无人机目标检测

导师给了个练手项目，记录下第一个项目过程：

项目要求：检测无人机降落时是否偏移，识别降落点。

想法是用opencv，使用传统的轮廓检测

首先把视频流转换成图片：只提取最后两分钟降落的片段

import cv2
import os
dir=r"..\wrong\video"
file_name="2022-06-07.mp4"
if os.path.exists(dir+"/img")==False:
    os.makedirs(dir+"/img")
file=os.path.join(dir,file_name)
cap=cv2.VideoCapture(file)
fps=cap.get(cv2.CAP_PROP_FPS)
frame_num=cap.get(cv2.CAP_PROP_FRAME_COUNT)
(width,height)=(cap.get(cv2.CAP_PROP_FRAME_WIDTH),cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
print(width,height,fps,frame_num)
num=0
while True:
    ret,frame=cap.read()
    if ret==False:
        break
    if num>frame_num-fps*120:
       export_path=os.path.join(dir,"img",file_name.split('.')[0]+str(num)+'.jpg')
       #print(export_path)
       a=cv2.imwrite(export_path,frame)
       #print(a)
    num+=1
print(num)

 对图像进行分割，根据面积和颜色特征提取降落点信息：

import cv2
import os
import numpy as np
from tqdm import tqdm
dir=r"..\video\CAPTURE_2022-06-07-14-35-13_img\descend"
files=os.listdir(dir)
qrcoder=cv2.QRCodeDetector()
writer = cv2.VideoWriter(dir + '.avi', cv2.VideoWriter_fourcc(*'mp4v'), 30, (1920, 1080), isColor=True)
for file in tqdm(files):
    if file.endswith(".jpg"):
        file=os.path.join(dir,file)
        img=cv2.imread(file)
        #points=qrcoder.detect(img)
        #if points !=(False,None):
           #img_qrcode = cv2.drawContours(img, [np.int32(points)], 0, (0, 0, 255), 2)
        #else:
           #img_qrcode=img
        img_gray=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
        img_binary=cv2.adaptiveThreshold(img_gray,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C,cv2.THRESH_BINARY_INV,5,0)
        ret,img_binary=cv2.threshold(img_gray,250,255,cv2.THRESH_BINARY_INV)
        kernel=np.ones((7,7))
        im_erode = cv2.erode(img_binary, kernel)
        im_erode = cv2.erode(im_erode, kernel)
        im_erode = cv2.erode(im_erode, kernel)
        im_dilate=cv2.dilate(im_erode,kernel)
        im_dilate = cv2.dilate(im_dilate, kernel)
        im_dilate = cv2.dilate(im_dilate, kernel)
        im_dilate = cv2.dilate(im_dilate, kernel)
        im_dilate = cv2.dilate(im_dilate, kernel)
        #img_open=cv2.morphologyEx(img_binary,cv2.MORPH_OPEN,kernel)
        img_blur=cv2.medianBlur(im_dilate,3)
        img_canny=cv2.Canny(img_blur,0,255)
        contours, hierarchy = cv2.findContours(img_blur, cv2.RETR_CCOMP, cv2.CHAIN_APPROX_SIMPLE)
        max_area=0
        for contour in contours:
            lenth = cv2.arcLength(contour, True)
            area = cv2.contourArea(contour)
            if lenth>0 and area/lenth>max_area and 400<area< 1000000:
                contour_max=contour
                max_area=area/lenth
        img_contours = cv2.drawContours(img, contour_max, -1, (0, 0, 255),3)
        box=cv2.minAreaRect(contour_max)
        box=cv2.boxPoints(box)
        box=np.int0(box)
        #img_contours =cv2.drawContours(img,[box],0,(0,0,255),2)
        #cv2.imshow("raw",img)
        #cv2.imshow("qrcode",img_qrcode)
        #cv2.imshow("ray",img_gray)
        #cv2.imshow("binary",img_binary)
        #cv2.imshow("close",img_open)
        #cv2.imshow("blur",img_blur)
        #cv2.imshow("canny", img_canny)
        #cv2.imshow("rota", img_rota)
        #cv2.imshow("img_connect", img_connect)
        #cv2.imshow("img_contours", img_contours)
        #cv2.waitKey()
        writer.write(img_contours)
writer.release()

 泛化性不太好，新到一批数据检测效果不佳，下一步考虑使用yolov5来检测。