOCODataSet/UltraSmall-COCO-Dataset_125'\n",
"#print(path.ls()) # prints subdirectories\n",
"os.listdir(IMAGE_DIR)\n",
"image_directory = IMAGE_DIR\n",
"annotation_file = IMAGE_DIR + '/labels_overlappchromosomes_2021-07-05-09-18-52.json'\n",
"example_coco = COCO(annotation_file)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"id": "uR1lasdQgaMh",
"outputId": "7d930859-6e07-4aa1-8c0b-399eb79baa60",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"description: overlappchromosomes\n",
"None\n",
"Custom COCO categories: \n",
"chromosome\n",
"\n",
"[1]\n",
"125\n",
"{'id': 38, 'width': 211, 'height': 210, 'file_name': 'grey0070886.png'}\n",
"[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125]\n"
]
}
],
"source": [
"print(example_coco.info())\n",
"categories = example_coco.loadCats(example_coco.getCatIds())\n",
"category_names = [category['name'] for category in categories]\n",
"print('Custom COCO categories: \\n{}\\n'.format(' '.join(category_names)))\n",
"\n",
"category_ids = example_coco.getCatIds(catNms=['chromosome'])\n",
"image_ids = example_coco.getImgIds(catIds=category_ids)\n",
"image_data = example_coco.loadImgs(image_ids[np.random.randint(0, len(image_ids))])[0]\n",
"\n",
"print(category_ids)\n",
"print(len(image_ids))\n",
"print(image_data)\n",
"print(image_ids)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"id": "Mqei1u6pgaMn",
"outputId": "3163be75-b397-4fc2-d690-3a1bfbe8c3f9",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 423
}
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"[74, 75]\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
}
],
"source": [
"# load and display instance annotations\n",
"image = io.imread(image_directory + '/'+ image_data['file_name'])\n",
"plt.Figure(figsize=(30.0,30.0))\n",
"plt.imshow(image, cmap=plt.cm.gray ); plt.axis('off')\n",
"#pylab.rcParams['figure.figsize'] = (8.0, 10.0)\n",
"annotation_ids = example_coco.getAnnIds(imgIds=image_data['id'], catIds=category_ids, iscrowd=None)\n",
"annotations = example_coco.loadAnns(annotation_ids)\n",
"\n",
"print(annotation_ids)\n",
"\n",
"example_coco.showAnns(annotations)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "VtIqTcAogaMo"
},
"source": [
"# Load the data images+annotation file with PyTorch-lightning\n",
" * there's 125 images\n",
" * an annotation files following the COCO format\n",
" \n",
"Some internet ressource about dataset and dataloader with FiftyOne:\n",
"\n",
" * https://towardsdatascience.com/stop-wasting-time-with-pytorch-datasets-17cac2c22fa8"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "e1pTnQm7gaMp"
},
"source": [
"## Can we use lightning-flash to load the dataset?"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"id": "WU1TdJ0qgaMs"
},
"outputs": [],
"source": [
"from functools import partial\n",
"\n",
"import flash\n",
"from flash.core.utilities.imports import example_requires\n",
"from flash.image import InstanceSegmentation, InstanceSegmentationData\n",
"example_requires(\"image\")\n",
"#import icedata # noqa: E402"
]
},
{
"cell_type": "markdown",
"source": [
"## Load the dataset with lightning-flash:\n",
"https://lightning-flash.readthedocs.io/en/latest/api/generated/flash.image.instance_segmentation.data.InstanceSegmentationData.html#flash.image.instance_segmentation.data.InstanceSegmentationData"
],
"metadata": {
"id": "gRqjGjjOckLs"
}
},
{
"cell_type": "code",
"source": [
"IMAGE_DIR = 'gdrive/MyDrive/Data Science/SmallCOCODataSet/UltraSmall-COCO-Dataset_125'\n",
"#print(path.ls()) # prints subdirectories\n",
"os.listdir(IMAGE_DIR)\n",
"image_directory = IMAGE_DIR\n",
"annotation_file = IMAGE_DIR + '/labels_overlappchromosomes_2021-07-05-09-18-52.json'\n",
"datamodule = InstanceSegmentationData.from_coco(train_folder= IMAGE_DIR, train_ann_file = annotation_file,\n",
" transform_kwargs=dict(image_size=(174, 175)), batch_size=2)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 98,
"referenced_widgets": [
"c5d3017471794f51a538788f287d8217",
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"8de4742bbb61493b9bfed382966e7fe0",
"a1c32a45aefe43b5a54d8a1b855d4494",
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},
"id": "Hlzzby71I8hm",
"outputId": "e963ee78-212e-4acd-9846-84ba4ff516cb"
},
"execution_count": 13,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
" 0%| | 0/250 [00:00<?, ?it/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "c5d3017471794f51a538788f287d8217"
}
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"\u001b[1m\u001b[1mINFO \u001b[0m\u001b[1m\u001b[0m - \u001b[1m\u001b[34m\u001b[1mAutofixing records\u001b[0m\u001b[1m\u001b[34m\u001b[0m\u001b[1m\u001b[0m | \u001b[36micevision.parsers.parser\u001b[0m:\u001b[36mparse\u001b[0m:\u001b[36m122\u001b[0m\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
" 0%| | 0/125 [00:00<?, ?it/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "68bb713547d74dbe8014064ec6d4e874"
}
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"print(datamodule.labels)\n",
"print(datamodule.num_classes)\n",
"#help(datamodule.viz)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Wx41kYUgauWS",
"outputId": "e07ab133-466c-4052-e860-da5e2a4e56b9"
},
"execution_count": 16,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"['background', 'chromosome']\n",
"2\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"# 2. Build the task"
],
"metadata": {
"id": "KN5hzW9oTeMC"
}
},
{
"cell_type": "code",
"source": [
"model = InstanceSegmentation(\n",
" head=\"mask_rcnn\",\n",
" backbone=\"resnet18_fpn\",\n",
" num_classes=datamodule.num_classes,pretrained = False,\n",
")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 84,
"referenced_widgets": [
"8ffa517695ee4c2291278680fdfe2c22",
"44d9552bab8341f7b4573f16756906bf",
"084b896ee93843a0b8d51e193df4f369",
"008bb67478544c2d87b8661ae4335a01",
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"3308c7abbe15472a847a6e0a661c8ae2",
"3d52ea47d59644668fa29a8312956692",
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"38c0024befbb448ea8df9f2b7694b65c",
"1c2373f9be544fc89cb3aca3ffe43197"
]
},
"id": "0ECDoaOGTec7",
"outputId": "a948b1ee-a043-464c-dce7-b1bedfc63c93"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"Downloading: \"https://download.pytorch.org/models/resnet18-f37072fd.pth\" to /root/.cache/torch/hub/checkpoints/resnet18-f37072fd.pth\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
" 0%| | 0.00/44.7M [00:00<?, ?B/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "8ffa517695ee4c2291278680fdfe2c22"
}
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"INFO:pytorch_lightning.utilities.rank_zero:Using 'mask_rcnn' provided by airctic/IceVision (https://github.com/airctic/icevision) and PyTorch/torchvision (https://github.com/pytorch/vision).\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"# 3. Create the trainer and finetune the model"
],
"metadata": {
"id": "G-y2T7nkTKPq"
}
},
{
"cell_type": "code",
"source": [
"trainer = flash.Trainer(max_epochs=10)\n",
"trainer.finetune(model, datamodule=datamodule, strategy=\"freeze\")"
],
"metadata": {
"id": "h3QEbRWxb4zY",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 344,
"referenced_widgets": [
"a48cd9ada3324e5c9135692dcff6fd1b",
"fca572fa85fb46b886fb606550258d97",
"e81dbc20c2744c1f92b604308198e13c",
"17557db3e6eb461f98e0a2732cb10688",
"ffc0c6340d0b402eabcc5e4e62463404",
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]
},
"outputId": "72f511dd-4e33-49d4-dda6-c322e833ba51"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"INFO:pytorch_lightning.utilities.rank_zero:GPU available: False, used: False\n",
"INFO:pytorch_lightning.utilities.rank_zero:TPU available: False, using: 0 TPU cores\n",
"INFO:pytorch_lightning.utilities.rank_zero:IPU available: False, using: 0 IPUs\n",
"INFO:pytorch_lightning.utilities.rank_zero:HPU available: False, using: 0 HPUs\n",
"INFO:pytorch_lightning.callbacks.model_summary:\n",
" | Name | Type | Params\n",
"-----------------------------------------------------------------------\n",
"0 | train_metrics | ModuleDict | 0 \n",
"1 | val_metrics | ModuleDict | 0 \n",
"2 | test_metrics | ModuleDict | 0 \n",
"3 | adapter | IceVisionInstanceSegmentationAdapter | 30.9 M\n",
"-----------------------------------------------------------------------\n",
"16.5 M Trainable params\n",
"14.4 M Non-trainable params\n",
"30.9 M Total params\n",
"123.588 Total estimated model params size (MB)\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Training: 0it [00:00, ?it/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "a48cd9ada3324e5c9135692dcff6fd1b"
}
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"INFO:pytorch_lightning.utilities.rank_zero:`Trainer.fit` stopped: `max_epochs=10` reached.\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"%ls gdrive/MyDrive/Data\\ Science/SmallCOCODataSet/UltraSmall-COCO-Dataset_125/Test/"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "ye7YYpcLmpeP",
"outputId": "476564fd-19f8-41c5-d46e-959d333a0930"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"grey0000001.png grey0084860.png grey0090400.png grey0094960.png\n",
"grey0017010.png grey0087002.png grey0090800.png\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"overlap1 = image_directory + '/Test/grey0000001.png'\n",
"overlap2 = image_directory + '/Test/grey0084860.png'\n",
"overlap3 = image_directory + '/Test/grey0090400.png'\n",
"overlap4 = image_directory + '/Test/grey0094960.png'\n",
"overlap5 = image_directory + '/Test/grey0017010.png'\n",
"overlap6 = image_directory + '/Test/grey0087002.png'\n",
"overlap7 = image_directory + '/Test/grey0090800.png'"
],
"metadata": {
"id": "G0ErPxA9my-e"
},
"execution_count": 19,
"outputs": []
},
{
"cell_type": "code",
"source": [
"\n",
"# 4. Detect objects in a few images!\n",
"datamodule = InstanceSegmentationData.from_files(\n",
" predict_files=[overlap1,overlap2,overlap3,overlap4,overlap5,overlap6,overlap7],\n",
" batch_size=7,\n",
")\n",
"predictions = trainer.predict(model, datamodule=datamodule)\n",
"print(predictions)\n",
"SinglePredict = \n",
"\n"
],
"metadata": {
"id": "Hqe4qIC-TTk7"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"# 5. Save the model!"
],
"metadata": {
"id": "Pb1muYrhcUvn"
}
},
{
"cell_type": "code",
"source": [
"trainer.save_checkpoint(\"gdrive/MyDrive/Data Science/SmallCOCODataSet/UltraSmall-COCO-Dataset_125/instance_segmentation_model_10epoch.pt\")"
],
"metadata": {
"id": "P1wpnLc-cT9B"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"#Load trained model"
],
"metadata": {
"id": "x_uIO4KLvXPi"
}
},
{
"cell_type": "code",
"source": [
"from flash import Trainer\n",
"\n",
"datamodule_2 = InstanceSegmentationData.from_files(\n",
" predict_files=[overlap2],\n",
" batch_size=1,\n",
")\n",
"\n",
"model = InstanceSegmentation.load_from_checkpoint(\"gdrive/MyDrive/Data Science/SmallCOCODataSet/UltraSmall-COCO-Dataset_125/instance_segmentation_model_10epoch.pt\")\n",
"\n",
"\n",
"trainer = Trainer()\n",
"single_prediction = trainer.predict(model, datamodule=datamodule_2)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 208,
"referenced_widgets": [
"6f5c12becf144a6aa5bf848d8441b2fb",
"d6d2f3bfb44a439597599580ba2518d3",
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"5e696aca659e4d259d764c0395a68d70",
"a529782447f74aedae7b861cdb1b9b9b",
"5b144b3dab2d453d9ca0f8790c39b050",
"ce7401f5b76248359e62cd1d7caee923",
"e8cdfa1fb877448d806f5ba15d25b659",
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"e2245e36b47a416381d35f6efb0cefb5",
"b93c9c358c34474683f5dbbaa2fb11fd"
]
},
"id": "g1TF-W6jvdfX",
"outputId": "9e362f9d-2c62-4ad9-8451-e74e505c9306"
},
"execution_count": 23,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"INFO:pytorch_lightning.utilities.rank_zero:Using 'mask_rcnn' provided by airctic/IceVision (https://github.com/airctic/icevision) and PyTorch/torchvision (https://github.com/pytorch/vision).\n",
"INFO:pytorch_lightning.utilities.rank_zero:GPU available: False, used: False\n",
"INFO:pytorch_lightning.utilities.rank_zero:TPU available: False, using: 0 TPU cores\n",
"INFO:pytorch_lightning.utilities.rank_zero:IPU available: False, using: 0 IPUs\n",
"INFO:pytorch_lightning.utilities.rank_zero:HPU available: False, using: 0 HPUs\n",
"WARNING:pytorch_lightning.loggers.tensorboard:Missing logger folder: /content/lightning_logs\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Predicting: 0it [00:00, ?it/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "6f5c12becf144a6aa5bf848d8441b2fb"
}
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.7/dist-packages/torch/functional.py:445: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:2157.)\n",
" return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined]\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"# See inside prediction"
],
"metadata": {
"id": "c-NmRDpooJcs"
}
},
{
"cell_type": "code",
"source": [
"# 4. Detect objects in a few images!\n",
"#datamodule_2 = InstanceSegmentationData.from_files(predict_files=[overlap2], batch_size=1,)\n",
"#single_prediction = trainer.predict(model, datamodule=datamodule_2)\n",
"print(single_prediction)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "omrkjY_rrxNl",
"outputId": "83a71c84-e817-4878-ab63-83408c69cdb5"
},
"execution_count": 24,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"[[{'bboxes': [{'xmin': 42.61557, 'ymin': 24.91679, 'width': 50.055023, 'height': 76.79202}], 'masks': [array([[0, 0, 0, ..., 0, 0, 0],\n",
" [0, 0, 0, ..., 0, 0, 0],\n",
" [0, 0, 0, ..., 0, 0, 0],\n",
" ...,\n",
" [0, 0, 0, ..., 0, 0, 0],\n",
" [0, 0, 0, ..., 0, 0, 0],\n",
" [0, 0, 0, ..., 0, 0, 0]], dtype=uint8)], 'labels': [1], 'scores': [0.5334059]}]]\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"len(single_prediction),type(single_prediction[0]), len(single_prediction[0])"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "i-UgbyXpk1Fu",
"outputId": "5183290b-4722-409c-b60d-7688b111b005"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(1, list, 1)"
]
},
"metadata": {},
"execution_count": 60
}
]
},
{
"cell_type": "code",
"source": [
"P0 = single_prediction[0]\n",
"print(P0[0])#, P0[1]\n",
"P0[0]['masks']\n",
"seg0 = P0[0]\n",
"seg0.keys(), seg0.values(), seg0['bboxes']\n",
"seg0['masks']"
],
"metadata": {
"id": "KKNtDLOHoVsc",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "2d50a488-3f43-4665-f7e5-f6f0b3356aec"
},
"execution_count": 30,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"{'bboxes': [{'xmin': 42.61557, 'ymin': 24.91679, 'width': 50.055023, 'height': 76.79202}], 'masks': [array([[0, 0, 0, ..., 0, 0, 0],\n",
" [0, 0, 0, ..., 0, 0, 0],\n",
" [0, 0, 0, ..., 0, 0, 0],\n",
" ...,\n",
" [0, 0, 0, ..., 0, 0, 0],\n",
" [0, 0, 0, ..., 0, 0, 0],\n",
" [0, 0, 0, ..., 0, 0, 0]], dtype=uint8)], 'labels': [1], 'scores': [0.5334059]}\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[array([[0, 0, 0, ..., 0, 0, 0],\n",
" [0, 0, 0, ..., 0, 0, 0],\n",
" [0, 0, 0, ..., 0, 0, 0],\n",
" ...,\n",
" [0, 0, 0, ..., 0, 0, 0],\n",
" [0, 0, 0, ..., 0, 0, 0],\n",
" [0, 0, 0, ..., 0, 0, 0]], dtype=uint8)]"
]
},
"metadata": {},
"execution_count": 30
}
]
},
{
"cell_type": "code",
"source": [
"mask01 = seg0['masks'][0]\n",
"overlap2\n",
"image = io.imread(overlap2)\n",
"plt.Figure(figsize=(30.0,30.0))\n",
"plt.subplot(121)\n",
"plt.imshow(mask01, cmap=plt.cm.gray ); plt.axis('off')\n",
"plt.subplot(122)\n",
"plt.imshow(image, cmap=plt.cm.gray ); plt.axis('off')"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 279
},
"id": "E00eprjlpZPX",
"outputId": "348a941b-7b0b-4262-b885-b27ba351fe08"
},
"execution_count": 31,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(-0.5, 210.5, 209.5, -0.5)"
]
},
"metadata": {},
"execution_count": 31
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 640x480 with 2 Axes>"
],
"image/png": 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