def camera_detector(self,cap,wait = 10):
detect_timer = Timer()
ret,_ = cap.read()
while ret:
ret,frame = cap.read()
detect_timer.tic()
result = self.detect(frame)
detect_timer.toc()
print('Average detecting time: {:.3f}s'.format( #统计处理一帧数据的平均检测时间
detect_timer.average_time))
self.draw_result(frame,result)
cv2.imshow('camera',frame)
cv2.waiKey(wait)
ret,frame = cap.read()
def img_cv_show(self, image_data, contour=[], centroid=[]):
if (contour != [] and centroid != [])
cv2.drawContours(image_data,
contours=[contour],
contourIdx=1,
color=(0,255,0),
thickness=2)
offCenterX = str(centroid[0] - image_data.shape[0]/2)
offCenterY = str(image_data.shape[1] - centroid[1])
offCenter = "(" + offCenterX + "," + offCenterY + ")"
cv2.putText(image_data,
text=offCenter,
org=(centroid[0]-20, centroid[1]-20),
fontFace=cv2.FONT_HERSHEY_SIMPLEX,
fontScale=0.5,
color=(0,0,0))
cv2.imshow('Image Window', image_data)
cv2.waiKey(3)
print 'processing %s...' % fn,
img = cv2.imread(fn, 0)
if img is None:
print "Failed to load", fn
continue
h, w = img.shape[:2]
print("%d,%d" %(h,w))
found, corners = cv2.findChessboardCorners(img, pattern_size)
if found:
term = ( cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_COUNT, 30, 0.1 )
cv2.cornerSubPix(img, corners, (5, 5), (-1, -1), term)
if debug_dir:
vis = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
cv2.drawChessboardCorners(vis, pattern_size, corners, found)
cv2.imshow("Image",vis)
cv2.waiKey(0)
# path, name, ext = splitfn(fn)
# cv2.imwrite('%s/%s_chess.bmp' % (debug_dir, name), vis)
if not found:
print 'chessboard not found'
continue
img_points.append(corners.reshape(-1, 2))
obj_points.append(pattern_points)
print 'ok'
rms, camera_matrix, dist_coefs, rvecs, tvecs = cv2.calibrateCamera(obj_points, img_points, (w, h), None, None)
print "RMS:", rms
print "camera matrix:\n", camera_matrix
print "distortion coefficients: ", dist_coefs.ravel()
cv2.destroyAllWindows()
from picamera.array import PiRGBArray
from picamera import PiCamera
import cv2
import time
camera = PiCamera()
rawCapture = PiRGBArray(camera)
time.sleep(0.1)
camera.capture(rawCapture, format="bgr")
image = rawCapture.array
cv2.imshow("Image", image)
cv2.waiKey(0)
# CV_CAP_PROP_FORMAT由返回的Mat对象的格式retrieve()。
# CV_CAP_PROP_MODE特定于后端的值，指示当前的捕获模式。
# CV_CAP_PROP_BRIGHTNESS图像的亮度（仅适用于相机）。
# CV_CAP_PROP_CONTRAST图像的对比度（仅适用于相机）。
# CV_CAP_PROP_SATURATION图像的饱和度（仅适用于相机）。
# CV_CAP_PROP_HUE图像的色相（仅适用于相机）。
# CV_CAP_PROP_GAIN图像的增益（仅适用于相机）。
# CV_CAP_PROP_EXPOSURE曝光（仅适用于相机）。
# CV_CAP_PROP_CONVERT_RGB布尔型标志，指示是否应将图像转换为RGB。
# CV_CAP_PROP_WHITE_BALANCE_U白平衡设置的U值（注意：当前仅由DC1394 v 2.x后端支持）
# CV_CAP_PROP_WHITE_BALANCE_V白平衡设置的V值（注意：当前仅受DC1394 v 2.x后端支持）
# CV_CAP_PROP_RECTIFICATION立体摄像机的纠正标志（注意：当前仅受DC1394 v 2.x后端支持）
# CV_CAP_PROP_ISO_SPEED摄像机的ISO速度（注意：当前仅受DC1394 v 2.x后端支持）
CV_CAP_PROP_BUFFERSIZE存储在内部缓冲存储器中的帧数（注意：当前仅受DC1394 v 2.x后端支持）
注意：在播放每一帧时，使用 cv2.waiKey() 设置适当的持续时间。如果设置的太低视频就会播放的非常快，如果设置的太高就
会播放的很慢（你可以使用这种方法控制视频的播放速度）。通常情况下 25 毫秒就可以了。
cv2.VideoWriter() # 保存视频流、
cv2.VideoWriter_fourcc(*'XVID') # 视频的编码格式
In Fedora: DIVX, XVID, MJPG, X264, WMV1, WMV2. (XVID is more preferable. MJPG results in high size video. X264 givesvery small size video)
In Windows: DIVX (More to be tested and added)
In OSX : (I don’t have access to OSX. Can some one fill this?)
FourCC 码以下面的格式传给程序，以 MJPG 为例：
cv2.cv.FOURCC('M','J','P','G') 或者 cv2.cv.FOURCC(*'MJPG')。
cv2.VideoWriter('output.avi',fourcc, 20.0, (640,480)) #1.保存视频流名称，编码格式，帧率，每一帧大小
for (i,c) in enumerate(cnts):
#zapewniamy wykrycie jedynie 3 markerów
#break if more than 3 contours appears
if i >= 3:
break
M=cv2.moments(c)
cX=int((M['m10']/(0.00001+M['m00']))*ratio)
cY=int((M['m01']/(0.00001+M['m00']))*ratio)
shape=sd.detect(c)
#rysujemy kontury z nazwą kształtu
c=c.astype('float')
c*=ratio
c=c.astype('int')
cv2.drawContours(frame, [c], -1, (0, 255, 0), 2)
cv2.putText(frame, shape, (cX, cY), cv2.FONT_HERSHEY_SIMPLEX,
0.5, (255,255,255), 2)
cv2.imshow('Image', frame)
key=cv2.waiKey(1)&0xFF
if key==ord('q'):
break
cv2.destroyAllWindows()
vs.stop()
lables=[]
faceData=[]
label=0
for nameDir in peoplelist:
personPath= dataPath + '/' + nameDir
print ('Leyendo las imagenes')
for fileName in os.listdir(personPath):
print ('Rostros:', nameDir + '/' +fileName)
labels.append (label)
faceData.append(cv2.imread (personPath+'/'+fileName,0))
image= cv2.imread(personPath+'/'+fileName, 0)
cv2.imshow( "image", image)
cv2.waiKey(10)
label= label +1
print ('labels',labels)
print ('Número de etiquetas 0: ' np.count_nonzero(np.array(labels)==0))
print ('Número de etiquetas 1: ' np.count_nonzero(np.array(labels)==1))
face_recognizer = cv2.face.EigenFaceRecognizer_create()
#Entrenando el reconocedor de rostros
print('Entrenando...')
face_recognizer.train(facesData, np.array(lables))
#Almacenamiendo el modelo obtenido
face_recognizer.write('modeloEigenFace.xml')
#1.导入与显示
#导入模块
import cv2 as cv
#读取图片
img = cv.imread("test1.jpg") #路径非中文；
#显示图片
cv.imshow("read_img", img)
#等待键盘输入 单位毫秒 0为无限等待 1000ms=1s
cv.waitKey(0)
cv.waiKey(3000)
#释放内存 由于Opencv底层由C++编写
cv.destroyAllWindows()