Update Accuracy_Testing.ipynb

Accuracy_Testing.ipynb

@@ -142,6 +142,7 @@
     "from hyperlpr import  calculation_raito_origin  as  fm\n",
     "from hyperlpr import finemapping_vertical as fv\n",
     "from hyperlpr import segmentation_0124 as segmentation\n",
+    "import plate_judgment\n"
     "\n",
     "\n",
     "import numpy as np\n",
@@ -165,17 +166,16 @@
     "\n",
     "###車牌辨識方法\n",
     "def MYSimpleRecognizePlate(img):\n",
-    "    car=img\n",
-    "    w,h=car.shape[:2]\n",
-    "    h=int(w*500/h)\n",
-    "    w=500\n",
-    "    car=cv2.resize(car,(w,h))\n",
-    "    images=detect.detectPlateRough(car,car.shape[0])\n",
+    "    y1,y2,x1,x2=0,1080,0,1000  ##限定區域\n"
+    "    car=img[y1:y2,x1:x2]\n",
+    "    images=detect.detectPlateRough(car,500)\n",
     "\n",
     "    origin_plate_list=[]\n",
     "    image_rgb_rl_list=[]\n",
     "    for j,plate in enumerate(images):\n",
     "            plate, rect, origin_plate,flg  =plate\n",
+    "            judgment_type=plate_judgment.SimplePredict(origin_plate)"
+    "            if(judgment_type==0):"
     "               img =cv2.resize(origin_plate,(300,100))\n",
     "               img = cv2.bitwise_not(img)\n",
     "               ### 車排上下定位\n",
@@ -220,7 +220,7 @@
     "               ### 辨識結果\n",
     "               print('車牌',name,'可信度',con,'字數',nums)\n",
     "               \n",
-    "            return cut_data\n",
+    "               return name,con,nums\n",
     "        \n",
     "        \n",
     "LPR=MYSimpleRecognizePlate(img)\n",