计算速度优化，摄像头读取逻辑改变，分类器更新。

.gitignore

@@ -4,6 +4,7 @@ build
 Mark
 armor_box_photo
 tools/TrainCNN/.idea
+tools/TrainCNN/model
 tools/TrainCNN/__pycache__
 others/include/config/config.h
 .DS_Store

armor/include/armor_finder/armor_finder.h

@@ -51,10 +51,11 @@ extern std::map<string, int> prior_red;
 class LightBlob {
 public:
     cv::RotatedRect rect;   //灯条位置
+    double areaRatio;
     double length;          //灯条长度
     uint8_t blob_color;      //灯条颜色
 
-    LightBlob(cv::RotatedRect &r, uint8_t color) : rect(r), blob_color(color) {
+    LightBlob(cv::RotatedRect &r, double ratio, uint8_t color) : rect(r), areaRatio(ratio), blob_color(color) {
         length = max(rect.size.height, rect.size.width);
     };
     LightBlob() = default;

armor/include/armor_finder/classifier/classifier.h

@@ -18,8 +18,8 @@ class Classifier {
 private:
     bool state;
 
-    vector<vector<MatrixXd>> conv1_w, conv2_w;
+    vector<vector<MatrixXd>> conv1_w, conv2_w, conv3_w;
-    vector<double> conv1_b, conv2_b;
+    vector<double> conv1_b, conv2_b, conv3_b;
     MatrixXd fc1_w, fc2_w;
     VectorXd fc1_b, fc2_b;
 
@@ -30,8 +30,10 @@ private:
 
     MatrixXd softmax(const MatrixXd &input);
     MatrixXd relu(const MatrixXd &input);
+    MatrixXd leaky_relu(const MatrixXd &input, float alpha);
     vector<vector<MatrixXd>> apply_bias(const vector<vector<MatrixXd>> &input, const vector<double> &bias);
     vector<vector<MatrixXd>> relu(const vector<vector<MatrixXd>> &input);
+    vector<vector<MatrixXd>> leaky_relu(const vector<vector<MatrixXd>> &input, float alpha);
     vector<vector<MatrixXd>> max_pool(const vector<vector<MatrixXd>> &input, int size);
     vector<vector<MatrixXd>> mean_pool(const vector<vector<MatrixXd>> &input, int size);
     vector<vector<MatrixXd>> pand(const vector<vector<MatrixXd>> &input, int val);

armor/src/armor_finder/anti_top/anti_top.cpp

@@ -3,12 +3,9 @@
 //
 
 #include <armor_finder/armor_finder.h>
+#include <additions/additions.h>
 #include <log.h>
 
-static double getTimeIntervalms(const timeval& now, const timeval &last){
-    return (now.tv_sec-last.tv_sec)*1000.0 + (now.tv_usec-last.tv_usec)/1000.0;
-}
-
 void ArmorFinder::antiTop() {
     static double top_periodms = 0;
     static double last_top_periodms = 0;

armor/src/armor_finder/armor_finder.cpp

@@ -58,8 +58,8 @@ ArmorFinder::ArmorFinder(uint8_t &color, Serial &u, const string &paras_folder,
 }
 
 void ArmorFinder::run(cv::Mat &src) {
-//    stateSearchingTarget(src);                    // for debug
+    stateSearchingTarget(src);                    // for debug
-//    goto end;
+    goto end;
     switch (state) {
         case SEARCHING_STATE:
             if (stateSearchingTarget(src)) {

armor/src/armor_finder/classifier/classifier.cpp

@@ -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;

armor/src/armor_finder/find/find_armor_box.cpp

@@ -6,6 +6,7 @@
 #include <show_images/show_images.h>
 #include <options/options.h>
 #include <opencv2/highgui.hpp>
+#define DO_NOT_CNT_TIME
 #include <log.h>
 
 
@@ -27,7 +28,7 @@ static bool lengthJudge(const LightBlob &light_blob_i, const LightBlob &light_bl
     double side_length;
     cv::Point2f centers = light_blob_i.rect.center - light_blob_j.rect.center;
     side_length = sqrt(centers.ddot(centers));
-    return (side_length / light_blob_i.length < 8 && side_length / light_blob_i.length > 0.5);
+    return (side_length / light_blob_i.length < 10 && side_length / light_blob_i.length > 0.5);
 }
 
 static bool lengthRatioJudge(const LightBlob &light_blob_i, const LightBlob &light_blob_j) {
@@ -99,6 +100,7 @@ bool matchArmorBoxes(const cv::Mat &src, const LightBlobs &light_blobs, ArmorBox
             if (min_x < 0 || max_x > src.cols || min_y < 0 || max_y > src.rows) {
                 continue;
             }
+            if((max_x-min_x)/(max_y-min_y) < 0.8) continue;
             LightBlobs pair_blobs = {light_blobs.at(i), light_blobs.at(j)};
             armor_boxes.emplace_back(
                     cv::Rect2d(min_x, min_y, max_x - min_x, max_y - min_y),
@@ -116,31 +118,35 @@ bool ArmorFinder::findArmorBox(const cv::Mat &src, ArmorBox &box) {
 
     box.rect = cv::Rect2d(0, 0, 0, 0);
     box.id = -1;
-
+    CNT_TIME("blob", {
         if (!findLightBlobs(src, light_blobs)) {
             return false;
         }
+    });
     if (show_light_blobs && src.size() == cv::Size(640, 480)) {
         showLightBlobs("light_blobs", src, light_blobs);
         cv::waitKey(1);
     }
-
+    CNT_TIME("boxes",{
         if (!matchArmorBoxes(src, light_blobs, armor_boxes, enemy_color)) {
     //        cout << "Box fail!" << endl;
             return false;
         }
+    });
     if (show_armor_boxes && src.size() == cv::Size(640, 480)) {
         showArmorBoxes("boxes", src, armor_boxes);
         cv::waitKey(1);
     }
 
     if (classifier && use_classifier) {
+        CNT_TIME("classify: %d", {
             for (auto &armor_box : armor_boxes) {
                 cv::Mat roi = src(armor_box.rect).clone();
                 cv::resize(roi, roi, cv::Size(48, 36));
                 int c = classifier(roi);
                 armor_box.id = c;
             }
+        }, armor_boxes.size());
         sort(armor_boxes.begin(), armor_boxes.end());
         if(armor_boxes[0].id != 0){
             box = armor_boxes[0];

armor/src/armor_finder/find/find_light_blobs.cpp

@@ -95,6 +95,11 @@ static double get_blob_color_opt(const cv::Mat &src, cv::RotatedRect blobPos) {
     }
 }
 
+static bool isSameBlob(LightBlob blob1, LightBlob blob2) {
+    auto dist = blob1.rect.center - blob2.rect.center;
+    return (dist.x * dist.x + dist.y * dist.y) < 9;
+}
+
 static void imagePreProcess(cv::Mat &src) {
     static cv::Mat kernel_erode = getStructuringElement(cv::MORPH_RECT, cv::Size(3, 5));
     erode(src, src, kernel_erode);
@@ -126,7 +131,6 @@ bool ArmorFinder::findLightBlobs(const cv::Mat &src, LightBlobs &light_blobs) {
     }
 
 
-
     cv::threshold(color_channel, src_bin_light, 200, 255, CV_THRESH_BINARY); // 二值化对应通道
     if (src_bin_light.empty()) return false;
     imagePreProcess(src_bin_light);                                  // 开闭运算
@@ -141,19 +145,55 @@ bool ArmorFinder::findLightBlobs(const cv::Mat &src, LightBlobs &light_blobs) {
     }
 
     std::vector<std::vector<cv::Point>> light_contours_light, light_contours_dim;
-    cv::findContours(src_bin_light, light_contours_light, CV_RETR_LIST, CV_CHAIN_APPROX_NONE);
+    LightBlobs light_blobs_light, light_blobs_dim;
-    cv::findContours(src_bin_dim, light_contours_dim, CV_RETR_LIST, CV_CHAIN_APPROX_NONE);
+    std::vector<cv::Vec4i> hierarchy_light, hierarchy_dim;
-    for (auto &light_contour : light_contours_light) {
+    cv::findContours(src_bin_light, light_contours_light, hierarchy_light, CV_RETR_CCOMP, CV_CHAIN_APPROX_NONE);
-        cv::RotatedRect rect = cv::minAreaRect(light_contour);
+    cv::findContours(src_bin_dim, light_contours_dim, hierarchy_dim, CV_RETR_CCOMP, CV_CHAIN_APPROX_NONE);
-        if (isValidLightBlob(light_contour, rect)) {
+    for (int i = 0; i < light_contours_light.size(); i++) {
-            light_blobs.emplace_back(rect, get_blob_color(src, rect));
+        if (hierarchy_light[i][2] == -1) {
+            cv::RotatedRect rect = cv::minAreaRect(light_contours_light[i]);
+            if (isValidLightBlob(light_contours_light[i], rect)) {
+                light_blobs_light.emplace_back(
+                        rect, areaRatio(light_contours_light[i], rect), get_blob_color(src, rect)
+                );
             }
         }
-    for (auto &light_contour : light_contours_dim) {
-        cv::RotatedRect rect = cv::minAreaRect(light_contour);
-        if (isValidLightBlob(light_contour, rect)) {
-            light_blobs.emplace_back(rect, get_blob_color(src, rect));
     }
+    for (int i = 0; i < light_contours_dim.size(); i++) {
+        if (hierarchy_dim[i][2] == -1) {
+            cv::RotatedRect rect = cv::minAreaRect(light_contours_dim[i]);
+            if (isValidLightBlob(light_contours_dim[i], rect)) {
+                light_blobs_dim.emplace_back(
+                        rect, areaRatio(light_contours_dim[i], rect), get_blob_color(src, rect)
+                );
+            }
+        }
+    }
+    vector<int> light_to_remove, dim_to_remove;
+    for (int l = 0; l != light_blobs_light.size(); l++) {
+        for (int d = 0; d != light_blobs_dim.size(); d++) {
+            if (isSameBlob(light_blobs_light[l], light_blobs_dim[d])) {
+                if (light_blobs_light[l].areaRatio > light_blobs_dim[d].areaRatio) {
+                    dim_to_remove.emplace_back(d);
+                } else {
+                    light_to_remove.emplace_back(l);
+                }
+            }
+        }
+    }
+    sort(light_to_remove.begin(), light_to_remove.end(), [](int a, int b) { return a > b; });
+    sort(dim_to_remove.begin(), dim_to_remove.end(), [](int a, int b) { return a > b; });
+    for (auto x : light_to_remove) {
+        light_blobs_light.erase(light_blobs_light.begin() + x);
+    }
+    for (auto x : dim_to_remove) {
+        light_blobs_dim.erase(light_blobs_dim.begin() + x);
+    }
+    for (const auto &light : light_blobs_light) {
+        light_blobs.emplace_back(light);
+    }
+    for (const auto &dim : light_blobs_dim) {
+        light_blobs.emplace_back(dim);
     }
     return light_blobs.size() >= 2;
 }

main.cpp

@@ -20,7 +20,7 @@
 #include <additions/additions.h>
 #include <config/setconfig.h>
 
-#define DO_NOT_CNT_TIME
+//#define DO_NOT_CNT_TIME
 
 #include <log.h>
 
@@ -63,8 +63,8 @@ int main(int argc, char *argv[]) {
     while (true) {
         // 打开视频源
         if (from_camera) {
-            video_gimbal = new CameraWrapper(ARMOR_CAMERA_GAIN, 0/*, "armor"*/);
+            video_gimbal = new CameraWrapper(ARMOR_CAMERA_GAIN, 2/*, "armor"*/);
-            video_chassis = new CameraWrapper(ENERGY_CAMERA_GAIN, 0/*, "energy"*/);
+            video_chassis = new CameraWrapper(ENERGY_CAMERA_GAIN, 2/*, "energy"*/);
         } else {
             video_gimbal = new VideoWrapper("/home/sun/项目/energy_video/7.27.avi");
             video_chassis = new VideoWrapper("/home/sun/项目/energy_video/7.27.avi");
@@ -93,14 +93,14 @@ int main(int argc, char *argv[]) {
         bool ok = true;
         cout << "start running" << endl;
         do {
-//            CNT_TIME("Total", {
+            CNT_TIME("Total", {
             if (mcuData.state == BIG_ENERGY_STATE) {//大能量机关模式
                 if (last_state != BIG_ENERGY_STATE) {//若上一帧不是大能量机关模式，即刚往完成切换，则需要初始化
                     LOGM(STR_CTR(WORD_BLUE, "Start Big Energy!"));
                     destroyAllWindows();
                     if (from_camera) {
                         delete video_gimbal;
-                        video_gimbal = new CameraWrapper(ENERGY_CAMERA_GAIN, 0/*, "armor"*/);
+                        video_gimbal = new CameraWrapper(ENERGY_CAMERA_GAIN, 2/*, "armor"*/);
                         if (video_gimbal->init()) {
                             LOGM("video_gimbal source initialization successfully.");
                         } else {
@@ -130,7 +130,7 @@ int main(int argc, char *argv[]) {
                     destroyAllWindows();
                     if (from_camera) {
                         delete video_gimbal;
-                        video_gimbal = new CameraWrapper(ENERGY_CAMERA_GAIN, 0/*, "armor"*/);
+                        video_gimbal = new CameraWrapper(ENERGY_CAMERA_GAIN, 2/*, "armor"*/);
                         if (video_gimbal->init()) {
                             LOGM("video_gimbal source initialization successfully.");
                         } else {
@@ -154,7 +154,7 @@ int main(int argc, char *argv[]) {
                     destroyAllWindows();
                     if (from_camera) {
                         delete video_gimbal;
-                        video_gimbal = new CameraWrapper(ARMOR_CAMERA_GAIN, 0/*, "armor"*/);
+                        video_gimbal = new CameraWrapper(ARMOR_CAMERA_GAIN, 2/*, "armor"*/);
                         if (video_gimbal->init()) {
                             LOGM("video_gimbal source initialization successfully.");
                         } else {
@@ -163,19 +163,23 @@ int main(int argc, char *argv[]) {
                     }
                 }
                 last_state = mcuData.state;
-                ok = checkReconnect(video_gimbal->read(gimbal_src));
+                CNT_TIME(STR_CTR(WORD_GREEN, "read img"), {
+                    if(!checkReconnect(video_gimbal->read(gimbal_src))) continue;
+                });
 #ifdef GIMBAL_FLIP_MODE
                 flip(gimbal_src, gimbal_src, GIMBAL_FLIP_MODE);
 #endif
+//                CNT_TIME("something whatever", {
                     if (!from_camera) extract(gimbal_src);
                     if (save_video) saveVideos(gimbal_src);
                     if (show_origin) showOrigin(gimbal_src);
-                CNT_TIME("Armor Time", {
+//                });
+                CNT_TIME(STR_CTR(WORD_CYAN, "Armor Time"), {
                     armorFinder.run(gimbal_src);
                 });
             }
 //                cv::waitKey(0);
-//            });
+            });
         } while (ok);
         delete video_gimbal;
         video_gimbal = nullptr;

others/include/additions/additions.h

@@ -6,6 +6,7 @@
 #define _ADDITIONS_H_
 
 #include <stdint.h>
+#include <sys/time.h>
 #include <serial/serial.h>
 
 struct mcu_data{
@@ -30,5 +31,6 @@ void showOrigin(const cv::Mat &gimbal_src, const cv::Mat &chassis_src);
 void showOrigin(const cv::Mat &gimbal_src);
 void extract(cv::Mat &gimbal_src, cv::Mat &chassis_src);
 void extract(cv::Mat &gimbal_src);
+double getTimeIntervalms(const timeval& now, const timeval &last);
 
 #endif /* _ADDITIONS_H_ */

others/include/camera/camera_wrapper.h

@@ -9,11 +9,12 @@
 
 #include <stdio.h>
 #include <iostream>
+#include <thread>
 #include "opencv2/core/core.hpp"
 #include "opencv2/highgui/highgui.hpp"
 #include <opencv2/imgproc/imgproc.hpp>
-
 #include "camera/wrapper_head.h"
+
 #ifdef Windows
     #include "camera/CameraApi.h"
 #elif defined(Linux) || defined(Darwin)
@@ -21,10 +22,13 @@
 #endif
 
 class CameraWrapper: public WrapperHead {
+    friend void cameraCallback(CameraHandle hCamera, BYTE *pFrameBuffer, tSdkFrameHead* pFrameHead,PVOID pContext);
 private:
     const std::string name;
     int mode;
 
+    bool init_done;
+
     unsigned char* rgb_buffer;
     int camera_cnts;
     int camera_status;
@@ -38,6 +42,11 @@ private:
     IplImage* iplImage;
     int channel;
 
+    cv::Mat src_queue[2];
+    volatile int qhead;
+    volatile int qtail;
+
+    std::thread *readThread;
 public:
     int gain;
 
@@ -48,6 +57,8 @@ public:
     bool read(cv::Mat& src) final;
     bool readRaw(cv::Mat& src);
     bool readProcessed(cv::Mat& src);
+    bool readCallback(cv::Mat& src);
+    bool readWithThread(cv::Mat &src);
     bool changeBrightness(int brightness);
 //    bool once
 };

others/include/log.h

@@ -146,7 +146,7 @@
 			gettimeofday(&ts, NULL); \
 			codes; \
 			gettimeofday(&te, NULL); \
-			LOGM(tag": %.1lfms", (te.tv_sec-ts.tv_sec)*1000.0+(te.tv_usec-ts.tv_usec)/1000.0); \
+			LOGM(tag": %.1lfms", ##__VA_ARGS__, (te.tv_sec-ts.tv_sec)*1000.0+(te.tv_usec-ts.tv_usec)/1000.0); \
 		}while (0)
     #else
         #warning "Unsupport plantform for CNT_TIME"

others/src/additions/additions.cpp

@@ -169,3 +169,7 @@ void extract(cv::Mat &gimbal_src) {//图像预处理，将视频切成640×480
         gimbal_src = gimbal_src(Rect(0, (width - 480) / 2, 640, 480));
     }
 }
+
+double getTimeIntervalms(const timeval& now, const timeval &last){
+    return (now.tv_sec-last.tv_sec)*1000.0 + (now.tv_usec-last.tv_usec)/1000.0;
+}

others/src/camera/camera_wrapper.cpp

@@ -4,6 +4,7 @@
 
 #include <camera/camera_wrapper.h>
 #include <log.h>
+#include <additions/additions.h>
 #include <options/options.h>
 #include <config/setconfig.h>
 
@@ -15,15 +16,33 @@ using namespace cv;
 
 CameraWrapper::CameraWrapper(int gain, int camera_mode, const std::string &n) :
         name(n),
+        init_done(false),
         mode(camera_mode),
         camera_cnts(2),
         camera_status(-1),
         iplImage(nullptr),
         rgb_buffer(nullptr),
         channel(3),
-        gain(gain){
+        gain(gain),
+        qhead(0),
+        qtail(0),
+        readThread(nullptr){
 }
 
+void cameraCallback(CameraHandle hCamera, BYTE *pFrameBuffer, tSdkFrameHead* pFrameHead,PVOID pContext){
+    CameraWrapper *c = (CameraWrapper*)pContext;
+    CameraImageProcess(hCamera, pFrameBuffer, c->rgb_buffer, pFrameHead);
+    auto iplImage = cvCreateImageHeader(cvSize(pFrameHead->iWidth, pFrameHead->iHeight), IPL_DEPTH_8U, c->channel);
+    cvSetData(iplImage, c->rgb_buffer, pFrameHead->iWidth * c->channel);  //此处只是设置指针，无图像块数据拷贝，不需担心转换效率
+    c->src_queue[c->qhead] = cv::cvarrToMat(iplImage).clone();
+    if((c->qhead+1)%2 == c->qtail){
+        c->qhead = (c->qhead+1)%2;
+        c->qtail = (c->qtail+1)%2;
+    } else {
+        c->qhead = (c->qhead+1)%2;
+    }
+//    LOGM("Get image, [%d %d]", c->qhead, c->qtail);
+}
 
 bool CameraWrapper::init() {
     CameraSdkInit(1);
@@ -76,6 +95,8 @@ bool CameraWrapper::init() {
     }
     LOGM("successfully loaded %s!", filepath);
 #elif defined(Linux)
+    CameraReadParameterFromFile(h_camera, PROJECT_DIR"/others/MV-UB31-Group0.config");
+    CameraLoadParameter(h_camera, PARAMETER_TEAM_A);
     CameraSetAeState(h_camera, false);
     CameraSetExposureTime(h_camera, CAMERA_EXPOSURE * 1000);
 #ifndef WITH_TIME_BASED_CAMERA_GAIN
@@ -106,14 +127,13 @@ bool CameraWrapper::init() {
     }
     CameraSetAnalogGain(h_camera, gain);
 #endif
-    if (mode == 0) {
+
-        CameraSetGain(h_camera, CAMERA_RED_GAIN, CAMERA_GREEN_GAIN, CAMERA_BLUE_GAIN);
-        CameraSetLutMode(h_camera, LUTMODE_PRESET);
-    }
 #endif
     double t;
+    int g;
     CameraGetExposureTime(h_camera, &t);
-    LOGM("Exposure time: %lfms", t / 1000.0);
+    CameraGetAnalogGain(h_camera, &g);
+    LOGM("Exposure time: %lfms, gain:%d", t / 1000.0, g);
     /*让SDK进入工作模式，开始接收来自相机发送的图像
     数据。如果当前相机是触发模式，则需要接收到
     触发帧以后才会更新图像。    */
@@ -135,6 +155,10 @@ bool CameraWrapper::init() {
         CameraSetIspOutFormat(h_camera, CAMERA_MEDIA_TYPE_BGR8);
         LOGM("camera %s color ", camera_name);
     }
+    if(mode == 2){
+        CameraSetCallbackFunction(h_camera, cameraCallback, this, nullptr);
+    }
+    init_done = true;
     return true;
 }
 
@@ -144,12 +168,18 @@ bool CameraWrapper::changeBrightness(int brightness) {
 }
 
 bool CameraWrapper::read(cv::Mat &src) {
+    if(init_done) {
         if (mode == 0)return readProcessed(src);
         if (mode == 1)return readRaw(src);
+        if (mode == 2)return readCallback(src);
+        if (mode == 3)return readWithThread(src);
+    } else {
+        return false;
+    }
 }
 
 bool CameraWrapper::readRaw(cv::Mat &src) {
-    if (CameraGetImageBuffer(h_camera, &frame_info, &pby_buffer, 1000) == CAMERA_STATUS_SUCCESS) {
+    if (CameraGetImageBuffer(h_camera, &frame_info, &pby_buffer, 500) == CAMERA_STATUS_SUCCESS) {
         if (iplImage) {
             cvReleaseImageHeader(&iplImage);
         }
@@ -173,7 +203,7 @@ bool CameraWrapper::readRaw(cv::Mat &src) {
 
 bool CameraWrapper::readProcessed(cv::Mat &src) {
 //	cerr << "Get-1" << endl;
-    if (CameraGetImageBuffer(h_camera, &frame_info, &pby_buffer, 1000) == CAMERA_STATUS_SUCCESS) {
+    if (CameraGetImageBuffer(h_camera, &frame_info, &pby_buffer, 500) == CAMERA_STATUS_SUCCESS) {
         CameraImageProcess(h_camera, pby_buffer, rgb_buffer,
                            &frame_info);  // this function is super slow, better not to use it.
         if (iplImage) {
@@ -192,9 +222,56 @@ bool CameraWrapper::readProcessed(cv::Mat &src) {
     }
 }
 
+bool CameraWrapper::readCallback(cv::Mat &src) {
+    timeval ts, te;
+    gettimeofday(&ts, NULL);
+    while(qtail==qhead){
+        gettimeofday(&te, NULL);
+        if(getTimeIntervalms(te, ts) > 500){
+            return false;
+        }
+    }
+
+    src = src_queue[qtail];
+//    cout << src.size << endl;
+    qtail = (qtail+1)%2;
+    return true;
+}
+
+bool CameraWrapper::readWithThread(cv::Mat &src){
+    if(readThread != nullptr){
+        readThread->join();
+        src = src_queue[qtail];
+        qtail = (qtail+1)%2;
+        delete readThread;
+        readThread = new std::thread([&](){
+            readProcessed(src_queue[qhead]);
+            qhead = (qhead+1)%2;
+        });
+    }else{
+        readThread = new std::thread([&](){
+            readProcessed(src_queue[qhead]);
+            qhead = (qhead+1)%2;
+        });
+        readThread->join();
+        src = src_queue[qtail];
+        qtail = (qtail+1)%2;
+        delete readThread;
+        readThread = new std::thread([&](){
+            readProcessed(src_queue[qhead]);
+            qhead = (qhead+1)%2;
+        });
+    }
+    return true;
+}
+
 CameraWrapper::~CameraWrapper() {
     CameraUnInit(h_camera);
     //注意，先反初始化后再free
     if (rgb_buffer != nullptr)
         free(rgb_buffer);
+    if(readThread != nullptr){
+        readThread->detach();
+        delete readThread;
+    }
 }

tools/TrainCNN/backward.py

@@ -1,6 +1,7 @@
 #!/usr/bin/python3
 print("Preparing...")
 import tensorflow as tf
+import os
 from tqdm import tqdm
 import generate
 import forward
@@ -35,28 +36,22 @@ def save_bias(fp, val):
         print(val[i], file=fp)
 
 
-def save_para(folder, paras):
+def save_para(folder, paras, names, info):
-    with open(folder + "/conv1_w", "w") as fp:
+    os.system("mkdir %s/%s" % (folder, info))
-        save_kernal(fp, paras[0])
+    for para, name in zip(paras, names):
-    with open(folder + "/conv1_b", "w") as fp:
+        fp = open("%s/%s/%s" % (folder, info, name), "w")
-        save_bias(fp, paras[1])
+        if name[-1:] == "b":
-    with open(folder + "/conv2_w", "w") as fp:
+            save_bias(fp, para)
-        save_kernal(fp, paras[2])
+        elif name[:2] == "fc":
-    with open(folder + "/conv2_b", "w") as fp:
+            save_weight_mat(fp, para)
-        save_bias(fp, paras[3])
+        elif name[:4] == "conv":
-    with open(folder + "/fc1_w", "w") as fp:
+            save_kernal(fp, para)
-        save_weight_mat(fp, paras[4])
+        fp.close()
-    with open(folder + "/fc1_b", "w") as fp:
-        save_bias(fp, paras[5])
-    with open(folder + "/fc2_w", "w") as fp:
-        save_weight_mat(fp, paras[6])
-    with open(folder + "/fc2_b", "w") as fp:
-        save_bias(fp, paras[7])
 
 
-STEPS = 60000
+STEPS = 50000
-BATCH = 50
+BATCH = 30
-LEARNING_RATE_BASE  = 0.001
+LEARNING_RATE_BASE  = 0.0005
 LEARNING_RATE_DECAY = 0.99
 MOVING_AVERAGE_DECAY = 0.99
 
@@ -65,7 +60,7 @@ def train(dataset, show_bar=False):
     x = tf.placeholder(tf.float32, [None, generate.SRC_ROWS, generate.SRC_COLS, generate.SRC_CHANNELS])
     y_= tf.placeholder(tf.float32, [None, forward.OUTPUT_NODES])
     keep_rate = tf.placeholder(tf.float32)
-    nodes, vars = forward.forward(x, 0.01)
+    nodes, vars, vars_name = forward.forward(x, 0.01)
     y = nodes[-1]
 
     ce  = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=y, labels=tf.argmax(y_, 1))
@@ -101,12 +96,11 @@ def train(dataset, show_bar=False):
 
             _, loss_value, step = sess.run(
                 [train_op, loss, global_step],
-                feed_dict={x: images_samples, y_: labels_samples, keep_rate:0.2}
+                feed_dict={x: images_samples, y_: labels_samples, keep_rate:0.4}
             )
 
-            if (i-1) % 100 == 0:
+            if step % 500 == 0:
-                if (i-1) % 500 == 0:
+                test_images, test_labels = dataset.sample_test_sets(6000)
-                    test_images, test_labels = dataset.sample_test_sets(5000)
                 test_acc, output = sess.run([accuracy, y], feed_dict={x: test_images, y_: test_labels, keep_rate:1.0})
                 output = np.argmax(output, axis=1)
                 real = np.argmax(test_labels, axis=1)
@@ -115,7 +109,7 @@ def train(dataset, show_bar=False):
                     print("label: %d, precise: %f, recall: %f" %
                         (n, np.mean(real[output==n]==n), np.mean(output[real==n]==n)))
 
-                    train_images, train_labels = dataset.sample_train_sets(5000)
+                train_images, train_labels = dataset.sample_train_sets(6000)
                 train_acc, output = sess.run([accuracy, y], feed_dict={x: train_images, y_: train_labels, keep_rate:1.0})
                 output = np.argmax(output, axis=1)
                 real = np.argmax(train_labels, axis=1)
@@ -124,12 +118,16 @@ def train(dataset, show_bar=False):
                     print("label: %d, precise: %f, recall: %f" %
                           (n, np.mean(real[output==n]==n), np.mean(output[real==n]==n)))
                 print("\n")
-
+                if train_acc >= 0.99 and test_acc >= 0.99:
-                bar.set_postfix({"loss": loss_value, "train_acc": train_acc, "test_acc": test_acc})
-
                     vars_val = sess.run(vars)
-        save_para("/home/xinyang/Workspace/RM_auto-aim/tools/para", vars_val)
+                    save_para(
-        print("save done!")
+                        "model",
+                        vars_val,
+                        vars_name,
+                        "steps:%d-train_acc:%f-test_acc:%f" % (step, train_acc, test_acc)
+                    )
+                bar.set_postfix({"loss": loss_value, "train_acc": train_acc, "test_acc": test_acc})
+        # print("save done!")
 
         # pred = sess.run(y, feed_dict={x: test_images, keep_rate:1.0})
 

tools/TrainCNN/forward.py

@@ -29,16 +29,22 @@ def max_pool_2x2(x):
 CONV1_KERNAL_SIZE = 5
 
 # 第一层卷积输出通道数
-CONV1_OUTPUT_CHANNELS = 8
+CONV1_OUTPUT_CHANNELS = 4
 
 # 第二层卷积核大小
 CONV2_KERNAL_SIZE = 3
 
 # 第二层卷积输出通道数
-CONV2_OUTPUT_CHANNELS = 16
+CONV2_OUTPUT_CHANNELS = 6
+
+# 第三层卷积核大小
+CONV3_KERNAL_SIZE = 3
+
+# 第三层卷积输出通道数
+CONV3_OUTPUT_CHANNELS = 8
 
 # 第一层全连接宽度
-FC1_OUTPUT_NODES = 100
+FC1_OUTPUT_NODES = 50
 
 # 第二层全连接宽度（输出标签类型数）
 FC2_OUTPUT_NODES = 15
@@ -49,6 +55,7 @@ OUTPUT_NODES = FC2_OUTPUT_NODES
 
 def forward(x, regularizer=None, keep_rate=tf.constant(1.0)):
     vars = []
+    vars_name = []
     nodes = []
 
     conv1_w = get_weight(
@@ -57,7 +64,10 @@ def forward(x, regularizer=None, keep_rate=tf.constant(1.0)):
     conv1_b = get_bias([CONV1_OUTPUT_CHANNELS])
     conv1   = tf.nn.relu(tf.nn.bias_add(conv2d(x, conv1_w), conv1_b))
     pool1   = avg_pool_2x2(conv1)
+    print("conv1: ", conv1.shape)
+    print("pool1: ", pool1.shape)
     vars.extend([conv1_w, conv1_b])
+    vars_name.extend(["conv1_w", "conv1_b"])
     nodes.extend([conv1, pool1])
 
     conv2_w = get_weight(
@@ -66,27 +76,41 @@ def forward(x, regularizer=None, keep_rate=tf.constant(1.0)):
     conv2_b = get_bias([CONV2_OUTPUT_CHANNELS])
     conv2 = tf.nn.relu(tf.nn.bias_add(conv2d(pool1, conv2_w), conv2_b))
     pool2 = avg_pool_2x2(conv2)
+    print("conv2: ", conv2.shape)
+    print("pool2: ", pool2.shape)
     vars.extend([conv2_w, conv2_b])
+    vars_name.extend(["conv2_w", "conv2_b"])
     nodes.extend([conv2, pool2])
 
-    pool_shape = pool2.get_shape().as_list()
+    conv3_w = get_weight(
-    node = pool_shape[1] * pool_shape[2] * pool_shape[3]
+        [CONV3_KERNAL_SIZE, CONV3_KERNAL_SIZE, CONV2_OUTPUT_CHANNELS, CONV3_OUTPUT_CHANNELS]
-    reshaped = tf.reshape(pool2, [-1, node])
+    )
+    conv3_b = get_bias([CONV3_OUTPUT_CHANNELS])
+    conv3 = tf.nn.relu(tf.nn.bias_add(conv2d(pool2, conv3_w), conv3_b))
+    print("conv3: ", conv3.shape)
+    vars.extend([conv3_w, conv3_b])
+    vars_name.extend(["conv3_w", "conv3_b"])
+    nodes.extend([conv3])
+
+    conv_shape = conv3.get_shape().as_list()
+    node = conv_shape[1] * conv_shape[2] * conv_shape[3]
+    reshaped = tf.reshape(conv3, [-1, node])
     reshaped = tf.nn.dropout(reshaped, keep_rate)
+    print("reshaped: ", reshaped.shape)
 
     fc1_w = get_weight([node, FC1_OUTPUT_NODES], regularizer)
     fc1_b = get_bias([FC1_OUTPUT_NODES])
     fc1   = tf.nn.relu(tf.matmul(reshaped, fc1_w) + fc1_b)
-    fc1   = tf.nn.dropout(fc1, keep_rate)
     vars.extend([fc1_w, fc1_b])
+    vars_name.extend(["fc1_w", "fc1_b"])
     nodes.extend([fc1])
 
     fc2_w = get_weight([FC1_OUTPUT_NODES, FC2_OUTPUT_NODES], regularizer)
     fc2_b = get_bias([FC2_OUTPUT_NODES])
-#    fc2   = tf.nn.softmax(tf.matmul(fc1, fc2_w) + fc2_b)
     fc2 = tf.matmul(fc1, fc2_w) + fc2_b
     vars.extend([fc2_w, fc2_b])
+    vars_name.extend(["fc2_w", "fc2_b"])
     nodes.extend([fc2])
 
-    return nodes, vars
+    return nodes, vars, vars_name
 

tools/para/conv1_b

@@ -1,9 +1,5 @@
-8
+4
--0.19897088
+0.30789205
-2.2773967
+-0.043525748
--0.07212669
+0.2050164
--0.22893764
+0.74044687
--0.022769619
-0.9122422
-1.3221853
--0.21709026

tools/para/conv1_w

@@ -1,604 +1,304 @@
 3
-8
+4
 5
 5
--0.42449534
+-0.10729549
--0.14145629
+0.09332296
-0.12018829
+0.16133511
-0.4759054
+-0.030649675
-0.54840434
+0.06557881
-0.28412497
+-0.053242918
-0.40357414
+0.19213735
-0.6718681
+0.15569621
-0.92518467
+0.3419906
-0.68381476
+0.1786888
-0.28161842
+0.19507813
-0.5942867
+0.24212381
-0.594364
+0.4231631
-0.7217546
+0.08838954
-0.77334726
+0.14048508
-0.18300368
+0.15044038
-0.3455078
+0.28268903
-0.270947
+0.13327996
-0.3452664
+0.15814759
-0.3419373
+-0.112561114
--0.07475002
+0.056091413
--0.23191033
+0.16789797
--0.5056218
+-0.11966196
--0.5653023
+0.038136892
--0.49549124
+-0.14447927
--0.356733
+0.5679466
--0.70811594
+0.48397887
--0.6640918
+0.70058227
--0.5230208
+0.5070652
--0.5567686
+0.62130994
--0.34954152
+0.5227163
--0.49958408
+0.6784681
--0.31039435
+0.5526853
--0.55455387
+0.5210595
--0.3291702
+0.396152
--0.0030061502
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--0.13863257
+0.459686
--0.40160847
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--0.53651094
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--0.24113898
+0.36256674
--0.1733842
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--0.2476929
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--0.5121031
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--0.47203135
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--0.43864155
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--0.058570273
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--0.33658004
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--0.6619966
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--0.73419136
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--0.7045188
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+-0.0047787298
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+-0.04889371
-0.8347145
+-0.065829955
-0.7488403
+-0.04788549
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+-0.33305293
--0.90990585
+-0.19116391
--0.6184357
+-0.28003454
--0.1404704
+-0.0820173
-0.20029725
+-0.26577657
-0.23383331
+-0.11826798
--0.5078964
+-0.39622292
-0.07829093
+-0.36510658
-0.41757903
+-0.11750732
-0.5164328
+-0.24734864
-0.5336988
+-0.2803419
--0.21864063
+-0.1519
-0.07738885
+-0.23317525
-0.46755105
+-0.007902931
-0.6110745
+-0.16537969
-0.64779043
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--0.17151009
+-0.023329364
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--0.014565518
+-0.24868572
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+-0.029912082
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--0.42132428
+-0.31355116
--0.4472937
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--0.686964
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--0.6123315
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--0.36468038
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--0.6651138
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--0.6533556
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--0.8055196
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--0.70568013
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--0.59960866
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--0.38753736
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--0.6284249
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--0.64662766
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--0.6305302
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--0.5739134
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--0.8985509
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--0.5367204
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--0.17939755
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--0.29208726
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--0.6158809
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--0.87346137
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--0.80676347
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--0.70183337
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--0.6015298
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-1.3865718
+-0.2411913
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