From ae9d25225597d97f548c86d2bf28729aa75f2eaa Mon Sep 17 00:00:00 2001
From: panda_home <1415243231@qq.com>
Date: Tue, 23 Dec 2025 09:30:56 +0800
Subject: [PATCH] =?UTF-8?q?=E8=A7=82=E8=B5=8F=E9=B1=BC=E5=81=A5=E5=BA=B7?=
 =?UTF-8?q?=E7=B3=BB=E7=BB=9Fv1.0?=
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---
 fish_tracker.py | 73 +++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 73 insertions(+)
 create mode 100644 fish_tracker.py

diff --git a/fish_tracker.py b/fish_tracker.py
new file mode 100644
index 0000000..4eb4772
--- /dev/null
+++ b/fish_tracker.py
@@ -0,0 +1,73 @@
+# fish_tracker.py
+import cv2
+import numpy as np
+from collections import defaultdict, deque
+from ultralytics import YOLO
+from deep_sort_realtime.deepsort_tracker import DeepSort
+
+class FishTracker:
+    def __init__(self, model_path="yolov8n.pt", conf_thresh=0.5):
+        self.model = YOLO(model_path)
+        self.tracker = DeepSort(max_age=30, n_init=3, nn_budget=100)
+        self.conf_thresh = conf_thresh
+        self.trajectories = defaultdict(lambda: deque(maxlen=30))  # 存最近30帧轨迹
+        self.alerts = []
+
+    def process_frame(self, frame):
+        self.alerts.clear()
+        h, w = frame.shape[:2]
+
+        # 1. YOLO 检测（只检测 "fish" 类别，COCO 中类别 ID=17）
+        results = self.model(frame, verbose=False)[0]
+        detections = []
+        for box in results.boxes:
+            if int(box.cls.item()) == 17 and box.conf.item() > self.conf_thresh:  # COCO: fish=17
+                x1, y1, x2, y2 = map(int, box.xyxy[0])
+                conf = float(box.conf.item())
+                detections.append([[x1, y1, x2 - x1, y2 - y1], conf, "fish"])
+
+        # 2. DeepSORT 追踪
+        tracks = self.tracker.update_tracks(detections, frame=frame)
+
+        # 3. 更新轨迹 & 绘制
+        output_frame = frame.copy()
+        for track in tracks:
+            if not track.is_confirmed():
+                continue
+            track_id = track.track_id
+            ltrb = track.to_ltrb()
+            x1, y1, x2, y2 = map(int, ltrb)
+            cx, cy = (x1 + x2) // 2, (y1 + y2) // 2
+
+            # 记录轨迹
+            self.trajectories[track_id].append((cx, cy, cv2.getTickCount()))
+
+            # 计算速度（像素/秒）
+            traj = self.trajectories[track_id]
+            if len(traj) >= 2:
+                total_dist = 0
+                total_time = 0
+                for i in range(1, len(traj)):
+                    dx = traj[i][0] - traj[i-1][0]
+                    dy = traj[i][1] - traj[i-1][1]
+                    dist = np.sqrt(dx*dx + dy*dy)
+                    dt = (traj[i][2] - traj[i-1][2]) / cv2.getTickFrequency()
+                    total_dist += dist
+                    total_time += dt
+                speed = total_dist / total_time if total_time > 0 else 0
+            else:
+                speed = 0
+
+            # 判断是否沉底（底部 20% 区域）
+            is_bottom = cy > h * 0.8
+
+            # 异常规则
+            if speed < 15 and is_bottom:
+                self.alerts.append(f"⚠️ 鱼 {track_id}: 低速({speed:.1f}px/s) + 沉底")
+
+            # 绘制框和ID
+            cv2.rectangle(output_frame, (x1, y1), (x2, y2), (0, 255, 0), 2)
+            cv2.putText(output_frame, f"Fish-{track_id}", (x1, y1 - 10),
+                        cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0), 2)
+
+        return output_frame, self.alerts
\ No newline at end of file