def getIris(frame): iris = [] copyImg = cv.CloneImage(frame) resImg = cv.CloneImage(frame) grayImg = cv.CreateImage(cv.GetSize(frame), 8, 1) mask = cv.CreateImage(cv.GetSize(frame), 8, 1) storage = cv.CreateMat(frame.width, 1, cv.CV_32FC3) cv.CvtColor(frame,grayImg,cv.CV_BGR2GRAY) cv.Canny(grayImg, grayImg, 5, 70, 3) cv.Smooth(grayImg,grayImg,cv.CV_GAUSSIAN, 7, 7) circles = getCircles(grayImg) iris.append(resImg) for circle in circles: rad = int(circle[0][2]) global radius radius = rad cv.Circle(mask, centroid, rad, cv.CV_RGB(255,255,255), cv.CV_FILLED) cv.Not(mask,mask) cv.Sub(frame,copyImg,resImg,mask) x = int(centroid[0] - rad) y = int(centroid[1] - rad) w = int(rad * 2) h = w cv.SetImageROI(resImg, (x,y,w,h)) cropImg = cv.CreateImage((w,h), 8, 3) cv.Copy(resImg,cropImg) cv.ResetImageROI(resImg) return(cropImg) return (resImg)
def sample_frame(self, frame):
# Get an average of the green channel in on the forehead
cv.SetImageROI(frame, self.face_tracker.get_forehead())
sample = cv.Avg(frame)[1]
cv.ResetImageROI(frame)
return sample
def get_card(color_capture, corners):
target = [(0, 0), (223, 0), (223, 310), (0, 310)]
mat = cv2.createMat(3, 3, cv2.CV_32FC1)
cv2.GetPerspectiveTransform(corners, target, mat)
warped = color_capture.clone() #cv2.CloneImage(color_capture)
cv2.WarpPerspective(color_capture, warped, mat)
cv2.SetImageROI(warped, (0, 0, 223, 310))
return warped
def getAverage(self, distances, segRange):
""" calculate the average pixel value over a perticular range in the image """
# get segment information
segWidth, segHeight = self.segmentSize
pixelsPerSegment = segWidth * segHeight
# calculate average
cv.SetImageROI(distances, segRange)
average = cv.Avg(distances)
return average[0]
def detectFace(self, cam_img, faceCascade, eyeCascade, mouthCascade): # cam_img should be cv2.cv.iplcam_img
min_size = (20, 20)
image_scale = 2
haar_scale = 1.2
min_neighbors = 2
haar_flags = 0
image_width = int(cam_img.get(cv.CV_CAP_PROP_FRAME_WIDTH))
image_height = int(cam_img.get(cv.CV_CAP_PROP_FRAME_HEIGHT))
# Allocate the temporary images
gray = cv.CreateImage((image_width, image_height), 8, 1) # tuple as the first arg
smallImage = cv.CreateImage((cv.Round(image_width / image_scale), cv.Round(image_height / image_scale)), 8, 1)
(ok, img) = cam_img.read()
# print 'gray is of ',type(gray) >>> gray is of <type 'cv2.cv.iplimage'>
# print type(smallImage) >>> <type 'cv2.cv.iplimage'>
# print type(image) >>> <type 'cv2.VideoCapture'>
# print type(img) >>> <type 'numpy.ndarray'>
# convert numpy.ndarray to iplimage
ipl_img = cv2.cv.CreateImageHeader((img.shape[1], img.shape[0]), cv.IPL_DEPTH_8U, 3)
cv2.cv.SetData(ipl_img, img.tostring(), img.dtype.itemsize * 3 * img.shape[1])
# Convert color input image to grayscale
cv.CvtColor(ipl_img, 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)
# => The function returns a list of tuples, (rect, neighbors) , where rect is a CvRect specifying the object’s extents and neighbors is a number of neighbors.
# => CvRect cvRect(int x, int y, int width, int height)
# If faces are found
if faces:
face = faces[0]
self.faceX = face[0][0]
self.faceY = face[0][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(ipl_img, pt1, pt2, cv.RGB(0, 0, 255), 3, 8, 0)
# face_region = cv.GetSubRect(ipl_img,(x,int(y + (h/4)),w,int(h/2)))
cv.SetImageROI(ipl_img, (pt1[0],
pt1[1],
pt2[0] - pt1[0],
int((pt2[1] - pt1[1]) * 0.7)))
eyes = cv.HaarDetectObjects(ipl_img, 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(ipl_img, # image
(eye[0][0], # vertex pt1
eye[0][1]),
(eye[0][0] + eye[0][2], # vertex pt2 opposite to pt1
eye[0][1] + eye[0][3]),
cv.RGB(255, 0, 0), 1, 4, 0) # color,thickness,lineType(8,4,cv.CV_AA),shift
cv.ResetImageROI(ipl_img)
return ipl_img
"/usr/local/Cellar/opencv/2.4.11/share/OpenCV/haarcascades/haarcascade_eye.xml"
)
frameN = 0 # frameカウント用に
while (1):
ret, im = cap.read() # 1フレームずつ読み込み？
if ret: # 読み込まれている時のみ処理
frameN = frameN + 1
im = cv2.resize(im,
(im.shape[1] / 2, im.shape[0] / 2)) # 動画サイズを半分に
gray = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY) # 画像をグレーに
gray = cv2.equalizeHist(gray) # ヒストグラムの均一化
# 顔探索(画像,縮小スケール,最低矩形数)
face = cascade.detectMultiScale(gray, 1.1, 3)
face_ROI = cv2.SetImageROI(gray, face) # ROIの設定
eye = cascade_eye.detectMultiScale(face_ROI, 1.1, 3) # 目の探索
# 顔検出した部分を長方形で囲う
for (x, y, w, h) in face:
cv2.rectangle(im, (x, y), (x + w, y + h), (0, 50, 255), 3)
out_list.append([videos] + [frameN] + [x] + [y] + [w] +
[h]) # データのリストを作成
# 画像表示（リアルタイム表示：重い）
# cv2.imshow('Video Stream', gray)
out.write(im) # 動画の書き込み
else:
break
win = dlib.image_window()
image = np.copy(frame)#cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
detected_faces = face_detector(image, 1)
#print("Total number of faces detected: {}".format(len(detected_faces)))
for d in detected_faces:
rr,cc = polygon_perimeter([d.top(), d.top(), d.bottom(), d.bottom()], [d.right(), d.left(), d.left(), d.right()])
if max(rr)<image.shape[0] and max(cc)<image.shape[1]:
image[rr, cc] = (255, 0, 0)
#cv2.imshow('Detected_frame', image)
# Define an initial bounding box
bbox = (d.left(), d.top(), d.right(), d.bottom())
# Uncomment the line below to select a different bounding box
#bbox = cv2.selectROI(image)
cv2.SetImageROI(frame, bbox)
#frame = image
# Initialize tracker with first frame and bounding box
ok = tracker.init(frame, bbox)
while True:
# Read a new frame
ok, frame = video.read()
if not ok:
break
# Start timer
timer = cv2.getTickCount()
# Update tracker
ok, bbox = tracker.update(frame)
import cv2 as cv
img = cv.LoadImage("friend1.jpg")
image_size = cv.GetSize(img) #获取图片的大小
greyscale = cv.CreateImage(image_size, 8, 1) #建立一个相同大小的灰度图像
cv.CvtColor(img, 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
# detect objects
cascade = cv.Load('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, (50, 50))
#获得人脸所在位置的数据
j = 0 #记录个数
for (x, y, w, h), n in faces:
j += 1
cv.SetImageROI(img, (x, y, w, h)) #获取头像的区域
cv.SaveImage("face" + str(j) + ".jpg", img)
#保存下来