opencvpython实战_OpenCV计算机视觉实战(Python版)

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()