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()