{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "name": "Medical AI Course Materials : 06_Blood_Cell_Detection.ipynb", "version": "0.3.2", "provenance": [], "collapsed_sections": [] }, "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" } }, "cells": [ { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "YwLb83DcpMOO" }, "source": [ "# 実践編: 血液の顕微鏡画像からの細胞検出\n", "\n", "ここでは血液細胞の検出タスクに取り組みます.人の血液の顕微鏡画像が与えられたときに,\n", "\n", "- 赤血球(Red Blood Cell; RBC)\n", "- 白血球(White Blood Cell; WBC)\n", "- 血小板(Platelet)\n", "\n", "の3種の細胞について,それぞれ**何がどの位置にあるか**を個別に認識する方法を考えます.\n", "これが可能になると,与えられた画像内に**それらの細胞が何個ずつあるか,また,どういう位置にあるか**,ということが分かります.\n", "\n", "このようなタスクは一般に**物体検出(object detection)**と呼ばれます.画像を入力として,対象の物体(ここでは例えば,上の3種の細胞)ごとに,個別に\n", "\n", "1. 物体を包含する最小面積の矩形(Bounding boxと呼ばれる)\n", "2. 「内側にある物体が何か」=クラスラベル\n", "\n", "を推定することを目的とします.\n", "ただし,**画像中にいくつの物体が含まれるかは事前に分からない**ため,任意個(または十分な数)の物体の**Bounding boxとクラスラベルの予測値の組**を出力できるような手法である必要があります.\n", "\n", "Bounding box(以下bbox)は,[`矩形の左上のy座標`, `矩形の左上のx座標`, `矩形の右下のy座標`, `矩形の右下のx座標`]のような形式で定義されることが多く,クラスは物体の種類ごとに割り振られたID(以下クラスラベル)で表されることが多いようです.例えば,RBCは0,WBCは1,Plateletは2といったように,対象とする物体に1対1対応した非負整数が割り当てられるのが一般的です.\n", "\n", "以下に,今回この資料で用いる**細胞画像のデータセット**から1例を取り出し,その画像の上に正解として与えられているbboxと,それに対応するクラスの名前を可視化したものを示します.\n", "\n", "赤い長方形がbboxと呼ばれるものです.対象となる血液細胞を一つ一つ,別々の長方形が囲っていることがわかります.この長方形の上辺に重なるように白いラベルが表示されています.それがその矩形の内部にある物体の種類(クラス)を表しています.\n", "\n", "![血液の顕微鏡画像からRBC, WBC, Plateletを検出している例](https://github.com/mitmul/medical-ai-course-materials/raw/master/notebooks/images/detection_samples.png)\n" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "mksH_eu3NxKB" }, "source": [ "## 環境構築\n", "\n", "まず環境構築のためColab上で以下のセルを実行してChainerCVのインストールを済ませましょう.\n", "これらのステップは前回までと同様です." ] }, { "cell_type": "code", "metadata": { "colab_type": "code", "id": "d7wNK-q472Qo", "colab": { "base_uri": "https://localhost:8080/", "height": 397 }, "outputId": "1327e77a-c6d7-4609-aeec-eb699b18af50" }, "source": [ "!pip install chainercv # ChainerCVのインストール" ], "execution_count": 1, "outputs": [ { "output_type": "stream", "text": [ "Collecting chainercv\n", "\u001b[?25l Downloading https://files.pythonhosted.org/packages/e8/1c/1f267ccf5ebdf1f63f1812fa0d2d0e6e35f0d08f63d2dcdb1351b0e77d85/chainercv-0.13.1.tar.gz (260kB)\n", "\r\u001b[K |█▎ | 10kB 22.7MB/s eta 0:00:01\r\u001b[K |██▌ | 20kB 2.1MB/s eta 0:00:01\r\u001b[K |███▊ | 30kB 3.1MB/s eta 0:00:01\r\u001b[K |█████ | 40kB 2.0MB/s eta 0:00:01\r\u001b[K |██████▎ | 51kB 2.5MB/s eta 0:00:01\r\u001b[K |███████▌ | 61kB 3.0MB/s eta 0:00:01\r\u001b[K |████████▉ | 71kB 3.4MB/s eta 0:00:01\r\u001b[K |██████████ | 81kB 3.9MB/s eta 0:00:01\r\u001b[K 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"metadata": { "colab_type": "text", "id": "4HJ3fgCTppe6" }, "source": [ "それでは,先程のセルの実行によって環境のセットアップが成功したことを以下のセルを実行して確認しましょう." ] }, { "cell_type": "code", "metadata": { "colab_type": "code", "id": "J4eLK_2xpDV5", "outputId": "4cd1e7cd-76be-4c22-a061-b12722520399", "colab": { "base_uri": "https://localhost:8080/", "height": 323 } }, "source": [ "import chainer\n", "import cupy\n", "import chainercv\n", "import matplotlib\n", "\n", "chainer.print_runtime_info()\n", "print('ChainerCV:', chainercv.__version__)\n", "print('matplotlib:', matplotlib.__version__)" ], "execution_count": 2, "outputs": [ { "output_type": "stream", "text": [ "Platform: Linux-4.14.137+-x86_64-with-Ubuntu-18.04-bionic\n", "Chainer: 6.5.0\n", "ChainerX: Not Available\n", "NumPy: 1.17.4\n", "CuPy:\n", " CuPy Version : 6.5.0\n", " CUDA Root : /usr/local/cuda\n", " CUDA Build Version : 10000\n", " CUDA Driver Version : 10010\n", " CUDA Runtime Version : 10000\n", " cuDNN Build Version : 7603\n", " cuDNN Version : 7603\n", " NCCL Build Version : 2402\n", " NCCL Runtime Version : 2402\n", "iDeep: 2.0.0.post3\n", "ChainerCV: 0.13.1\n", "matplotlib: 3.1.1\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "57IgNNGD4-Q2" }, "source": [ "## 物体検出(Object detection)\n", "\n", "物体検出(object detection)は,Computer Visionの応用分野で現在も活発に研究が行われているタスクの一つで,自動運転やロボティクスなど幅広い領域で重要な役割を果たす技術です.Semantic Segmentationと違い,物体の形(輪郭)までは認識しませんが,**物の種類と位置を,物体ごとに個別に出力**します.\n", "\n", "「物の種類」をクラスと呼ぶとき,そのクラスに属する個別の物体をインスタンスと呼ぶことができます.すると,犬が2匹写っている写真があるとき,それは「犬」というクラスに属しているインスタンスが2個ある,という状態だと言えます.つまり,この前の章で学習したSemantic Segmentationというタスクでは**インスタンスごとに領域が区別されて出力されるわけではなかった**一方で,**物体検出の出力はインスタンスごとになる(インスタンスごとに別々のbboxが出力される)**という違いがあります.こういった出力の形を \"instance-wise\" という言葉で表現する場合もあります.\n", "\n", "ニューラルネットワークを用いた物体検出手法は,[R-CNN](https://arxiv.org/abs/1311.2524)という2014年に発表された手法を皮切りに,様々な改善手法が提案されてきました.まず一つの流れとして,[R-CNN](https://arxiv.org/abs/1311.2524), [Fast R-CNN](https://arxiv.org/abs/1504.08083), そして[Faster R-CNN](https://arxiv.org/abs/1506.01497)という物体検出手法は,はじめに物体の候補を推定し,次に各候補毎に物体のクラスや位置を詳細に推定します.これは**two stageタイプ**と呼ばれています.\n", "\n", "それに対して,同じくCNNをベースとはしているものの,**single stageタイプ**と呼ばれている手法があります.[SSD](https://arxiv.org/abs/1512.02325)や[YOLO](https://arxiv.org/abs/1506.02640),[YOLOv2](https://arxiv.org/abs/1612.08242),[YOLOv3](https://arxiv.org/abs/1804.02767)などがsingle stageタイプとしてよく知られています.これらは物体の候補を生成せず.直接各物体のクラスと位置を推定します.一般的にsingle stageタイプの方がtwo stageタイプよりも処理速度は高速である一方,精度が低いと言われています.ただし,最近はこれらの手法の境界は曖昧になり,性能差もほとんどなくなってきています.\n", "\n", "ここでは,single stageタイプの物体検出手法の一つ,SSDを使って,細胞画像から三種類の細胞の位置と種類を抽出するタスクに挑戦します." ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "wNTZ0qnEpvHr" }, "source": [ "## データセットの準備" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "wtoqG815k6rm" }, "source": [ "### データセットダウンロード\n", "\n", "まずは[BCCD Dataset](https://github.com/Shenggan/BCCD_Dataset)という,血液の顕微鏡画像のデータセットを用意します.このデータセットには,364枚の画像と,その画像それぞれに対応したファイル名のXMLファイルが含まれています.XMLファイルには,対応する画像中に登場したRBC, WBC, Plateletの3つのいずれかの細胞を囲むBounding boxの座標情報が格納されています.一つの画像中に複数の細胞が含まれている場合があるため,XMLファイルには複数の細胞についての記載が含まれる場合があります.\n", "\n", "BCCD Datasetは広く物体検出の研究に用いられているようなベンチマークデータセットに比べると非常に小規模であり,Github上で配布されています.以下のセルを実行してまずはデータセットをダウンロードしてみましょう." ] }, { "cell_type": "code", "metadata": { "colab_type": "code", "id": "AXaoRznUquZ-", "outputId": "f95865d7-9799-4327-bb5a-5a25daf2de84", "colab": { "base_uri": "https://localhost:8080/", "height": 107 } }, "source": [ "!if [ ! -d BCCD_Dataset ]; then git clone https://github.com/Shenggan/BCCD_Dataset.git; fi" ], "execution_count": 3, "outputs": [ { "output_type": "stream", "text": [ "Cloning into 'BCCD_Dataset'...\n", "remote: Enumerating objects: 786, done.\u001b[K\n", "remote: Total 786 (delta 0), reused 0 (delta 0), pack-reused 786\u001b[K\n", "Receiving objects: 100% (786/786), 7.34 MiB | 23.63 MiB/s, done.\n", "Resolving deltas: 100% (375/375), done.\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "bBg6xjP6qviM" }, "source": [ "ダウンロードが完了したら,`BCCD_Dataset`ディレクトリ以下のファイル構成を見てみましょう.このデータセットは,以下のようなファイル構成で配布されています.\n", "\n", "
\n", "| BCCD\n", "| |-- Annotations\n", "| | |\n", "| | `-- BloodImage_00XYZ.xml (364 items)\n", "| |\n", "| |-- ImageSets\n", "| | |\n", "| | `-- Main\n", "| | |\n", "| | |-- test.txt\n", "| | |-- train.txt\n", "| | `-- val.txt\n", "| |\n", "| `-- JPEGImages\n", "| |\n", "| `-- BloodImage_00XYZ.jpg (364 items)\n", "\n", "\n", "この構成は,長年物体検出の標準的ベンチマークデータセットとして用いられてきた**Pascal VOCデータセット**の形式に沿ったものとなっています.そのため,ChainerCVが用意しているPascal VOCデータセットを容易に扱えるようにするクラスをほとんどそのまま流用することが可能です.\n", "\n", "実際には他にもディレクトリがありますが,今回用いるのは上記のファイルツリーに含まれているものだけとなります.それぞれのディレクトリに含まれているものを説明します.\n", "\n", "- **Annotationsディレクトリ:**Pascal VOCデータセットと同様の形式で細胞画像それぞれに対して**どの位置に何があるか**という正解情報が格納されています.正解情報はXMLファイルとして格納されており,画像ファイルとの対応がわかりやすいように拡張子を除いて同一のファイル名で保存されています.\n", "- **ImageSetsディレクトリ:**学習用データセット(train)・検証用データセット(val)・テスト用データセット(test)のそれぞれに用いる画像のリストが記されたテキストファイルが入っています.これらのリストに従って,データセットを三分割し,それぞれ`train.txt`にリストアップされた画像を学習に,`val.txt`にリストアップされた画像を検証(学習中に汎化性能を大雑把に調べるために使うデータセットスプリット)に,`test.txt`にリストアップされた画像を学習終了後の最終的な性能評価に用います.\n", "- **JPEGImagesディレクトリ:**このデータセットに含まれるすべての画像データが入っています." ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "YNCwXii8W4It" }, "source": [ "### データセットオブジェクト作成\n", "\n", "ChainerCVにはPascal VOCデータセットを簡単に読み込むための便利なクラスが用意されています.これを継承し,`_get_annotations`メソッドをオーバーライドして,今回使用するデータセットを読み込み可能にします.変更が必要な行は1行だけです.[こちら](https://github.com/chainer/chainercv/blob/v0.10.0/chainercv/datasets/voc/voc_bbox_dataset.py#L90-L115)から該当するコード(`_get_annotations`メソッドの部分)をコピーしてきて,以下の変更を行い,`VOCBboxDataset`を継承する`BCCDDataset`クラスのメソッドとして追加してみましょう.\n", "(以下はdiff形式とよばれ,-でははじまる行を削除し,+で始まる行を追加するという意味です)\n", "\n", "```\n", "- label.append(voc_utils.voc_bbox_label_names.index(name))\n", "+ label.append(bccd_labels.index(name))\n", "```" ] }, { "cell_type": "code", "metadata": { "colab_type": "code", "id": "raamj0eXHDxj", "colab": {} }, "source": [ "import os\n", "import xml.etree.ElementTree as ET\n", "\n", "import numpy as np\n", "\n", "from chainercv.datasets import VOCBboxDataset\n", "\n", "\n", "bccd_labels = ('rbc', 'wbc', 'platelets')\n", "\n", "\n", "class BCCDDataset(VOCBboxDataset):\n", "\n", " def _get_annotations(self, i):\n", " id_ = self.ids[i]\n", " \n", " # Pascal VOC形式のアノテーションデータは,XML形式で配布されています\n", " anno = ET.parse(\n", " os.path.join(self.data_dir, 'Annotations', id_ + '.xml'))\n", "\n", " # XMLを読み込んで,bboxの座標・大きさ,bboxごとのクラスラベルなどの\n", " # 情報を取り出し,リストに追加していきます\n", " bbox = []\n", " label = []\n", " difficult = []\n", " for obj in anno.findall('object'):\n", " bndbox_anno = obj.find('bndbox')\n", " \n", " # bboxの座標値が0-originになるように1を引いています\n", " # subtract 1 to make pixel indexes 0-based\n", " bbox.append([\n", " int(bndbox_anno.find(tag).text) - 1\n", " for tag in ('ymin', 'xmin', 'ymax', 'xmax')])\n", " name = obj.find('name').text.lower().strip()\n", " label.append(bccd_labels.index(name))\n", " bbox = np.stack(bbox).astype(np.float32)\n", " label = np.stack(label).astype(np.int32)\n", " \n", " # オリジナルのPascal VOCには,difficultという\n", " # 属性が画像ごとに真偽値で与えられていますが,今回は用いません\n", " # (今回のデータセットでは全画像がdifficult = 0に設定されているため)\n", " # When `use_difficult==False`, all elements in `difficult` are False.\n", " difficult = np.array(difficult, dtype=np.bool)\n", " return bbox, label, difficult" ], "execution_count": 4, "outputs": [] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "JYW5zyhnPKKq" }, "source": [ "さて,これで学習や検証,テストなどにデータセットを用いるためのデータ読み込み等を行うクラスを準備することができました.さっそくこのクラスを用いて学習・検証・テスト用のデータセットオブジェクトを作成してみましょう." ] }, { "cell_type": "code", "metadata": { "colab_type": "code", "id": "sw3_oHpz442i", "outputId": "41464f98-844f-4666-d7b3-0d924d46cdcf", "colab": { "base_uri": "https://localhost:8080/", "height": 73 } }, "source": [ "train_dataset = BCCDDataset('BCCD_Dataset/BCCD', 'train')\n", "valid_dataset = BCCDDataset('BCCD_Dataset/BCCD', 'val')\n", "test_dataset = BCCDDataset('BCCD_Dataset/BCCD', 'test')" ], "execution_count": 5, "outputs": [ { "output_type": "stream", "text": [ "/usr/local/lib/python3.6/dist-packages/chainercv/datasets/voc/voc_bbox_dataset.py:63: UserWarning: please pick split from 'train', 'trainval', 'val'for 2012 dataset. For 2007 dataset, you can pick 'test' in addition to the above mentioned splits.\n", " 'please pick split from \\'train\\', \\'trainval\\', \\'val\\''\n" ], "name": "stderr" } ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "xPOfLkNZY8Bn" }, "source": [ "ここで警告が表示されるかもしれませんが,特に気にしなくても大丈夫です.本来Pascal VOCデータセットだけに特化して作られたクラスをBCCD Datasetに使っているため出ているものです.\n", "\n", "さて,3つのデータセットオブジェクトを作成することができました.それぞれの大きさ(いくつのデータが含まれているか)を確認してみましょう." ] }, { "cell_type": "code", "metadata": { "colab_type": "code", "id": "4wLDoPNDZDaU", "outputId": "80c26e65-6729-426d-9225-a7bb361e549f", "colab": { "base_uri": "https://localhost:8080/", "height": 71 } }, "source": [ "print('Number of images in \"train\" dataset:', len(train_dataset))\n", "print('Number of images in \"valid\" dataset:', len(valid_dataset))\n", "print('Number of images in \"test\" dataset:', len(test_dataset))" ], "execution_count": 6, "outputs": [ { "output_type": "stream", "text": [ "Number of images in \"train\" dataset: 205\n", "Number of images in \"valid\" dataset: 87\n", "Number of images in \"test\" dataset: 72\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "p6_rUqLVZ_Hp" }, "source": [ "では,`train_dataset`の1つ目のデータにアクセスしてみましょう." ] }, { "cell_type": "code", "metadata": { "colab_type": "code", "id": "NaROwCSZaATl", "colab": {} }, "source": [ "first_datum = train_dataset[0]" ], "execution_count": 7, "outputs": [] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "bDtylp_LaGax" }, "source": [ "さて,`train_dataset`は`VOCBboxDataset`を継承した`BCCDDataset`クラスのオブジェクトでした.そのため,上でオーバーライドした`_get_annotations`メソッド以外は,`VOCBboxDataset`クラスが提供する機能を継承しているはずです.どのような機能が提供されているのか,`VOCBboxDataset`クラスのドキュメントを見て確認してみましょう:[VOCBboxDataset](https://chainercv.readthedocs.io/en/stable/reference/datasets.html?highlight=VOCBboxDataset#vocbboxdataset)\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "pL6tzyZpp5rX" }, "source": [ "以下のような表が記載されています.このデータセットは,それぞれの要素に以下のようなものを持つリストのようになっています.\n", "\n", "| name\t| shape |\tdtype | \tformat |\n", "|:--|:--|:--|:--|\n", "| img | (3,H,W) | float32 | RGB, [0,255] |\n", "| bbox | (R,4) | float32 | (ymin,xmin,ymax,xmax) |\n", "| label | (R,) | int32 | [0,#fg_class−1] |\n", "| difficult (optional)* | (R,) | bool | – |\n", "\n", "* #fg_classはforeground(前景)のクラス数\n", "* difficultは `return_difficult = True` のときのみ有効\n", "\n", "ただし,今回データセットオブジェクトを作成する際に`return_difficult`オプションを明示的に`True`と指定していないので,デフォルト値の`False`が使われています.そのため上の表の最後の行にある`difficult`という要素は返ってきません.\n", "\n", "今回作成した3つのデータセットオブジェクトはすべて,それぞれの要素が`(画像データ, 正解のbboxリスト, 各bboxごとのクラス)`という3つの配列となっています." ] }, { "cell_type": "code", "metadata": { "colab_type": "code", "id": "cJgzQGoqpyhl", "outputId": "0cee0e19-d7c0-413f-ce99-113cf7d19a26", "colab": { "base_uri": "https://localhost:8080/", "height": 35 } }, "source": [ "len(first_datum)" ], "execution_count": 8, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "3" ] }, "metadata": { "tags": [] }, "execution_count": 8 } ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "VM2lxzkHa2DY" }, "source": [ "確かに,要素数は3でした.では,画像データを取り出して,そのshapeとdtypeを見てみます." ] }, { "cell_type": "code", "metadata": { "colab_type": "code", "id": "zyXkCAJSraM4", "outputId": "3d4d4c57-370c-4064-cb2d-56e3c7096e2e", "colab": { "base_uri": "https://localhost:8080/", "height": 35 } }, "source": [ "print(first_datum[0].shape, first_datum[0].dtype)" ], "execution_count": 9, "outputs": [ { "output_type": "stream", "text": [ "(3, 480, 640) float32\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "bkixGpqRrdVb" }, "source": [ "確かに,`(3=チャンネル数, H=高さ, W=幅)`という形になっており,またデータ型は`float32`になっています.上の表にあったとおりでした.ではbboxはどのような形式になっているのでしょうか.中身と,そのshapeを表示して見てみます." ] }, { "cell_type": "code", "metadata": { "colab_type": "code", "id": "-P12EOkCr617", "outputId": "83e1f0ba-8ae0-4016-aa7d-df2c0f8284c9", "colab": { "base_uri": "https://localhost:8080/", "height": 377 } }, "source": [ "print(first_datum[1])\n", "print(first_datum[1].shape)" ], "execution_count": 10, "outputs": [ { "output_type": "stream", "text": [ "[[314. 67. 479. 285.]\n", " [360. 345. 453. 445.]\n", " [178. 52. 298. 145.]\n", " [399. 448. 479. 535.]\n", " [131. 460. 211. 547.]\n", " [294. 453. 374. 540.]\n", " [282. 416. 382. 507.]\n", " [341. 277. 450. 368.]\n", " [ 61. 544. 158. 635.]\n", " [ 90. 484. 187. 575.]\n", " [170. 375. 252. 437.]\n", " [176. 328. 270. 394.]\n", " [ 58. 290. 167. 406.]\n", " [ 0. 298. 67. 403.]\n", " [ 25. 345. 137. 448.]\n", " [ 0. 133. 94. 240.]\n", " [ 37. 0. 163. 97.]\n", " [159. 164. 263. 256.]\n", " [208. 463. 318. 565.]]\n", "(19, 4)\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "C-204SZmsFmd" }, "source": [ "19個のbboxの情報が並んでおり,ひとつひとつは`(y_min, x_min, y_max, x_max)`という4つの数字で表されています.この4つの数字はbboxの左上と右下の画像座標値(画像平面上の位置)を表しています.\n", "\n", "画像内に登場している物体のそれぞれについて,この4つの数字を出力するというのが物体検出の一つの目的となります.ただし,それだけでなく,それぞれのbboxがどのクラスに属しているか(そのbboxの内部にある物体の種類)も出力する必要があります.これについての正解情報が,最後の要素に入っています.これを表示してみます." ] }, { "cell_type": "code", "metadata": { "colab_type": "code", "id": "JB3MoEt1s0ii", "outputId": "66aa0693-ea55-4260-9a53-31282e2d5bcc", "colab": { "base_uri": "https://localhost:8080/", "height": 53 } }, "source": [ "print(first_datum[2])\n", "print(first_datum[2].shape)" ], "execution_count": 11, "outputs": [ { "output_type": "stream", "text": [ "[1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n", "(19,)\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "e8uxffsXs4rH" }, "source": [ "19個の数字が入っていました.これはそれぞれ,上で表示してみたbbox(`first_datum[1]`)に順番に対応しており,それぞれのbboxがどのクラスに属する物体か(0: RBC, 1: WBC, 2: Platelet)を表しています." ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "WFV-jPd36VHo" }, "source": [ "ではこの節の最後に,これら3つの要素で一括りとされているデータセット中の1つのデータを,可視化して確認してみます.\n", "trainデータセットから取り出した画像一つと,それに対応するbbox,それぞれのクラスラベルを取り出し,ChainerCVが用意している可視化用の便利な関数を使って,画像を表示した上でそこにbounding boxと対応するクラスの名前を重ねて表示してみます." ] }, { "cell_type": "code", "metadata": { "colab_type": "code", "id": "tzDvP_8W8V63", "outputId": "3938dc2f-350e-419d-9248-e24c015fb108", "colab": { "base_uri": "https://localhost:8080/", "height": 301 } }, "source": [ "%matplotlib inline\n", "from chainercv.visualizations import vis_bbox\n", "\n", "img, bbox, label = train_dataset[0]\n", "ax = vis_bbox(img, bbox, label, label_names=bccd_labels)\n", "ax.set_axis_off()\n", "ax.figure.tight_layout()" ], "execution_count": 12, "outputs": [ { "output_type": "display_data", "data": { "image/png": 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I2u+keXAFFcW8KogsaErRm9nodIWbs91mkvRYFihOoUAGS0qvCdGO3ke2NlYrGyhgFjGG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\nAHjoZS8F8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CFb15BakVxdYKiSJ7VuQXpE2UzQIhlntlBto5JgsM5u/ccj/9UrbD72wogF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eXL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ZmC/fHFv/+P/+iP958dqTEj7kRdjYHRN4z8+2b53MohPVBKgd9vnBu3FMzqAdWyjz/93vd7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\nP7AeQyf6Qvjch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