小编Dee*_*kar的帖子

我正在构建一个图像处理分类器,这段代码是一个API,用于预测整个代码运行的图像的图像类,除了这一行(pred = model.predict_classes(test_image))这个API是在Django框架中制作的,我正在使用python 2.7

如果我正常运行此代码(没有制作API)它运行完美,这是一个重点

def classify_image(request):
if request.method == 'POST' and request.FILES['test_image']:

    fs = FileSystemStorage()
    fs.save(request.FILES['test_image'].name, request.FILES['test_image'])


    test_image = cv2.imread('media/'+request.FILES['test_image'].name)

    if test_image is not None:
        test_image = cv2.resize(test_image, (128, 128))
        test_image = np.array(test_image)
        test_image = test_image.astype('float32')
        test_image /= 255
        print(test_image.shape)
    else:
        print('image didnt load')

    test_image = np.expand_dims(test_image, axis=0)
    print(test_image)
    print(test_image.shape)

    pred = model.predict_classes(test_image)
    print(pred)

return JsonResponse(pred, safe=False)

更新

我正在研究机器学习文本分类，并且使用 svc 线性内核，整个代码都在工作，除了最后一行代码 (print (svm_model_linear.predict_proba(test)) 实际上是在构建一个分类器，其中有 3 个类别循环、足球和羽毛球，我有一些被标记为这些类别的人的 Facebook 状态，我也使用 train_test_split 训练了测试的分类器，之后我有一些未标记的状态，我想对它们进行分类，但最后一行代码给出我的错误

from sklearn.feature_extraction.text import CountVectorizer
cv = CountVectorizer(max_features = 700)
X = cv.fit_transform(corpus).toarray()
print X
y = dataset.iloc[:, 1].values
print y

# Splitting the dataset into the Training set and Test set
from sklearn.cross_validation import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 
0.20, random_state = 0)


from sklearn.svm import SVC
svm_model_linear = SVC(kernel ='linear', C = 1, 
probability=True).fit(X_train, y_train)
svm_predictions = svm_model_linear.predict(X_test)



# model accuracy …

我正在构建图像处理分类器,除了这一行以外的整个代码 -

input_img_resize=cv2.resize(input_img,(128,128))

这条线给我一个错误

('error: /io/opencv/modules/imgproc/src/imgwarp.cpp:3483: error: (-215) ssize.width > 0 && ssize.height > 0 in function resize')

我的代码 -

PATH = os.getcwd()
# Define data path
data_path = PATH + '/data'
data_dir_list = os.listdir(data_path)

img_rows=128
img_cols=128
num_channel=3
num_epoch=30

num_classes = 67

img_data_list=[]

for dataset in data_dir_list:
    img_list=os.listdir(data_path+'/'+ dataset)
    print ('Loaded the images of dataset-'+'{}\n'.format(dataset))
    for img in img_list:
        input_img=cv2.imread(data_path + '/'+ dataset + '/'+ img )

        input_img_resize=cv2.resize(input_img,(128,128))
        img_data_list.append(input_img_resize)