COCOデータセットを利用した物体検出#
物体検出タスクを行うための基本的なステップを説明
以下の手順は、Pythonと機械学習ライブラリであるPyTorchと torchvisionを用いて、COCOデータセットを利用した物体検出を行う例
1. 必要なライブラリのインストール#
# # poetry環境を用いない場合
# ! pip install numpy matplotlib opencv-python pillow
# ! pip install torch torchvision
# ! pip install pycocotools
2. COCOデータセットのダウンロードと準備#
# !mkdir datasets
# !cd datasets
# COCO 2017 train images
!wget http://images.cocodataset.org/zips/train2017.zip
!unzip train2017.zip
# # COCO 2017 val images
# !wget http://images.cocodataset.org/zips/val2017.zip
# !unzip val2017.zip
# # COCO 2017 annotations
# !wget http://images.cocodataset.org/annotations/annotations_trainval2017.zip
# !unzip annotations_trainval2017.zip
# !mv datasets ../data/
# !mv annotations ../data/
# !mv val2017 ../data/
# !rm annotations_trainval2017.zip
# !rm train2017.zip
# !rm val2017.zip
--2023-07-10 13:54:23-- http://images.cocodataset.org/zips/train2017.zip
Resolving images.cocodataset.org (images.cocodataset.org)... 3.5.21.109, 52.217.140.153, 3.5.10.154, ...
Connecting to images.cocodataset.org (images.cocodataset.org)|3.5.21.109|:80... connected.
HTTP request sent, awaiting response... 200 OK
Length: 19336861798 (18G) [application/zip]
Saving to: ‘train2017.zip’
train2017.zip 100%[===================>] 18.01G 2.56MB/s in 92m 23s
2023-07-10 15:26:47 (3.33 MB/s) - ‘train2017.zip’ saved [19336861798/19336861798]
Archive: train2017.zip
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3. COCOデータセットの読み込みとアノテーションデータの解析#
import torchvision
from torchvision.models.detection.faster_rcnn import FastRCNNPredictor
from torchvision.datasets import CocoDetection
# COCOデータセットの読み込み
# ここでは訓練データの例を示しています
img_dir = "../data/datasets/train2017"
anno_file = "../data/datasets/annotations/instances_train2017.json"
dataset = CocoDetection(img_dir, anno_file)
# 画像とアノテーションデータを取得
img, target = dataset[0]
# アノテーションデータは辞書型で以下のような内容が含まれます
# 'boxes' - bounding boxの座標が[n_objects, 4]の形状で格納されています
# 'labels' - 各オブジェクトのラベルが[n_objects]の形状で格納されています
# 'image_id' - 画像のIDが格納されています
# 'area' - bounding boxの面積が格納されています
# 'iscrowd' - アノテーションがcrowd（集合体）であるかどうかのフラグ
loading annotations into memory...
---------------------------------------------------------------------------
FileNotFoundError Traceback (most recent call last)
Cell In[12], line 9
7 img_dir = "../data/datasets/train2017"
8 anno_file = "../data/datasets/annotations/instances_train2017.json"
----> 9 dataset = CocoDetection(img_dir, anno_file)
11 # 画像とアノテーションデータを取得
12 img, target = dataset[0]
File /mnt/workspace/jupyter-book-python/.venv/lib/python3.10/site-packages/torchvision/datasets/coco.py:36, in CocoDetection.__init__(self, root, annFile, transform, target_transform, transforms)
33 super().__init__(root, transforms, transform, target_transform)
34 from pycocotools.coco import COCO
---> 36 self.coco = COCO(annFile)
37 self.ids = list(sorted(self.coco.imgs.keys()))
File /mnt/workspace/jupyter-book-python/.venv/lib/python3.10/site-packages/pycocotools/coco.py:81, in COCO.__init__(self, annotation_file)
79 print('loading annotations into memory...')
80 tic = time.time()
---> 81 with open(annotation_file, 'r') as f:
82 dataset = json.load(f)
83 assert type(dataset)==dict, 'annotation file format {} not supported'.format(type(dataset))
FileNotFoundError: [Errno 2] No such file or directory: '../data/datasets/annotations/instances_train2017.json'
torchvision.__version__
'0.15.1+cu117'