我制作了一个模型来预测图像上的字符,以进行车牌识别。它在我的电脑上工作得很好,但我需要把它放在 Android 应用程序中。所以我开发了一个小应用程序并将我的 keras 模型转换为 tflite。现在它总是预测同一个字符。
我使用以下方法转换了模型:
mod_path = "License_character_recognition.h5"
def load_model(path,custom_objects={},verbose=0):
#from tf.keras.models import model_from_json
path = splitext(path)[0]
with open('MobileNets_character_recognition.json','r') as json_file:
model_json = json_file.read()
model = tf.keras.models.model_from_json(model_json, custom_objects=custom_objects)
model.load_weights('%s.h5' % path)
if verbose: print('Loaded from %s' % path)
return model
keras_mod = load_model(mod_path)
converter = tf.lite.TFLiteConverter.from_keras_model(keras_mod)
tflite_model = converter.convert()
# Save the TF Lite model.
with tf.io.gfile.GFile('ocr.tflite', 'wb') as f:
f.write(tflite_model)
是否有更好的方法来转换模型,还是我遗漏了什么?
编辑:这就是我管理位图所做的
try {
Mat bis = Utils.loadResource(MainActivity.this, R.drawable.plaque, Imgcodecs.IMREAD_COLOR);
cvtColor(bis, bis, COLOR_BGR2RGB);
Mat m = Utils.loadResource(MainActivity.this, R.drawable.plaque,Imgcodecs.IMREAD_GRAYSCALE);
blur(m, blur, new Size(2,2));
threshold(blur, bin, 0, 255, THRESH_BINARY_INV + THRESH_OTSU);
ArrayList<MatOfPoint> contours;
contours = getContours(bin);
//Try to sort from left to right
Collections.sort(contours, new SortByTopLeft());
Log.d("Contour", String.valueOf(contours.size()));
int i = 0;
for (MatOfPoint c : contours){
Rect cont = boundingRect(c);
float ratio = (float) (cont.height/cont.width);
Log.d("Ratio", String.valueOf(ratio));
float pourcent = ((float) cont.height/ (float) bin.height());
Log.d("pourcent", String.valueOf(pourcent));
if (ratio >= 1 && ratio <= 2.5){
if(pourcent >=0.5){
Log.d("Ui", String.valueOf(cont));
rectangle(bis, cont, new Scalar(0,255,0), 2);
//Separate numbers
Mat curr_num = new Mat(bin, cont);
Bitmap curbit = Bitmap.createBitmap(curr_num.cols(), curr_num.rows(), Bitmap.Config.ARGB_8888);
Utils.matToBitmap(curr_num, curbit);
images[i].setImageBitmap(curbit);
int charac = classifier.classify(curbit);
Log.d("Result", String.valueOf(charac));
result.setText(String.valueOf(charac));
if (i < 6){
i++;
}
}
}
最佳答案
您可以使用 TensorFlow Lite Android Support Library .这library旨在帮助处理 TensorFlow Lite 模型的输入和输出,并使 TensorFlow Lite 解释器更易于使用。
像下面这样使用它并在 this article 找到更多信息:
Bitmap assetsBitmap = getBitmapFromAsset(mContext, "picture.jpg");
// Initialization code
// Create an ImageProcessor with all ops required. For more ops, please
// refer to the ImageProcessor Architecture.
ImageProcessor imageProcessor =
new ImageProcessor.Builder()
.add(new ResizeOp(32, 32, ResizeOp.ResizeMethod.BILINEAR))
//.add(new NormalizeOp(127.5f, 127.5f))
.build();
// Create a TensorImage object. This creates the tensor of the corresponding
// tensor type (flot32 in this case) that the TensorFlow Lite interpreter needs.
TensorImage tImage = new TensorImage(DataType.FLOAT32);
// Analysis code for every frame
// Preprocess the image
tImage.load(assetsBitmap);
tImage = imageProcessor.process(tImage);
// Create a container for the result and specify that this is not a quantized model.
// Hence, the 'DataType' is defined as FLOAT32
TensorBuffer probabilityBuffer = TensorBuffer.createFixedSize(new int[]{1, 10}, DataType.FLOAT32);
interpreter.run(tImage.getBuffer(), probabilityBuffer.getBuffer());
Log.i("RESULT", Arrays.toString(probabilityBuffer.getFloatArray()));
return getSortedResult(result);
}
关于python - 为什么 tflite 模型的准确性与 keras 模型如此不同?,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/63726309/