[update]:防止由于车牌占用大，导致检测模型识别失败 的测试用例

open-anpr-test/src/main/java/com/visual/open/anpr/exps/PlateRecognitionExample.java

@@ -15,9 +15,9 @@ public class PlateRecognitionExample {
 
     static{ nu.pattern.OpenCV.loadShared(); }
     //本地开发模式
-//    public static String serverHost = "http://127.0.0.1:8080";
+    public static String serverHost = "http://127.0.0.1:8080";
     //docker部署模式
-    public static String serverHost = "http://172.16.36.228:56790";
+//    public static String serverHost = "http://127.0.0.1:56790";
     //远程测试服务
     //public static String serverHost = "http://open-anpr.diven.nat300.top";
 
@@ -27,14 +27,14 @@ public class PlateRecognitionExample {
     public static void recognition() {
         String searchPath = "open-anpr-test/src/main/resources/image";
         File [] files = Objects.requireNonNull(new File(searchPath).listFiles());
-//        for(int u=0; u<=1000; u++)
+        System.out.println(files.length);
-        for(int i=0; i< files.length-1; i++){
+        for(int i=0; i < files.length; i++){
+            System.out.println(i);
             File imageA = files[i];
             String imageBase64 = Base64Util.encode(imageA.getAbsolutePath());
             Response<List<RecognitionRep>> response = plateRecognition.detection().recognition(
                 Recognition.build().setImage(imageBase64).setLimit(5)
             );
-
             DrawImage drawImage = DrawImage.build(imageA.getAbsolutePath());
             if(response.ok()){
                 for(RecognitionRep recognitionRep : response.getData()){