计算机视觉应用开发案例教程（OpenCV）习题答案 第6章

第6章一、选择题答案B解析：Haar级联分类器仅支持单通道图像，检测前需将彩色图转为灰度图，A、C、D错误，B正确。B解析：scaleFactor是检测时图像的缩放比例，值越小检测越精细，A是minSize的作用，C是minNeighbors的作用，D是maxSize的作用，B正确。B解析：减小minNeighbors会降低检测的严格程度，减少漏检，A、C、D会增加漏检或无影响，B正确。B解析：cv2.VideoCapture(0)打开默认摄像头，1表示外接摄像头，A、C、D函数或参数错误，B正确。C解析：提取人脸ROI，仅在ROI内检测眼睛，避免背景干扰，A、B、D无此作用，C正确。二、填空题答案cv2.CascadeClassifier()；cv2.CascadeClassifier.detectMultiScale()解析：前者加载模型，后者检测目标，需写完整函数名（含类名）。左上角；宽度和高度解析：(x,y)是目标左上角像素坐标，w是宽度（水平方向），h是高度（垂直方向）。cv2.VideoCapture.read()；cv2.VideoCapture.release()解析：read()返回(ret,frame)，release()释放摄像头资源，避免占用。ROI（或感兴趣区域）解析：仅对人脸ROI滤波，不影响背景，是局部处理的核心。实操题答案1.代码示例：importcv2#1.加载眼睛级联模型（替换为你的路径）eye_model_path="haarcascade_eye.xml"eye_clf=cv2.CascadeClassifier(eye_model_path)ifeye_clf.empty():print("眼睛模型加载失败！请检查路径。")else:#2.读取含眼睛的图像img=cv2.imread("lena.jpg",cv2.IMREAD_COLOR)ifimgisNone:print("图像读取失败！")else:#3.灰度化gray=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)#4.检测眼睛（参数：眼睛较小，minSize设小）eyes=eye_clf.detectMultiScale(gray,scaleFactor=1.1,minNeighbors=3,minSize=(15,15),#眼睛尺寸小，minSize=15maxSize=(100,100))#5.处理结果iflen(eyes)==0:print("未检测到眼睛！可尝试减小minNeighbors或minSize。")cv2.putText(img,"NoEyesDetected",(50,50),cv2.FONT_HERSHEY_SIMPLEX,1,(0,0,255),2)else:print(f"检测到{len(eyes)}只眼睛！")for(x,y,w,h)ineyes:#绘制蓝色矩形框（B=255,G=0,R=0）cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),2)cv2.putText(img,"Eye",(x,y-10),cv2.FONT_HERSHEY_SIMPLEX,0.7,(255,0,0),2)#6.显示与保存cv2.imshow("EyeDetectionResult",img)cv2.imwrite("eye_detection.jpg",img)print("眼睛检测结果已保存！")cv2.waitKey(0)cv2.destroyAllWindows()解析：眼睛检测的关键是minSize设小（15~30），因眼睛尺寸通常比人脸小；若未检测到，可减小minNeighbors至2或scaleFactor至1.05。2.代码示例：importcv2#1.加载人脸和微笑模型（替换为你的路径）face_model="haarcascade_frontalface_default.xml"smile_model="haarcascade_smile.xml"face_clf=cv2.CascadeClassifier(face_model)smile_clf=cv2.CascadeClassifier(smile_model)ifface_clf.empty()orsmile_clf.empty():print("模型加载失败！")else:#2.打开默认摄像头cap=cv2.VideoCapture(0)ifnotcap.isOpened():print("摄像头打开失败！")else:print("摄像头已打开，按'q'退出！")whileTrue:ret,frame=cap.read()ifnotret:print("无法读取帧！")breakgray=cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY)#3.检测人脸faces=face_clf.detectMultiScale(gray,1.2,5,(50,50))for(x_face,y_face,w_face,h_face)infaces:#绘制人脸框（绿色）cv2.rectangle(frame,(x_face,y_face),(x_face+w_face,y_face+h_face),(0,255,0),2)cv2.putText(frame,"Face",(x_face,y_face-10),cv2.FONT_HERSHEY_SIMPLEX,0.7,(0,255,0),2)#4.提取人脸ROI（微笑检测仅在ROI内）#微笑通常在人脸下半部分，可缩小ROI范围（y_face+h_face//2开始）smile_roi_gray=gray[y_face+h_face//2:y_face+h_face,x_face:x_face+w_face]smile_roi_color=frame[y_face+h_face//2:y_face+h_face,x_face:x_face+w_face]#5.检测微笑（参数宽松，因微笑特征不明显）smiles=smile_clf.detectMultiScale(smile_roi_gray,scaleFactor=1.1,minNeighbors=2,minSize=(30,30))#绘制微笑框（黄色）for(x_smile,y_smile,w_smile,h_smile)insmiles:cv2.rectangle(smile_roi_color,(x_smile,y_smile),(x_smile+w_smile,y_smile+h_smile),(0,255,255),2)cv2.putText(smile_roi_color,"Smile",(x_smile,y_smile-5),cv2.FONT_HERSHEY_SIMPLEX,0.5,(0,255,255),1)#6.显示实时帧cv2.imshow("Real-timeFace+SmileDetection",frame)#按q退出ifcv2.waitKey(1)&0xFF==ord