Python实现简单的"导弹"自动追踪原理解析

前言

本文介绍如何使用Python实现一个简单的"导弹"自动追踪功能。该功能主要包括两个部分，首先是识别并实时跟踪目标的位置；其次是对目标进行自动追踪。本文将分别介绍二者的实现过程。

识别目标位置

获取视频流

首先需要获取视频流，并将其转换为一系列帧。这可以通过使用OpenCV库来实现。

import cv2

# Open the camera
cap = cv2.VideoCapture(0)

while True:
    # Read the current frame
    ret, frame = cap.read()

    # Display the frame
    cv2.imshow('frame', frame)

    # Press 'q' to exit
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

# Release the camera and close all windows
cap.release()
cv2.destroyAllWindows()

在获取视频流后，我们需要对每一帧进行处理。具体来说，我们需要将每一帧图像转换为灰度图像，这样可以简化后续操作，并提高程序处理速度。

import cv2

# Open the camera
cap = cv2.VideoCapture(0)

while True:
    # Read the current frame
    ret, frame = cap.read()

    # Convert the frame to grayscale
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

    # Display the frame
    cv2.imshow('frame', gray)

    # Press 'q' to exit
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

# Release the camera and close all windows
cap.release()
cv2.destroyAllWindows()

目标检测

有了灰度图像后，我们就可以对图片进行目标检测。其中最常用的目标检测算法是Haar特征级联分类器。简单来说，这个算法就是通过比较已知的正样本和负样本特征来进行目标检测的。

下面是一个检测人脸的例子。需要先下载一个训练好的分类器文件（haarcascade_frontalface_default.xml），并与代码放在同一目录中。

import cv2

# Load the classifier
face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')

# Open the camera
cap = cv2.VideoCapture(0)

while True:
    # Read the current frame
    ret, frame = cap.read()

    # Convert the frame to grayscale
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

    # Detect faces in the frame
    faces = face_cascade.detectMultiScale(gray, 1.3, 5)

    # Draw rectangles around the faces
    for (x,y,w,h) in faces:
        cv2.rectangle(frame,(x,y),(x+w,y+h),(255,0,0),2)

    # Display the frame
    cv2.imshow('frame', frame)

    # Press 'q' to exit
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

# Release the camera and close all windows
cap.release()
cv2.destroyAllWindows()

实现目标追踪

基于特征匹配的追踪

一种简单的目标追踪方法是通过特征匹配来实现。具体来说，我们首先提取出目标图像的特征，然后在以后的帧中找到与目标相似的特征点，这些点的坐标即为目标的位置。

下面是一个基于ORB算法的特征匹配实现。需要在OpenCV中安装contrib库。

import cv2
import numpy as np

# Create ORB object
orb = cv2.ORB_create()

# Initialize the matcher
bf = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)

# Open the camera
cap = cv2.VideoCapture(0)

# Load the target image
target = cv2.imread('target.jpg', 0)
target_kp, target_des = orb.detectAndCompute(target, None)

while True:
    # Read the current frame
    ret, frame = cap.read()

    # Convert the frame to grayscale
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

    # Detect keypoints and descriptors in the current frame
    kp, des = orb.detectAndCompute(gray, None)

    # Match the descriptors with the target descriptors
    matches = bf.match(target_des, des)

    # Sort the matches by distance (good ones first) and keep the top N
    matches = sorted(matches, key=lambda x:x.distance)
    matches = matches[:10]

    # Draw the matches
    img_matches = cv2.drawMatches(target, target_kp, gray, kp, matches, None)

    # Get the location of the target in the frame
    target_pts = np.float32([ target_kp[m.queryIdx].pt for m in matches ]).reshape(-1,1,2)
    current_pts = np.float32([ kp[m.trainIdx].pt for m in matches ]).reshape(-1,1,2)

    # Compute the perspective transform between the target and the current frame
    M, mask = cv2.findHomography(target_pts, current_pts, cv2.RANSAC, 5.0)

    # Get the corners of the target in the current frame
    h,w = target.shape
    target_corners = np.float32([ [0,0], [0,h-1], [w-1,h-1], [w-1,0] ]).reshape(-1,1,2)
    current_corners = cv2.perspectiveTransform(target_corners, M)

    # Draw the bounding box around the target
    frame = cv2.polylines(frame, [np.int32(current_corners)], True, (255,0,0), 2)

    # Display the frame
    cv2.imshow('frame', img_matches)

    # Press 'q' to exit
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

# Release the camera and close all windows
cap.release()
cv2.destroyAllWindows()

基于光流的追踪

另一种常用的目标追踪方法是基于光流的追踪。光流是指图像中像素随着时间的变化所形成的矢量场。通过跟踪这些矢量，我们可以大致得出物体在图像平面上的运动状态。

下面是一个基于Lucas-Kanade算法的光流追踪实现。

import cv2
import numpy as np

# Open the camera
cap = cv2.VideoCapture(0)

# Create some random colors
color = np.random.randint(0, 255, (100,3))

# Take the first frame
ret, old_frame = cap.read()
old_gray = cv2.cvtColor(old_frame, cv2.COLOR_BGR2GRAY)

# Create a mask image for drawing purposes
mask = np.zeros_like(old_frame)

# Set the ROI for tracking
x,y,w,h = 300,200,100,50
track_window = (x,y,w,h)

# Set up the ROI for tracking
roi = old_gray[y:y+h, x:x+w]
hsv_roi = cv2.cvtColor(old_frame, cv2.COLOR_BGR2HSV)
mask_roi = cv2.inRange(hsv_roi, np.array((0., 60.,32.)), np.array((180.,255.,255.)))
hist = cv2.calcHist([hsv_roi],[0],mask_roi,[180],[0,180])
cv2.normalize(hist,hist,0,255,cv2.NORM_MINMAX)
term_crit = ( cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 1 )

while True:
    # Read the current frame
    ret, frame = cap.read()

    if ret == True:
        # Convert the frame to grayscale
        frame_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

        # Calculate optical flow between the current frame and the previous frame
        p1, st, err = cv2.calcOpticalFlowPyrLK(old_gray, frame_gray, 
                                               np.array([track_window]), None, maxLevel=5, 
                                               winSize=(50, 50), criteria=(cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 0.03))

        # Get the new position of the target
        x, y = p1[0][0], p1[0][1]

        # Update the track window
        track_window = (x, y, w, h)

        # Draw the tracking window on the frame
        cv2.rectangle(frame, (x,y), (x+w,y+h), (0, 255, 0), 2)

        # Display the frame
        cv2.imshow('frame', frame)

        # Set the current frame as the previous frame for the next iteration
        old_gray = frame_gray.copy()

    # Press 'q' to exit
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

# Release the camera and close all windows
cap.release()
cv2.destroyAllWindows()

结语

本文介绍了如何使用Python实现一个简单的"导弹"自动追踪功能。根据实际需求，可以选择不同的追踪方法，例如基于特征匹配的追踪或基于光流的追踪。