Merge remote-tracking branch 'origin/master'

CMakeLists.txt

@@ -1,34 +1,34 @@
-cmake_minimum_required(VERSION 3.5)
+CMAKE_MINIMUM_REQUIRED(VERSION 3.5)
 
-project(AutoAim)
+PROJECT(AutoAim)
-set(CMAKE_CXX_STANDARD 11)
+SET(CMAKE_CXX_STANDARD 11)
-SET(CMAKE_BUILD_TYPE DEBUG)
+SET(CMAKE_BUILD_TYPE RELEASE)
+SET(CMAKE_CXX_FLAGS "-DPROJECT_DIR=\"\\\"${PROJECT_SOURCE_DIR}\\\"\"")
 
 FIND_PROGRAM(CCACHE_FOUND ccache)
 IF(CCACHE_FOUND)
-    set_property(GLOBAL PROPERTY RULE_LAUNCH_COMPILE ccache)
+    SET_PROPERTY(GLOBAL PROPERTY RULE_LAUNCH_COMPILE ccache)
-    set_property(GLOBAL PROPERTY RULE_LAUNCH_LINK ccache)
+    SET_PROPERTY(GLOBAL PROPERTY RULE_LAUNCH_LINK ccache)
-    message("< Use ccache for compiler >")
+    MESSAGE("< Use ccache for compiler >")
 ENDIF()
 
 FIND_PACKAGE(OpenCV 3 REQUIRED)
 FIND_PACKAGE(Eigen3 REQUIRED)
 FIND_PACKAGE(Threads)
 
-include_directories( ${EIGEN3_INCLUDE_DIR} )
+INCLUDE_DIRECTORIES(${EIGEN3_INCLUDE_DIR})
-include_directories( ${PROJECT_SOURCE_DIR}/energy/include )
+INCLUDE_DIRECTORIES(${PROJECT_SOURCE_DIR}/energy/include)
-include_directories( ${PROJECT_SOURCE_DIR}/armor/include )
+INCLUDE_DIRECTORIES(${PROJECT_SOURCE_DIR}/armor/include)
-include_directories( ${PROJECT_SOURCE_DIR}/others/include )
+INCLUDE_DIRECTORIES(${PROJECT_SOURCE_DIR}/others/include)
 
 FILE(GLOB_RECURSE sourcefiles "others/src/*.cpp" "energy/src/*cpp" "armor/src/*.cpp")
-add_executable(run main.cpp ${sourcefiles} )
+ADD_EXECUTABLE(run main.cpp ${sourcefiles} )
 
 TARGET_LINK_LIBRARIES(run ${CMAKE_THREAD_LIBS_INIT})
 TARGET_LINK_LIBRARIES(run ${OpenCV_LIBS})
 TARGET_LINK_LIBRARIES(run ${PROJECT_SOURCE_DIR}/others/libMVSDK.so)
 
-# Todo
+ADD_CUSTOM_TARGET(train COMMAND "gnome-terminal" "-x" "bash" "-c" "\"${PROJECT_SOURCE_DIR}/tools/TrainCNN/backward.py\"" )
-# ADD_CUSTOM_TARGET(bind-monitor COMMAND "")
 
 # Todo
-# ADD_CUSTOM_TARGET(train COMMAND "")
+# ADD_CUSTOM_TARGET(bind-monitor COMMAND "")

armor/src/armor_finder/armor_finder.cpp

@@ -2,47 +2,42 @@
 // Created by xinyang on 19-3-27.
 //
 #include <log.h>
+#include <options/options.h>
+#include <show_images/show_images.h>
+#include <opencv2/highgui.hpp>
 #include <armor_finder/armor_finder.h>
 
 ArmorFinder::ArmorFinder(EnemyColor color, Uart &u, string paras_folder) :
-                            uart(u),
+            uart(u),
-                            enemy_color(color),
+            enemy_color(color),
-                            state(STANDBY_STATE),
+            state(STANDBY_STATE),
-                            classifier(std::move(paras_folder)),
+            classifier(std::move(paras_folder)),
-                            contour_area(0)
+            contour_area(0)
-                            {
+            {
-    auto para = TrackerToUse::Params();
-    para.desc_npca = 1;
-    para.desc_pca = 0;
-    tracker = TrackerToUse::create(para);
-    if(!tracker){
-        LOGW("Tracker Not init");
-    }
 }
 
 void ArmorFinder::run(cv::Mat &src) {
     cv::Mat src_use;
-//    if (src.type() == CV_8UC3) {
+    src_use = src.clone();
-//        cv::cvtColor(src, src_use, CV_RGB2GRAY);
-//    }else{
-        src_use = src.clone();
-//    }
     cv::cvtColor(src_use, src_gray, CV_RGB2GRAY);
 
-    stateSearchingTarget(src_use);
+    if(show_armor_box){
-    return;
+        showArmorBox("box", src, armor_box);
-
+        cv::waitKey(1);
+    }
+//    stateSearchingTarget(src_use);
+//    return;
     switch (state){
         case SEARCHING_STATE:
             if(stateSearchingTarget(src_use)){
                 if((armor_box & cv::Rect2d(0, 0, 640, 480)) == armor_box) {
-                    cv::Mat roi = src_use.clone()(armor_box);
+                    if(!classifier){
-                    cv::threshold(roi, roi, 200, 255, cv::THRESH_BINARY);
+                        cv::Mat roi = src_use.clone()(armor_box), roi_gray;
-                    contour_area = cv::countNonZero(roi);
+                        cv::cvtColor(roi, roi_gray, CV_RGB2GRAY);
-                    auto para = TrackerToUse::Params();
+                        cv::threshold(roi_gray, roi_gray, 180, 255, cv::THRESH_BINARY);
-                    para.desc_npca = 1;
+                        contour_area = cv::countNonZero(roi_gray);
-                    para.desc_pca = 0;
+                    }
-                    tracker = TrackerToUse::create(para);
+                    tracker = TrackerToUse::create();
                     tracker->init(src_use, armor_box);
                     state = TRACKING_STATE;
                     LOGW("into track");
@@ -50,7 +45,7 @@ void ArmorFinder::run(cv::Mat &src) {
             }
             break;
         case TRACKING_STATE:
-            if(!stateTrackingTarget(src_gray)){
+            if(!stateTrackingTarget(src_use)){
                 state = SEARCHING_STATE;
                 //std::cout << "into search!" << std::endl;
             }

armor/src/armor_finder/classifier/classifier.cpp

@@ -259,7 +259,7 @@ Classifier::Classifier(const string &folder) : state(true){
     fc2_w = load_fc_w(folder+"fc2_w");
     fc2_b = load_fc_b(folder+"fc2_b");
     if(state){
-        LOGM("Load paras success!");
+        LOGM("Load para success!");
     }
 }
 

armor/src/armor_finder/state_machine/searching_state/searching_state.cpp

@@ -7,7 +7,6 @@
 #include "image_process/image_process.h"
 #include <log.h>
 #include <show_images/show_images.h>
-
 #include <options/options.h>
 
 typedef std::vector<LightBlob> LightBlobs;
@@ -32,11 +31,10 @@ static void pipelineLightBlobPreprocess(cv::Mat &src) {
 }
 
 static bool findLightBlobs(const cv::Mat &src, LightBlobs &light_blobs) {
-    static cv::Mat src_bin;
+//    static cv::Mat src_bin;
 
-    cv::threshold(src, src_bin, 80, 255, CV_THRESH_BINARY);
     std::vector<std::vector<cv::Point> > light_contours;
-    cv::findContours(src_bin, light_contours, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_NONE);
+    cv::findContours(src, light_contours, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_NONE);
     for (auto &light_contour : light_contours) {
         cv::RotatedRect rect = cv::minAreaRect(light_contour);
         if(isValidLightBlob(rect)){
@@ -117,7 +115,7 @@ static bool findArmorBoxes(LightBlobs &light_blobs, std::vector<cv::Rect2d> &arm
             min_x = fmin(rect_left.x, rect_right.x);
             max_x = fmax(rect_left.x + rect_left.width, rect_right.x + rect_right.width);
             min_y = fmin(rect_left.y, rect_right.y) - 5;
-            max_y = fmax(rect_left.y + rect_left.height, rect_right.y + rect_right.height) + 5;
+            max_y = fmax(rect_left.y + rect_left.height, rect_right.y + rect_right.height);
             if (min_x < 0 || max_x > 640 || min_y < 0 || max_y > 480) {
                 continue;
             }
@@ -149,19 +147,21 @@ bool judge_light_color(std::vector<LightBlob> &light, std::vector<LightBlob> &co
 }
 
 bool ArmorFinder::stateSearchingTarget(cv::Mat &src) {
-    cv::Mat split, pmsrc=src.clone();
+    cv::Mat split, pmsrc=src.clone(), src_bin;
     LightBlobs light_blobs, pm_light_blobs, light_blobs_real;
     std::vector<cv::Rect2d> armor_boxes, boxes_one, boxes_two, boxes_three;
 
 //    cv::resize(src, pmsrc, cv::Size(320, 240));
     imageColorSplit(src, split, enemy_color);
-    imagePreProcess(split);
+    cv::threshold(split, src_bin, 130, 255, CV_THRESH_BINARY);
-    cv::resize(split, split, cv::Size(640, 480));
+    imagePreProcess(src_bin);
+//    cv::imshow("bin", src_bin);
+//    cv::resize(split, split, cv::Size(640, 480));
 //    pipelineLightBlobPreprocess(pmsrc);
 //    if(!findLightBlobs(pmsrc, pm_light_blobs)){
 //        return false;
 //    }
-    if(!findLightBlobs(split, light_blobs)){
+    if(!findLightBlobs(src_bin, light_blobs)){
         return false;
     }
 //    if(!judge_light_color(light_blobs, pm_light_blobs, light_blobs_real)){
@@ -182,10 +182,7 @@ bool ArmorFinder::stateSearchingTarget(cv::Mat &src) {
         for(auto box : armor_boxes){
             cv::Mat roi = src(box).clone();
             cv::resize(roi, roi, cv::Size(48, 36));
-//            cv::imshow("roi", roi);
-//            cv::waitKey(0);
             int c = classifier(roi);
-//            cout << c << endl;
             switch(c){
                 case 1:
                     boxes_one.emplace_back(box);
@@ -204,6 +201,8 @@ bool ArmorFinder::stateSearchingTarget(cv::Mat &src) {
             armor_box = boxes_two[0];
         }else if(!boxes_three.empty()){
             armor_box = boxes_three[0];
+        } else{
+            return false;
         }
         if(show_armor_box){
             showArmorBoxClass("class", src, boxes_one, boxes_two, boxes_three);
@@ -211,10 +210,6 @@ bool ArmorFinder::stateSearchingTarget(cv::Mat &src) {
     }else{
         armor_box = armor_boxes[0];
     }
-    if(show_armor_box){
-        showArmorBox("box", src, armor_box);
-        cv::waitKey(1);
-    }
     if(split.size() == cv::Size(320, 240)){
         armor_box.x *= 2;
         armor_box.y *= 2;

armor/src/armor_finder/state_machine/tracking_state/tracking_state.cpp

@@ -5,17 +5,27 @@
 #include <armor_finder/armor_finder.h>
 
 bool ArmorFinder::stateTrackingTarget(cv::Mat &src) {
-    auto last = armor_box;
+    if(!tracker->update(src, armor_box)){
-    tracker->update(src, armor_box);
+        return false;
-
+    }
     if((armor_box & cv::Rect2d(0, 0, 640, 480)) != armor_box){
         return false;
     }
-    cv::Mat roi = src(armor_box);
-    threshold(roi, roi, 200, 255, cv::THRESH_BINARY);
 
-    if(abs(cv::countNonZero(roi) - contour_area) > contour_area * 0.3){
+    cv::Mat roi = src.clone()(armor_box);
-        return false;
+    if(classifier){
+        cv::resize(roi, roi, cv::Size(48, 36));
+        if(classifier(roi) == 0){
+            return false;
+        }
+    }else{
+        cv::Mat roi_gray;
+        cv::cvtColor(roi, roi_gray, CV_RGB2GRAY);
+        cv::threshold(roi_gray, roi_gray, 180, 255, cv::THRESH_BINARY);
+        contour_area = cv::countNonZero(roi_gray);
+        if(abs(cv::countNonZero(roi_gray) - contour_area) > contour_area * 0.3){
+            return false;
+        }
     }
     return sendBoxPosition();
 }

main.cpp

@@ -13,32 +13,33 @@
 #include <camera/wrapper_head.h>
 #include <armor_finder/armor_finder.h>
 #include <options/options.h>
+#include <thread>
+
+//#define DO_NOT_CNT_TIME
 #include <log.h>
 
-#include <thread>
+#define PATH PROJECT_DIR
+#define ENERGY_STATE 1
+#define ARMOR_STATE 0
 
 using namespace cv;
 using namespace std;
 
-#define ENERGY_STATE 1
-#define ARMOR_STATE 0
 
-int state = ENERGY_STATE;
+int state = ARMOR_STATE;
 float curr_yaw=0, curr_pitch=0;
 float mark_yaw=0, mark_pitch=0;
 int mark = 0;
 
 void uartReceive(Uart* uart);
 
-int main(int argc, char *argv[])
+int main(int argc, char *argv[]){
-{
     process_options(argc, argv);
     Uart uart;
     thread receive(uartReceive, &uart);
 	bool flag = true;
 
-	while (flag)
+	while (flag){
-	{
         int ally_color = ALLY_RED;
         int energy_part_rotation = CLOCKWISE;
 
@@ -51,46 +52,50 @@ int main(int argc, char *argv[])
 		WrapperHead *video_armor;
         WrapperHead *video_energy;
 		if(from_camera) {
-//            video_armor = new CameraWrapper();
+            video_armor = new CameraWrapper(0);
-            video_energy = new CameraWrapper();
+//            video_energy = new CameraWrapper(1);
         }else {
-            video_armor = new VideoWrapper("r_l_640.avi");
+            video_armor = new VideoWrapper("/home/xinyang/Desktop/Video.mp4");
-            video_energy = new VideoWrapper("r_l_640.avi");
+            video_energy = new VideoWrapper("/home/xinyang/Desktop/Video.mp4");
         }
-		if (video_energy->init()) {
+		if (video_armor->init()) {
 			cout << "Video source initialization successfully." << endl;
 		}
 
 		Mat energy_src, armor_src;
 
-		ArmorFinder armorFinder(ENEMY_BLUE, uart, "/home/xinyang/Desktop/AutoAim/tools/para/");
+		ArmorFinder armorFinder(ENEMY_BLUE, uart, PATH"/tools/para/");
 
         Energy energy(uart);
         energy.setAllyColor(ally_color);
         energy.setRotation(energy_part_rotation);
 
-		while (video_energy->read(energy_src) && video_energy->read(armor_src))
+        bool ok=true;
-		{
+
-		    if(show_origin) {
+		while (ok){
-                imshow("enery src", energy_src);
+		    CNT_TIME(WORD_LIGHT_CYAN, "Total", {
-                imshow("armor src", armor_src);
+                ok = video_armor->read(energy_src) && video_armor->read(armor_src);
-            }
+                if (show_origin) {
-            if(state == ENERGY_STATE){
+                    imshow("enery src", energy_src);
-                if(from_camera==0){
+                    imshow("armor src", armor_src);
-                    energy.extract(energy_src);
                 }
-                energy.run(energy_src);
+                if (state == ENERGY_STATE) {
-            }else{
+                    if (from_camera == 0) {
-                CNT_TIME(WORD_LIGHT_BLUE, "Armor Time", {
+                        energy.extract(energy_src);
-                    armorFinder.run(armor_src);
+                    }
-                });
+                    energy.run(energy_src);
-            }
+                } else {
-			if (waitKey(1) == 'q') {
+                    CNT_TIME(WORD_LIGHT_BLUE, "Armor Time", {
-				flag = false;
+                        armorFinder.run(armor_src);
-				break;
+                    });
-			}
+                }
+                if (waitKey(1) == 'q') {
+                    flag = false;
+                    break;
+                }
+            });
 		}
-		delete video_energy;
+		delete video_armor;
 		cout << "Program fails. Restarting" << endl;
 	}
 

others/include/camera/camera_wrapper.h

@@ -36,11 +36,7 @@ private:
 
 
 public:
-    CameraWrapper();
+    CameraWrapper(int camera_mode=1, const std::string &n="NULL");
-    CameraWrapper(const std::string &n);
-    CameraWrapper(int camera_mode);
-    CameraWrapper(const std::string &n,int camera_mode);
-
     ~CameraWrapper() final;
 
     bool init() final;

others/src/camera/camera_wrapper.cpp

@@ -9,39 +9,13 @@ using std::cout;
 using std::endl;
 using namespace cv;
 
-CameraWrapper::CameraWrapper():
+CameraWrapper::CameraWrapper(int camera_mode, const std::string &n):
-        name("NULL"),
-        mode(1),
-        camera_cnts(2),
-        camera_status(-1),
-        iplImage(nullptr),
-        channel(3){
-}
-
-CameraWrapper::CameraWrapper(const std::string &n):
-        name(n),
-        mode(1),
-        camera_cnts(2),
-        camera_status(-1),
-        iplImage(nullptr),
-        channel(3){
-}
-
-CameraWrapper::CameraWrapper(int camera_mode):
-        name("NULL"),
-        mode(camera_mode),
-        camera_cnts(2),
-        camera_status(-1),
-        iplImage(nullptr),
-        channel(3){
-}
-
-CameraWrapper::CameraWrapper(const std::string &n,int camera_mode):
         name(n),
         mode(camera_mode),
         camera_cnts(2),
         camera_status(-1),
         iplImage(nullptr),
+        rgb_buffer(nullptr),
         channel(3){
 }
 
@@ -51,9 +25,9 @@ bool CameraWrapper::init() {
 
     //枚举设备，并建立设备列表
     int camera_enumerate_device_status =  CameraEnumerateDevice(camera_enum_list, &camera_cnts);
-    //cout<<"camera enumerate device status: "<<camera_enumerate_device_status<<endl;
+    if(camera_enumerate_device_status != CAMERA_STATUS_SUCCESS){
-    //cout<<"camera number: "<<camera_cnts<<endl;
+        LOGE("CameraEnumerateDevice fail with %d!", camera_enumerate_device_status);
-
+    }
     //没有连接设备
     if (camera_cnts == 0) {
         LOGE("No camera device detected!");
@@ -67,16 +41,17 @@ bool CameraWrapper::init() {
         camera_status = CameraInit(&camera_enum_list[i], -1, -1, &h_camera);
         if (camera_status != CAMERA_STATUS_SUCCESS) {
             LOGE("Camera 0 initialization failed with code %d. See camera_status.h to find the code meaning.", camera_status);
-            return false;
+            goto stop;
         }
         CameraGetFriendlyName(h_camera, camera_name);
         if(name=="NULL" || strcmp(name.data(), camera_name)==0){
             break;
         }
+stop:
         CameraUnInit(h_camera);
     }
     if(i >= camera_cnts){
-        LOGE("No device name %s!", name.data());
+        LOGE("No device name %s or device open error!!", name.data());
         return false;
     }
 
@@ -90,11 +65,11 @@ bool CameraWrapper::init() {
             tCapability.sResolutionRange.iWidthMax * 3);
     if(mode == 0){
         // 不使用自动曝光
-        CameraSetAeState(h_camera, false);
+        CameraSetAeState(h_camera, true);
         // 曝光时间10ms
-        CameraSetExposureTime(h_camera, 10000);
+//        CameraSetExposureTime(h_camera, 10000);
         // 模拟增益4
-        CameraSetAnalogGain(h_camera, 63);
+        CameraSetAnalogGain(h_camera, 64);
         // 使用预设LUT表
         CameraSetLutMode(h_camera, LUTMODE_PRESET);
         // 抗频闪
@@ -188,6 +163,7 @@ CameraWrapper::~CameraWrapper()
 {
     CameraUnInit(h_camera);
     //注意，先反初始化后再free
-    free(rgb_buffer);
+    if(rgb_buffer != nullptr)
+        free(rgb_buffer);
 }
 

tools/TrainCNN/backward.py

@@ -1,9 +1,12 @@
+#!/usr/bin/python3
+print("Preparing...")
 import tensorflow as tf
-from progressive.bar import Bar
+from tqdm import tqdm
 import generate
 import forward
 import cv2
 import numpy as np
+print("Finish!")
 
 def save_kernal(fp, val):
     print(val.shape[2], file=fp)
@@ -51,7 +54,7 @@ def save_para(folder, paras):
 
 
 STEPS = 20000
-BATCH = 10
+BATCH = 30
 LEARNING_RATE_BASE  = 0.01
 LEARNING_RATE_DECAY = 0.99
 MOVING_AVERAGE_DECAY = 0.99
@@ -85,18 +88,13 @@ def train(dataset, show_bar=False):
 
     correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
     accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
-    acc = 0
 
     with tf.Session() as sess:
         init_op = tf.global_variables_initializer()
         sess.run(init_op)
 
-        if show_bar:
+        bar = tqdm(range(STEPS), dynamic_ncols=True)
-            bar = Bar(max_value=STEPS, width=u'50%')
+        for i in bar:
-            bar.cursor.clear_lines(1)
-            bar.cursor.save()
-
-        for i in range(STEPS):
             images_samples, labels_samples = dataset.sample_train_sets(BATCH)
 
             _, loss_value, step = sess.run(
@@ -107,18 +105,21 @@ def train(dataset, show_bar=False):
             if i % 100 == 0:
                 if i % 1000 == 0:
                     acc = sess.run(accuracy, feed_dict={x: test_images, y_: test_labels})
+                bar.set_postfix({"loss": loss_value, "acc": acc})
 
-            if show_bar:
+
-                bar.title = "step: %d, loss: %f, acc: %f" % (step, loss_value, acc)
+            # if show_bar:
-                bar.cursor.restore()
+            #     bar.title = "step: %d, loss: %f, acc: %f" % (step, loss_value, acc)
-                bar.draw(value=i+1)
+            #     bar.cursor.restore()
+            #     bar.draw(value=i+1)
 
         # video = cv2.VideoCapture("/home/xinyang/Desktop/Video.mp4")
         # _ = True
         # while _:
         #     _, frame = video.read()
         #     cv2.imshow("Video", frame)
-        #     if cv2.waitKey(10) == 113:
+        #     k = cv2.waitKey(10)
+        #     if k == ord(" "):
         #         bbox = cv2.selectROI("frame", frame, False)
         #         print(bbox)
         #         roi = frame[bbox[1]:bbox[1]+bbox[3], bbox[0]:bbox[0]+bbox[2]]
@@ -131,6 +132,22 @@ def train(dataset, show_bar=False):
         #         res = sess.run(y, feed_dict={x: roi})
         #         res = res.reshape([forward.OUTPUT_NODES])
         #         print(np.argmax(res))
+        #     elif k==ord("q"):
+        #         break
+        # keep = True
+        # while keep:
+        #     n = input()
+        #     im = cv2.imread(n)
+        #     im = cv2.resize(im, (48, 36))
+        #     cv2.imshow("im", im)
+        #     if cv2.waitKey(0) == ord("q"):
+        #         keep = False
+        #     im = im.astype(np.float32)
+        #     im /= 255.0
+        #     im = im.reshape([1, 36, 48, 3])
+        #     res = sess.run(y, feed_dict={x: im})
+        #     res = res.reshape([forward.OUTPUT_NODES])
+        #     print(np.argmax(res))
 
         vars_val = sess.run(vars)
         save_para("/home/xinyang/Desktop/AutoAim/tools/para", vars_val)
@@ -139,5 +156,7 @@ def train(dataset, show_bar=False):
 
 
 if __name__ == "__main__":
-    dataset = generate.DataSet("/home/xinyang/Desktop/DataSets")
+    print("Loading data sets...")
+    dataset = generate.DataSet("/home/xinyang/Desktop/DataSets/box")
+    print("Finish!")
     train(dataset, show_bar=True)

tools/TrainCNN/forward.py

@@ -29,13 +29,13 @@ def max_pool_2x2(x):
 CONV1_KERNAL_SIZE = 5
 
 # 第一层卷积输出通道数
-CONV1_OUTPUT_CHANNELS = 4
+CONV1_OUTPUT_CHANNELS = 6
 
 # 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 = 8
+CONV2_OUTPUT_CHANNELS = 10
 
 # 第一层全连接宽度
 FC1_OUTPUT_NODES = 16

tools/TrainCNN/generate.py

@@ -3,6 +3,9 @@ import os
 import cv2
 import random
 from forward import OUTPUT_NODES
+import sys
+import os
+from tqdm import tqdm
 # 原图像行数
 SRC_ROWS = 36
 
@@ -12,6 +15,7 @@ SRC_COLS = 48
 # 原图像通道数
 SRC_CHANNELS = 3
 
+
 class DataSet:
     def __init__(self, folder):
         self.train_samples = []
@@ -21,10 +25,14 @@ class DataSet:
         self.generate_data_sets(folder)
 
     def file2nparray(self, name):
-        image = cv2.imread(name)
+        try:
-        image = cv2.resize(image, (SRC_COLS, SRC_ROWS))
+            image = cv2.imread(name)
-        image = image.astype(np.float32)
+            image = cv2.resize(image, (SRC_COLS, SRC_ROWS))
-        return image / 255.0
+            image = image.astype(np.float32)
+            return image / 255.0
+        except:
+            print(name)
+            sys.exit(-1)
 
     def id2label(self, id):
         a = np.zeros([OUTPUT_NODES])
@@ -36,13 +44,14 @@ class DataSet:
         for i in range(OUTPUT_NODES):
             dir = "%s/%d" % (folder, i)
             files = os.listdir(dir)
-            for file in files:
+            for file in tqdm(files, postfix={"loading id": i}, dynamic_ncols=True):
-                if random.random() > 0.2:
+                if file[-3:] == "jpg":
-                    self.train_samples.append(self.file2nparray("%s/%s" % (dir, file)))
+                    if random.random() > 0.2:
-                    self.train_labels.append(self.id2label(i))
+                        self.train_samples.append(self.file2nparray("%s/%s" % (dir, file)))
-                else:
+                        self.train_labels.append(self.id2label(i))
-                    self.test_samples.append(self.file2nparray("%s/%s" % (dir, file)))
+                    else:
-                    self.test_labels.append(self.id2label(i))
+                        self.test_samples.append(self.file2nparray("%s/%s" % (dir, file)))
+                        self.test_labels.append(self.id2label(i))
         self.train_samples = np.array(self.train_samples)
         self.train_labels = np.array(self.train_labels)
         self.test_samples = np.array(self.test_samples)

tools/monitor.sh

@@ -1,5 +1,8 @@
 #!/bin/sh
 
+# usage: monitor.sh "<the exe file's absolute path and its shell parameter>"
+# And then when ever the exe shutdonw it will be automatically restart
+
 exe=$1
 while true; do
 	state=`ps aux | grep "$1" | grep -v grep | grep -v $0`

tools/para/conv1_b

@@ -0,0 +1,7 @@
+6
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tools/para/conv1_w

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tools/para/conv2_b

@@ -0,0 +1,11 @@
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tools/para/conv2_w

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