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

前提・実現したいこと

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、
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
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。

発生している問題・エラーメッセージ

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
TypeError: only size-1 arrays can be converted to Python scalars

The above exception was the direct cause of the following exception:

ValueError                                Traceback (most recent call last)
ValueError: setting an array element with a sequence.

該当のソースコード

python
1import pandas as pd
2import numpy as np
3import glob
4import cv2
5
6df2 = pd.read_csv("data2.csv")
7df2.shape
8
9
10#file pathの設定(フォルダ内の画像データを読み込み 画像データは6486枚)
11files = glob.glob("/Users/a440/Desktop/happy_images/*")
12len(files)
13
14#filesのデータフレームを作成
15df_files = pd.DataFrame({"data":files})
16
17#dataのformatを合わせる
18df2["data"] = "/Users/a440/Desktop/happy_images/" + df2["data"] + ".jpg"
19df2 = df2.drop("Unnamed: 0", axis=1)
20df2.to_csv("data2-2.csv")
21
22#df2とdf_filesをdata列でマージする
23df_join = pd.merge(df2, df_files, how="inner",on="data",indicator=True)
24df_join.shape

OUT
16441, 3


df_join.to_csv("df_join.csv")

**#targetにsmileのカテゴリを代入
smiles =df_join["smile"]
target = []

for smile in smiles:
    data = np.asarray(smile)
    target.append(data)
y = np.array(target)**
y

OUT
1array([1, 3, 1, ..., 3, 3, 3])

#photo_array に画像データの配列を代入
titles = df_join["data"]

    
image_size = 50

photo_array = []

for title in titles:
    bgr = cv2.imread(title, cv2.IMREAD_GRAYSCALE)
    bgr = np.asarray(bgr)
    photo_array.append(bgr)
x = np.array(photo_array)
x[0]

OUT
1array([[217, 217, 217, ..., 188, 196, 203],
2       [217, 217, 217, ..., 188, 196, 203],
3       [217, 217, 217, ..., 188, 196, 204],
4       ...,
5       [  4,   4,   4, ...,  44,  43,  42],
6       [  4,   4,   4, ...,  44,  43,  42],
7       [  4,   4,   4, ...,  44,  43,  41]], dtype=uint8)

from sklearn import linear_model
clf = linear_model.LogisticRegression()

from sklearn.model_selection import StratifiedKFold
ss = StratifiedKFold(n_splits=10,
          shuffle=True)

for train_index, test_index in ss.split(x, y):
    x_train, x_test = x[train_index], x[test_index]
    y_train, y_test = y[train_index], y[test_index]  
    
clf.fit(x_train, y_train)
print(clf.score(x_test, y_test))

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
TypeError: only size-1 arrays can be converted to Python scalars

The above exception was the direct cause of the following exception:

ValueError                                Traceback (most recent call last)
<ipython-input-15-305a5bf690e8> in <module>
     10     y_train, y_test = y[train_index], y[test_index]
     11 
---> 12 clf.fit(x_train, y_train)
     13 print(clf.score(x_test, y_test))
     14 

/opt/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_logistic.py in fit(self, X, y, sample_weight)
   1342         X, y = self._validate_data(X, y, accept_sparse='csr', dtype=_dtype,
   1343                                    order="C",
-> 1344                                    accept_large_sparse=solver != 'liblinear')
   1345         check_classification_targets(y)
   1346         self.classes_ = np.unique(y)

/opt/anaconda3/lib/python3.7/site-packages/sklearn/base.py in _validate_data(self, X, y, reset, validate_separately, **check_params)
    430                 y = check_array(y, **check_y_params)
    431             else:
--> 432                 X, y = check_X_y(X, y, **check_params)
    433             out = X, y
    434 

/opt/anaconda3/lib/python3.7/site-packages/sklearn/utils/validation.py in inner_f(*args, **kwargs)
     70                           FutureWarning)
     71         kwargs.update({k: arg for k, arg in zip(sig.parameters, args)})
---> 72         return f(**kwargs)
     73     return inner_f
     74 

/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)
    800                     ensure_min_samples=ensure_min_samples,
    801                     ensure_min_features=ensure_min_features,
--> 802                     estimator=estimator)
    803     if multi_output:
    804         y = check_array(y, accept_sparse='csr', force_all_finite=True,

/opt/anaconda3/lib/python3.7/site-packages/sklearn/utils/validation.py in inner_f(*args, **kwargs)
     70                           FutureWarning)
     71         kwargs.update({k: arg for k, arg in zip(sig.parameters, args)})
---> 72         return f(**kwargs)
     73     return inner_f
     74 

/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)
    596                     array = array.astype(dtype, casting="unsafe", copy=False)
    597                 else:
--> 598                     array = np.asarray(array, order=order, dtype=dtype)
    599             except ComplexWarning:
    600                 raise ValueError("Complex data not supported\n"

/opt/anaconda3/lib/python3.7/site-packages/numpy/core/_asarray.py in asarray(a, dtype, order)
     81 
     82     """
---> 83     return array(a, dtype, copy=False, order=order)
     84 
     85 

ValueError: setting an array element with a sequence.