fix bug

2019-07-09 00:03:06 +08:00
parent 960769689b
commit 13344f3e71
12 changed files with 12501 additions and 9 deletions
--- a/armor/src/armor_finder/searching_state/searching_state.cpp
+++ b/armor/src/armor_finder/searching_state/searching_state.cpp
@@ -268,7 +268,7 @@ bool ArmorFinder::stateSearchingTarget(cv::Mat &src) {
        color = channels[0];                /* 根据目标颜色进行通道提取 */
    else if (enemy_color == ENEMY_RED)      /*                      */
        color = channels[2];                /************************/
-    cv::threshold(color, src_bin, 170, 255, CV_THRESH_BINARY); // 二值化对应通道
+    cv::threshold(color, src_bin, 190, 255, CV_THRESH_BINARY); // 二值化对应通道
    imagePreProcess(src_bin);                                  // 开闭运算
    if (!findLightBlobs(src_bin, light_blobs)) {
        return false;
--- a/main.cpp
+++ b/main.cpp
@@ -21,10 +21,10 @@ using namespace std;
 mcu_data mcuData = {
        0,
        0,
-        SMALL_ENERGY_STATE,
+        ARMOR_STATE,
        0,
        1,
-        ENEMY_BLUE,
+        ENEMY_RED,
 };

 WrapperHead *video_gimble = nullptr;
@@ -111,7 +111,6 @@ int main(int argc, char *argv[]) {
                        energy.runBig(gimble_src);
                    }
                }
-                cv::waitKey(100);
            });
        } while (ok);

--- a/others/src/camera/camera_wrapper.cpp
+++ b/others/src/camera/camera_wrapper.cpp
@@ -76,7 +76,7 @@ bool CameraWrapper::init() {
 #elif defined(Linux)
    CameraSetAeState(h_camera, false);
    CameraSetExposureTime(h_camera, 10*1000);
-    CameraSetAnalogGain(h_camera, 30);
+    CameraSetAnalogGain(h_camera, 50);
    if(mode == 0){
        CameraSetGain(h_camera, 100, 100, 100);
        CameraSetLutMode(h_camera, LUTMODE_PRESET);
--- a/tools/TrainCNN/backward.py
+++ b/tools/TrainCNN/backward.py
@@ -70,8 +70,8 @@ def train(dataset, show_bar=False):
    nodes, vars = forward.forward(x, 0.01)
    y = nodes[-1]

-#    ce  = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=y, labels=tf.argmax(y_, 1))
-    ce  = tf.nn.weighted_cross_entropy_with_logits(logits=y, labels=tf.argmax(y_, 1), pos_weight=1)
+    ce  = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=y, labels=tf.argmax(y_, 1))
+#    ce  = tf.nn.weighted_cross_entropy_with_logits(logits=y, labels=tf.argmax(y_, 1), pos_weight=1)
    cem = tf.reduce_mean(ce)
    loss= cem + tf.add_n(tf.get_collection("losses"))

@@ -114,7 +114,7 @@ def train(dataset, show_bar=False):


        vars_val = sess.run(vars)
-        save_para("/home/xinyang/Desktop/RM_auto-aim/tools/para", vars_val)
+        save_para("/home/xinyang/Workspace/RM_auto-aim/tools/para", vars_val)
        print("save done!")
 #        nodes_val = sess.run(nodes, feed_dict={x:test_images})
 #        return vars_val, nodes_val
@@ -204,5 +204,5 @@ def train(dataset, show_bar=False):


 if __name__ == "__main__":
-    dataset = generate.DataSet("/home/xinyang/Desktop/box_cut")
+    dataset = generate.DataSet("/home/xinyang/Workspace/dataset/box_cut")
    train(dataset, show_bar=True)
--- a/tools/para/conv1_b
+++ b/tools/para/conv1_b
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--- a/tools/para/conv1_w
+++ b/tools/para/conv1_w
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--- a/tools/para/conv2_b
+++ b/tools/para/conv2_b
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--- a/tools/para/conv2_w
+++ b/tools/para/conv2_w
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--- a/tools/para/fc1_b
+++ b/tools/para/fc1_b
@@ -0,0 +1,17 @@
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--- a/tools/para/fc1_w
+++ b/tools/para/fc1_w
--- a/tools/para/fc2_b
+++ b/tools/para/fc2_b
@@ -0,0 +1,16 @@
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--- a/tools/para/fc2_w
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@@ -0,0 +1,242 @@
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