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计算速度优化，摄像头读取逻辑改变，分类器更新。

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This commit is contained in:
xinyang
2019-07-28 15:59:01 +08:00
parent a5259cbd1f
commit 5a0fdb30af
24 changed files with 17667 additions and 115529 deletions
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25
armor/src/armor_finder/classifier/classifier.cpp
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@@ -162,6 +162,12 @@ MatrixXd Classifier::relu(const MatrixXd &input){
});
}
MatrixXd Classifier::leaky_relu(const MatrixXd &input, float alpha){
return input.unaryExpr([&](double val){
return (val>0)?(val):(alpha*val);
});
}
vector<vector<MatrixXd>> Classifier::relu(const vector<vector<MatrixXd>> &input){
vector<vector<MatrixXd>> result;
for(int samples=0; samples<input.size(); samples++){
@@ -174,6 +180,18 @@ vector<vector<MatrixXd>> Classifier::relu(const vector<vector<MatrixXd>> &input)
return result;
}
vector<vector<MatrixXd>> Classifier::leaky_relu(const vector<vector<MatrixXd>> &input, float alpha){
vector<vector<MatrixXd>> result;
for(int samples=0; samples<input.size(); samples++){
vector<MatrixXd> sub;
for(int channels=0; channels<input[0].size(); channels++){
sub.emplace_back(leaky_relu(input[samples][channels], alpha));
}
result.emplace_back(sub);
}
return result;
}
vector<vector<MatrixXd>> Classifier::pand(const vector<vector<MatrixXd>> &input, int val){
vector<vector<MatrixXd>> result;
for(int sample=0; sample<input.size(); sample++){
@@ -254,6 +272,8 @@ Classifier::Classifier(const string &folder) : state(true){
conv1_b = load_conv_b(folder+"conv1_b");
conv2_w = load_conv_w(folder+"conv2_w");
conv2_b = load_conv_b(folder+"conv2_b");
conv3_w = load_conv_w(folder+"conv3_w");
conv3_b = load_conv_b(folder+"conv3_b");
fc1_w = load_fc_w(folder+"fc1_w");
fc1_b = load_fc_b(folder+"fc1_b");
fc2_w = load_fc_w(folder+"fc2_w");
@@ -273,7 +293,8 @@ MatrixXd Classifier::calculate(const vector<vector<MatrixXd>> &input) {
vector<vector<MatrixXd>> pool1_result = mean_pool(conv1_result, 2);
vector<vector<MatrixXd>> conv2_result = relu(apply_bias(conv2(conv2_w, pool1_result), conv2_b));
vector<vector<MatrixXd>> pool2_result = mean_pool(conv2_result, 2);
MatrixXd flattened = flatten(pool2_result);
vector<vector<MatrixXd>> conv3_result = relu(apply_bias(conv2(conv3_w, pool2_result), conv3_b));
MatrixXd flattened = flatten(conv3_result);
MatrixXd y1 = fc1_w * flattened;
y1.colwise() += fc1_b;
MatrixXd fc1 = relu(y1);
@@ -303,7 +324,7 @@ int Classifier::operator()(const cv::Mat &image) {
// cout << result << "==============" <<endl;
MatrixXd::Index minRow, minCol;
result.maxCoeff(&minRow, &minCol);
if(result(minRow, minCol) > 0.9){
if(result(minRow, minCol) > 0.90){
return minRow;
}else{
return 0;