响应式网站概况,淮北建筑大学,有免费推广平台,自己写的网页怎么发布根据计算眼睛、嘴巴的变化#xff0c;判断是什么表情结合以下两篇文章来理解表情识别的实现方法 基于 dilib 库的人脸检测 https://blog.csdn.net/weixin_73504499/article/details/142977202?spm1001.2014.3001.5501 基于 dlib 库的人脸关键点定位 https://blog.csdn.net/we…根据计算眼睛、嘴巴的变化判断是什么表情结合以下两篇文章来理解表情识别的实现方法 基于 dilib 库的人脸检测 https://blog.csdn.net/weixin_73504499/article/details/142977202?spm1001.2014.3001.5501 基于 dlib 库的人脸关键点定位 https://blog.csdn.net/weixin_73504499/article/details/142990867?spm1001.2014.3001.5501 完整代码如下import numpy as np
import dlib
import cv2
from sklearn.metrics.pairwise import euclidean_distances
from PIL import Image, ImageDraw, ImageFont# 计算眼睛的宽高比
def eye_aspect_ratio(eye):A euclidean_distances(eye[1].reshape(1, 2), eye[5].reshape(1, 2))B euclidean_distances(eye[2].reshape(1, 2), eye[4].reshape(1, 2))C euclidean_distances(eye[0].reshape(1, 2), eye[3].reshape(1, 2))ear ((A B) / 2.0) / Creturn ear# 计算嘴的宽高比
def MAR(shape):x shape[50]y shape[50].reshape(1, 2)A euclidean_distances(shape[50].reshape(1, 2), shape[58].reshape(1, 2))B euclidean_distances(shape[51].reshape(1, 2), shape[57].reshape(1, 2))C euclidean_distances(shape[52].reshape(1, 2), shape[56].reshape(1, 2))D euclidean_distances(shape[48].reshape(1, 2), shape[54].reshape(1, 2))return ((A B C) / 3) / D# 计算嘴宽度与脸颊宽度的比值
def MJR(shape):M euclidean_distances(shape[48].reshape(1, 2), shape[54].reshape(1, 2)) # 嘴宽度J euclidean_distances(shape[3].reshape(1, 2), shape[13].reshape(1, 2)) # 下颌的宽度return M / J 向图片中添加中文
def cv2AddChineseText(img, text, position, textColor(0, 255, 0), textSize30):if (isinstance(img, np.ndarray)): # 判断是否是OpenCV图片类型img Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB)) # 实现 array 到 image 的转换draw ImageDraw.Draw(img) # 在img图片上创建一个绘图的对象# 字体的格式 C 盘中的 Windows/Fonts 中复制到此文件夹下可看到文件名fontStyle ImageFont.truetype(simsun.ttc, textSize, encodingutf-8)draw.text(position, text, textColor, fontfontStyle) # 绘制文本return cv2.cvtColor(np.asarray(img), cv2.COLOR_RGB2BGR) # 转换回 OpenCV 格式# 构建脸部位置检测器
detector dlib.get_frontal_face_detector()# 读取人脸关键点定位模型
predictor dlib.shape_predictor(shape_predictor_68_face_landmarks.dat)# 打开摄像头或视频
cap cv2.VideoCapture(0)while True:ret, frame cap.read()frame cv2.flip(frame, 1)faces detector(frame, 0) # 获取图片中全部人脸位置for face in faces:shape predictor(frame, face) # 获取关键点# 将关键点转换为坐标(x,y)的形式shape np.array([[p.x, p.y] for p in shape.parts()])# 计算嘴部的高宽比mar MAR(shape)# 计算 “最宽/脸颊宽”mjr MJR(shape)rightEye shape[36:42] # 右眼关键点索引从36到41(不包含42)leftEye shape[42:48] # 左眼关键点索引从42到47(不包含48)rightEAR eye_aspect_ratio(rightEye) # 计算右眼纵横比leftEAR eye_aspect_ratio(leftEye) # 计算左眼纵横比ear (rightEAR leftEAR) / 2.0 # 均值处理result 正常 # 默认是正常表情# 打印出实际值可以根据该值更改阈值print(mar, mar, \tmjr, mjr, \tear, ear)if mar 0.5 and ear 0.28:result 大笑elif mar 0.5 and ear 0.28:result 愤怒elif mjr 0.45:result 微笑# 输出中文# frame cv2AddChineseText(frame, result, (50, 100))# cv2.putText()#输出英文mouthHull cv2.convexHull(shape[48:61]) # 嘴型构造凸包frame cv2AddChineseText(frame, result, mouthHull[0, 0]) # 多人脸cv2.drawContours(frame, [mouthHull], -1, (0, 255, 0), 1) # 画出凸包cv2.imshow(Frame, frame)if cv2.waitKey(1) 27:breakcv2.destroyAllWindows()
cap.release()
