When I use opencv and tensorflow, I run it. It throws many errors. I can post my code here because it is many file. But I see it throws errors on library file of open cv or tensorflow
/Applications/Xcode.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/bin/../include/c++/v1/__functional_base:80:16: error: no template named 'unary_function'; did you mean 'binary_function'?
static unary_function<_Ap, _Rp>
^
/Applications/Xcode.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/bin/../include/c++/v1/__functional_base:27:29: note: 'binary_function' declared here
struct _LIBCPP_TEMPLATE_VIS binary_function
^
/Applications/Xcode.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/bin/../include/c++/v1/__functional_base:80:16: error: too few template arguments for class template 'binary_function'
static unary_function<_Ap, _Rp>
^
/Applications/Xcode.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/bin/../include/c++/v1/__functional_base:27:29: note: template is declared here
struct _LIBCPP_TEMPLATE_VIS binary_function
^
/Applications/Xcode.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/bin/../include/c++/v1/__functional_base:81:31: error: no template named 'unary_function'; did you mean 'binary_function'?
__test(const volatile unary_function<_Ap, _Rp>*);
^
/Applications/Xcode.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/bin/../include/c++/v1/__functional_base:27:29: note: 'binary_function' declared here
struct _LIBCPP_TEMPLATE_VIS binary_function
In file included from /Applications/Xcode.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/bin/../include/c++/v1/memory:657:
In file included from /Applications/Xcode.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/bin/../include/c++/v1/utility:203:
In file included from /Applications/Xcode.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/bin/../include/c++/v1/cstring:60:
In file included from /Users/truonglocbinh/Desktop/Project/agora-flutter-sdk/ios/Classes/tflite-dist/include/tensorflow/lite/string.h:19:
In file included from /Applications/Xcode.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/bin/../include/c++/v1/string:504:
In file included from /Applications/Xcode.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/bin/../include/c++/v1/string_view:175:
In file included from /Applications/Xcode.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/bin/../include/c++/v1/__string:56:
In file included from /Applications/Xcode.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/bin/../include/c++/v1/algorithm:644:
In file included from /Applications/Xcode.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/bin/../include/c++/v1/functional:498:
In file included from /Applications/Xcode.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/bin/../include/c++/v1/tuple:141:
It notices error on #include <memory> in library file below
/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/
/// Deserialization infrastructure for tflite. Provides functionality
/// to go from a serialized tflite model in flatbuffer format to an
/// interpreter.
///
#ifndef TENSORFLOW_LITE_MODEL_H_
#define TENSORFLOW_LITE_MODEL_H_
#include <memory>
#include "tensorflow/lite/c/c_api_internal.h"
#include "tensorflow/lite/core/api/error_reporter.h"
#include "tensorflow/lite/core/api/op_resolver.h"
#include "tensorflow/lite/interpreter.h"
#include "tensorflow/lite/mutable_op_resolver.h"
#include "tensorflow/lite/schema/schema_generated.h"
namespace tflite {
/// Abstract interface that verifies whether a given model is legit.
/// It facilitates the use-case to verify and build a model without loading it
/// twice.
class TfLiteVerifier {
public:
/// Returns true if the model is legit.
virtual bool Verify(const char* data, int length,
ErrorReporter* reporter) = 0;
virtual ~TfLiteVerifier() {}
};
/// An RAII object that represents a read-only tflite model, copied from disk,
/// or mmapped. This uses flatbuffers as the serialization format.
///
/// NOTE: The current API requires that a FlatBufferModel instance be kept alive
/// by the client as long as it is in use by any dependent Interpreter
/// instances.
/// <pre><code>
/// using namespace tflite;
/// StderrReporter error_reporter;
/// auto model = FlatBufferModel::BuildFromFile("interesting_model.tflite",
/// &error_reporter);
/// MyOpResolver resolver; // You need to subclass OpResolver to provide
/// // implementations.
/// InterpreterBuilder builder(*model, resolver);
/// std::unique_ptr<Interpreter> interpreter;
/// if(builder(&interpreter) == kTfLiteOk) {
/// .. run model inference with interpreter
/// }
/// </code></pre>
///
/// OpResolver must be defined to provide your kernel implementations to the
/// interpreter. This is environment specific and may consist of just the
/// builtin ops, or some custom operators you defined to extend tflite.
class FlatBufferModel {
public:
/// Builds a model based on a file.
/// Caller retains ownership of `error_reporter` and must ensure its lifetime
/// is longer than the FlatBufferModel instance.
/// Returns a nullptr in case of failure.
static std::unique_ptr<FlatBufferModel> BuildFromFile(
const char* filename,
ErrorReporter* error_reporter = DefaultErrorReporter());
/// Verifies whether the content of the file is legit, then builds a model
/// based on the file.
/// The extra_verifier argument is an additional optional verifier for the
/// file contents. By default, we always check with tflite::VerifyModelBuffer.
/// If extra_verifier is supplied, the file contents is also checked against
/// the extra_verifier after the check against tflite::VerifyModelBuilder.
/// Caller retains ownership of `error_reporter` and must ensure its lifetime
/// is longer than the FlatBufferModel instance.
/// Returns a nullptr in case of failure.
static std::unique_ptr<FlatBufferModel> VerifyAndBuildFromFile(
const char* filename, TfLiteVerifier* extra_verifier = nullptr,
ErrorReporter* error_reporter = DefaultErrorReporter());
/// Builds a model based on a pre-loaded flatbuffer.
/// Caller retains ownership of the buffer and should keep it alive until
/// the returned object is destroyed. Caller also retains ownership of
/// `error_reporter` and must ensure its lifetime is longer than the
/// FlatBufferModel instance.
/// Returns a nullptr in case of failure.
/// NOTE: this does NOT validate the buffer so it should NOT be called on
/// invalid/untrusted input. Use VerifyAndBuildFromBuffer in that case
static std::unique_ptr<FlatBufferModel> BuildFromBuffer(
const char* caller_owned_buffer, size_t buffer_size,
ErrorReporter* error_reporter = DefaultErrorReporter());
/// Verifies whether the content of the buffer is legit, then builds a model
/// based on the pre-loaded flatbuffer.
/// The extra_verifier argument is an additional optional verifier for the
/// buffer. By default, we always check with tflite::VerifyModelBuffer. If
/// extra_verifier is supplied, the buffer is checked against the
/// extra_verifier after the check against tflite::VerifyModelBuilder. The
/// caller retains ownership of the buffer and should keep it alive until the
/// returned object is destroyed. Caller retains ownership of `error_reporter`
/// and must ensure its lifetime is longer than the FlatBufferModel instance.
/// Returns a nullptr in case of failure.
static std::unique_ptr<FlatBufferModel> VerifyAndBuildFromBuffer(
const char* buffer, size_t buffer_size,
TfLiteVerifier* extra_verifier = nullptr,
ErrorReporter* error_reporter = DefaultErrorReporter());
/// Builds a model directly from a flatbuffer pointer
/// Caller retains ownership of the buffer and should keep it alive until the
/// returned object is destroyed. Caller retains ownership of `error_reporter`
/// and must ensure its lifetime is longer than the FlatBufferModel instance.
/// Returns a nullptr in case of failure.
static std::unique_ptr<FlatBufferModel> BuildFromModel(
const tflite::Model* caller_owned_model_spec,
ErrorReporter* error_reporter = DefaultErrorReporter());
// Releases memory or unmaps mmaped memory.
~FlatBufferModel();
// Copying or assignment is disallowed to simplify ownership semantics.
FlatBufferModel(const FlatBufferModel&) = delete;
FlatBufferModel& operator=(const FlatBufferModel&) = delete;
bool initialized() const { return model_ != nullptr; }
const tflite::Model* operator->() const { return model_; }
const tflite::Model* GetModel() const { return model_; }
ErrorReporter* error_reporter() const { return error_reporter_; }
const Allocation* allocation() const { return allocation_.get(); }
// Returns the minimum runtime version from the flatbuffer. This runtime
// version encodes the minimum required interpreter version to run the
// flatbuffer model. If the minimum version can't be determined, an empty
// string will be returned.
// Note that the returned minimum version is a lower-bound but not a strict
// lower-bound; ops in the graph may not have an associated runtime version,
// in which case the actual required runtime might be greater than the
// reported minimum.
string GetMinimumRuntime() const;
/// Returns true if the model identifier is correct (otherwise false and
/// reports an error).
bool CheckModelIdentifier() const;
private:
/// Loads a model from a given allocation. FlatBufferModel will take over the
/// ownership of `allocation`, and delete it in destructor. The ownership of
/// `error_reporter`remains with the caller and must have lifetime at least
/// as much as FlatBufferModel. This is to allow multiple models to use the
/// same ErrorReporter instance.
FlatBufferModel(std::unique_ptr<Allocation> allocation,
ErrorReporter* error_reporter = DefaultErrorReporter());
/// Loads a model from Model flatbuffer. The `model` has to remain alive and
/// unchanged until the end of this flatbuffermodel's lifetime.
FlatBufferModel(const Model* model, ErrorReporter* error_reporter);
/// Flatbuffer traverser pointer. (Model* is a pointer that is within the
/// allocated memory of the data allocated by allocation's internals.
const tflite::Model* model_ = nullptr;
/// The error reporter to use for model errors and subsequent errors when
/// the interpreter is created
ErrorReporter* error_reporter_;
/// The allocator used for holding memory of the model. Note that this will
/// be null if the client provides a tflite::Model directly.
std::unique_ptr<Allocation> allocation_;
};
/// Build an interpreter capable of interpreting `model`.
///
/// model: A model whose lifetime must be at least as long as any
/// interpreter(s) created by the builder. In principle multiple interpreters
/// can be made from a single model.
/// op_resolver: An instance that implements the OpResolver interface, which
/// maps
/// custom op names and builtin op codes to op registrations. The lifetime
/// of the provided `op_resolver` object must be at least as long as the
/// InterpreterBuilder; unlike `model` and `error_reporter`, the `op_resolver`
/// does not need to exist for the duration of any created Interpreter
/// objects.
/// error_reporter: a functor that is called to report errors that handles
/// printf var arg semantics. The lifetime of the `error_reporter` object must
/// be greater than or equal to the Interpreter created by operator().
///
/// Returns a kTfLiteOk when successful and sets interpreter to a valid
/// Interpreter. Note: The user must ensure the model lifetime (and error
/// reporter, if provided) is at least as long as interpreter's lifetime.
class InterpreterBuilder {
public:
InterpreterBuilder(const FlatBufferModel& model,
const OpResolver& op_resolver);
/// Builds an interpreter given only the raw flatbuffer Model object (instead
/// of a FlatBufferModel). Mostly used for testing.
/// If `error_reporter` is null, then DefaultErrorReporter() is used.
InterpreterBuilder(const ::tflite::Model* model,
const OpResolver& op_resolver,
ErrorReporter* error_reporter = DefaultErrorReporter());
~InterpreterBuilder();
InterpreterBuilder(const InterpreterBuilder&) = delete;
InterpreterBuilder& operator=(const InterpreterBuilder&) = delete;
TfLiteStatus operator()(std::unique_ptr<Interpreter>* interpreter);
TfLiteStatus operator()(std::unique_ptr<Interpreter>* interpreter,
int num_threads);
private:
TfLiteStatus BuildLocalIndexToRegistrationMapping();
TfLiteStatus ParseNodes(
const flatbuffers::Vector<flatbuffers::Offset<Operator>>* operators,
Subgraph* subgraph);
TfLiteStatus ParseTensors(
const flatbuffers::Vector<flatbuffers::Offset<Buffer>>* buffers,
const flatbuffers::Vector<flatbuffers::Offset<Tensor>>* tensors,
Subgraph* subgraph);
TfLiteStatus ApplyDelegates(Interpreter* interpreter);
TfLiteStatus ParseQuantization(const QuantizationParameters* src_quantization,
TfLiteQuantization* quantization,
const std::vector<int>& dims);
const ::tflite::Model* model_;
const OpResolver& op_resolver_;
ErrorReporter* error_reporter_;
std::vector<const TfLiteRegistration*> flatbuffer_op_index_to_registration_;
std::vector<TfLiteRegistration> unresolved_custom_ops_;
std::vector<BuiltinOperator> flatbuffer_op_index_to_registration_types_;
const Allocation* allocation_ = nullptr;
bool has_flex_op_ = false;
};
} // namespace tflite
#endif // TENSORFLOW_LITE_MODEL_H_
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