回答編集履歴
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追補
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追補
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補完方法にどれだけ差があるか試したことがなかったため、KERASの方法を正としてOpenCV、PILの方法を比較しました。
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結果： 補完方法による「差はない」は言い過ぎですが、**データ増強の変化量と比べればないも同然程度の差**しかありませんでした。
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```Python3
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from PIL import Image
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import cv2
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import os
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import numpy as np
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from keras.preprocessing.image import ImageDataGenerator,img_to_array
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path = ".\lenna.png"
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size = (256,256)
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def PIL2OpenCV(img):
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img = np.asarray(img)
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if len(img.shape)==3:
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# RGB --> BGR
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img = cv2.cvtColor(img,cv2.COLOR_RGB2BGR)
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return img
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def pure_PIL(path,size):
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img_PIL = Image.open(path)
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img_PIL = img_PIL.resize(size)
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return PIL2OpenCV(img_PIL)
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def pure_OpenCV(path,size,flag):
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img_OpenCV = cv2.imread(path)
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img_OpenCV = cv2.resize(img_OpenCV,size,interpolation=flag)
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return img_OpenCV
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def by_keras(path,size):
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img_by_keras = ImageDataGenerator.flow_from_directory(path,target_size=size)
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print(img_by_keras)
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return img_by_keras
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if __name__=="__main__":
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img_by_keras = pure_OpenCV(path,size,cv2.INTER_LANCZOS4)
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# cv2.imshow("KERAS",cv2.absdiff(img_by_keras,img_by_keras))
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img_PIL = pure_PIL(path,size)
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# cv2.imshow("PIL",cv2.absdiff(img_by_keras,img_PIL))
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img_nearest = pure_OpenCV(path,size,cv2.INTER_NEAREST)
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# cv2.imshow("OpenCV (NEAREST)",cv2.absdiff(img_by_keras,img_nearest))
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img_linear = pure_OpenCV(path,size,cv2.INTER_LINEAR)
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# cv2.imshow("OpenCV (LINEAR)",cv2.absdiff(img_by_keras,img_linear))
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img_cubic = pure_OpenCV(path,size,cv2.INTER_CUBIC)
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# cv2.imshow("OpenCV (CUBIC)",cv2.absdiff(img_by_keras,img_cubic))
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img_area = pure_OpenCV(path,size,cv2.INTER_AREA)
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# cv2.imshow("OpenCV (AREA)",cv2.absdiff(img_by_keras,img_area))
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img_lanczos = pure_OpenCV(path,size,cv2.INTER_LANCZOS4)
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# cv2.imshow("OpenCV (LANCZOS4)",cv2.absdiff(img_by_keras,img_lanczos))
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# img_strange = img_to_array(img_nearest)
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# cv2.imshow("STRANGE CODE",img_strange)
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img_stacked = np.hstack((cv2.absdiff(img_by_keras,img_by_keras),
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cv2.absdiff(img_by_keras,img_PIL),
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cv2.absdiff(img_by_keras,img_nearest),
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cv2.absdiff(img_by_keras,img_linear),
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cv2.absdiff(img_by_keras,img_cubic),
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cv2.absdiff(img_by_keras,img_area),
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cv2.absdiff(img_by_keras,img_lanczos)
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))
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cv2.imwrite("./stacked.png",img_stacked)
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cv2.imshow("KERAS/PIL/NEAREST/LINEAR/CUBIC/AREA/LANCZOS/STRANGE",img_stacked)
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cv2.waitKey(0)
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```
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また、
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> 学習した特徴量（kerasでリサイズ）がモデルを使用するときの画像（opencvでリサイズ）に使えない状態
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これについて検証するため、掲載のコードを手元のWindowsで動くように若干修正したのが以下のコードです。
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結果： OpenCVではBGRに対して、PILではRGBでデータが格納されています。この違いにより、PIL経由でデータを表示させたものは異常な色になりました。これが学習がうまくいかなかった原因と思われます。
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これを防ぐには、上記の`PIL2OpenCV`をかませる等で対応してください。
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```Python3
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from PIL import Image
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import cv2
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import os
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import numpy as np
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from keras.preprocessing.image import ImageDataGenerator,img_to_array
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path = ".\lenna.png"
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size = (256,256)
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def PIL2OpenCV(img):
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img = np.asarray(img)
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if len(img.shape)==3:
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# RGB --> BGR
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img = cv2.cvtColor(img,cv2.COLOR_RGB2BGR)
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return img
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def pure_PIL(path,size):
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img_PIL = Image.open(path)
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img_PIL = img_PIL.resize(size)
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return PIL2OpenCV(img_PIL)
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def pure_OpenCV(path,size,flag):
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img_OpenCV = cv2.imread(path)
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img_OpenCV = cv2.resize(img_OpenCV,size,interpolation=flag)
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return img_OpenCV
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def by_keras(path,size):
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img_by_keras = ImageDataGenerator.flow_from_directory(path,target_size=size)
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print(img_by_keras)
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return img_by_keras
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if __name__=="__main__":
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img_by_keras = pure_OpenCV(path,size,cv2.INTER_LANCZOS4)
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cv2.imshow("KERAS",cv2.absdiff(img_by_keras,img_by_keras))
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img_PIL = pure_PIL(path,size)
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cv2.imshow("PIL",cv2.absdiff(img_by_keras,img_PIL))
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img_OpenCV = pure_OpenCV(path,size,cv2.INTER_NEAREST)
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cv2.imshow("OpenCV (NEAREST)",cv2.absdiff(img_by_keras,img_OpenCV))
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img_OpenCV = pure_OpenCV(path,size,cv2.INTER_LINEAR)
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cv2.imshow("OpenCV (LINEAR)",cv2.absdiff(img_by_keras,img_OpenCV))
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img_OpenCV = pure_OpenCV(path,size,cv2.INTER_CUBIC)
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cv2.imshow("OpenCV (CUBIC)",cv2.absdiff(img_by_keras,img_OpenCV))
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img_OpenCV = pure_OpenCV(path,size,cv2.INTER_AREA)
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cv2.imshow("OpenCV (AREA)",cv2.absdiff(img_by_keras,img_OpenCV))
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img_OpenCV = pure_OpenCV(path,size,cv2.INTER_LANCZOS4)
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cv2.imshow("OpenCV (LANCZOS4)",cv2.absdiff(img_by_keras,img_OpenCV))
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img_OpenCV = img_to_array(img_OpenCV)
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cv2.imshow("STRANGE CODE",img_OpenCV)
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cv2.waitKey(0)
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```
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---
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**■リファレンスの情報**
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- [Kerasのload_imgのリサイズはパスを入れて**PIL形式を返す**](https://www.tensorflow.org/api_docs/python/tf/keras/preprocessing/image/load_img)です。
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つまり、
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