質問編集履歴
3
文章表現
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画像データを
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画像データの特徴量を抽出し機械学習用のデータセットを作りたい
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### 前提・実現したいこと
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自前の画像データから機械学習用のデータセットを作成し学習させたいのですが、
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下記エラーにより実行できません。画像データを配列にする
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モデルをfitさせた時に下記エラーにより実行できません。画像データを配列にする
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と考えているのですが、解決方法をご存知の方ご教示ください。
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ときの方法が不適切ではと考えているのですが、解決方法をご存知の方ご教示ください。
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### 発生している問題・エラーメッセージ
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import glob
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import cv2
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#1.anotation_results_form_check.ipynbで作成したdata2.csvを読み込み
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df2 = pd.read_csv("data2.csv")
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df2.shape
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df_join.to_csv("df_join.csv")
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#targetにsmileのカテゴリを代入
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**#targetにsmileのカテゴリを代入
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smiles =df_join["smile"]
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target = []
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for smile in smiles:
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data = np.asarray(smile)
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target.append(data)
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y = np.array(target)
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y = np.array(target)**
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y
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```
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```OUT
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bgr = np.asarray(bgr)
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photo_array.append(bgr)
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x = np.array(photo_array)
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x[0]
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```
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```OUT
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array([[217, 217, 217, ..., 188, 196, 203],
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[217, 217, 217, ..., 188, 196, 203],
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[217, 217, 217, ..., 188, 196, 204],
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...,
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[ 4, 4, 4, ..., 44, 43, 42],
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[ 4, 4, 4, ..., 44, 43, 42],
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[ 4, 4, 4, ..., 44, 43, 41]], dtype=uint8)
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```
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```
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from sklearn import linear_model
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clf = linear_model.LogisticRegression()
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文法の修正
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画像データ
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画像データをnumpy配列にし機械学習用のデータセットを作りたい
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File without changes
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不具合内容を変更
title
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画像データから機械学習用のデータセットを作る時にエラーが発生(setting an array element with a sequence.)
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```
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---------------------------------------------------------------------------
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TypeError Traceback (most recent call last)
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<ipython-input-14-70537278e5a3> in <module>
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8 for train_index, test_index in ss.split(photo_array, target):
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----> 9 x_train, x_test = photo_array[train_index], photo_array[test_index]
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TypeError: only size-1 arrays can be converted to Python scalars
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The above exception was the direct cause of the following exception:
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ValueError Traceback (most recent call last)
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ValueError: setting an array element with a sequence.
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```
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### 該当のソースコード
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df_join.to_csv("df_join.csv")
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#targetにsmileのカテゴリを代入
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smiles =df_join["smile"]
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target
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target = []
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for smile in smiles:
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data = np.asarray(smile)
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target.append(data)
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y = np.array(target)
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y
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```
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```OUT
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array([1, 3, 1, ..., 3, 3, 3])
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```
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```
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#photo_array に画像データの配列を代入
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titles = df_join["data"]
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image_size = 50
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photo_array = []
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for title in titles:
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bgr = cv2.imread(title, cv2.IMREAD_GRAYSCALE)
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bgr = np.
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bgr = np.asarray(bgr)
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photo_array.append(bgr)
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photo_array
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x = np.array(photo_array)
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```
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```OUT
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array([[217, 217, 217, ..., 188, 196, 203],
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[217, 217, 217, ..., 188, 196, 203],
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[217, 217, 217, ..., 188, 196, 204],
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...,
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[ 4, 4, 4, ..., 44, 43, 42],
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[ 4, 4, 4, ..., 44, 43, 42],
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[ 4, 4, 4, ..., 44, 43, 41]], dtype=uint8)
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```
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```
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from sklearn import linear_model
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clf = linear_model.LogisticRegression()
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ss = StratifiedKFold(n_splits=10,
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shuffle=True)
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for train_index, test_index in ss.split(
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for train_index, test_index in ss.split(x, y):
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x_train, x_test =
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x_train, x_test = x[train_index], x[test_index]
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y_train, y_test =
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y_train, y_test = y[train_index], y[test_index]
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clf.fit(x_train, y_train)
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print(clf.score(x_test, y_test))
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```
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```エラーメッセージ
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---------------------------------------------------------------------------
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TypeError Traceback (most recent call last)
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TypeError: only size-1 arrays can be converted to Python scalars
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The above exception was the direct cause of the following exception:
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ValueError Traceback (most recent call last)
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<ipython-input-15-305a5bf690e8> in <module>
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10 y_train, y_test = y[train_index], y[test_index]
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---> 12 clf.fit(x_train, y_train)
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13 print(clf.score(x_test, y_test))
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/opt/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_logistic.py in fit(self, X, y, sample_weight)
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1342 X, y = self._validate_data(X, y, accept_sparse='csr', dtype=_dtype,
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1343 order="C",
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-> 1344 accept_large_sparse=solver != 'liblinear')
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1345 check_classification_targets(y)
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1346 self.classes_ = np.unique(y)
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/opt/anaconda3/lib/python3.7/site-packages/sklearn/base.py in _validate_data(self, X, y, reset, validate_separately, **check_params)
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430 y = check_array(y, **check_y_params)
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431 else:
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--> 432 X, y = check_X_y(X, y, **check_params)
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433 out = X, y
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/opt/anaconda3/lib/python3.7/site-packages/sklearn/utils/validation.py in inner_f(*args, **kwargs)
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70 FutureWarning)
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71 kwargs.update({k: arg for k, arg in zip(sig.parameters, args)})
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---> 72 return f(**kwargs)
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73 return inner_f
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/opt/anaconda3/lib/python3.7/site-packages/sklearn/utils/validation.py in check_X_y(X, y, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, estimator)
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800 ensure_min_samples=ensure_min_samples,
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801 ensure_min_features=ensure_min_features,
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--> 802 estimator=estimator)
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803 if multi_output:
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804 y = check_array(y, accept_sparse='csr', force_all_finite=True,
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/opt/anaconda3/lib/python3.7/site-packages/sklearn/utils/validation.py in inner_f(*args, **kwargs)
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70 FutureWarning)
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71 kwargs.update({k: arg for k, arg in zip(sig.parameters, args)})
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---> 72 return f(**kwargs)
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73 return inner_f
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/opt/anaconda3/lib/python3.7/site-packages/sklearn/utils/validation.py in check_array(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator)
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596 array = array.astype(dtype, casting="unsafe", copy=False)
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597 else:
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--> 598 array = np.asarray(array, order=order, dtype=dtype)
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599 except ComplexWarning:
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600 raise ValueError("Complex data not supported\n"
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/opt/anaconda3/lib/python3.7/site-packages/numpy/core/_asarray.py in asarray(a, dtype, order)
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82 """
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---> 83 return array(a, dtype, copy=False, order=order)
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ValueError: setting an array element with a sequence.
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```
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