更新到教程5

Author: DarlingZeroX

5 Realtime Dectect/Main.py

@@ -0,0 +1,34 @@
+import cv2
+import mss
+import numpy as np
+
+from YoloObjectDetection import YoloObjectDetection
+
+#实例化Yolo目标检测类
+yod = YoloObjectDetection()
+
+#实时截屏的区域
+rect = {"left" :0, "top": 0, "width": 1000, "height": 500}
+
+with mss.mss() as m:
+    while True:
+        #截屏
+        img = m.grab(rect)
+        #转成numpy数组
+        img = np.array(img)
+        #这里要转出Yolo能输入的格式
+        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+
+        #进行目标检测
+        yod.detectObjects(img)
+
+        #在图片上用方框标记出来
+        yod.labelImg(img)
+
+        #实时显示图片
+        cv2.imshow("DNF Yolo Auto", img)
+
+        #按Q键退出
+        if cv2.waitKey(5) & 0xFF == ord('q'):
+            cv2.destroyAllWindows()
+            break

5 Realtime Dectect/TorchSupport.py

@@ -0,0 +1,16 @@
+import torch
+
+def checkTorch():
+    print("torch版本",torch.__version__)
+    print("GPU是否可用",torch.cuda.is_available())
+    print("GPU个数",torch.cuda.device_count())
+    print("对应cudnn版本号",torch.backends.cudnn.version())
+    print("对应cuda版本号",torch.version.cuda)
+    print("--------------------------------------------------")
+    #查看torchvision和torch版本是否匹配
+    import torchvision
+    print(torch.__version__)
+    print(torchvision.__version__)
+
+if __name__ == "__main__":
+    checkTorch()

5 Realtime Dectect/YoloObjectDetection.py

@@ -0,0 +1,65 @@
+from ultralytics import YOLO
+
+import cv2
+
+class YoloObjectDetection:
+    mModel = None
+    mDetectResult = []
+
+    def __init__(self):
+        #加载Yolo模型
+        self.mModel = YOLO('yolov8n.pt', verbose=False)
+
+    def getDetectResult(self):
+        return self.mDetectResult
+
+    # 调用Yolo模型进行目标检测
+    def detectObjects(self, img):
+        #调用Yolo模型进行预测并返回结果
+        results = self.mModel(img, stream=True,verbose=False)
+
+        self.mDetectResult = []
+
+        # 读取检测的多个结果并保存
+        for result in results:
+            if len(result.boxes.cls) > 0:
+                for i in range(len(result.boxes.cls)):
+                    # 类别ID
+                    leibie_id = int(result.boxes.cls[i].item())
+
+                    # 类别
+                    leibie = result.names[leibie_id]
+
+                    # 相似度
+                    xiangsidu = str(result.boxes.conf[i].item())[0:3]
+
+                    # 坐标
+                    zuobiao = result.boxes.xyxy[i].tolist()
+
+                    self.mDetectResult.append({'类别': leibie, '相似度': xiangsidu, '坐标': zuobiao})
+
+        return self.mDetectResult
+
+    #把检测的结果在传入的图片上用方框标记出来
+    def labelImg(self, img, detectResult = None):
+        if detectResult is None:
+            detectResult = self.mDetectResult
+
+        if len(detectResult) > 0:
+            for i in detectResult:
+                # 画框
+                cv2.rectangle(img, (int(i['坐标'][0]), int(i['坐标'][1])), (int(i['坐标'][2]), int(i['坐标'][3])),
+                              (0, 255, 0), 2)
+
+                # 标记类型
+                cv2.putText(img, f" {i['类别']}", (int(i['坐标'][0]), int(i['坐标'][1]) + 15),
+                            cv2.FONT_HERSHEY_SIMPLEX, 0.6,
+                            (255, 255, 255), 1, cv2.LINE_AA)
+
+                # 相似度
+                cv2.putText(img, f" {i['相似度']}", (int(i['坐标'][0]) + 80, int(i['坐标'][1]) + 15),
+                            cv2.FONT_HERSHEY_SIMPLEX, 0.6,
+                            (255, 255, 255), 1, cv2.LINE_AA)
+
+
+