ADD file via upload

advanced/track/animal_track.py

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+import tkinter as tk
+from tkinter import filedialog
+import numpy as np
+import cv2
+import os
+import time
+
+# 获取当前脚本文件的目录
+base_path = os.path.dirname(os.path.abspath(__file__))
+
+def select_file_and_run():
+    """
+    弹出文件选择对话框，选择视频文件并运行检测和追踪程序。
+    """
+    file_path = filedialog.askopenfilename(filetypes=[("Video files", "*.mp4;*.avi")])
+    if file_path:
+        run_detection_and_tracking(file_path)
+
+def run_detection_and_tracking(file_path):
+    """
+    运行目标检测和多目标追踪程序。
+    参数：
+    file_path - 视频文件路径
+    """
+    # 加载YOLO模型
+    model_path1 = os.path.join(base_path, 'yolo/yolov3.weights')  # 权重文件路径
+    model_path2 = os.path.join(base_path, 'yolo/yolov3.cfg')      # 配置文件路径
+    model_path3 = os.path.join(base_path, 'yolo/coco.names')      # 类别名称文件路径
+    net = cv2.dnn.readNet(model_path1, model_path2)               # 加载YOLO模型
+    layer_names = net.getLayerNames()                             # 获取模型层名称
+    output_layers = [layer_names[i - 1] for i in net.getUnconnectedOutLayers()]  # 获取输出层名称
+    classes = []
+    with open(model_path3, "r") as f:
+        classes = [line.strip() for line in f.readlines()]        # 读取类别名称
+
+    # 初始化视频捕捉
+    cap = cv2.VideoCapture(file_path)                             # 打开视频文件
+    fps = cap.get(cv2.CAP_PROP_FPS)                               # 获取视频帧率
+    speed_up_factor = 2                                           # 加速倍数
+    delay = int(1000 / fps / speed_up_factor)                     # 计算加速后每帧之间的时间间隔，以毫秒为单位
+
+    # 创建多目标追踪器
+    trackers = []
+    # 创建用于存储已追踪对象信息的列表
+    tracked_objects = []
+
+    while True:
+        ret, frame = cap.read()                                   # 读取视频帧
+        if not ret:
+            break
+
+        start_time = time.time()
+
+        # 检测目标
+        height, width, channels = frame.shape
+        blob = cv2.dnn.blobFromImage(frame, 0.00392, (416, 416), (0, 0, 0), True, crop=False)
+        net.setInput(blob)                                        # 设置输入数据
+        outs = net.forward(output_layers)                         # 执行前向传播，获取检测结果
+
+        # 获取检测结果
+        new_objects = []  # 用于存储当前帧检测到的新对象
+
+        class_ids = []  # 存储检测到的目标的类别ID
+        confidences = []  # 存储检测到的目标的置信度
+        boxes = []  # 存储检测到的目标的边界框
+        for out in outs:
+            for detection in out:
+                scores = detection[5:]  # 获取每个检测框的所有类别得分
+                class_id = np.argmax(scores)  # 获取得分最高的类别ID，即检测到的物体类别
+                confidence = scores[class_id]  # 获取该类别的置信度
+                if confidence > 0.5:  # 筛选置信度超过0.5的检测框
+                    # 目标检测
+                    center_x = int(detection[0] * width)  # 中心点x坐标
+                    center_y = int(detection[1] * height)  # 中心点y坐标
+                    w = int(detection[2] * width)  # 边界框宽度
+                    h = int(detection[3] * height)  # 边界框高度
+                    x = int(center_x - w / 2)  # 左上角x坐标
+                    y = int(center_y - h / 2)  # 左上角y坐标
+                    boxes.append([x, y, w, h])  # 存储边界框
+                    confidences.append(float(confidence))  # 存储置信度
+                    class_ids.append(class_id)  # 存储类别ID
+                    new_objects.append((x, y, w, h, class_id))  # 存储新检测到的对象信息
+
+        # 非最大值抑制
+        indices = cv2.dnn.NMSBoxes(boxes, confidences, 0.5, 0.4)
+
+        # 更新追踪器或添加新的追踪器
+        for i in indices.flatten():  # 遍历NMS后的检测框索引
+            x, y, w, h = boxes[i]  # 获取检测框的左上角坐标和宽高
+            label = str(classes[class_ids[i]])  # 获取检测框的类别标签
+            if label in ["dog", "cat", "bird", "horse", "sheep", "cow", "elephant", "bear", "zebra",
+                         "giraffe"]:  # 只选择动物目标
+                # 检查是否已经有相似的追踪器在追踪相同类型的对象
+                found_similar = False
+                for tracked_object in tracked_objects:
+                    if tracked_object[4] == class_ids[i]:  # 检查类别是否相同
+                        # 计算当前检测到的对象与已有追踪器的距离或重叠度
+                        existing_bbox = (tracked_object[0], tracked_object[1], tracked_object[0] + tracked_object[2],
+                                         tracked_object[1] + tracked_object[3])
+                        new_bbox = (x, y, x + w, y + h)
+                        overlap_area = calculate_overlap(existing_bbox, new_bbox)
+                        if overlap_area > 0.5:  # 如果重叠度超过阈值，认为是同一个对象，不再重复追踪
+                            found_similar = True
+                            break
+                if not found_similar:
+                    tracker = cv2.TrackerKCF_create()  # 创建KCF追踪器
+                    trackers.append(tracker)  # 将追踪器添加到追踪器列表
+                    trackers[-1].init(frame, (x, y, w, h))  # 初始化追踪器
+                    tracked_objects.append((x, y, w, h, class_ids[i]))  # 添加到已追踪对象列表
+
+        # 绘制追踪框
+        for tracker in trackers:
+            success, bbox = tracker.update(frame)                   # 更新追踪器
+            if success:
+                # 画出追踪框
+                p1 = (int(bbox[0]), int(bbox[1]))
+                p2 = (int(bbox[0] + bbox[2]), int(bbox[1] + bbox[3]))
+                cv2.rectangle(frame, p1, p2, (255, 0, 0), 2)
+            else:
+                # 追踪失败
+                cv2.putText(frame, "Tracking failure detected", (100, 80), cv2.FONT_HERSHEY_SIMPLEX,
+                            0.75, (0, 0, 255),2)
+
+        # 显示结果
+        cv2.imshow('Tracking', frame)
+
+        # 按下ESC键退出或关闭窗口退出
+        if cv2.waitKey(delay) & 0xFF == 27:
+            break
+        if cv2.getWindowProperty('Tracking', cv2.WND_PROP_VISIBLE) < 1:
+            break
+
+    cap.release()
+    cv2.destroyAllWindows()
+
+def calculate_overlap(bbox1, bbox2):
+    """
+    计算两个边界框的重叠面积比（IoU）。
+    参数：
+    bbox1, bbox2 - 分别是 (x1, y1, x2, y2) 格式的边界框坐标，其中 (x1, y1) 是左上角坐标，(x2, y2) 是右下角坐标
+    返回值：
+    iou - 重叠面积比
+    """
+    # 计算交集部分的坐标
+    inter_x1 = max(bbox1[0], bbox2[0])
+    inter_y1 = max(bbox1[1], bbox2[1])
+    inter_x2 = min(bbox1[2], bbox2[2])
+    inter_y2 = min(bbox1[3], bbox2[3])
+
+    # 计算交集区域的面积
+    inter_area = max(0, inter_x2 - inter_x1 + 1) * max(0, inter_y2 - inter_y1 + 1)
+
+    # 计算各自的区域面积
+    area_bbox1 = (bbox1[2] - bbox1[0] + 1) * (bbox1[3] - bbox1[1] + 1)
+    area_bbox2 = (bbox2[2] - bbox2[0] + 1) * (bbox2[3] - bbox2[1] + 1)
+
+    # 计算并返回重叠区域的IoU
+    iou = inter_area / float(area_bbox1 + area_bbox2 - inter_area)
+
+    return iou