Minor fix

README.md

@@ -313,6 +313,8 @@ Training Yolo v3:
 
   * change line batch to [`batch=64`](https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L3)
   * change line subdivisions to [`subdivisions=8`](https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L4)
+  * change line max_batches to (`classes*2000`), f.e. [`max_batches=6000`](https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L20) if you train for 3 classes
+  * change line steps to 80% and 90% of max_batches, f.e. [`steps=4800,5400`](https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L22)
   * change line `classes=80` to your number of objects in each of 3 `[yolo]`-layers:
       * https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L610
       * https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L696
@@ -467,6 +469,8 @@ Or just train with `-map` flag:
 
 So you will see mAP-chart (red-line) in the Loss-chart Window. mAP will be calculated for each 4 Epochs using `valid=valid.txt` file that is specified in `obj.data` file (`1 Epoch = images_in_train_txt / batch` iterations)
 
+(to change the max x-axis value - change [`max_batches=`](https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L20) parameter to `2000*classes`, f.e. `max_batches=6000` for 3 classes)
+
 ![loss_chart_map_chart](https://hsto.org/webt/yd/vl/ag/ydvlagutof2zcnjodstgroen8ac.jpeg)
 
 Example of custom object detection: `darknet.exe detector test data/obj.data yolo-obj.cfg yolo-obj_8000.weights`

src/blas.c

@@ -234,7 +234,7 @@ void smooth_l1_cpu(int n, float *pred, float *truth, float *delta, float *error)
         }
         else {
             error[i] = 2*abs_val - 1;
-            delta[i] = (diff < 0) ? 1 : -1;
+            delta[i] = (diff > 0) ? 1 : -1;
         }
     }
 }

src/http_stream.cpp

@@ -489,11 +489,14 @@ public:
 };
 // ----------------------------------------
 
+static std::mutex mtx_mjpeg;
+
 struct mat_cv : cv::Mat { int a[0]; };
 
 void send_mjpeg(mat_cv* mat, int port, int timeout, int quality)
 {
     try {
+        std::lock_guard<std::mutex> lock(mtx_mjpeg);
         static MJPG_sender wri(port, timeout, quality);
         //cv::Mat mat = cv::cvarrToMat(ipl);
         wri.write(*mat);

src/image_opencv.cpp

@@ -862,7 +862,7 @@ void draw_detections_cv_v3(mat_cv* mat, detection *dets, int num, float thresh,
             }
         }
         if (class_id >= 0) {
-            int width = show_img->rows * .006;
+            int width = std::max(1.0f, show_img->rows * .002f);
 
             //if(0){
             //width = pow(prob, 1./2.)*10+1;
@@ -908,7 +908,8 @@ void draw_detections_cv_v3(mat_cv* mat, detection *dets, int num, float thresh,
             //int b_height = bot - top;
             //sprintf(labelstr, "%d x %d - w: %d, h: %d", b_x_center, b_y_center, b_width, b_height);
 
-            float const font_size = show_img->rows / 1200.F;
+            float const font_size = show_img->rows / 1000.F;
+            cv::Size const text_size = cv::getTextSize(labelstr, cv::FONT_HERSHEY_COMPLEX_SMALL, font_size, 1, 0);
             CvPoint pt1, pt2, pt_text, pt_text_bg1, pt_text_bg2;
             pt1.x = left;
             pt1.y = top;
@@ -917,8 +918,9 @@ void draw_detections_cv_v3(mat_cv* mat, detection *dets, int num, float thresh,
             pt_text.x = left;
             pt_text.y = top - 4;// 12;
             pt_text_bg1.x = left;
-            pt_text_bg1.y = top - (3 + 25 * font_size);
+            pt_text_bg1.y = top - (1 + 18 * font_size);
             pt_text_bg2.x = right;
+            if ((right - left) < text_size.width) pt_text_bg2.x = left + text_size.width;
             pt_text_bg2.y = top;
             CvScalar color;
             color.val[0] = red * 256;
@@ -952,7 +954,8 @@ void draw_detections_cv_v3(mat_cv* mat, detection *dets, int num, float thresh,
             cv::rectangle(*show_img, pt_text_bg1, pt_text_bg2, color, width, 8, 0);
             cv::rectangle(*show_img, pt_text_bg1, pt_text_bg2, color, CV_FILLED, 8, 0);    // filled
             cv::Scalar black_color = CV_RGB(0,0,0);
-            cv::putText(*show_img, labelstr, pt_text, CV_FONT_HERSHEY_SIMPLEX, font_size, black_color, font_size * 3, CV_AA);
+            cv::putText(*show_img, labelstr, pt_text, CV_FONT_HERSHEY_COMPLEX_SMALL, font_size, black_color, 2*font_size, CV_AA);
+            // CV_FONT_HERSHEY_COMPLEX_SMALL, CV_FONT_HERSHEY_SIMPLEX
         }
     }
     if (ext_output) {