knownEmbeddings = []
knownNames = []
for (i, imagePath) in enumerate(imagePaths):
## SOME CODE
knownNames.append(name)
knownEmbeddings.append(vec.flatten())
data = {"embeddings": knownEmbeddings, "names": knownNames}
f = open('file.json', "wb")
f.write(json.dumps(data, indent=4))
f.close()
data如下：
{'embeddings': [array([ 2.23568859e-04, -4.08176295e-02, -1.56606492e-02, -1.40566211e-02,
5.53448219e-04, 1.34807974e-01, 2.10583732e-02, -7.99260102e-03,
8.04360434e-02, 2.51036473e-02, -2.45967298e-03, 8.73192959e-03,
1.08047323e-02, 8.02712217e-02, 6.31465465e-02, 9.41963419e-02],
dtype=float32), array([-5.54675907e-02, 1.19409459e-02, -3.03599555e-02, -2.86714472e-02,
6.26528710e-02, 1.25348523e-01, -2.16291733e-02, -4.60545160e-02,
6.25465512e-02, -7.61162862e-02, 4.28330414e-02, 8.57844874e-02,
3.75184380e-02, -8.10878351e-02, -8.96525383e-02, 8.15552175e-02,
-9.75750014e-02, -8.24848488e-02, 9.30746570e-02, 1.71318889e-01,
1.00642473e-01, 5.39120510e-02, 1.12627009e-02, 1.40678780e-02,
-4.41719554e-02, 1.03237763e-01, 4.38372791e-02, 7.53327608e-02],
dtype=float32), array([-0.03736538, -0.0485549 , -0.0976112 , -0.06195155, 0.00269726,
0.07389018, -0.07325964, 0.06653353, -0.04690087, -0.02606474,
0.03597135, 0.06904668, 0.02198682, -0.06437466, -0.04554454,
0.01083081, -0.06293067, 0.07047471, 0.02824293, -0.15522538,
-0.01900602, 0.10689866, -0.07408814, -0.0419201 , 0.10475922,
0.04784475, -0.09085421, -0.20360689, 0.08321641, 0.08441921,
0.01941148, 0.03566081, -0.05956643, 0.005247 , -0.03989819,
0.02512971, 0.00458561, 0.13706829], dtype=float32), array([ 7.87236728e-03, 5.65276742e-02, -7.17918649e-02, -1.84332877e-02,
1.28411269e-02, 2.85971135e-01, 3.13642109e-03, 2.48481780e-02,
-9.48547944e-02, 2.89725009e-02, 4.33916636e-02, 9.01726633e-02,
4.36290540e-02, -1.02897413e-01, 2.22285688e-02, -5.19381762e-02,
1.52556881e-01, -1.25146270e-01, 3.18806712e-03, -2.51053665e-02,
-4.36606398e-03, 7.19061792e-02, 4.66747172e-02, 8.13280419e-02],
dtype=float32), array([ 0.09142991, -0.05100765, -0.09615178, -0.03553161, 0.11363017,
0.19886988, 0.11280693, 0.0229619 , -0.0220201 , -0.01211688,
0.07489388, 0.0802715 , 0.16185616, -0.0904082 , 0.0025941 ,
0.12167819, -0.07357537, -0.01442344, -0.01343578, 0.16952834,
0.03366659, -0.0534111 , -0.01595308, 0.15053654, -0.07398864,
0.04694209, -0.06523879, 0.01342433], dtype=float32), array([-0.05331187, 0.08159426, -0.01742208, 0.00992642, -0.01155609,
0.25759327, -0.00505029, -0.09290393, 0.01588799, -0.00478396,
0.08572742, -0.05053008, 0.05197625, 0.1267016 , 0.15398905,
0.13668832, -0.13869229, 0.02502107, -0.04443422, -0.05987623,
0.14948404, 0.03311499, 0.12621029], dtype=float32), array([ 0.1219558 , -0.0371135 , -0.13762642, 0.00431138, 0.20073804,
0.09986125, 0.21617071, 0.02764285, -0.1352063 , 0.02268699,
-0.04734468, 0.10888206, 0.13558514, -0.00319178, 0.02979032,
0.03558976, -0.07293532, -0.05351996, -0.02449711, 0.1459181 ,
-0.00320001, 0.01020296, -0.05007216, 0.05868218, -0.03522768,
-0.01064874, -0.0732395 , -0.05393502], dtype=float32), array([ 0.10833652, 0.08779355, -0.15162815, -0.03925862, 0.08713786,
0.2850307 , 0.13499181, 0.01792248, -0.1405847 , 0.08626581,
0.02001712, -0.06957201, -0.00727825, 0.01650161, 0.11886367,
0.07897119, -0.14108546, 0.03840445, 0.05881708, 0.03361814,
-0.0106756 , -0.04287936, -0.06621028], dtype=float32), array([ 4.90577929e-02, 9.13119391e-02, -2.76884548e-02, -5.19143604e-02,
1.50506735e-01, 1.86451554e-01, 9.94046330e-02, -7.73873506e-03,
-1.91362634e-01, 4.69892733e-02, -5.67045361e-02, 2.81608831e-02,
5.74332848e-02, -9.09122005e-02, 1.46917075e-01, 4.63287433e-04,
4.22818065e-02, -2.01395284e-02, 1.31114023e-02, -6.61114752e-02],
dtype=float32), array([ 1.13848910e-01, 1.16239523e-03, -6.73869327e-02, 8.96331621e-05,
6.71111122e-02, 2.01299891e-01, 1.76381439e-01, 1.44544961e-02,
-1.36415318e-01, -3.18108648e-02, -3.51585075e-02, 1.24862537e-01,
6.54390603e-02, -1.79662079e-01, 8.39038659e-03, -6.52492717e-02,
-4.79320846e-02, -4.05376814e-02, -1.82695538e-02, 1.35992825e-01,
6.97307214e-02, -5.41270301e-02, 3.14575769e-02, 2.86752880e-02,
9.04180668e-03, 3.10734902e-02, -3.88299376e-02, -7.43401796e-02],
dtype=float32), array([ 0.09236415, 0.05246023, -0.03693461, 0.05469636, 0.05779893,
0.13331857, 0.21085702, -0.01114039, -0.09325632, 0.07158454,
0.03167493, 0.13376454, 0.13156445, -0.12092946, -0.02573274,
-0.05352074, 0.00177706, 0.05248505, -0.07331309, 0.06653137,
-0.02102634, 0.00347302, -0.19828801, -0.08791062, 0.05434143,
0.07060813, -0.09335811, -0.04778329, -0.02983012, 0.1595401 ,
0.01018381, -0.04852933, -0.03336967, 0.02886004, 0.05975606,
0.0974864 , -0.00946077, -0.06796782], dtype=float32), array([ 7.70701468e-02, 2.34568515e-03, -1.22838043e-01, 6.06604481e-05,
1.08674861e-01, 2.40898028e-01, 7.23511800e-02, -4.14036550e-02,
-1.20636895e-01, -2.74732499e-03, -1.84729435e-02, 6.18617162e-02,
8.97722915e-02, -1.62845016e-01, -1.34318219e-02, 5.31670935e-02,
-8.27090293e-02, -1.22121066e-01, 1.53016988e-02, 1.22807577e-01,
-1.36648446e-01, 1.32556446e-02, -8.84201974e-02, 8.29895660e-02,
5.18502928e-02, -7.32250437e-02, 6.30651340e-02, -9.98577196e-03,
-4.71815281e-02, 6.02727272e-02, -7.98970908e-02, -2.12689787e-02],
dtype=float32)], 'names': ['adam', 'adam', 'adam', 'adam', 'adam', 'adam', 'bhalla', 'bhalla', 'bhalla', 'bhalla', 'bhalla', 'bhalla']}
我需要将其保存在json文件中。当我调试时，data显示为类型dict，因此我使用json.dumps(data)将其保存在json文件中，但是它抛出以下错误：
TypeError: Object of type 'ndarray' is not JSON serializable
我该如何解决
答案 0 :(得分：2)
从以前的答案和这个 post 中添加，你可以做这样的事情：
class NumpyEncoder(json.JSONEncoder):
""" Special json encoder for numpy types """
def default(self, obj):
if isinstance(obj, (numpy.int_, numpy.intc, numpy.intp, numpy.int8,
numpy.int16, numpy.int32, numpy.int64, numpy.uint8,
numpy.uint16, numpy.uint32, numpy.uint64)):
return int(obj)
elif isinstance(obj, (numpy.float_, numpy.float16, numpy.float32,
numpy.float64)):
return float(obj)
elif isinstance(obj, (numpy.ndarray,)):
return obj.tolist()
return json.JSONEncoder.default(self, obj)
然后你可以打电话
import json
operation = json.dumps(data, cls=NumpyEncoder)
如果您正在使用字典并希望保存修改后的字典但收到类似错误
<块引用>'TypeError：intc 类型的对象不是 JSON 可序列化的'
你可以使用
dumped = json.dumps(data, cls=NumpyEncoder)
with open('json_file_path.json', 'a') as f:
f.write(dumped + '\n')
代码确保保存的 json 文件不包含 here 中所述的 \ splashes
答案 1 :(得分：0)
data的确是dict，但是它包含ndarrays的列表，这些列表也需要序列化，并且不能（本机）序列化。
这里您需要提供自己的JSONEncoder，知道如何处理ndarray（如bharatk所建议的那样将它们转换为列表，这是最明显的解决方案）。
答案 2 :(得分：0)
您想首先转换为64位float的列表，例如
import json
import numpy as np
arr = np.array([1], dtype=np.float32)
json.dumps(list(arr))
给我：TypeError: Object of type float32 is not JSON serializable
如果我在序列化为JSON之前将其转换为64位浮点值，它会起作用：
json.dumps(list(arr.astype(float)))
我成功找回'[1.0]'
还请注意，如果您有文件对象，则最好改用json.dump，例如尝试做：
with open('file.json', "wb") as f:
f.write(json.dump(data, f, indent=4))
答案 3 :(得分：0)
如果要转换为列表，以遵循上述巴拉特克的解决方案，我建议使用Numpy tolist（）方法。
data = {"embeddings": knownEmbeddings.tolist(), "names": knownNames.tolist()}
祝你好运！