fix cpp precision (#1095)

deploy/cpp/docs/linux_build.md

 # Linux平台编译指南
 
 ## 说明
-本文档在 `Linux`平台使用`GCC 4.8.5` 和 `GCC 4.9.4`测试过，如果需要使用更高G++版本编译使用，则需要重新编译Paddle预测库，请参考: [从源码编译Paddle预测库](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/advanced_guide/inference_deployment/inference/build_and_install_lib_cn.html)。
+本文档在 `Linux`平台使用`GCC 4.8.5` 和 `GCC 4.9.4`测试过，如果需要使用更高G++版本编译使用，则需要重新编译Paddle预测库，请参考: [从源码编译Paddle预测库](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/advanced_guide/inference_deployment/inference/build_and_install_lib_cn.html)。本文档使用的预置的opencv库是在ubuntu 16.04上用gcc4.8编译的，如果需要在ubuntu 16.04以外的系统环境编译，那么需自行编译opencv库。
 
 ## 前置条件
 * G++ 4.8.2 ~ 4.9.4

deploy/cpp/src/main.cc

@@ -94,7 +94,7 @@ void PredictImage(const std::string& image_path,
   std::vector<PaddleDetection::ObjectResult> result;
   det->Predict(im, &result);
   for (const auto& item : result) {
-    printf("class=%d confidence=%.2f rect=[%d %d %d %d]\n",
+    printf("class=%d confidence=%.4f rect=[%d %d %d %d]\n",
         item.class_id,
         item.confidence,
         item.rect[0],

deploy/cpp/src/object_detector.cc

@@ -11,8 +11,10 @@
 // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 // See the License for the specific language governing permissions and
 // limitations under the License.
-
-# include "include/object_detector.h"
+#include <sstream>
+// for setprecision
+#include <iomanip>
+#include "include/object_detector.h"
 
 namespace PaddleDetection {
 
@@ -71,13 +73,15 @@ cv::Mat VisualizeResult(const cv::Mat& img,
     cv::Rect roi = cv::Rect(results[i].rect[0], results[i].rect[2], w, h);
 
     // Configure color and text size
-    std::string text = lable_list[results[i].class_id];
+    std::ostringstream oss;
+    oss << std::setiosflags(std::ios::fixed) << std::setprecision(4);
+    oss << lable_list[results[i].class_id] << " ";
+    oss << results[i].confidence;
+    std::string text = oss.str();
     int c1 = colormap[3 * results[i].class_id + 0];
     int c2 = colormap[3 * results[i].class_id + 1];
     int c3 = colormap[3 * results[i].class_id + 2];
     cv::Scalar roi_color = cv::Scalar(c1, c2, c3);
-    text += " ";
-    text += std::to_string(static_cast<int>(results[i].confidence * 100)) + "%";
     int font_face = cv::FONT_HERSHEY_COMPLEX_SMALL;
     double font_scale = 0.5f;
     float thickness = 0.5;

deploy/python/infer.py

@@ -456,7 +456,7 @@ class Detector():
         expect_boxes = (np_boxes[:, 1] > threshold) & (np_boxes[:, 0] > -1)
         np_boxes = np_boxes[expect_boxes, :]
         for box in np_boxes:
-            print('class_id:{:d}, confidence:{:.2f},'
+            print('class_id:{:d}, confidence:{:.4f},'
                   'left_top:[{:.2f},{:.2f}],'
                   ' right_bottom:[{:.2f},{:.2f}]'.format(
                       int(box[0]), box[1], box[2], box[3], box[4], box[5]))

deploy/python/visualize.py

@@ -180,7 +180,7 @@ def draw_box(im, np_boxes, labels):
             fill=color)
 
         # draw label
-        text = "{} {:.2f}".format(labels[clsid], score)
+        text = "{} {:.4f}".format(labels[clsid], score)
         tw, th = draw.textsize(text)
         draw.rectangle(
             [(xmin + 1, ymin - th), (xmin + tw + 1, ymin)], fill=color)