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