计算机视觉应用开发案例教程（OpenCV）习题答案 第5章

第5章一、选择题答案B解析：A选项角点是灰度变化剧烈的区域，错误；C选项边缘仅一个方向变化，不属于角点，错误；D选项平面区域灰度无变化，不易误判为角点，错误；B选项正确。B解析：cv2.goodFeaturesToTrack()是Shi-Tomasi角点检测的专用函数，A选项用cv2.cornerHarris()，C/D用cv2.SIFT_create()/cv2.ORB_create()，B正确。C解析：SIFT和SURF受专利保护，ORB是OpenCV开源免费的，C正确。B解析：暴力匹配穷举查询图每个特征点与训练图所有特征点的距离，A/C是FLANN的原理，D无依据，B正确。C解析：单应性矩阵描述坐标映射关系，A是特征检测的作用，B是特征计算的作用，D是匹配筛选的作用，C正确。二、填空题答案角点响应值；0.02~0.04；多（或密集）解析：k控制角点判定的宽松程度，值越小，更多疑似区域被判定为角点。32；128解析：ORB描述子32维，SIFT128维，低维度减少计算量，提高匹配速度。检查的邻域数量；高解析：checks越大，FLANN搜索越彻底，精度越高，但耗时增加。去除外点（或错误匹配对）解析：RANSAC算法能剔除不符合投影关系的错误匹配（外点），确保单应性矩阵准确。三、实操题答案代码示例：importcv2importnumpyasnp#1.读取棋盘格图像img=cv2.imread("chessboard.jpg",cv2.IMREAD_GRAYSCALE)ifimgisNone:print("图像读取失败！")exit()img_color=cv2.cvtColor(img,cv2.COLOR_GRAY2BGR)h,w=img.shape#2.定义3组k值k_values=[0.02,0.04,0.06]result_imgs=[]corner_counts=[]forkink_values:#Harris角点检测img_float=np.float32(img)dst=cv2.cornerHarris(img_float,blockSize=2,ksize=3,k=k)dst=cv2.dilate(dst,None)#统计角点数量threshold=0.01*dst.max()count=np.sum(dst>threshold)corner_counts.append(count)#绘制角点temp_img=img_color.copy()temp_img[dst>threshold]=[0,0,255]#添加标注cv2.putText(temp_img,f"k={k},Count={count}",(20,40),cv2.FONT_HERSHEY_SIMPLEX,1,(255,255,255),2)result_imgs.append(temp_img)#3.拼接结果图（水平拼接）final_img=np.hstack(result_imgs)#4.显示与保存cv2.imshow("HarriskParameterTuning",final_img)cv2.imwrite("harris_param_tune.jpg",final_img)#5.打印统计结果print("Harris角点检测参数统计：")fork,countinzip(k_values,corner_counts):print(f"k={k}：{count}个角点")print("结论：k值越小，检测到的角点越多，密集程度越高；k值越大，角点越少越稀疏。")cv2.waitKey(0)cv2.destroyAllWindows()2.代码示例：importcv2importnumpyasnp#1.读取目标图和场景图img_target=cv2.imread("target.jpg",cv2.IMREAD_COLOR)#目标图img_scene=cv2.imread("scene.jpg",cv2.IMREAD_COLOR)#场景图（gray_target=cv2.cvtColor(img_target,cv2.COLOR_BGR2GRAY)gray_scene=cv2.cvtColor(img_scene,cv2.COLOR_BGR2GRAY)ifimg_targetisNoneorimg_sceneisNone:print("图像读取失败！")exit()#2.ORB特征检测orb=cv2.ORB_create(nfeatures=1000)kp_tar,des_tar=orb.detectAndCompute(gray_target,None)kp_scene,des_scene=orb.detectAndCompute(gray_scene,None)#3.暴力匹配与筛选matcher=cv2.BFMatcher(cv2.NORM_HAMMING,crossCheck=False)matches=matcher.knnMatch(des_tar,des_scene,k=2)good_matches=[]form,ninmatches:ifm.distance<0.75*n.distance:good_matches.append(m)print(f"筛选后匹配对数：{len(good_matches)}")#4.绘制匹配结果img_matches=cv2.drawMatches(img_target,kp_tar,img_scene,kp_scene,good_matches[:30],None,flags=2)cv2.putText(img_matches,f"ORBMatches(Top30)",(20,40),cv2.FONT_HERSHEY_SIMPLEX,1,(0,0,255),2)#5.标记场景图中的目标（匹配对数>30时）img_marked=img_scene.copy()iflen(good_matches)>30:#提取场景图中的目标点坐标pts_scene=np.float32([kp_scene[m.trainIdx].ptformingood_matches])#计算目标点的最小/最大坐标，确定矩形框x_min,y_min=int(min(pts_scene[:,0])),int(min(pts_scene[:,1]))x_max,y_max=int(max(pts_scene[:,0])),int(max(pts_scene[:,1]))#绘制矩形框（绿色，线宽2）cv2.rectangle(img_marked,(x_min,y_min),(x_max,y_max),(0,255,0),2)cv2.putText(img_marked,"Target",(x_min,y_min-10),cv2.FONT_HERSHEY_SIMPLEX,1,(0,255,0),2)print("场景图中检测到目标，已标记！")else:cv2.putText(img_marked,"TargetNotDetected",(20,40),cv2.FONT_HERSHEY_SIMPLEX,1,(0,0,255),2)print("匹配对数不足，未检测到目标！")#6.显示与保存cv2.imshow("1.ORBMatches",img_matches)cv2.imshow("2.MarkedScene",img_marked)cv2.imwrite("target_matches.jpg",img_matche