Opencv获取导入DNN模型的各层信息
2024-06-05 10:29:20
1_Opencv获取导入DNN模型的各层信息
- 一、代码
- 1、C++代码
- 2、Python代码
- 二、相关说明
- 1、支持深度学习框架
- 2、导入模型的有三个参数
一、代码
1、C++代码
#include <opencv2/opencv.hpp>
#include <opencv2/dnn.hpp>
#include <iostream>using namespace cv;
using namespace cv::dnn;
using namespace std;int main(int argc, char** argv) {string bin_model = "E:/workOpencv/JZG_opencv/opencv_tutorial/data/models/googlenet/bvlc_googlenet.caffemodel";string protxt = "E:/workOpencv/JZG_opencv/opencv_tutorial/data/models/googlenet/bvlc_googlenet.prototxt";// load CNN modelNet net = dnn::readNet(bin_model, protxt);// 获取各层信息 # vector就是容器，相当于是一个动态的数组 vector <类型> 标识符vector<String> layer_names = net.getLayerNames();for (int i = 0; i < layer_names.size(); i++) {int id = net.getLayerId(layer_names[i]);auto layer = net.getLayer(id);printf("layer id:%d, type: %s, name:%s \n", id, layer->type.c_str(), layer->name.c_str());}system("pause"); // 用双引号，不要用单引号return 0;
}
输出结果：
Attempting to upgrade input file specified using deprecated V1LayerParameter: E:/workOpencv/JZG_opencv/opencv_tutorial/data/models/googlenet/bvlc_googlenet.caffemodel
Successfully upgraded file specified using deprecated V1LayerParameter
layer id:1, type: Convolution, name:conv1/7x7_s2
layer id:2, type: ReLU, name:conv1/relu_7x7
layer id:3, type: Pooling, name:pool1/3x3_s2
layer id:4, type: LRN, name:pool1/norm1
layer id:5, type: Convolution, name:conv2/3x3_reduce
layer id:6, type: ReLU, name:conv2/relu_3x3_reduce
layer id:7, type: Convolution, name:conv2/3x3
layer id:8, type: ReLU, name:conv2/relu_3x3
layer id:9, type: LRN, name:conv2/norm2
layer id:10, type: Pooling, name:pool2/3x3_s2
layer id:11, type: Convolution, name:inception_3a/1x1
layer id:12, type: ReLU, name:inception_3a/relu_1x1
layer id:13, type: Convolution, name:inception_3a/3x3_reduce
layer id:14, type: ReLU, name:inception_3a/relu_3x3_reduce
layer id:15, type: Convolution, name:inception_3a/3x3
layer id:16, type: ReLU, name:inception_3a/relu_3x3
layer id:17, type: Convolution, name:inception_3a/5x5_reduce
layer id:18, type: ReLU, name:inception_3a/relu_5x5_reduce
layer id:19, type: Convolution, name:inception_3a/5x5
layer id:20, type: ReLU, name:inception_3a/relu_5x5
layer id:21, type: Pooling, name:inception_3a/pool
layer id:22, type: Convolution, name:inception_3a/pool_proj
layer id:23, type: ReLU, name:inception_3a/relu_pool_proj
layer id:24, type: Concat, name:inception_3a/output
layer id:25, type: Convolution, name:inception_3b/1x1
layer id:26, type: ReLU, name:inception_3b/relu_1x1
layer id:27, type: Convolution, name:inception_3b/3x3_reduce
layer id:28, type: ReLU, name:inception_3b/relu_3x3_reduce
layer id:29, type: Convolution, name:inception_3b/3x3
layer id:30, type: ReLU, name:inception_3b/relu_3x3
layer id:31, type: Convolution, name:inception_3b/5x5_reduce
layer id:32, type: ReLU, name:inception_3b/relu_5x5_reduce
layer id:33, type: Convolution, name:inception_3b/5x5
layer id:34, type: ReLU, name:inception_3b/relu_5x5
layer id:35, type: Pooling, name:inception_3b/pool
layer id:36, type: Convolution, name:inception_3b/pool_proj
layer id:37, type: ReLU, name:inception_3b/relu_pool_proj
layer id:38, type: Concat, name:inception_3b/output
layer id:39, type: Pooling, name:pool3/3x3_s2
layer id:40, type: Convolution, name:inception_4a/1x1
layer id:41, type: ReLU, name:inception_4a/relu_1x1
layer id:42, type: Convolution, name:inception_4a/3x3_reduce
layer id:43, type: ReLU, name:inception_4a/relu_3x3_reduce
layer id:44, type: Convolution, name:inception_4a/3x3
layer id:45, type: ReLU, name:inception_4a/relu_3x3
layer id:46, type: Convolution, name:inception_4a/5x5_reduce
layer id:47, type: ReLU, name:inception_4a/relu_5x5_reduce
layer id:48, type: Convolution, name:inception_4a/5x5
layer id:49, type: ReLU, name:inception_4a/relu_5x5
layer id:50, type: Pooling, name:inception_4a/pool
layer id:51, type: Convolution, name:inception_4a/pool_proj
layer id:52, type: ReLU, name:inception_4a/relu_pool_proj
layer id:53, type: Concat, name:inception_4a/output
layer id:54, type: Convolution, name:inception_4b/1x1
layer id:55, type: ReLU, name:inception_4b/relu_1x1
layer id:56, type: Convolution, name:inception_4b/3x3_reduce
layer id:57, type: ReLU, name:inception_4b/relu_3x3_reduce
layer id:58, type: Convolution, name:inception_4b/3x3
layer id:59, type: ReLU, name:inception_4b/relu_3x3
layer id:60, type: Convolution, name:inception_4b/5x5_reduce
layer id:61, type: ReLU, name:inception_4b/relu_5x5_reduce
layer id:62, type: Convolution, name:inception_4b/5x5
layer id:63, type: ReLU, name:inception_4b/relu_5x5
layer id:64, type: Pooling, name:inception_4b/pool
layer id:65, type: Convolution, name:inception_4b/pool_proj
layer id:66, type: ReLU, name:inception_4b/relu_pool_proj
layer id:67, type: Concat, name:inception_4b/output
layer id:68, type: Convolution, name:inception_4c/1x1
layer id:69, type: ReLU, name:inception_4c/relu_1x1
layer id:70, type: Convolution, name:inception_4c/3x3_reduce
layer id:71, type: ReLU, name:inception_4c/relu_3x3_reduce
layer id:72, type: Convolution, name:inception_4c/3x3
layer id:73, type: ReLU, name:inception_4c/relu_3x3
layer id:74, type: Convolution, name:inception_4c/5x5_reduce
layer id:75, type: ReLU, name:inception_4c/relu_5x5_reduce
layer id:76, type: Convolution, name:inception_4c/5x5
layer id:77, type: ReLU, name:inception_4c/relu_5x5
layer id:78, type: Pooling, name:inception_4c/pool
layer id:79, type: Convolution, name:inception_4c/pool_proj
layer id:80, type: ReLU, name:inception_4c/relu_pool_proj
layer id:81, type: Concat, name:inception_4c/output
layer id:82, type: Convolution, name:inception_4d/1x1
layer id:83, type: ReLU, name:inception_4d/relu_1x1
layer id:84, type: Convolution, name:inception_4d/3x3_reduce
layer id:85, type: ReLU, name:inception_4d/relu_3x3_reduce
layer id:86, type: Convolution, name:inception_4d/3x3
layer id:87, type: ReLU, name:inception_4d/relu_3x3
layer id:88, type: Convolution, name:inception_4d/5x5_reduce
layer id:89, type: ReLU, name:inception_4d/relu_5x5_reduce
layer id:90, type: Convolution, name:inception_4d/5x5
layer id:91, type: ReLU, name:inception_4d/relu_5x5
layer id:92, type: Pooling, name:inception_4d/pool
layer id:93, type: Convolution, name:inception_4d/pool_proj
layer id:94, type: ReLU, name:inception_4d/relu_pool_proj
layer id:95, type: Concat, name:inception_4d/output
layer id:96, type: Convolution, name:inception_4e/1x1
layer id:97, type: ReLU, name:inception_4e/relu_1x1
layer id:98, type: Convolution, name:inception_4e/3x3_reduce
layer id:99, type: ReLU, name:inception_4e/relu_3x3_reduce
layer id:100, type: Convolution, name:inception_4e/3x3
layer id:101, type: ReLU, name:inception_4e/relu_3x3
layer id:102, type: Convolution, name:inception_4e/5x5_reduce
layer id:103, type: ReLU, name:inception_4e/relu_5x5_reduce
layer id:104, type: Convolution, name:inception_4e/5x5
layer id:105, type: ReLU, name:inception_4e/relu_5x5
layer id:106, type: Pooling, name:inception_4e/pool
layer id:107, type: Convolution, name:inception_4e/pool_proj
layer id:108, type: ReLU, name:inception_4e/relu_pool_proj
layer id:109, type: Concat, name:inception_4e/output
layer id:110, type: Pooling, name:pool4/3x3_s2
layer id:111, type: Convolution, name:inception_5a/1x1
layer id:112, type: ReLU, name:inception_5a/relu_1x1
layer id:113, type: Convolution, name:inception_5a/3x3_reduce
layer id:114, type: ReLU, name:inception_5a/relu_3x3_reduce
layer id:115, type: Convolution, name:inception_5a/3x3
layer id:116, type: ReLU, name:inception_5a/relu_3x3
layer id:117, type: Convolution, name:inception_5a/5x5_reduce
layer id:118, type: ReLU, name:inception_5a/relu_5x5_reduce
layer id:119, type: Convolution, name:inception_5a/5x5
layer id:120, type: ReLU, name:inception_5a/relu_5x5
layer id:121, type: Pooling, name:inception_5a/pool
layer id:122, type: Convolution, name:inception_5a/pool_proj
layer id:123, type: ReLU, name:inception_5a/relu_pool_proj
layer id:124, type: Concat, name:inception_5a/output
layer id:125, type: Convolution, name:inception_5b/1x1
layer id:126, type: ReLU, name:inception_5b/relu_1x1
layer id:127, type: Convolution, name:inception_5b/3x3_reduce
layer id:128, type: ReLU, name:inception_5b/relu_3x3_reduce
layer id:129, type: Convolution, name:inception_5b/3x3
layer id:130, type: ReLU, name:inception_5b/relu_3x3
layer id:131, type: Convolution, name:inception_5b/5x5_reduce
layer id:132, type: ReLU, name:inception_5b/relu_5x5_reduce
layer id:133, type: Convolution, name:inception_5b/5x5
layer id:134, type: ReLU, name:inception_5b/relu_5x5
layer id:135, type: Pooling, name:inception_5b/pool
layer id:136, type: Convolution, name:inception_5b/pool_proj
layer id:137, type: ReLU, name:inception_5b/relu_pool_proj
layer id:138, type: Concat, name:inception_5b/output
layer id:139, type: Pooling, name:pool5/7x7_s1
layer id:140, type: Dropout, name:pool5/drop_7x7_s1
layer id:141, type: InnerProduct, name:loss3/classifier
layer id:142, type: Softmax, name:prob
OpenCV DNN 获取导入模型各层信息
2、Python代码
import cv2 as cv
import numpy as npbin_model = "googlenet/bvlc_googlenet.caffemodel"
protxt = "googlenet/bvlc_googlenet.prototxt"# Load names of classes
classes = None
with open("googlenet/classification_classes_ILSVRC2012.txt", 'rt') as f:classes = f.read().rstrip('\n').split('\n')# load CNN model
net = cv.dnn.readNet(bin_model, protxt)# 获取各层信息
layer_names = net.getLayerNames()
print(layer_names)
for name in layer_names:id = net.getLayerId(name)layer = net.getLayer(id)print("layer id : %d, type : %s, name: %s"%(id, layer.type, layer.name))print("successfully loaded model...")
二、相关说明
1、支持深度学习框架
模型支持1000个类别的图像分类，OpenCV DNN模块支持下面框架的预训练模型的前馈网络(预测图)使用
- Caffe
- Tensorflow
- Torch
- DLDT
- Darknet
2、导入模型的有三个参数
同时还支持自定义层解析、非最大抑制操作、获取各层的信息等。OpenCV加载模型的通用API为
Net cv::dnn::readNet(
const String & model,
const String & config = “”,
const String & framework = “”
)
1、第一个参数model
model是训练好的二进制网络权重文件，可能来自支持的网络框架，不同的深度学习框架对应的二进制模型扩展名如下：
*.caffemodel (Caffe, http://caffe.berkeleyvision.org/)
*.pb (TensorFlow, https://www.tensorflow.org/)
*.t7 | *.net (Torch, http://torch.ch/)
*.weights (Darknet, https://pjreddie.com/darknet/)
*.bin (DLDT, https://software.intel.com/openvino-toolkit)
2、第二个参数config
config针对模型二进制的描述文件，不同的框架配置文件有不同扩展名，如下：
*.prototxt (Caffe, http://caffe.berkeleyvision.org/)
*.pbtxt (TensorFlow, https://www.tensorflow.org/)
*.cfg (Darknet, https://pjreddie.com/darknet/)
*.xml (DLDT, https://software.intel.com/openvino-toolkit)
3、第三个参数framework
framework显示声明参数，说明模型使用哪个框架训练出来的
后面的两个参数并不是很重要，第一参数一定要有
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