Caffe based 3D images descriptor. A class to extract features from an image. The so obtained descriptors can be used for classification and pose estimation goals [229].
More...
#include <opencv2/cnn_3dobj.hpp>
|
| | descriptorExtractor (const String &device_type, int device_id=0) |
| | Set the device for feature extraction, if the GPU is used, there should be a device_id. More...
|
| |
| void | extract (InputArrayOfArrays inputimg, OutputArray feature, String feature_blob) |
| | Extract features from a single image or from a vector of images. If loadNet was not called before, this method invocation will fail. More...
|
| |
| int | getDeviceId () |
| | Get device ID information for feature extraction. More...
|
| |
| String | getDeviceType () |
| | Get device type information for feature extraction. More...
|
| |
| void | loadNet (const String &model_file, const String &trained_file, const String &mean_file="") |
| | Initiate a classification structure, the net work parameter is stored in model_file, the network structure is stored in trained_file, you can decide whether to use mean images or not. More...
|
| |
| void | setDeviceId (const int &device_id) |
| | Set device ID information for feature extraction. Useful to change device without the need to reload the net. Only used for GPU. More...
|
| |
| void | setDeviceType (const String &device_type) |
| | Set device type information for feature extraction. Useful to change device without the need to reload the net. More...
|
| |
Caffe based 3D images descriptor. A class to extract features from an image. The so obtained descriptors can be used for classification and pose estimation goals [229].
◆ descriptorExtractor()
| cv::cnn_3dobj::descriptorExtractor::descriptorExtractor |
( |
const String & |
device_type, |
|
|
int |
device_id = 0 |
|
) |
| |
Set the device for feature extraction, if the GPU is used, there should be a device_id.
- Parameters
-
| device_type | CPU or GPU. |
| device_id | ID of GPU. |
◆ extract()
Extract features from a single image or from a vector of images. If loadNet was not called before, this method invocation will fail.
- Parameters
-
| inputimg | Input images. |
| feature | Output features. |
| feature_blob | Layer which the feature is extracted from. |
◆ getDeviceId()
| int cv::cnn_3dobj::descriptorExtractor::getDeviceId |
( |
| ) |
|
Get device ID information for feature extraction.
◆ getDeviceType()
| String cv::cnn_3dobj::descriptorExtractor::getDeviceType |
( |
| ) |
|
Get device type information for feature extraction.
◆ loadNet()
| void cv::cnn_3dobj::descriptorExtractor::loadNet |
( |
const String & |
model_file, |
|
|
const String & |
trained_file, |
|
|
const String & |
mean_file = "" |
|
) |
| |
Initiate a classification structure, the net work parameter is stored in model_file, the network structure is stored in trained_file, you can decide whether to use mean images or not.
- Parameters
-
| model_file | Path of caffemodel which including all parameters in CNN. |
| trained_file | Path of prototxt which defining the structure of CNN. |
| mean_file | Path of mean file(option). |
◆ setDeviceId()
| void cv::cnn_3dobj::descriptorExtractor::setDeviceId |
( |
const int & |
device_id | ) |
|
Set device ID information for feature extraction. Useful to change device without the need to reload the net. Only used for GPU.
- Parameters
-
◆ setDeviceType()
| void cv::cnn_3dobj::descriptorExtractor::setDeviceType |
( |
const String & |
device_type | ) |
|
Set device type information for feature extraction. Useful to change device without the need to reload the net.
- Parameters
-
The documentation for this class was generated from the following file: