Image_Process/advanced/track/animal_track.py

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():
    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):
    # 加载YOLO模型
    model_path1 = os.path.join(base_path, 'yolo/yolov3.weights')
    model_path2 = os.path.join(base_path, 'yolo/yolov3.cfg')
    model_path3 = os.path.join(base_path, 'yolo/coco.names')
    net = cv2.dnn.readNet(model_path1, model_path2)
    layer_names = net.getLayerNames()
    output_layers = [layer_names[i - 1] for i in net.getUnconnectedOutLayers()]
    classes = []
    with open(model_path3, "r") as f:
        classes = [line.strip() for line in f.readlines()]

    # 初始化视频捕捉
    cap = cv2.VideoCapture(file_path)
    fps = cap.get(cv2.CAP_PROP_FPS)
    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()
        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)
        outs = net.forward(output_layers)

        # 获取检测结果
        new_objects = []  # 用于存储当前帧检测到的新对象

        class_ids = []
        confidences = []
        boxes = []
        for out in outs:
            for detection in out:
                scores = detection[5:]
                class_id = np.argmax(scores)
                confidence = scores[class_id]
                if confidence > 0.5:
                    # 目标检测
                    center_x = int(detection[0] * width)
                    center_y = int(detection[1] * height)
                    w = int(detection[2] * width)
                    h = int(detection[3] * height)
                    x = int(center_x - w / 2)
                    y = int(center_y - h / 2)
                    boxes.append([x, y, w, h])
                    confidences.append(float(confidence))
                    class_ids.append(class_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():
            x, y, w, h = boxes[i]
            label = str(classes[class_ids[i]])
            if label in ["dog", "cat", "bird"]:  # 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()
                    trackers.append(tracker)
                    trackers[-1].init(frame, (x, y, w, h))
                    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)
            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):
    # bbox1 和 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

    # 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