新增摄像头移动后对物体的跟踪

main/camera.py

@@ -7,55 +7,83 @@ cap.set(3, 320)
 cap.set(4, 240)
 cap.set(cv2.CAP_PROP_BUFFERSIZE, 1)
 
-ret, prev_frame = cap.read()
-prev_gray = cv2.cvtColor(prev_frame, cv2.COLOR_BGR2GRAY)
-prev_gray = cv2.GaussianBlur(prev_gray, (5,5), 0)
+# ===== 核心改动：用背景减法器替代帧差法 =====
+# MOG2: 能适应背景变化，适合移动摄像头场景
+# history: 保留多少帧来建立背景模型（越大适应越慢，但越稳定）
+# varThreshold: 判断前景的灵敏度（越小越敏感，但噪声越多）
+fgbg = cv2.createBackgroundSubtractorMOG2(history=100, varThreshold=25, detectShadows=False)
+
+# 可选：用KNN背景减法器（效果类似，速度稍快）
+# fgbg = cv2.createBackgroundSubtractorKNN(history=100, dist2Threshold=400, detectShadows=False)
+
+# 形态学核（用于去噪）
+kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5))
+
+# 用于平滑追踪的卡尔曼滤波器（可选，减少抖动）
+kalman = cv2.KalmanFilter(4, 2)
+kalman.measurementMatrix = np.array([[1,0,0,0], [0,1,0,0]], np.float32)
+kalman.transitionMatrix = np.array([[1,0,1,0], [0,1,0,1], [0,0,1,0], [0,0,0,1]], np.float32)
+kalman.processNoiseCov = np.eye(4, dtype=np.float32) * 0.03
+kalman.measurementNoiseCov = np.eye(2, dtype=np.float32) * 0.5
+kalman.statePost = np.zeros((4,1), np.float32)
 
 while True:
     ret, frame = cap.read()
     if not ret:
         break
-
-    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
-    gray = cv2.GaussianBlur(gray, (5,5), 0)
-
-    # 帧差
-    diff = cv2.absdiff(prev_gray, gray)
-    diff = cv2.convertScaleAbs(diff, alpha=2.5)
-
-    _, thresh = cv2.threshold(diff, 20, 255, cv2.THRESH_BINARY)
-
-    contours, _ = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
-
+    
+    # 1. 背景减法：得到前景掩码（白色=运动物体）
+    fgmask = fgbg.apply(frame)
+    
+    # 2. 形态学处理：去噪 + 填充空洞
+    fgmask = cv2.morphologyEx(fgmask, cv2.MORPH_OPEN, kernel)   # 先开运算去噪点
+    fgmask = cv2.morphologyEx(fgmask, cv2.MORPH_CLOSE, kernel)  # 再闭运算填充空洞
+    
+    # 3. 找轮廓
+    contours, _ = cv2.findContours(fgmask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+    
     # ===== 核心：选最大目标 =====
     target = None
     max_area = 0
-
+    
     for cnt in contours:
         area = cv2.contourArea(cnt)
-
+        
         if area < 300:  # 过滤噪声
             continue
-
+        
         if area > max_area:
             max_area = area
             target = cnt
-
-    # ===== 只处理一个目标 =====
+    
+    # ===== 处理目标 =====
     if target is not None:
         x, y, w, h = cv2.boundingRect(target)
         cx = x + w // 2
         cy = y + h // 2
-
+        
+        # 卡尔曼滤波预测/更新（可选，让追踪更平滑）
+        measurement = np.array([[np.float32(cx)], [np.float32(cy)]])
+        kalman.correct(measurement)
+        prediction = kalman.predict()
+        pred_x, pred_y = int(prediction[0]), int(prediction[1])
+        
+        # 画原始检测框
         cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 255, 0), 2)
         cv2.circle(frame, (cx, cy), 5, (0, 0, 255), -1)
-
-        print("唯一目标:", cx, cy, "area:", max_area)
-
+        # 画卡尔曼平滑后的位置（黄色）
+        cv2.circle(frame, (pred_x, pred_y), 5, (0, 255, 255), -1)
+        
+        print(f"目标位置: ({cx}, {cy}), 面积: {max_area}")
+    else:
+        # 没有检测到目标时，只预测不更新
+        prediction = kalman.predict()
+        # 可选：根据预测位置保持追踪（即使被短暂遮挡）
+    
+    # 可选：显示前景掩码（调试用）
+    cv2.imshow("foreground mask", fgmask)
     cv2.imshow("tracking", frame)
-
-    prev_gray = gray.copy()
-
+    
     if cv2.waitKey(1) & 0xFF == 27:
         break