def detect_face(self, image):
min_size = (20, 20)
image_scale = 2
haar_scale = 1.1
min_neighbors = 2
haar_flags = 0
# Allocate the temporary images
gray = cv.CreateImage((image.width, image.height), 8, 1)
smallImage = cv.CreateImage((cv.Round(
image.width / image_scale), cv.Round(image.height / image_scale)),
8, 1)
# Convert color input image to grayscale
cv.CvtColor(image, gray, cv.CV_BGR2GRAY)
# Scale input image for faster processing
cv.Resize(gray, smallImage, cv.CV_INTER_LINEAR)
# Equalize the histogram
cv.EqualizeHist(smallImage, smallImage)
# Detect the faces
faces = cv.HaarDetectObjects(smallImage, self.cascade,
cv.CreateMemStorage(0), haar_scale,
min_neighbors, haar_flags, min_size)
return faces
def show_img(self):
global face_rect
global cam
# 一个死循环，用来不间断的显示图片
while True:
img = cv.QueryFrame(cam) # 取出视频中的一帧
# 保存三通道的图片
src = cv.CreateImage((img.width, img.height), 8, 3)
cv.Resize(img, src, cv.CV_INTER_LINEAR)
# 保存灰度图片
gray = cv.CreateImage((img.width, img.height), 8, 1)
cv.CvtColor(img, gray, cv.CV_BGR2GRAY) # 将rgb图片变成灰度图
cv.EqualizeHist(gray, gray) # 对灰度图进行直方图均衡化
rects = detect(gray, cascade) # 传入图片和分类器，如果检测到人脸，返回人脸的坐标和大小
face_rect = rects
# 话那个绿色的人脸框
draw_rects(src, rects, (0, 255, 0))
# 显示画框的人脸
cv.ShowImage('DeepFace ZhangLi', src)
#path = 'C:/Users/ZhangLi/Desktop/demo.jpg'
#showImage = QtGui.QImage(img.data, img.shape[1], img.shape[0], QtGui.QImage.Format_RGB888)
#self.show_video.setPixmap(QtGui.QPixmap.fromImage(showImage))
#png = QPixmap(path)
#self.show_video.setPixmap(png)
cv2.waitKey(5) == 27
cv2.destroyAllWindows()
def normalize(self, image):
#Checks whether inputs are correct
if self.image_check(image) < 0:
return -1
#chaning the image to grayscale
gsimage = cv.CreateImage(cv.GetSize(image), cv.IPL_DEPTH_8U, 1)
newgsimage = cv.CreateImage(cv.GetSize(image), cv.IPL_DEPTH_8U, 1)
cv.CvtColor(image, gsimage, cv.CV_RGB2GRAY)
cv.EqualizeHist(gsimage, newgsimage)
if self.visualize:
while True:
cv.NamedWindow("Normal")
cv.ShowImage("Normal", gsimage)
cv.WaitKey(5)
cv.NamedWindow("Histogram Equalized")
cv.ShowImage("Histogram Equalized", newgsimage)
if cv.WaitKey(5) == 1048603:
break
cv.DestroyAllWindows()
return newgsimage
def detect_and_draw(img, face_cascade):
gray = cv.CreateImage((img.width, img.height), 8, 1)
image_scale = img.width / smallwidth
small_img = cv.CreateImage((cv.Round(
img.width / image_scale), cv.Round(img.height / image_scale)), 8, 1)
# gray = cv.CreateImage((img.width,img.height), 8, 1)
image_scale = img.width / smallwidth
# small_img = cv.CreateImage((cv.Round(img.width / image_scale), cv.Round (img.height / image_scale)), 8, 1)
# convert color input image to grayscale
cv.CvtColor(img, gray, cv.CV_BGR2GRAY)
# scale input image for faster processing
cv.Resize(gray, small_img, cv.CV_INTER_LINEAR)
cv.EqualizeHist(small_img, small_img)
faces = cv.HaarDetectObjects(small_img, face_cascade,
cv.CreateMemStorage(0), haar_scale,
min_neighbors, haar_flags, min_size)
if opencv_preview and faces:
for ((x, y, w, h), n) in faces:
# the input to cv.HaarDetectObjects was resized, so scale the
# bounding box of each face and convert it to two CvPoints
pt1 = (int(x * image_scale), int(y * image_scale))
pt2 = (int((x + w) * image_scale), int((y + h) * image_scale))
cv.Rectangle(img, pt1, pt2, cv.RGB(255, 0, 0), 3, 8, 0)
if verbose:
print "Face at: ", pt1[0], ",", pt2[0], "\t", pt1[1], ",", pt2[
1]
return True if faces else False
def detect_and_draw(img, cascade):
# allocate temporary images
gray = cv.CreateImage((img.width, img.height), 8, 1)
small_img = cv.CreateImage((cv.Round(img.width / image_scale),
cv.Round(img.height / image_scale)), 8, 1)
# convert color input image to grayscale
cv.CvtColor(img, gray, cv.CV_BGR2GRAY)
# scale input image for faster processing
cv.Resize(gray, small_img, cv.CV_INTER_LINEAR)
cv.EqualizeHist(small_img, small_img)
if(cascade):
t = cv.GetTickCount()
faces = cv.HaarDetectObjects(small_img, cascade, cv.CreateMemStorage(0),
haar_scale, min_neighbors, haar_flags, min_size)
t = cv.GetTickCount() - t
print "detection time = %gms" % (t / (cv.GetTickFrequency() * 1000.))
if faces:
for ((x, y, w, h), n) in faces:
# the input to cv.HaarDetectObjects was resized, so scale the
# bounding box of each face and convert it to two CvPoints
pt1 = (int(x * image_scale), int(y * image_scale))
pt2 = (int((x + w) * image_scale), int((y + h) * image_scale))
cv.Rectangle(img, pt1, pt2, cv.RGB(255, 0, 0), 3, 8, 0)
cv.ShowImage("result", img)
def detectFace(img, cascade):
# allocate temporary images
gray = cv.CreateImage((img.width, img.height), 8, 1)
small_img = cv.CreateImage(
(cv.Round(img.width / imageScale), cv.Round(img.height / imageScale)),
8, 1)
# convert color input image to grayscale
cv.CvtColor(img, gray, cv.CV_BGR2GRAY)
# scale input image for faster processing
cv.Resize(gray, small_img, cv.CV_INTER_LINEAR)
cv.EqualizeHist(small_img, small_img)
faces = cv.HaarDetectObjects(small_img, cascade, cv.CreateMemStorage(0),
haarScale, minNeighbors, haarFlags, minSize)
if faces:
print "\tDetected ", len(faces), " object(s)"
for ((x, y, w, h), n) in faces:
#the input to cv.HaarDetectObjects was resized, scale the
#bounding box of each face and convert it to two CvPoints
pt1 = (int(x * imageScale), int(y * imageScale))
pt2 = (int((x + w) * imageScale), int((y + h) * imageScale))
cv.Rectangle(img, pt1, pt2, cv.RGB(255, 0, 0), 3, 8, 0)
return img
else:
return False
def capture():
"""
Using the intel training set to capture the face in the video.
Most of them are frameworks in OpenCV.
"""
j = 0
g = os.walk("origin")
for path, d, filelist in g:
for filename in filelist:
img = cv.LoadImage(os.path.join(path, filename))
image_size = cv.GetSize(img)
greyscale = cv.CreateImage(image_size, 8, 1)
cv.CvtColor(img, greyscale, cv.CV_BGR2GRAY)
storage = cv.CreateMemStorage(0)
cv.EqualizeHist(greyscale, greyscale)
cascade = cv.Load('haarcascade_frontalface_alt2.xml')
faces = cv.HaarDetectObjects(greyscale, cascade, storage, 1.2, 2,
cv.CV_HAAR_DO_CANNY_PRUNING, (50, 50))
for (x, y, w, h), n in faces:
j += 1
cv.SetImageROI(img, (x, y, w, h))
cv.SaveImage("captured/face" + str(j) + ".png", img)
def DetectFace(image, faceCascade, returnImage=False):
# This function takes a grey scale cv image and finds
# the patterns defined in the haarcascade function
# modified from: http://www.lucaamore.com/?p=638
#variables
min_size = (30, 30)
#image_scale = 2
haar_scale = 1.1
min_neighbors = 2
haar_flags = 0
# Equalize the histogram
cv.EqualizeHist(image, image)
# Detect the faces
faces = cv.HaarDetectObjects(image, faceCascade, cv.CreateMemStorage(0),
haar_scale, min_neighbors, haar_flags,
min_size)
# If faces are found
if faces and returnImage:
for ((x, y, w, h), n) in faces:
# Convert bounding box to two CvPoints
pt1 = (int(x), int(y))
pt2 = (int(x + w), int(y + h))
cv.Rectangle(image, pt1, pt2, cv.RGB(255, 0, 0), 5, 8, 0)
if returnImage:
return image
else:
return faces
def detect_and_draw(self, img, cascade, camera_position=0):
min_size = (20, 20)
image_scale = self.horizontalSlider_3.value()
haar_scale = 1.2
min_neighbors = 2
haar_flags = 0
# allocate temporary images
gray = cv.CreateImage((img.width, img.height), 8, 1)
small_img_height = cv.Round(img.height / image_scale)
small_img = cv.CreateImage(
(cv.Round(img.width / image_scale), small_img_height), 8, 1)
# convert color input image to grayscale
cv.CvtColor(img, gray, cv.CV_BGR2GRAY)
# scale input image for faster processing
cv.Resize(gray, small_img, cv.CV_INTER_LINEAR)
cv.EqualizeHist(small_img, small_img)
faces = cv.HaarDetectObjects(small_img, cascade,
cv.CreateMemStorage(0), haar_scale,
min_neighbors, haar_flags, min_size)
if faces:
for ((x, y, w, h), n) in faces:
if self.face_cert < n:
x2, y2, w2, h2 = self.make_the_rectangle_bigger(
x, y, w, h, 1.22, small_img_height, image_scale)
self.create_person_and_add_to_room(img, (x2, y2, w2, h2),
camera_position)
if self.mark_detected_objects[camera_position]:
pt2 = (int(x2 + w2), int(y2 + h2))
cv.Rectangle(img, (x2, y2), pt2, cv.RGB(255, 0, 0), 3,
8, 0)
if self.show_main_view[camera_position]:
cv.ShowImage("result" + str(camera_position), img)
def Magnitude(self, dx, dy, Mask=None, precise=True, method="cv"):
'''Calculates the magnitude of the gradient using precise and fast approach'''
dxconv = cv.CreateImage(cv.GetSize(dx), cv.IPL_DEPTH_32F, dx.channels)
dyconv = cv.CreateImage(cv.GetSize(dx), cv.IPL_DEPTH_32F, dx.channels)
dxdest = cv.CreateImage(cv.GetSize(dx), cv.IPL_DEPTH_32F, dx.channels)
dydest = cv.CreateImage(cv.GetSize(dx), cv.IPL_DEPTH_32F, dx.channels)
magdest = cv.CreateImage(cv.GetSize(dx), cv.IPL_DEPTH_32F, dx.channels)
magnitude = cv.CreateImage(cv.GetSize(dx), cv.IPL_DEPTH_32F,
dx.channels)
magnitudetemp = cv.CreateImage(cv.GetSize(dx), cv.IPL_DEPTH_32F,
dx.channels)
zero = cv.CreateImage(cv.GetSize(dx), cv.IPL_DEPTH_32F, dx.channels)
cv.Convert(dx, dxconv)
cv.Convert(dy, dyconv)
if precise:
cv.Pow(dxconv, dxdest, 2)
cv.Pow(dyconv, dydest, 2)
cv.Add(dxdest, dydest, magdest)
cv.Pow(magdest, magnitude, 1. / 2)
else:
#Add the |dx| + |dy|
return None
if method == "slow":
size = cv.GetSize(magnitude)
for x in range(size[0]):
for y in range(size[1]):
if Mask == None:
pass
elif Mask[y, x] > 0:
pass
else:
magnitude[y, x] = 0
final = cv.CreateImage(cv.GetSize(dx), cv.IPL_DEPTH_8U,
dx.channels)
cv.ConvertScaleAbs(magnitude, final)
else:
cv.Add(zero, magnitude, magnitudetemp, Mask)
final = cv.CreateImage(cv.GetSize(dx), cv.IPL_DEPTH_8U,
dx.channels)
cv.ConvertScaleAbs(magnitudetemp, final)
if self.visualize:
magnitude2 = cv.CreateImage(cv.GetSize(dy), cv.IPL_DEPTH_8U, 1)
cv.EqualizeHist(final, magnitude2)
while True:
cv.NamedWindow("Magnitude")
cv.ShowImage("Magnitude", magnitude2)
c = cv.WaitKey(5)
if c > 0:
break
cv.DestroyAllWindows()
return final
def detect_and_draw(img, cascade, jpg_cnt):
# allocate temporary images
gray = cv.CreateImage((img.width, img.height), 8, 1)
small_img = cv.CreateImage((cv.Round(
img.width / image_scale), cv.Round(img.height / image_scale)), 8, 1)
# convert color input image to grayscale
cv.CvtColor(img, gray, cv.CV_BGR2GRAY)
# scale input image for faster processing
cv.Resize(gray, small_img, cv.CV_INTER_LINEAR)
cv.EqualizeHist(small_img, small_img)
if (cascade):
t = cv.GetTickCount()
faces = cv.HaarDetectObjects(small_img, cascade,
cv.CreateMemStorage(0), haar_scale,
min_neighbors, haar_flags, min_size)
t = cv.GetTickCount() - t
print "detection time = %gms" % (t / (cv.GetTickFrequency() * 10000))
if faces:
for ((x, y, w, h), n) in faces:
# the input to cv.HaarDetectObjects was resized, so scale the
# bounding box of each face and convert it to two CvPoints
pt1 = (int(x * image_scale), int(y * image_scale))
pt2 = (int((x + w) * image_scale), int((y + h) * image_scale))
cv.Rectangle(img, pt1, pt2, cv.RGB(255, 0, 0), 3, 8, 0)
if jpg_cnt % 50 == 1:
print('capture completed')
cv.SaveImage('test_' + str(jpg_cnt) + '.jpg', img)
print("aaa1")
url = 'http://210.94.185.52:8080/upload.php'
#files={ 'upfiles' : open('/home/lee/test_'+str(jpg_cnt)+'.jpg','rb')}
files = {
'upfiles':
open('/home/lee/test_' + str(jpg_cnt) + '.jpg', 'rb')
}
print("aaa2")
r = requests.post(url, files=files)
print("aaa3")
print(r.text)
for i in r.text.split():
try:
op = float(i)
break
except:
continue
print(op)
#LED
if op >= 0.9:
lock_on()
else:
print('no')
cv.ShowImage("result", img)
def track(img, threshold=100):
'''Accepts BGR image and optional object threshold between 0 and 255 (default = 100).
Returns: (x,y) coordinates of centroid if found
(-1,-1) if no centroid was found
None if user hit ESC
'''
cascade = cv.Load("haarcascade_frontalface_default.xml")
gray = cv.CreateImage((img.width, img.height), 8, 1)
small_img = cv.CreateImage((cv.Round(
img.width / image_scale), cv.Round(img.height / image_scale)), 8, 1)
# convert color input image to grayscale
cv.CvtColor(img, gray, cv.CV_BGR2GRAY)
# scale input image for faster processing
cv.Resize(gray, small_img, cv.CV_INTER_LINEAR)
cv.EqualizeHist(small_img, small_img)
center = (-1, -1)
faces = []
original_size_faces = []
#import ipdb; ipdb.set_trace()
if (cascade):
t = cv.GetTickCount()
# HaarDetectObjects takes 0.02s
faces = cv.HaarDetectObjects(small_img, cascade,
cv.CreateMemStorage(0), haar_scale,
min_neighbors, haar_flags, min_size)
t = cv.GetTickCount() - t
if faces:
for ((x, y, w, h), n) in faces:
# the input to cv.HaarDetectObjects was resized, so scale the
# bounding box of each face and convert it to two CvPoints
pt1 = (int(x * image_scale), int(y * image_scale))
pt2 = (int((x + w) * image_scale), int((y + h) * image_scale))
# cv.Rectangle(img, pt1, pt2, cv.RGB(255, 0, 0), 3, 8, 0)
#cv.Rectangle(img, (x,y), (x+w,y+h), 255)
# get the xy corner co-ords, calc the center location
x1 = pt1[0]
x2 = pt2[0]
y1 = pt1[1]
y2 = pt2[1]
centerx = x1 + ((x2 - x1) / 2)
centery = y1 + ((y2 - y1) / 2)
center = (centerx, centery)
scaled = ((x1, y1, x2 - x1, y2 - y1), n)
original_size_faces.append(scaled)
# print scaled
# cv.NamedWindow(WINDOW_NAME, 1)
# cv.ShowImage(WINDOW_NAME, img)
# if cv.WaitKey(5) == 27:
# center = None
return (center, original_size_faces)
def histogramequalization():
src = cv.LoadImage(getpath(), cv.CV_LOAD_IMAGE_GRAYSCALE)
dst = cv.CreateImage((src.width, src.height), src.depth, src.channels)
cv.EqualizeHist(src, dst)
cv.NamedWindow("SourceImage", 1)
cv.NamedWindow("EqualizedImage", 1)
cv.ShowImage("SourceImage", src)
cv.ShowImage("EqualizedImage", dst)
cv.WaitKey(0)
def DetectRedEyes(image, faceCascade, eyeCascade): min_size = (20,20) image_scale = 2 haar_scale = 1.2 min_neighbors = 2 haar_flags = 0 # Allocate the temporary images gray = cv.CreateImage((image.width, image.height), 8, 1) smallImage = cv.CreateImage((cv.Round(image.width / image_scale),cv.Round (image.height / image_scale)), 8 ,1) # Convert color input image to grayscale cv.CvtColor(image, gray, cv.CV_BGR2GRAY) # Scale input image for faster processing cv.Resize(gray, smallImage, cv.CV_INTER_LINEAR) # Equalize the histogram cv.EqualizeHist(smallImage, smallImage) # Detect the faces faces = cv.HaarDetectObjects(smallImage, faceCascade, cv.CreateMemStorage(0), haar_scale, min_neighbors, haar_flags, min_size) # If faces are found if faces: for ((x, y, w, h), n) in faces: # the input to cv.HaarDetectObjects was resized, so scale the # bounding box of each face and convert it to two CvPoints pt1 = (int(x * image_scale), int(y * image_scale)) pt2 = (int((x + w) * image_scale), int((y + h) * image_scale)) cv.Rectangle(image, pt1, pt2, cv.RGB(255, 0, 0), 3, 8, 0) face_region = cv.GetSubRect(image,(x,int(y + (h/4)),w,int(h/2))) cv.SetImageROI(image, (pt1[0], pt1[1], pt2[0] - pt1[0], int((pt2[1] - pt1[1]) * 0.7))) eyes = cv.HaarDetectObjects(image, eyeCascade, cv.CreateMemStorage(0), haar_scale, min_neighbors, haar_flags, (15,15)) if eyes: # For each eye found for eye in eyes: # Draw a rectangle around the eye cv.Rectangle(image, (eye[0][0], eye[0][1]), (eye[0][0] + eye[0][2], eye[0][1] + eye[0][3]), cv.RGB(255, 0, 0), 1, 8, 0) cv.ResetImageROI(image) return image
def readWholeImg(imgname):
curFrame = cv.LoadImage(imgname, 1)
gray = cv.CreateImage((curFrame.width, curFrame.height), 8, 1)
cv.CvtColor(curFrame, gray, cv.CV_BGR2GRAY)
img48 = cv.CreateImage((48, 48), 8, 1)
cv.Resize(gray, img48, cv.CV_INTER_LINEAR)
cv.EqualizeHist(img48, img48)
face_vector = np.asarray(img48[:, :])
face_vector = face_vector.reshape(48 * 48)
return [[face_vector], curFrame]
def OnPaint(self, evt):
if not self.timer.IsRunning() :
dc = wx.BufferedDC(wx.ClientDC(self), wx.NullBitmap, wx.BUFFER_VIRTUAL_AREA)
dc.SetBackground(wx.Brush(wx.Colour(0, 0, 0)))
return
# Capture de l'image
frame = cv.QueryFrame(CAMERA)
cv.CvtColor(frame, frame, cv.CV_BGR2RGB)
Img = wx.EmptyImage(frame.width, frame.height)
Img.SetData(frame.tostring())
self.bmp = wx.BitmapFromImage(Img)
width, height = frame.width, frame.height
# Détection des visages
min_size = (20, 20)
image_scale = 2
haar_scale = 1.2
min_neighbors = 2
haar_flags = 0
gray = cv.CreateImage((frame.width, frame.height), 8, 1)
small_img = cv.CreateImage((cv.Round(frame.width / image_scale), cv.Round (frame.height / image_scale)), 8, 1)
cv.CvtColor(frame, gray, cv.CV_BGR2GRAY)
cv.Resize(gray, small_img, cv.CV_INTER_LINEAR)
cv.EqualizeHist(small_img, small_img)
listeVisages = cv.HaarDetectObjects(small_img, CASCADE, cv.CreateMemStorage(0), haar_scale, min_neighbors, haar_flags, min_size)
# Affichage de l'image
x, y = (0, 0)
try:
dc = wx.BufferedDC(wx.ClientDC(self), wx.NullBitmap, wx.BUFFER_VIRTUAL_AREA)
try :
dc.SetBackground(wx.Brush(wx.Colour(0, 0, 0)))
except :
pass
dc.Clear()
dc.DrawBitmap(self.bmp, x, y)
# Dessin des rectangles des visages
if listeVisages :
for ((x, y, w, h), n) in listeVisages :
dc.SetBrush(wx.TRANSPARENT_BRUSH)
dc.SetPen(wx.Pen(wx.Colour(255, 0, 0), 2))
dc.DrawRectangle(x* image_scale, y* image_scale, w* image_scale, h* image_scale)
self.listeVisages = listeVisages
del dc
del Img
except TypeError:
pass
except wx.PyDeadObjectError:
pass
def detect_and_draw(img, cascade, detected):
# allocate temporary images
gray = cv.CreateImage((img.width, img.height), 8, 1)
image_scale = img.width / smallwidth
small_img = cv.CreateImage((cv.Round(
img.width / image_scale), cv.Round(img.height / image_scale)), 8, 1)
# convert color input image to grayscale
cv.CvtColor(img, gray, cv.CV_BGR2GRAY)
# scale input image for faster processing
cv.Resize(gray, small_img, cv.CV_INTER_LINEAR)
cv.EqualizeHist(small_img, small_img)
if (cascade):
t = cv.GetTickCount()
faces = cv.HaarDetectObjects(small_img, cascade,
cv.CreateMemStorage(0), haar_scale,
min_neighbors, haar_flags, min_size)
# t = cv.GetTickCount() - t
# print "detection time = %gms" % (t/(cv.GetTickFrequency()*1000.))
if faces:
if detected == 0:
# os.system('festival --tts hi &')
detected = 1
for ((x, y, w, h), n) in faces:
# the input to cv.HaarDetectObjects was resized, so scale the
# bounding box of each face and convert it to two CvPoints
pt1 = (int(x * image_scale), int(y * image_scale))
pt2 = (int((x + w) * image_scale), int((y + h) * image_scale))
cv.Rectangle(img, pt1, pt2, cv.RGB(255, 0, 0), 3, 8, 0)
print "Face at: ", pt1[0], ",", pt2[0], "\t", pt1[1], ",", pt2[
1]
# find amount needed to pan/tilt
span = (pt1[0] + pt2[0]) / 2
stlt = (pt1[1] + pt2[1]) / 2
mid = smallwidth / 2
if span < mid:
print "left", mid - span
else:
print "right", span - mid
#os.system('echo "6="' + str(valTilt) + ' > /dev/pi-blaster')
#os.system('echo "7="' + str(valPan) + ' > /dev/pi-blaster')
else:
if detected == 1:
#print "Last seen at: ", pt1[0], ",", pt2[0], "\t", pt1[1], ",", pt2[1]
#os.system('festival --tts bye &')
status = "just disappeared"
detected = 0
cv.ShowImage("result", img)
return detected
def detect_and_draw(img, cascade, c):
# allocate temporary images
gray = cv.CreateImage((img.width, img.height), 8, 1)
small_img = cv.CreateImage((cv.Round(
img.width / image_scale), cv.Round(img.height / image_scale)), 8, 1)
# convert color input image to grayscale
cv.CvtColor(img, gray, cv.CV_BGR2GRAY)
# scale input image for faster processing
cv.Resize(gray, small_img, cv.CV_INTER_LINEAR)
cv.EqualizeHist(small_img, small_img)
face_flag = False
if (cascade):
t = cv.GetTickCount()
faces = cv.HaarDetectObjects(small_img, cascade,
cv.CreateMemStorage(0), haar_scale,
min_neighbors, haar_flags, min_size)
t = cv.GetTickCount() - t
print "detection time = %gms" % (t / (cv.GetTickFrequency() * 1000.))
if faces:
face_flag = True
for ((x, y, w, h), n) in faces:
# the input to cv.HaarDetectObjects was resized, so scale the
# bounding box of each face and convert it to two CvPoints
pt1 = (int(x * image_scale), int(y * image_scale))
pt2 = (int((x + w) * image_scale), int((y + h) * image_scale))
# ある程度顔が検出されたら
if c > 4:
# 画像の保存
global counter
counter = -1
d = datetime.today()
datestr = d.strftime('%Y-%m-%d_%H-%M-%S')
outputname = '/home/pi/fd/fd_' + datestr + '.jpg'
cv.SaveImage(outputname, img)
print 'Face Detect'
# 読み込みと切り取り
fimg = cv.LoadImage(outputname)
fimg_trim = fimg[pt1[1]:pt2[1], pt1[0]:pt2[0]]
outputname2 = '/home/pi/fd/face_' + datestr + '.jpg'
cv.SaveImage(outputname2, fimg_trim)
print 'Face Image Save'
cv.Rectangle(img, pt1, pt2, cv.RGB(255, 0, 0), 3, 8, 0)
cv.ShowImage("result", img)
return face_flag
def detect_and_draw(img, cascade):
# allocate temporary images
gray = cv.CreateImage((img.width,img.height), 8, 1)
small_img = cv.CreateImage((cv.Round(img.width / image_scale),
cv.Round (img.height / image_scale)), 8, 1)
# convert color input image to grayscale
cv.CvtColor(img, gray, cv.CV_BGR2GRAY)
# scale input image for faster processing
cv.Resize(gray, small_img, cv.CV_INTER_LINEAR)
cv.EqualizeHist(small_img, small_img)
if(cascade):
t = cv.GetTickCount()
faces = cv.HaarDetectObjects(small_img, cascade, cv.CreateMemStorage(0),
haar_scale, min_neighbors, haar_flags, min_size)
t = cv.GetTickCount() - t
#print "detection time = %gms" % (t/(cv.GetTickFrequency()*1000.))
if faces:
count = 0
stop = 1
name = 1
no = 1
dict = {}
for num in range(14):
dict[name] = no
name += 1
print dict
f = open('no.json','w')
json.dump(dict,f)
#for count in range(14):
#time.sleep(stop)
#count += 1
#print(count)
#time.sleep(stop)
#cv.PutText(img, "SAMPLE_TEXT", (0, 50), cv.CV_FONT_HERSHEY_PLAIN, cv.RGB(255, 255, 255))
#cv.PutText(img, "SAMPLE_TEXT", (0, 50), cv.CV_FONT_HERSHEY_PLAIN, 4, (255, 255, 255), 2, cv.CV_AA )
for ((x, y, w, h), n) in faces:
# the input to cv.HaarDetectObjects was resized, so scale the
# bounding box of each face and convert it to two CvPoints
#for count in range(14):
count += 1
print(count)
pt1 = (int(x * image_scale), int(y * image_scale))
pt2 = (int((x + w) * image_scale), int((y + h) * image_scale))
cv.Rectangle(img, pt1, pt2, cv.RGB(255, 0, 0), 3, 8, 0)
#count = count + 1
#print(count)
# cv.putText(img, "SAMPLE_TEXT", (0, 50), FONT_HERSHEY_PLAIN, 4, (255, 255, 255), 2, cv.CV_AA)
cv.ShowImage("result", img)
def dect_image(filename):
img = cv.LoadImage(filename)
gray = cv.CreateImage(cv.GetSize(img), 8, 1)
cv.CvtColor(img, gray, cv.CV_BGR2GRAY)
cv.EqualizeHist(gray, gray)
rects = detect(img, cascade)
if len(rects) != 0:
rect = (rects[0][0], rects[0][1], rects[0][2] - rects[0][0],
rects[0][3] - rects[0][1])
cv.SetImageROI(img, rect)
cv.SaveImage(filename, img)
def DetectRedEyes(image, faceCascade):
min_size = (20, 20)
image_scale = 2
haar_scale = 1.1
min_neighbors = 2
haar_flags = 0
# Allocate the temporary images
gray = cv.CreateImage((image.width, image.height), 8, 1)
smallImage = cv.CreateImage((cv.Round(
image.width / image_scale), cv.Round(image.height / image_scale)), 8,
1)
# Convert color input image to grayscale
cv.CvtColor(image, gray, cv.CV_BGR2GRAY)
# Scale input image for faster processing
cv.Resize(gray, smallImage, cv.CV_INTER_LINEAR)
# Equalize the histogram
cv.EqualizeHist(smallImage, smallImage)
# Detect the faces
faces = cv.HaarDetectObjects(smallImage, faceCascade,
cv.CreateMemStorage(0), haar_scale,
min_neighbors, haar_flags, min_size)
# If faces are found
if faces:
#print faces
for ((x, y, w, h), n) in faces:
# the input to cv.HaarDetectObjects was resized, so scale the
# bounding box of each face and convert it to two CvPoints
#print "face"
global line2
line2 = n
pt1 = (int(x * image_scale), int(y * image_scale))
pt2 = (int((x + w) * image_scale), int((y + h) * image_scale))
# print pt1
# print pt2
cv.Rectangle(image, pt1, pt2, cv.RGB(255, 0, 0), 1, 8, 0)
cv.PutText(image, "face" + str(h), pt1, font, cv.RGB(255, 0, 0))
cv.PutText(image, "Come close.", (0, 20), font, cv.RGB(255, 0, 0))
cv.PutText(image, "Ensure your forehead is well lit.", (0, 40),
font, cv.RGB(255, 0, 0))
cv.PutText(image, "Hit escape when done.", (0, 60), font,
cv.RGB(255, 0, 0))
cv.ResetImageROI(image)
return image
def detect_and_draw(img, cascade):
# allocate temporary images
gray = cv.CreateImage((img.width,img.height), 8, 1)
small_img = cv.CloneMat(img)# cv.CreateImage((img.width,img.height)) # (cv.Round(img.width / image_scale),cv.Round (img.height / image_scale)), 8, 1)
# convert color input image to grayscale
cv.CvtColor(img, gray, cv.CV_BGR2GRAY)
# scale input image for faster processing
cv.Resize(gray, small_img, cv.CV_INTER_LINEAR)
cv.EqualizeHist(small_img, small_img)
if(cascade):
t = cv.GetTickCount()
#Scan image and get an array of faces
faces = cv.HaarDetectObjects(small_img, cascade, cv.CreateMemStorage(0),
haar_scale, min_neighbors, haar_flags, min_size)
t = cv.GetTickCount() - t
#print "detection time = %gms" % (t/(cv.GetTickFrequency()*1000.))
if faces:
for ((x, y, w, h), n) in faces:
# the input to cv.HaarDetectObjects was resized, so scale the
# bounding box of each face and convert it to two CvPoints
pt1 = (int(x * image_scale), int(y * image_scale))
pt2 = (int((x + w) * image_scale), int((y + h) * image_scale))
cv.Rectangle(img, pt1, pt2, cv.RGB(255, 0, 0), 3, 8, 0)
print "X " , x
if int(x * image_scale) > (img.width * 0.45):
#print "X " , x
#print steppera.IsTurning()
if (steppera.IsTurning() == False):
if (stepperInUse[STEPPERA] == True):
sensor_value = "-4"
if isNumeric(sensor_value):
print "Moving to" , sensor_value
steppera.changeSpeed(int(100 * sign(int(float(sensor_value)) - 0)),abs(int(float(sensor_value)) - 0))
while (steppera.IsTurning() == True):
cv.WaitKey(100)
if int((x + w) * image_scale) < (img.width * 0.55):
#print "X " , x
#print steppera.IsTurning()
if (steppera.IsTurning() == False):
if (stepperInUse[STEPPERA] == True):
sensor_value = "4"
if isNumeric(sensor_value):
print "Moving to" , sensor_value
steppera.changeSpeed(int(100 * sign(int(float(sensor_value)) - 0)),abs(int(float(sensor_value)) - 0))
while (steppera.IsTurning() == True):
cv.WaitKey(100)
cv.ShowImage("result", img)
def ifFace(img, size):
gray = cv.CreateImage(size, 8, 1)
cv.CvtColor(img, gray, cv.CV_BGR2GRAY)
newMem = cv.CreateMemStorage(0)
cv.EqualizeHist(gray, gray)
face = cv.HaarDetectObjects(gray, c_f, newMem, 1.2, 3,
cv.CV_HAAR_DO_CANNY_PRUNING, (50, 50))
mouth = cv.HaarDetectObjects(gray, c_m, newMem, 1.2, 2,
cv.CV_HAAR_DO_CANNY_PRUNING, (0, 0))
if face and mouth:
print "有脸"
cv.SaveImage("img/out.jpg", img)
sys.exit(0)
def detect_and_draw(img, front_cascade, profile_cascade):
# allocate temporary images
gray = cv.CreateImage((img.width,img.height), 8, 1)
small_img = cv.CreateImage((cv.Round(img.width / image_scale),
cv.Round (img.height / image_scale)), 8, 1)
# convert color input image to grayscale
cv.CvtColor(img, gray, cv.CV_BGR2GRAY)
# scale input image for faster processing
cv.Resize(gray, small_img, cv.CV_INTER_LINEAR)
cv.EqualizeHist(small_img, small_img)
if(front_cascade):
# Test for frontal face
faces = cv.HaarDetectObjects(small_img, front_cascade, cv.CreateMemStorage(0),
haar_scale, min_neighbors, haar_flags, min_size)
if faces: # we've detected a face
return [faces, FRONTAL]
# Test for profile face
faces = cv.HaarDetectObjects(small_img, profile_cascade, cv.CreateMemStorage(0),
haar_scale, min_neighbors, haar_flags, min_size)
if faces: # we've detected a face
return [faces, PROFILE]
#t = cv.GetTickCount() - t
#print "detection time = %gms" % (t/(cv.GetTickFrequency()*1000.))
#if faces:
#for ((x, y, w, h), n) in faces:
## the input to cv.HaarDetectObjects was resized, so scale the
## bounding box of each face and convert it to two CvPoints
#pt1 = (int(x * image_scale), int(y * image_scale))
#pt2 = (int((x + w) * image_scale), int((y + h) * image_scale))
#imgWidth, imgHeight = cv.GetSize(img)
#croppedX = max(0, x*image_scale-w*image_scale/2)
#croppedY = max(0, y*image_scale-h*image_scale/2)
#croppedW = min(imgWidth, (2*w)*image_scale)
#croppedH = min(imgHeight, (2*h)*image_scale)
#imgCropped = cv.CreateImage((croppedW, croppedH), img.depth, img.nChannels)
#srcRegion = cv.GetSubRect(img, (croppedX, croppedY, croppedW, croppedH))
#cv.Copy(srcRegion, imgCropped)
#cv.ShowImage("cropped", imgCropped)
#cv.Rectangle(img, pt1, pt2, cv.RGB(255, 0, 0), 3, 8, 0)
return []
def detect_face(image, faceCascade):
#variables
min_size = (20, 20)
haar_scale = 1.1
min_neighbors = 3
haar_flags = 0
# Equalize the histogram
cv.EqualizeHist(image, image)
# Detect the faces
faces = cv.HaarDetectObjects(image, faceCascade, cv.CreateMemStorage(0),
haar_scale, min_neighbors, haar_flags,
min_size)
return faces
def ifFace(img,size):
gray=cv.CreateImage(size,8,1)
cv.CvtColor(img,gray,cv.CV_BGR2GRAY)
newMem1=cv.CreateMemStorage(0)
newMem2=cv.CreateMemStorage(0)
newMem3=cv.CreateMemStorage(0)
cv.EqualizeHist(gray,gray)
face=cv.HaarDetectObjects(gray,c_f,newMem1,1.2,3,cv.CV_HAAR_DO_CANNY_PRUNING,(50,50))
mouth=cv.HaarDetectObjects(gray,c_m,newMem2,1.2,2,cv.CV_HAAR_DO_CANNY_PRUNING,(10,10))
body=cv.HaarDetectObjects(gray,c_m,newMem3,1.2,2,cv.CV_HAAR_DO_CANNY_PRUNING,(100,100))
if face and mouth or body:
cv.SaveImage("img/out.jpg",img)
return 1
else:
return 0
def detect_and_draw(img, cascade):
# allocate temporary images
gray = cv.CreateImage((img.width, img.height), 8, 1)
small_img = cv.CreateImage((cv.Round(
img.width / image_scale), cv.Round(img.height / image_scale)), 8, 1)
# convert color input image to grayscale
cv.CvtColor(img, gray, cv.CV_BGR2GRAY)
# scale input image for faster processing
cv.Resize(gray, small_img, cv.CV_INTER_LINEAR)
cv.EqualizeHist(small_img, small_img)
if (cascade):
t = cv.GetTickCount()
faces = cv.HaarDetectObjects(small_img, cascade,
cv.CreateMemStorage(0), haar_scale,
min_neighbors, haar_flags, min_size)
t = cv.GetTickCount() - t
print "detection time = %gms" % (t / (cv.GetTickFrequency() * 1000.))
if faces:
facenum = 0
for ((x, y, w, h), n) in faces:
# the input to cv.HaarDetectObjects was resized, so scale the
# bounding box of each face and convert it to two CvPoints
pt1 = (int(x * image_scale), int(y * image_scale))
pt2 = (int((x + w) * image_scale), int((y + h) * image_scale))
cv.Rectangle(img, pt1, pt2, cv.RGB(255, 0, 0), 3, 8, 0)
#code copied from https://github.com/mitchtech/py_servo_facetracker/blob/master/facetracker_servo_gpio.py
x1 = pt1[0]
x2 = pt2[0]
y1 = pt1[1]
y2 = pt2[1]
midFaceX = x1 + ((x2 - x1) / 2)
midFaceY = y1 + ((y2 - y1) / 2)
facenum = facenum + 1
client.publish(topic + str(facenum),
str(midFaceX) + "," + str(midFaceY), 0)
print topic + str(facenum), str(midFaceX) + "," + str(midFaceY)
cv.ShowImage("result", img)
def get_faces(self, image):
"""
Given an opencv image, return a ((x,y,w,h), certainty) tuple for each face
detected.
"""
# Convert the image to grayscale and normalise
cv.CvtColor(image, self.gray, cv.CV_BGR2GRAY)
cv.EqualizeHist(self.gray, self.gray)
# Detect faces
return cv.HaarDetectObjects(self.gray,
self.cascade,
self.storage,
scale_factor=1.3,
min_neighbors=2,
flags=cv.CV_HAAR_DO_CANNY_PRUNING,
min_size=(40, 40))
def detect_and_draw(img,cascade):
gray=cv.CreateImage((img.width,img.height),8,1)
small_img=cv.CreateImage((cv.Round(img.width/image_scale),cv.Round(img.height/image_scale)),8,1)
cv.CvtColor(img,gray,cv.CV_BGR2GRAY)
cv.Resize(gray,small_img,cv.CV_INTER_LINEAR)
cv.EqualizeHist(small_img,small_img)
if(cascade):
t=cv.GetTickCount()
faces=cv.HaarDetectObjects(small_img,cascade,cv.CreateMemStorage(0),haar_scale,min_neighbors,haar_flags,min_size)
t=cv.GetTickCount()-t
print "time taken for detection = %gms"%(t/(cv.GetTickFrequency()*1000.))
if faces:
for ((x,y,w,h),n) in faces:
pt1=(int(x*image_scale),int(y*image_scale))
pt2=(int((x+w)*image_scale),int((y+h)*image_scale))
cv.Rectangle(img,pt1,pt2,cv.RGB(255,0,0),3,8,0)
cv.ShowImage("video",img)
def repeat():
#每次从摄像头获取一张图片
frame = cv.QueryFrame(capture)
image_size = cv.GetSize(frame)#获取图片的大小
#print image_size
greyscale = cv.CreateImage(image_size, 8, 1)#建立一个相同大小的灰度图像
cv.CvtColor(frame, greyscale, cv.CV_BGR2GRAY)#将获取的彩色图像，转换成灰度图像
storage = cv.CreateMemStorage(0)#创建一个内存空间，人脸检测是要利用，具体作用不清楚
cv.EqualizeHist(greyscale, greyscale)#将灰度图像直方图均衡化，貌似可以使灰度图像信息量减少，加快检测速度
#画图像分割线
cv.Line(frame, (210,0),(210,480), (0,255,255),1)
cv.Line(frame, (420,0),(420,480), (0,255,255),1)
cv.Line(frame, (0,160),(640,160), (0,255,255),1)
cv.Line(frame, (0,320),(640,320), (0,255,255),1)
# detect objects
cascade = cv.Load('/usr/share/OpenCV/haarcascades/haarcascade_frontalface_alt2.xml')
#加载Intel公司的训练库
#检测图片中的人脸，并返回一个包含了人脸信息的对象faces
faces = cv.HaarDetectObjects(greyscale, cascade, storage, 1.2, 2,
cv.CV_HAAR_DO_CANNY_PRUNING,
(100, 100))
#获得人脸所在位置的数据
for (x,y,w,h) , n in faces:
# print x,y
if x<210:
print "right"
elif x>310:
print "left"
cv.Rectangle(frame, (x,y), (x+w,y+h), (0,128,0),2)#在相应位置标识一个矩形 边框属性(0,0,255)红色 20宽度
cv.ShowImage("W1", greyscale)#显示互有边框的图片
cv.ShowImage("W1", frame)
