Question

我正在构建一个图像处理分类器，这个代码是一个API来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）它运行得很好

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)

Answer 1

您的test_image和tensorflow模型的输入不匹配。

# Your image shape is (, , 3)
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')

# Your image shape is (, , 4)
test_image = np.expand_dims(test_image, axis=0)
print(test_image)
print(test_image.shape)

pred = model.predict_classes(test_image)

以上只是假设。如果你想调试，我想你应该打印你的图像大小并与你的模型定义的第一个布局进行比较。并检查尺寸（宽度，高度，深度）是否匹配

/ image / Tensor Tensor上的ValueError（＆＃34; activation_5 / Softmax：0＆＃34;，shape =（？，4），dtype = float32）不是此图的元素

1 个答案: