質問編集履歴
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エラーが出るまでの全てのプログラムを書きました。
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```python
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!pip install icrawler
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```
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```python
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# from icrawler.builtin import GoogleImageCrawler
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# もつ煮の画像を100枚取得
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# crawler = GoogleImageCrawler(storage={"root_dir": "moosuni"})
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# crawler.crawl(keyword="もつ煮", max_num=100)
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from icrawler.builtin import BingImageCrawler
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# もつ煮の画像を100枚取得
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crawler = BingImageCrawler(storage={"root_dir": "motsuni"})
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crawler.crawl(keyword="もつ煮", max_num=100)
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```
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```python
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# ナポリタンの画像を100枚取得
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# crawler = GoogleImageCrawler(storage={"root_dir": "naporitan"})
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# crawler.crawl(keyword="ナポリタン", max_num=100)
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from icrawler.builtin import BingImageCrawler
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# ナポリタンの画像を100枚取得
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crawler = BingImageCrawler(storage={"root_dir": "naporitan"})
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crawler.crawl(keyword="ナポリタン", max_num=100)
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```
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```python
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# ラーメンの画像を100枚取得
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# crawler = GoogleImageCrawler(storage={"root_dir": "ra-men"})
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# crawler.crawl(keyword="ラーメン", max_num=100)
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from icrawler.builtin import BingImageCrawler
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# ラーメンの画像を100枚取得
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crawler = BingImageCrawler(storage={"root_dir": "ra-men"})
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crawler.crawl(keyword="醤油ラーメン", max_num=100)
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```
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```python
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from PIL import Image
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import os, glob
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import numpy as np
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from PIL import ImageFile
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# IOError: image file is truncated (0 bytes not processed)回避のため
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ImageFile.LOAD_TRUNCATED_IMAGES = True
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classes = ["motsuni", "naporitan", "ra-men"]
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num_classes = len(classes)
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image_size = 64
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num_testdata = 25
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X_train = []
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X_test = []
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y_train = []
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y_test = []
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for index, classlabel in enumerate(classes):
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photos_dir = "./" + classlabel
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files = glob.glob(photos_dir + "/*.jpg")
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for i, file in enumerate(files):
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image = Image.open(file)
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image = image.convert("RGB")
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image = image.resize((image_size, image_size))
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data = np.asarray(image)
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if i < num_testdata:
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X_test.append(data)
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y_test.append(index)
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else:
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for angle in range(-20, 20, 5):
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img_r = image.rotate(angle)
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data = np.asarray(img_r)
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X_train.append(data)
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y_train.append(index)
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img_trains = img_r.transpose(Image.FLIP_LEFT_RIGHT)
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data = np.asarray(img_trains)
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X_train.append(data)
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y_train.append(index)
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X_train = np.array(X_train)
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X_test = np.array(X_test)
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y_train = np.array(y_train)
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y_test = np.array(y_test)
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xy = (X_train, X_test, y_train, y_test)
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np.save("./ryouri.npy", xy)
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```
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```python
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from keras.models import Sequential
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from keras.layers import Conv2D, MaxPooling2D
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@@ -16,7 +186,7 @@
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classes = ["motsuni", "naporitan", "
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classes = ["motsuni", "naporitan", "ra-men"]
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num_classes = len(classes)
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@@ -206,4 +376,8 @@
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379
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使う画像は、上のコードでウェブから拾ってきています。
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３つ以上のファイルを機械学習するためにはどのようにすればいいか、教えていただけると嬉しいです。
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