疲劳检测
#导入工具包 from scipy.spatial import distance as dist from collections import OrderedDict import numpy as np import argparse import time import dlib import cv2 FACIAL_LANDMARKS_68_IDXS = OrderedDict([ ("mouth", (48, 68)), ("right_eyebrow", (17, 22)), ("left_eyebrow", (22, 27)), ("right_eye", (36, 42)), ("left_eye", (42, 48)), ("nose", (27, 36)), ("jaw", (0, 17)) ]) # http://vision.fe.uni-lj.si/cvww2016/proceedings/papers/05.pdf def eye_aspect_ratio(eye): # 计算距离,竖直的 A = dist.euclidean(eye[1], eye[5]) B = dist.euclidean(eye[2], eye[4]) # 计算距离,水平的 C = dist.euclidean(eye[0], eye[3]) # ear值 ear = (A + B) / (2.0 * C) return ear # 输入参数 ap = argparse.ArgumentParser() ap.add_argument("-p", "--shape-predictor", required=True, help="path to facial landmark predictor") ap.add_argument("-v", "--video", type=str, default="", help="path to input video file") args = vars(ap.parse_args()) # 设置判断参数 EYE_AR_THRESH = 0.3 EYE_AR_CONSEC_FRAMES = 3 # 初始化计数器 COUNTER = 0 TOTAL = 0 # 检测与定位工具 print("[INFO] loading facial landmark predictor...") detector = dlib.get_frontal_face_detector() predictor = dlib.shape_predictor(args["shape_predictor"]) # 分别取两个眼睛区域 (lStart, lEnd) = FACIAL_LANDMARKS_68_IDXS["left_eye"] (rStart, rEnd) = FACIAL_LANDMARKS_68_IDXS["right_eye"] # 读取视频 print("[INFO] starting video stream thread...") vs = cv2.VideoCapture(args["video"]) #vs = FileVideoStream(args["video"]).start() time.sleep(1.0) def shape_to_np(shape, dtype="int"): # 创建68*2 coords = np.zeros((shape.num_parts, 2), dtype=dtype) # 遍历每一个关键点 # 得到坐标 for i in range(0, shape.num_parts): coords[i] = (shape.part(i).x, shape.part(i).y) return coords # 遍历每一帧 while True: # 预处理 frame = vs.read()[1] if frame is None: break (h, w) = frame.shape[:2] width=1200 r = width / float(w) dim = (width, int(h * r)) frame = cv2.resize(frame, dim, interpolation=cv2.INTER_AREA) gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) # 检测人脸 rects = detector(gray, 0) # 遍历每一个检测到的人脸 for rect in rects: # 获取坐标 shape = predictor(gray, rect) shape = shape_to_np(shape) # 分别计算ear值 leftEye = shape[lStart:lEnd] rightEye = shape[rStart:rEnd] leftEAR = eye_aspect_ratio(leftEye) rightEAR = eye_aspect_ratio(rightEye) # 算一个平均的 ear = (leftEAR + rightEAR) / 2.0 # 绘制眼睛区域 leftEyeHull = cv2.convexHull(leftEye) rightEyeHull = cv2.convexHull(rightEye) cv2.drawContours(frame, [leftEyeHull], -1, (0, 255, 0), 1) cv2.drawContours(frame, [rightEyeHull], -1, (0, 255, 0), 1) # 检查是否满足阈值 if ear < EYE_AR_THRESH: COUNTER += 1 else: # 如果连续几帧都是闭眼的,总数算一次 if COUNTER >= EYE_AR_CONSEC_FRAMES: TOTAL += 1 # 重置 COUNTER = 0 # 显示 cv2.putText(frame, "Blinks: {}".format(TOTAL), (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2) cv2.putText(frame, "EAR: {:.2f}".format(ear), (300, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2) cv2.imshow("Frame", frame) key = cv2.waitKey(10) & 0xFF if key == 27: break vs.release() cv2.destroyAllWindows()
OpenCV计算机视觉实战
唐宇迪老师的课程讲的挺好的 就是贵了点
课程目录
01课程简介与环境配置
02图像基本操作
03阈值与平滑处理
04图像形态学操作
05图像梯度计算
06边缘检测
07图像金字塔与轮廓检测
08直方图与傅里叶变换
09项目实战-信用卡数字识别
10项目实战-文档扫描OCR识别
11图像特征-harris
12图像特征-sift
13案例实战-全景图像拼接
14项目实战-停车场车位识别
15项目实战-答题卡识别判卷
16背景建模
17光流估计
18Opencv的DNN模块
19项目实战-目标追踪
20卷积原理与操作
21项目实战-疲劳检测
#导入工具包from scipy.spatial import distance as distfrom collections import OrderedDictimport numpy as npimport argparseimport timeimport dlibimport cv2
FACIAL_LANDMARKS_68_IDXS = OrderedDict([("mouth", (48, 68)),("right_eyebrow", (17, 22)),("left_eyebrow", (22, 27)),("right_eye", (36, 42)),("left_eye", (42, 48)),("nose", (27, 36)),("jaw", (0, 17))])
# http://vision.fe.uni-lj.si/cvww2016/proceedings/papers/05.pdfdef eye_aspect_ratio(eye):# 计算距离,竖直的A = dist.euclidean(eye[1], eye[5])B = dist.euclidean(eye[2], eye[4])# 计算距离,水平的C = dist.euclidean(eye[0], eye[3])# ear值ear = (A + B) / (2.0 * C)return ear # 输入参数ap = argparse.ArgumentParser()ap.add_argument("-p", "--shape-predictor", required=True,help="path to facial landmark predictor")ap.add_argument("-v", "--video", type=str, default="",help="path to input video file")args = vars(ap.parse_args()) # 设置判断参数EYE_AR_THRESH = 0.3EYE_AR_CONSEC_FRAMES = 3
# 初始化计数器COUNTER = 0TOTAL = 0
# 检测与定位工具print("[INFO] loading facial landmark predictor...")detector = dlib.get_frontal_face_detector()predictor = dlib.shape_predictor(args["shape_predictor"])
# 分别取两个眼睛区域(lStart, lEnd) = FACIAL_LANDMARKS_68_IDXS["left_eye"](rStart, rEnd) = FACIAL_LANDMARKS_68_IDXS["right_eye"]
# 读取视频print("[INFO] starting video stream thread...")vs = cv2.VideoCapture(args["video"])#vs = FileVideoStream(args["video"]).start()time.sleep(1.0)
def shape_to_np(shape, dtype="int"):# 创建68*2coords = np.zeros((shape.num_parts, 2), dtype=dtype)# 遍历每一个关键点# 得到坐标for i in range(0, shape.num_parts):coords[i] = (shape.part(i).x, shape.part(i).y)return coords
# 遍历每一帧while True:# 预处理frame = vs.read()[1]if frame is None:break(h, w) = frame.shape[:2]width=1200r = width / float(w)dim = (width, int(h * r))frame = cv2.resize(frame, dim, interpolation=cv2.INTER_AREA)gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
# 检测人脸rects = detector(gray, 0)
# 遍历每一个检测到的人脸for rect in rects:# 获取坐标shape = predictor(gray, rect)shape = shape_to_np(shape)
# 分别计算ear值leftEye = shape[lStart:lEnd]rightEye = shape[rStart:rEnd]leftEAR = eye_aspect_ratio(leftEye)rightEAR = eye_aspect_ratio(rightEye)
# 算一个平均的ear = (leftEAR + rightEAR) / 2.0
# 绘制眼睛区域leftEyeHull = cv2.convexHull(leftEye)rightEyeHull = cv2.convexHull(rightEye)cv2.drawContours(frame, [leftEyeHull], -1, (0, 255, 0), 1)cv2.drawContours(frame, [rightEyeHull], -1, (0, 255, 0), 1)
# 检查是否满足阈值if ear < EYE_AR_THRESH:COUNTER += 1
else:# 如果连续几帧都是闭眼的,总数算一次if COUNTER >= EYE_AR_CONSEC_FRAMES:TOTAL += 1
# 重置COUNTER = 0
# 显示cv2.putText(frame, "Blinks: {}".format(TOTAL), (10, 30),cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)cv2.putText(frame, "EAR: {:.2f}".format(ear), (300, 30),cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)
cv2.imshow("Frame", frame)key = cv2.waitKey(10) & 0xFF if key == 27:break
vs.release()cv2.destroyAllWindows()