# OpenCV计算机视觉实战(Python版)资源

## pan.baidu.com/s/1Ng_-utB8BSrXlgVelc8ovw

#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()
help="path to facial landmark predictor")
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
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
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
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.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

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

pan。baidu。com/s/1Ng_-utB8BSrXlgVelc8ovw

posted @ 2019-08-15 11:31  罂粟花的博客  阅读(...)  评论(...编辑  收藏