From 741184485637f6662c7de1b791e66da986098740 Mon Sep 17 00:00:00 2001
From: xly <ggaq@uestc.edu.cn>
Date: Mon, 26 Aug 2024 18:16:03 +0800
Subject: [PATCH] =?UTF-8?q?=E4=B8=8A=E4=BC=A0=E6=96=87=E4=BB=B6=E8=87=B3?=
 =?UTF-8?q?=20/?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 detect_license_plate.py | 76 +++++++++++++++++++++++++++++++++++++++++
 1 file changed, 76 insertions(+)
 create mode 100644 detect_license_plate.py

diff --git a/detect_license_plate.py b/detect_license_plate.py
new file mode 100644
index 0000000..e6b58e9
--- /dev/null
+++ b/detect_license_plate.py
@@ -0,0 +1,76 @@
+# coding:utf-8
+from ultralytics import YOLO
+import cv2
+import detect_tools as tools
+from PIL import ImageFont
+from paddleocr import PaddleOCR
+import matplotlib.pyplot as plt
+import os
+
+def get_license_result(ocr, image):
+    """
+    image:输入的车牌截取照片
+    输出，车牌号与置信度
+    """
+    result = ocr.ocr(image, cls=True)[0]
+    if result:
+        license_name, conf = result[0][1]
+        if '·' in license_name:
+            license_name = license_name.replace('·', '')
+        return license_name, conf
+    else:
+        return None, None
+
+# 获取图片地址
+img_path = "images"
+files = os.listdir(img_path)
+s = []
+for file in files:
+    temp = img_path+"/"+file
+    s.append(temp)
+fontC = ImageFont.truetype("Fonts/msyhbd.ttc", 50, 0, encoding="utf-8")
+
+# 加载ocr模型
+cls_model_dir = 'paddleModels/whl/cls/ch_ppocr_mobile_v2.0_cls_infer'
+rec_model_dir = 'paddleModels/whl/rec/ch/ch_PP-OCRv4_rec_infer'
+ocr = PaddleOCR(use_angle_cls=False, lang="ch", det=False, cls_model_dir=cls_model_dir, rec_model_dir=rec_model_dir)
+
+# 所需加载的模型目录
+path = 'runs/detect/train/weights/best.pt'
+# 加载预训练模型
+# conf	0.25	object confidence threshold for detection
+# iou	0.7	int.ersection over union (IoU) threshold for NMS
+model = YOLO(path, task='detect')
+# model = YOLO(path, task='detect',conf=0.5)
+
+#检测图片
+i = 0
+for now_img in s:
+    now_img = tools.img_cvread(now_img)
+    results = model(now_img)[0]
+    location_list = results.boxes.xyxy.tolist()
+    if len(location_list) >= 1:
+        location_list = [list(map(int, e)) for e in location_list]
+        # 截取每个车牌区域的照片
+        license_imgs = []
+        for each in location_list:
+            x1, y1, x2, y2 = each
+            cropImg = now_img[y1:y2, x1:x2]
+            license_imgs.append(cropImg)
+        # 车牌识别结果
+        lisence_res = []
+        conf_list = []
+        for each in license_imgs:
+            license_num, conf = get_license_result(ocr, each)
+            if license_num:
+                lisence_res.append(license_num)
+                conf_list.append(conf)
+            else:
+                lisence_res.append('无法识别')
+                conf_list.append(0)
+        for text, box in zip(lisence_res, location_list):
+            now_img = tools.drawRectBox(now_img, box, text, fontC)
+            now_img = cv2.resize(now_img, dsize=None, fx=0.5, fy=0.5, interpolation=cv2.INTER_LINEAR)
+            # cv2.imshow("YOLOv8 Detection", now_img)
+            cv2.imwrite(f"./result/{i}.jpg", now_img)
+            i += 1
\ No newline at end of file