我正在尝试通过recyclerView中的意图将图像从数组列表中的一个活动发送到另一个活动。但是在putextra方法中，它显示错误，例如无法解析方法'putextra（java.lang.string，android.widget.imageview）'
ngOnInit(): void {
this.someObs.subscribe((elem: SomeType) => {
/* some code */
});
}
答案 0 :(得分：0)
您不能额外传递ImageView，您传递的对象必须是Parcelable或Serialiazable。
您的ImageView也仅与此活动有关。如果您尝试将图像发送到另一个活动，最好将路径发送到图像。
答案 1 :(得分：0)
您不能直接发送Sub Macro1()
Dim rng As Range, cell As Range
Set rng = Range("A3:A15")
For Each cell In rng
Range("A" & cell.Row & ":D" & cell.Row).Copy
Range("W" & cell.Row).PasteSpecial Paste:=xlPasteValues, Operation:=xlNone, SkipBlanks _
:=False, Transpose:=False
Range("E" & cell.Row & ":H" & cell.Row).Copy
Range("W" & cell.Row + 1).PasteSpecial Paste:=xlPasteValues, Operation:=xlNone, SkipBlanks _
:=False, Transpose:=False
Range("I" & cell.Row).Copy
Range("S" & cell.Row).PasteSpecial Paste:=xlPasteValues, Operation:=xlNone, SkipBlanks _
:=False, Transpose:=False
Range("R" & cell.Row + 1).PasteSpecial Paste:=xlPasteValues, Operation:=xlNone, SkipBlanks _
:=False, Transpose:=False
Range("J" & cell.Row).Copy
Range("Q" & cell.Row & ":Q" & cell.Row + 1).PasteSpecial Paste:=xlPasteValues, Operation:=xlNone, SkipBlanks _
:=False, Transpose:=False
Range("W" & cell.Row & ":W" & cell.Row + 1).Copy
Range("P" & cell.Row).PasteSpecial Paste:=xlPasteValues, Operation:=xlNone, SkipBlanks _
:=False, Transpose:=False
Range("W" & cell.Row).Copy
Range("AF" & cell.Row + 1).PasteSpecial Paste:=xlPasteValues, Operation:=xlNone, SkipBlanks _
:=False, Transpose:=False
Range("W" & cell.Row + 1).Copy
Range("AF" & cell.Row).PasteSpecial Paste:=xlPasteValues, Operation:=xlNone, SkipBlanks _
:=False, Transpose:=False
Range("AA" & cell.Row & ":AE" & cell.Row + 1).Copy
'Range("AA5").Select
ActiveSheet.Paste
'Range("Q8").Select
Next cell
End Sub
，因为image不支持
尝试此解决方案
例如，您可以使用intent发送image
ByteArrayholder.itemView.setOnClickListener(new View.OnClickListener() {
@Override
public void onClick(View view) {
ImageView img = images.get(position);
img.buildDrawingCache();
Bitmap bmp = ((BitmapDrawable)img.getDrawable()).getBitmap();
ByteArrayOutputStream stream = new ByteArrayOutputStream();
bmp.compress(Bitmap.CompressFormat.PNG, 100, stream);
byte[] byteArray = stream.toByteArray();
Intent intent = new Intent(this, Result.class);
intent.putExtra("Image", byteArray);
context.startActivity(intent);
}
});
中的
onCreate()答案 2 :(得分：0)
最后，我终于做到了，您只是创建了一个图像数组（注意：不要创建arrayList），以防万一您想在多个图像之间进行选择，并简单地将这些图像传递给整数。下面是代码...
onBindViewHolder（MyViewHolder持有人，最终int位置）上的public void {
Fit.Polynomial
System.Double[]
0, 1.5, 0.500000000000001
Polynomial.Fit
1.5x + 0.500000000000001x^2
//针对性活动
public void getdata（）{
def model(input,training = True):
parameters = []
with tf.name_scope('conv1_1') as scope:
kernel11 = tf.Variable(tf.truncated_normal([3, 3, 20, 32], dtype = tf.float64, stddev = 1e-1), name = 'kernel')
biases11 = tf.Variable(tf.constant(0.0, shape = [32], dtype = tf.float64),trainable = True, name = 'biases')
conv = tf.nn.conv2d(input, kernel11, [1, 1, 1, 1], padding = 'SAME')
output11 = tf.nn.bias_add(conv, biases11)
conv1_1 = tf.nn.relu(output11)
parameters += [kernel11, biases11]
#conv1_2
with tf.name_scope('conv1_2') as scope:
kernel12 = tf.Variable(tf.truncated_normal([3, 3, 32, 32], dtype=tf.float64, stddev=1e-1), name='kernel')
biases12 = tf.Variable(tf.constant(0.0, shape=[32], dtype=tf.float64), trainable=True, name='biases')
conv = tf.nn.conv2d(conv1_1, kernel12, [1, 1, 1, 1], padding='SAME')
output12 = tf.nn.bias_add(conv, biases12)
conv1_2 = tf.nn.relu(output12)
parameters += [kernel12, biases12]
#pooling layer1
pool1 = tf.nn.max_pool(conv1_2,
ksize = [1, 2, 2, 1],
strides = [1, 2, 2, 1], #stride in model01_deconv is [1, 2, 2, 2, 1]
padding = 'SAME',
name = 'pool1')
#conv2_1
with tf.name_scope('conv2_1') as scope:
kernel21 = tf.Variable(tf.truncated_normal([3, 3, 32, 64], dtype = tf.float64, stddev = 1e-1), name = 'kernel')
biases21 = tf.Variable(tf.constant(0.0, shape=[64], dtype=tf.float64), trainable=True, name='biases')
conv = tf.nn.conv2d(pool1, kernel21, [1, 1, 1, 1], padding='SAME')
output21 = tf.nn.bias_add(conv, biases21)
conv2_1 = tf.nn.relu(output21)
parameters += [kernel21, biases21]
#conv2_2
with tf.name_scope('conv2_2') as scope:
kernel22 = tf.Variable(tf.truncated_normal([3, 3, 64, 64], dtype = tf.float64, stddev = 1e-1), name = 'kernel')
biases22 = tf.Variable(tf.constant(0.0, shape=[64], dtype=tf.float64), trainable=True, name='biases')
conv = tf.nn.conv2d(conv2_1, kernel22, [1, 1, 1, 1], padding='SAME')
output22 = tf.nn.bias_add(conv, biases22)
conv2_2 = tf.nn.relu(output22)
parameters += [kernel22, biases22]
print("************* conv2_2 ******************* \n")
#pooling layer2
pool2 = tf.nn.max_pool(conv2_2,
ksize = [1, 2, 2, 1],
strides = [1, 2, 2, 1],
padding = 'SAME',
name = 'pool2')
#conv3_1
with tf.name_scope('conv3_1') as scope:
kernel31 = tf.Variable(tf.truncated_normal([3, 3, 64, 128], dtype = tf.float64, stddev = 1e-1), name = 'kernel')
biases31 = tf.Variable(tf.constant(0.0, shape=[128], dtype=tf.float64), trainable=True, name='biases')
conv = tf.nn.conv2d(pool2, kernel31, [1, 1, 1, 1], padding='SAME')
output31 = tf.nn.bias_add(conv, biases31)
conv3_1 = tf.nn.relu(output31)
parameters += [kernel31, kernel31]
#conv3_2
with tf.name_scope('conv3_2') as scope:
kernel32 = tf.Variable(tf.truncated_normal([3, 3, 128, 128], dtype = tf.float64, stddev = 1e-1), name = 'kernel')
biases32 = tf.Variable(tf.constant(0.0, shape=[128], dtype=tf.float64), trainable=True, name='biases')
conv = tf.nn.conv2d(conv3_1, kernel32, [1, 1, 1, 1], padding='SAME')
output32 = tf.nn.bias_add(conv, biases32)
conv3_2 = tf.nn.relu(output32)
parameters += [kernel32, biases32]
#conv3_3
with tf.name_scope('conv3_3') as scope:
kernel33 = tf.Variable(tf.truncated_normal([3, 3, 128, 128], dtype = tf.float64, stddev = 1e-1), name = 'kernel')
biases33 = tf.Variable(tf.constant(0.0, shape=[128], dtype=tf.float64), trainable=True, name='biases')
conv = tf.nn.conv2d(conv3_2, kernel33, [1, 1, 1, 1], padding='SAME')
output33 = tf.nn.bias_add(conv, biases33)
conv3_3 = tf.nn.relu(output33)
parameters += [kernel33, biases33]
#pooling layer3
pool3 = tf.nn.max_pool(conv3_3,
ksize = [1, 2, 2, 1],
strides = [1, 2, 2, 1],
padding = 'SAME',
name = 'pool3')
#conv4_1
with tf.name_scope('conv4_1') as scope:
kernel41 = tf.Variable(tf.truncated_normal([3, 3, 128, 256], dtype = tf.float64, stddev = 1e-1), name = 'kernel')
biases41 = tf.Variable(tf.constant(0.0, shape=[256], dtype=tf.float64), trainable=True, name='biases')
conv = tf.nn.conv2d(pool3, kernel41, [1, 1, 1, 1], padding='SAME')
output41 = tf.nn.bias_add(conv, biases41)
conv4_1 = tf.nn.relu(output41)
parameters += [kernel41, biases41]
#conv4_2
with tf.name_scope('conv4_2') as scope:
kernel42 = tf.Variable(tf.truncated_normal([3, 3, 256, 256], dtype = tf.float64, stddev = 1e-1), name = 'kernel')
biases42 = tf.Variable(tf.constant(0.0, shape=[256], dtype=tf.float64), trainable=True, name='biases')
conv = tf.nn.conv2d(conv4_1, kernel42, [1, 1, 1, 1], padding='SAME')
output42 = tf.nn.bias_add(conv, biases42)
conv4_2 = tf.nn.relu(output42)
parameters += [kernel42, biases42]
#conv4_3
with tf.name_scope('conv4_3') as scope:
kernel43 = tf.Variable(tf.truncated_normal([3, 3, 256, 256], dtype = tf.float64, stddev = 1e-1), name = 'kernel')
biases43 = tf.Variable(tf.constant(0.0, shape=[256], dtype=tf.float64), trainable=True, name='biases')
conv = tf.nn.conv2d(conv4_2, kernel43, [1, 1, 1, 1], padding='SAME')
output43 = tf.nn.bias_add(conv, biases43)
conv4_3 = tf.nn.relu(output43)
parameters += [kernel43, biases43]
#pooling layer4
pool4 = tf.nn.max_pool(conv4_3,
ksize = [1, 2, 2, 1],
strides = [1, 2, 2, 1],
padding = 'SAME',
name = 'pool4')
#FC 1
with tf.name_scope('fc1') as scope:
shape = int(np.prod(pool4.get_shape()[1:]))
fc1w = tf.Variable(tf.truncated_normal([shape, 4096], dtype = tf.float64, stddev = 1e-1), name = 'kernel')
fc1b = tf.Variable(tf.constant(1.0, shape = [4096], dtype = tf.float64), trainable = True, name = 'biases')
pool5_flat = tf.reshape(pool4, [-1, shape])
fc1l = tf.nn.bias_add(tf.matmul(pool5_flat, fc1w), fc1b)
fc1 = tf.nn.relu(fc1l)
parameters += [fc1w, fc1b]
#FC2
with tf.name_scope('fc2') as scope:
fc2w = tf.Variable(tf.truncated_normal([4096, 4096], dtype = tf.float64, stddev = 1e-1), name = 'kernel')
fc2b = tf.Variable(tf.constant(1.0, shape = [4096], dtype = tf.float64), trainable = True, name = 'biases')
fc2l = tf.nn.bias_add(tf.matmul(fc1, fc2w), fc2b)
fc2 = tf.nn.relu(fc2l)
parameters += [fc2w, fc2b]
#FC3
with tf.name_scope('fc3') as scope:
fc3w = tf.Variable(tf.truncated_normal([4096, 3], dtype=tf.float64, stddev=1e-1), name='kernel') #[4096, 1000] but i put 3: the shape of the bbox
fc3b = tf.Variable(tf.constant(1.0, shape=[3], dtype=tf.float64), trainable=True, name='biases') #shape=[1000]
fc3l = tf.nn.bias_add(tf.matmul(fc2, fc3w), fc3b)
parameters += [fc3w, fc3b]
print(' Done with convolution..')
return fc3l
def train(batch_size,lr =lr_rate, epochs_tr = epochs, training = True):
init_op_glob = tf.global_variables_initializer()
init_op_local = tf.local_variables_initializer()
with tf.Session() as sess:
print("############################### Start Training ################################# \n")
# vgg = model(x)
dataset = dataloader(bin_path, label_path, calib_path,batch_size)
sess.run([init_op_glob, init_op_local])
print(" Hiiii I'm Here 0000 \n ")
train_loss = []
test_loss = []
train_accuracy = []
test_accuracy = []
#initializing the variables
summary_writer = tf.summary.FileWriter('./OutputResult', sess.graph)
for epoch in range(epochs_tr):
print(" ######################################### Epoch ",epoch," ################################# \n", )
for data in dataset:
prediction_box = sess.run(model(x, training = True), feed_dict = {x : data[0]})
box = create_bbox(data[1],data[2],dataformat="bin", label_type="txt", is_velo_cam=True)
print(" Hiiii I'm Here 3 \n ")
loss = loss_function(prediction_box, box)
optimizer = create_optimizer(lr, loss)
opt = sess.run(optimizer)
loss, acc = sess.run([loss, accuracy])
print("Iteration " + str(epoch) + ", Loss = " + \
"{:.6f}".format(loss) + ", Training Accuracy = " + \
"{:.5f}".format(acc))
print("Optimization is Finished ")
train_loss.append(loss)
train_accuracy.append(acc)
summary_writer.close()
plt.plot(range(len(train_loss)), train_loss, 'b', label = 'Training_loss')
plt.title('Trainig loss')
plt.xlabel('Epochs', fontsize = 16)
plt.ylabel('Loss', fontsize =16)
plt.legend()
plt.figure()
plt.show()
plt.plot(range(len(train_accuracy)), train_accuracy, 'b', label='Training Accuracy')
plt.title('Training Accuracy')
plt.xlabel('Epochs ',fontsize=16)
plt.ylabel('Loss',fontsize=16)
plt.legend()
plt.figure()
plt.show()
//然后在onCreate方法中调用“ getdata（）;”