Question

我是Android Studio的新手，我正在尝试构建一个简单的QR码扫描仪。但是，当我尝试构建时，出现以下错误：

 failed app/src/main/java/com/example/myapplication/MainActivity.kt
 Unresolved reference: new
 Unresolved reference: initiateScan
 Type mismatch: inferred type is Int but Intent! was expected
 Type mismatch: inferred type is Int but Intent? was expected

我真的不确定要从哪里去修复它。任何帮助将不胜感激！这是我的代码：

//package com.example.myapplication
package com.example.vicky.qrcodescanner


import android.R.attr
import android.app.Activity
import android.content.Intent
import android.os.Bundle
import android.widget.Toast
import androidx.appcompat.app.AppCompatActivity
import com.example.myapplication.R
import com.google.zxing.integration.android.IntentIntegrator
import kotlinx.android.synthetic.main.activity_main.*


class MainActivity : AppCompatActivity() {
    fun OnCreate(savedInstanceState: Bundle) {
        super.onCreate(savedInstanceState)
        setContentView(R.layout.activity_main)

        btn_scan.setOnClickListener {
            new IntentIntegrator(this).initiateScan();
//            val scanner = IntentIntegrator(activity:this)
//
//            scanner.initiateScan()
        }
    }

    override fun onActivityResult(requestCode: Int, resultCode: Int, data: Intent?) {
        if(resultCode == Activity.RESULT_OK) {
            val result =
                IntentIntegrator.parseActivityResult(requestCode, resultCode, attr.data)
            if (result != null) {
                if (result.contents == null) {
                    Toast.makeText(this, "Cancelled", Toast.LENGTH_LONG).show()
                } else {
                    Toast.makeText(this, "Scanned: " + result.contents, Toast.LENGTH_LONG)
                        .show()
                }
            } else {
                super.onActivityResult(requestCode, resultCode, attr.data)
            }
        }
    }
}

Answer 1

更改

} else {
    super.onActivityResult(requestCode, resultCode, data)
}

到

#Get the paths of the directories which contain train and test data
train_dir = os.path.join('/ImageClassifier/cats_and_dogs_filtered', 'train')
validation_dir = os.path.join('/ImageClassifier/cats_and_dogs_filtered', 'validation')


train_cats_dir = os.path.join(train_dir, 'cats')  # directory with our training cat pictures
train_dogs_dir = os.path.join(train_dir, 'dogs')  # directory with our training dog pictures
validation_cats_dir = os.path.join(validation_dir, 'cats')  # directory with our validation cat pictures
validation_dogs_dir = os.path.join(validation_dir, 'dogs')  # directory with our validation dog pictures


num_cats_tr = len(os.listdir(train_cats_dir))
num_dogs_tr = len(os.listdir(train_dogs_dir))

num_cats_val = len(os.listdir(validation_cats_dir))
num_dogs_val = len(os.listdir(validation_dogs_dir))

total_train = num_cats_tr + num_dogs_tr
total_val = num_cats_val + num_dogs_val


batch_size = 128
epochs = 15
IMG_HEIGHT = 150
IMG_WIDTH = 150


train_image_generator = ImageDataGenerator(rescale=1./255) # Generator for our training data
validation_image_generator = ImageDataGenerator(rescale=1./255) # Generator for our validation data


train_data_gen = train_image_generator.flow_from_directory(batch_size=batch_size,
                                                           directory=train_dir,
                                                           shuffle=True,
                                                           target_size=(IMG_HEIGHT, IMG_WIDTH),
                                                           class_mode='binary')

val_data_gen = validation_image_generator.flow_from_directory(batch_size=batch_size,
                                                              directory=validation_dir,
                                                              target_size=(IMG_HEIGHT, IMG_WIDTH),
                                                              class_mode='binary')


model = Sequential([
    Conv2D(16, 3, padding='same', activation='relu', input_shape=(IMG_HEIGHT, IMG_WIDTH ,3)),
    MaxPooling2D(),
    Conv2D(32, 3, padding='same', activation='relu'),
    MaxPooling2D(),
    Conv2D(64, 3, padding='same', activation='relu'),
    MaxPooling2D(),
    Flatten(),
    Dense(512, activation='relu'),
    Dense(1)
])


model.compile(optimizer='adam',
              loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),
              metrics=['accuracy'])


history = model.fit_generator(
    train_data_gen,
    steps_per_epoch=total_train // batch_size,
    epochs=epochs,
    validation_data=val_data_gen,
    validation_steps=total_val // batch_size
)

如何修复构建错误？

1 个答案: