《计算机视觉》—— 表情识别
- 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
- 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) / C return 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), textSize=30): 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, encoding="utf-8") draw.text(position, text, textColor, font=fontStyle) # 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: break cv2.destroyAllWindows() cap.release()
原文地址：https://blog.csdn.net/weixin_73504499/article/details/143081668
免责声明：本站文章内容转载自网络资源，如本站内容侵犯了原著者的合法权益，可联系本站删除。更多内容请关注自学内容网（zxcms.com）！