[−][src]Struct opencv::dnn::ClassificationModel
This class represents high-level API for classification models.
ClassificationModel allows to set params for preprocessing input image. ClassificationModel creates net from file with trained weights and config, sets preprocessing input, runs forward pass and return top-1 prediction.
Implementations
impl ClassificationModel
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pub fn as_raw_ClassificationModel(&self) -> *const c_void
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pub fn as_raw_mut_ClassificationModel(&mut self) -> *mut c_void
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impl ClassificationModel
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pub fn new(model: &str, config: &str) -> Result<ClassificationModel>
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Create classification model from network represented in one of the supported formats. An order of @p model and @p config arguments does not matter.
Parameters
- model: Binary file contains trained weights.
- config: Text file contains network configuration.
C++ default parameters
- config: ""
pub fn new_1(network: &Net) -> Result<ClassificationModel>
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Trait Implementations
impl Boxed for ClassificationModel
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unsafe fn from_raw(ptr: *mut c_void) -> Self
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fn into_raw(self) -> *mut c_void
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fn as_raw(&self) -> *const c_void
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fn as_raw_mut(&mut self) -> *mut c_void
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impl ClassificationModelTrait for ClassificationModel
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fn as_raw_ClassificationModel(&self) -> *const c_void
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fn as_raw_mut_ClassificationModel(&mut self) -> *mut c_void
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fn classify(
&mut self,
frame: &dyn ToInputArray,
class_id: &mut i32,
conf: &mut f32
) -> Result<()>
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&mut self,
frame: &dyn ToInputArray,
class_id: &mut i32,
conf: &mut f32
) -> Result<()>
impl Drop for ClassificationModel
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impl ModelTrait for ClassificationModel
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fn as_raw_Model(&self) -> *const c_void
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fn as_raw_mut_Model(&mut self) -> *mut c_void
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fn set_input_size(&mut self, size: Size) -> Result<Model>
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fn set_input_size_1(&mut self, width: i32, height: i32) -> Result<Model>
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fn set_input_mean(&mut self, mean: Scalar) -> Result<Model>
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fn set_input_scale(&mut self, scale: f64) -> Result<Model>
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fn set_input_crop(&mut self, crop: bool) -> Result<Model>
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fn set_input_swap_rb(&mut self, swap_rb: bool) -> Result<Model>
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fn set_input_params(
&mut self,
scale: f64,
size: Size,
mean: Scalar,
swap_rb: bool,
crop: bool
) -> Result<()>
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&mut self,
scale: f64,
size: Size,
mean: Scalar,
swap_rb: bool,
crop: bool
) -> Result<()>
fn predict(
&mut self,
frame: &dyn ToInputArray,
outs: &mut dyn ToOutputArray
) -> Result<()>
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&mut self,
frame: &dyn ToInputArray,
outs: &mut dyn ToOutputArray
) -> Result<()>
impl NetTrait for ClassificationModel
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fn as_raw_Net(&self) -> *const c_void
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fn as_raw_mut_Net(&mut self) -> *mut c_void
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fn empty(&self) -> Result<bool>
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fn dump(&mut self) -> Result<String>
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fn dump_to_file(&mut self, path: &str) -> Result<()>
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fn add_layer(
&mut self,
name: &str,
typ: &str,
params: &mut LayerParams
) -> Result<i32>
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&mut self,
name: &str,
typ: &str,
params: &mut LayerParams
) -> Result<i32>
fn add_layer_to_prev(
&mut self,
name: &str,
typ: &str,
params: &mut LayerParams
) -> Result<i32>
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&mut self,
name: &str,
typ: &str,
params: &mut LayerParams
) -> Result<i32>
fn get_layer_id(&mut self, layer: &str) -> Result<i32>
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fn get_layer_names(&self) -> Result<Vector<String>>
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fn get_layer(&mut self, layer_id: Net_LayerId) -> Result<Ptr<Layer>>
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fn get_layer_inputs(
&mut self,
layer_id: Net_LayerId
) -> Result<Vector<Ptr<Layer>>>
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&mut self,
layer_id: Net_LayerId
) -> Result<Vector<Ptr<Layer>>>
fn connect_first_second(&mut self, out_pin: &str, inp_pin: &str) -> Result<()>
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fn connect(
&mut self,
out_layer_id: i32,
out_num: i32,
inp_layer_id: i32,
inp_num: i32
) -> Result<()>
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&mut self,
out_layer_id: i32,
out_num: i32,
inp_layer_id: i32,
inp_num: i32
) -> Result<()>
fn set_inputs_names(&mut self, input_blob_names: &Vector<String>) -> Result<()>
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fn set_input_shape(&mut self, input_name: &str, shape: &MatShape) -> Result<()>
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fn forward_single(&mut self, output_name: &str) -> Result<Mat>
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fn forward_async(&mut self, output_name: &str) -> Result<AsyncArray>
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fn forward_layer(
&mut self,
output_blobs: &mut dyn ToOutputArray,
output_name: &str
) -> Result<()>
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&mut self,
output_blobs: &mut dyn ToOutputArray,
output_name: &str
) -> Result<()>
fn forward(
&mut self,
output_blobs: &mut dyn ToOutputArray,
out_blob_names: &Vector<String>
) -> Result<()>
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&mut self,
output_blobs: &mut dyn ToOutputArray,
out_blob_names: &Vector<String>
) -> Result<()>
fn forward_and_retrieve(
&mut self,
output_blobs: &mut Vector<Vector<Mat>>,
out_blob_names: &Vector<String>
) -> Result<()>
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&mut self,
output_blobs: &mut Vector<Vector<Mat>>,
out_blob_names: &Vector<String>
) -> Result<()>
fn set_halide_scheduler(&mut self, scheduler: &str) -> Result<()>
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fn set_preferable_backend(&mut self, backend_id: i32) -> Result<()>
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fn set_preferable_target(&mut self, target_id: i32) -> Result<()>
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fn set_input(
&mut self,
blob: &dyn ToInputArray,
name: &str,
scalefactor: f64,
mean: Scalar
) -> Result<()>
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&mut self,
blob: &dyn ToInputArray,
name: &str,
scalefactor: f64,
mean: Scalar
) -> Result<()>
fn set_param(
&mut self,
layer: Net_LayerId,
num_param: i32,
blob: &Mat
) -> Result<()>
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&mut self,
layer: Net_LayerId,
num_param: i32,
blob: &Mat
) -> Result<()>
fn get_param(&mut self, layer: Net_LayerId, num_param: i32) -> Result<Mat>
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fn get_unconnected_out_layers(&self) -> Result<Vector<i32>>
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fn get_unconnected_out_layers_names(&self) -> Result<Vector<String>>
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fn get_layers_shapes(
&self,
net_input_shapes: &Vector<MatShape>,
layers_ids: &mut Vector<i32>,
in_layers_shapes: &mut Vector<Vector<MatShape>>,
out_layers_shapes: &mut Vector<Vector<MatShape>>
) -> Result<()>
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&self,
net_input_shapes: &Vector<MatShape>,
layers_ids: &mut Vector<i32>,
in_layers_shapes: &mut Vector<Vector<MatShape>>,
out_layers_shapes: &mut Vector<Vector<MatShape>>
) -> Result<()>
fn get_layers_shapes_1(
&self,
net_input_shape: &MatShape,
layers_ids: &mut Vector<i32>,
in_layers_shapes: &mut Vector<Vector<MatShape>>,
out_layers_shapes: &mut Vector<Vector<MatShape>>
) -> Result<()>
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&self,
net_input_shape: &MatShape,
layers_ids: &mut Vector<i32>,
in_layers_shapes: &mut Vector<Vector<MatShape>>,
out_layers_shapes: &mut Vector<Vector<MatShape>>
) -> Result<()>
fn get_layer_shapes(
&self,
net_input_shape: &MatShape,
layer_id: i32,
in_layer_shapes: &mut Vector<MatShape>,
out_layer_shapes: &mut Vector<MatShape>
) -> Result<()>
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&self,
net_input_shape: &MatShape,
layer_id: i32,
in_layer_shapes: &mut Vector<MatShape>,
out_layer_shapes: &mut Vector<MatShape>
) -> Result<()>
fn get_layer_shapes_1(
&self,
net_input_shapes: &Vector<MatShape>,
layer_id: i32,
in_layer_shapes: &mut Vector<MatShape>,
out_layer_shapes: &mut Vector<MatShape>
) -> Result<()>
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&self,
net_input_shapes: &Vector<MatShape>,
layer_id: i32,
in_layer_shapes: &mut Vector<MatShape>,
out_layer_shapes: &mut Vector<MatShape>
) -> Result<()>
fn get_flops(&self, net_input_shapes: &Vector<MatShape>) -> Result<i64>
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fn get_flops_1(&self, net_input_shape: &MatShape) -> Result<i64>
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fn get_flops_2(
&self,
layer_id: i32,
net_input_shapes: &Vector<MatShape>
) -> Result<i64>
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&self,
layer_id: i32,
net_input_shapes: &Vector<MatShape>
) -> Result<i64>
fn get_flops_3(&self, layer_id: i32, net_input_shape: &MatShape) -> Result<i64>
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fn get_layer_types(&self, layers_types: &mut Vector<String>) -> Result<()>
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fn get_layers_count(&self, layer_type: &str) -> Result<i32>
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fn get_memory_consumption(
&self,
net_input_shapes: &Vector<MatShape>,
weights: &mut size_t,
blobs: &mut size_t
) -> Result<()>
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&self,
net_input_shapes: &Vector<MatShape>,
weights: &mut size_t,
blobs: &mut size_t
) -> Result<()>
fn get_memory_consumption_1(
&self,
net_input_shape: &MatShape,
weights: &mut size_t,
blobs: &mut size_t
) -> Result<()>
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&self,
net_input_shape: &MatShape,
weights: &mut size_t,
blobs: &mut size_t
) -> Result<()>
fn get_memory_consumption_for_layer(
&self,
layer_id: i32,
net_input_shapes: &Vector<MatShape>,
weights: &mut size_t,
blobs: &mut size_t
) -> Result<()>
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&self,
layer_id: i32,
net_input_shapes: &Vector<MatShape>,
weights: &mut size_t,
blobs: &mut size_t
) -> Result<()>
fn get_memory_consumption_2(
&self,
layer_id: i32,
net_input_shape: &MatShape,
weights: &mut size_t,
blobs: &mut size_t
) -> Result<()>
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&self,
layer_id: i32,
net_input_shape: &MatShape,
weights: &mut size_t,
blobs: &mut size_t
) -> Result<()>
fn get_memory_consumption_for_layers(
&self,
net_input_shapes: &Vector<MatShape>,
layer_ids: &mut Vector<i32>,
weights: &mut Vector<size_t>,
blobs: &mut Vector<size_t>
) -> Result<()>
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&self,
net_input_shapes: &Vector<MatShape>,
layer_ids: &mut Vector<i32>,
weights: &mut Vector<size_t>,
blobs: &mut Vector<size_t>
) -> Result<()>
fn get_memory_consumption_3(
&self,
net_input_shape: &MatShape,
layer_ids: &mut Vector<i32>,
weights: &mut Vector<size_t>,
blobs: &mut Vector<size_t>
) -> Result<()>
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&self,
net_input_shape: &MatShape,
layer_ids: &mut Vector<i32>,
weights: &mut Vector<size_t>,
blobs: &mut Vector<size_t>
) -> Result<()>
fn enable_fusion(&mut self, fusion: bool) -> Result<()>
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fn get_perf_profile(&mut self, timings: &mut Vector<f64>) -> Result<i64>
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impl Send for ClassificationModel
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Auto Trait Implementations
impl RefUnwindSafe for ClassificationModel
impl !Sync for ClassificationModel
impl Unpin for ClassificationModel
impl UnwindSafe for ClassificationModel
Blanket Implementations
impl<T> Any for T where
T: 'static + ?Sized,
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T: 'static + ?Sized,
impl<T> Borrow<T> for T where
T: ?Sized,
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T: ?Sized,
impl<T> BorrowMut<T> for T where
T: ?Sized,
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T: ?Sized,
pub fn borrow_mut(&mut self) -> &mut T
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impl<T> From<T> for T
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impl<T, U> Into<U> for T where
U: From<T>,
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U: From<T>,
impl<T, U> TryFrom<U> for T where
U: Into<T>,
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U: Into<T>,
type Error = Infallible
The type returned in the event of a conversion error.
pub fn try_from(value: U) -> Result<T, <T as TryFrom<U>>::Error>
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impl<T, U> TryInto<U> for T where
U: TryFrom<T>,
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U: TryFrom<T>,