def detect(self, obj, event): # First, reset image, in case of previous detections: active_handle = self.get_active('Media') media = self.dbstate.db.get_media_from_handle(active_handle) self.load_image(media) min_face_size = (50, 50) # FIXME: get from setting self.cv_image = cv2.LoadImage(self.full_path, cv2.CV_LOAD_IMAGE_GRAYSCALE) o_width, o_height = self.cv_image.width, self.cv_image.height cv2.EqualizeHist(self.cv_image, self.cv_image) cascade = cv2.Load(HAARCASCADE_PATH) faces = cv2.HaarDetectObjects(self.cv_image, cascade, cv2.CreateMemStorage(0), 1.2, 2, cv2.CV_HAAR_DO_CANNY_PRUNING, min_face_size) references = self.find_references() rects = [] o_width, o_height = [ float(t) for t in (self.cv_image.width, self.cv_image.height) ] for ((x, y, width, height), neighbors) in faces: # percentages: rects.append((x / o_width, y / o_height, width / o_width, height / o_height)) self.draw_rectangles(rects, references)
def DetectFace(image, faceCascade): #modified from: http://www.lucaamore.com/?p=638 min_size = (20, 20) image_scale = 1 haar_scale = 1.1 min_neighbors = 3 haar_flags = 0 # Allocate the temporary images smallImage = cv2.CreateImage((cv2.Round( image.width / image_scale), cv2.Round(image.height / image_scale)), 8, 1) # Scale input image for faster processing cv2.Resize(image, smallImage, cv2.CV_INTER_LINEAR) # Equalize the histogram cv2.EqualizeHist(smallImage, smallImage) # Detect the faces faces = cv2.HaarDetectObjects(smallImage, faceCascade, cv2.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)) cv2.Rectangle(image, pt1, pt2, cv2.RGB(255, 0, 0), 5, 8, 0) return image
def DetectFace(image, faceCascade, returnImage=False): 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) # 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 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 = (20, 20) haar_scale = 1.1 min_neighbors = 3 haar_flags = 0 # Equalize the histogram cv2.EqualizeHist(image, image) # Detect the faces faces = cv2.HaarDetectObjects(image, faceCascade, cv2.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)) cv2.Rectangle(image, pt1, pt2, cv2.RGB(255, 0, 0), 5, 8, 0) if returnImage: return image else: return faces
def detect_faces(self, image_filename): """ Detects all faces and returns a list with images and corresponding coordinates""" logging.debug( 'Start method "detect_faces" for file %s (face-detector.py)' % image_filename) cascade = cv.Load(parameter.cascadefile) # load face cascade image = cv.LoadImage(image_filename) # loads and converts image # detect and save coordinates of detected faces coordinates = cv.HaarDetectObjects( image, cascade, cv.CreateMemStorage(), parameter.scaleFactor, parameter.minNeighbors, parameter.flags, parameter.min_facesize) # Convert to greyscale - better results when converting AFTER facedetection with viola jones if image.channels == 3: logging.debug( 'Bild %s wird in Graustufenbild umgewandelt (face-detector.py)' % image_filename) grey_face = (cv.CreateImage((image.width, image.height), 8, 1)) # Create grey-scale Image cv.CvtColor(image, grey_face, cv.CV_RGB2GRAY ) # convert Image to Greyscale (necessary for SURF) image = grey_face logging.debug( '%d faces successfully detected in file %s (face-detector.py)' % (len(coordinates), image_filename)) return image, coordinates
def detect_no_draw(self, img): # allocate temporary images gray = cv.CreateImage((img.width, img.height), 8, 1) small_img = cv.CreateImage((cv.Round(img.width / self.image_scale), cv.Round(img.height / self.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 self.cascade: t = cv.GetTickCount() faces = cv.HaarDetectObjects(small_img, self.cascade, cv.CreateMemStorage(0), self.haar_scale, self.min_neighbors, self.haar_flags, self.min_size) t = cv.GetTickCount() - t if faces: return True else: return False
def DetectEyes(imageCV, faceCascade, eyeCascade): minSize = (20, 20) imageScale = 2 haarScale = 1.2 minNeighbors = 2 haarFlags = 0 # Allocate the temporary images #gray = cv2.CreateImage((imageCV.width, image.height), 8, 1) #smallImage = cv.CreateImage((cv.Round(image.width / image_scale), cv2.Round (image.height / image_scale)), 8 ,1) # Convert color input image to grayscale cv2.cvtColor(image, gray, cv.CV_BGR2GRAY) # Scale input image for faster processing cv2.Resize(gray, smallImage, cv.CV_INTER_LINEAR) # Equalize the histogram cv2.EqualizeHist(smallImage, smallImage) # Detect the faces faces = cv2.HaarDetectObjects(smallImage, faceCascade, cv2.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)) cv2.Rectangle(image, pt1, pt2, cv2.RGB(255, 0, 0), 3, 8, 0)
def detect(image): image_faces = [] bitmap = cv.fromarray(image) faces = cv.HaarDetectObjects(bitmap, cascade, cv.CreateMemStorage(0)) if faces: for (x, y, w, h), n in faces: image_faces.append(image[y:(y + h), x:(x + w)]) #cv2.rectangle(image,(x,y),(x+w,y+h),(255,255,255),3) return image_faces
def faces_from_pil_image(pil_image): "Return a list of (x,y,h,w) tuples for faces detected in the PIL image" storage = cv2.CvMemStorage(0) facial_features = cv2.Load('haarcascade_frontalface_alt.xml', storage=storage) cv_im = cv2.CreateImageHeader(pil_image.size, cv.IPL_DEPTH_8U, 3) cv2.SetData(cv_im, pil_image.tostring()) faces = cv2.HaarDetectObjects(cv2_im, facial_features, storage) # faces includes a `neighbors` field that we aren't going to use here return [f[0] for f in faces]
def detect_faces(image_path, min_face_size): cv_image = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE) o_width, o_height = cv_image.shape[0], cv_image.shape[1] cv2.equalizeHist(cv_image, cv_image) cascade = cv2.CascadeClassifier(HAARCASCADE_PATH) # ??? faces = cv2.HaarDetectObjects(cv_image, cascade, cv2.CreateMemStorage(0), 1.2, 2, cv2.CV_HAAR_DO_CANNY_PRUNING, min_face_size) return faces
def detect_object(image): '''dect''' grayscale = cv2.CreateImage((image.width, image.height), 8, 1) cv2.CvtColor(image, grayscale, cv2.CV_BGR2GRAY) cascade = cv2.Load("data/haarcascades/haarcascade_frontalface_alt.xml") rect = cv2.HaarDetectObjects(grayscale, cascade, cv2.CreateMemStorage(), 1.1, 2, cv2.CV_HAAR_DO_CANNY_PRUNING, (20, 20)) result = [] for r in rect: result.append((r[0][0], r[0][1], r[0][0] + r[0][2], r[0][1] + r[0][3])) return result
def detect_object(image): '''检测图片,获取人脸在图片中的坐标''' grayscale = cv.CreateImage((image.width, image.height), 8, 1) cv.CvtColor(image, grayscale, cv.CV_BGR2GRAY) cascade = cv.Load( "/usr/local/opencv-2.4.9/data/haarcascades/haarcascade_frontalface_alt_tree.xml" ) rect = cv.HaarDetectObjects(grayscale, cascade, cv.CreateMemStorage(), 1.1, 2, cv.CV_HAAR_DO_CANNY_PRUNING, (20, 20)) result = [] for r in rect: result.append((r[0][0], r[0][1], r[0][0] + r[0][2], r[0][1] + r[0][3])) return result
def detect_object(image): print('aaa') '''检测图片,获取人脸在图片中的坐标''' grayscale = numpy.zeros(image.shape, numpy.uint8) # v2.CreateImage((image.width, image.height), 8, 1) cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) cascade = cv2.Load("C:/Users/polyv-1107/opencv-3.3.0/data/haarcascades/haarcascade_frontalface_alt_tree.xml") rect = cv2.HaarDetectObjects(grayscale, cascade, cv2.CreateMemStorage(), 1.1, 2, cv2.CV_HAAR_DO_CANNY_PRUNING, (20,20)) result = [] for r in rect: result.append((r[0][0], r[0][1], r[0][0]+r[0][2], r[0][1]+r[0][3])) return result
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(self, img): # allocate temporary images gray = cv.CreateImage((img.width, img.height), 8, 1) small_img = cv.CreateImage((cv.Round(img.width / self.image_scale), cv.Round(img.height / self.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 self.cascade: t = cv.GetTickCount() faces = cv.HaarDetectObjects(small_img, self.cascade, cv.CreateMemStorage(0), self.haar_scale, self.min_neighbors, self.haar_flags, self.min_size) t = cv.GetTickCount() - t # print "time taken for detection = %gms" % (t/(cv.GetTickFrequency()*1000.)) if faces: face_found = 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 * self.image_scale), int(y * self.image_scale)) pt2 = (int((x + w) * self.image_scale), int((y + h) * self.image_scale)) cv.Rectangle(img, pt1, pt2, cv.RGB(255, 0, 0), 3, 8, 0) else: face_found = False cv.ShowImage("video", img) return face_found
def detect_nose(cv2_image, storage): """Detects nose based on haar. Returns ponts""" return cv2.HaarDetectObjects(cv2image_grayscale(cv2_image), NOSE_HAAR, storage)
#BuildingES.py #!/usr/bin/python import cv2 #import the openCV lib to python import serial #import the pyserial module #Module -1: Image Processing hc = cv2.imread( '/home/george/PycharmProjects/Embeded image processing system/haarcascade_frontalface_alt2.xml' ) img = cv2.imshow('/home/jayneil/beautiful-faces.jpg', 0) faces = cv2.HaarDetectObjects(img, hc, cv2.CreateMemStorage()) a = 1 print(faces) for (x, y, w, h), n in faces: cv2.Rectangle(img, (x, y), (x + w, y + h), 255) cv2.SaveImage("faces_detected.jpg", img) dst = cv2.imread('faces_detected.jpg') cv2.NamedWindow('Face Detected', cv2.CV_WINDOW_AUTOSIZE) cv2.imshow('Face Detected', dst) cv2.WaitKey(5000) cv2.DestroyWindow('Face Detected') #Module -2: Trigger Pyserial if faces == []: ser = serial.Serial('/dev/ttyUSB0', 9600) print(ser) ser.write('N') else:
import cv2 import sys storage = cv2.CreateMemStorage() image_path = "yusei.jpg" img = cv2.imread(image_path) hc = cv2.Load("../data/haarcascades/haarcascade_frontalface_default.xml") faces = cv2.HaarDetectObjects(img, hc, storage, 1.1, 3, 0, (0, 0)) max = 0 maxh = 0 maxw = 0 resx = 0 resy = 0 for (x, y, w, h), n in faces: if max < w * h: maxw = w maxh = h resx = x resy = y max = w * h sub = cv2.GetSubRect(img, (resx, resy, maxw, maxh)) cv2.SaveImage("face_" + sys.argv[1], sub)
# convert color input image to grayscale cv.CvtColor(frame, 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) midFace = None 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: lights(50 if len(faces) == 0 else 0, 50 if len(faces) > 0 else 0, 0, 50) 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(frame, pt1, pt2, cv.RGB(100, 220, 255), 1, 8, 0) # get the xy corner co-ords, calc the midFace location x1 = pt1[0] x2 = pt2[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
def detect_and_draw(img, cascade): t = cv2.GetTickCount() ## start counter cv2.CvtColor(img, gray, cv2.CV_BGR2GRAY) cv2.Resize(gray, small_img, cv2.CV_INTER_LINEAR) #Ages all trackedFaces for f in trackedFaces: f.updateLife() #Remove expired faces for f in trackedFaces: if (f.isTooOld()): trackedFaces.remove(f) faces = cv2.HaarDetectObjects(small_img, cascade, storage, haar_scale, min_neighbors, haar_flags, min_size) drawline = 0 if faces: #found a face for ((x, y, w, h), n) in faces: matchedFace = False pt1 = (int(x * image_scale), int(y * image_scale)) pt2 = (int((x + w) * image_scale), int((y + h) * image_scale)) pt3 = (int(x * image_scale) + int( ((x + w) * image_scale - x * image_scale) / 3), int(y * image_scale)) pt4 = (int((x + w) * image_scale) - int( ((x + w) * image_scale - x * image_scale) / 3), int((y * image_scale) + int(( (y + h) * image_scale) - int(y * image_scale)) / 3)) #check if there are trackedFaces if (len(trackedFaces) > 0): #each face being tracked for f in trackedFaces: #the face is found (small movement) if ((abs(f.xpt - pt1[0]) < FACE_MAX_MOVEMENT) and (abs(f.ypt - pt1[1]) < FACE_MAX_MOVEMENT)): matchedFace = True f.updateFace(int(w * image_scale), int(h * image_scale), pt1[0], pt1[1]) mf = f break #if face not found, add a new face if (matchedFace == False): f = Face(0, int(w * image_scale), int(h * image_scale), pt1[0], pt1[1], 0) trackedFaces.append(f) mf = f #No tracked faces: adding one else: f = Face(0, int(w * image_scale), int(h * image_scale), pt1[0], pt1[1], 0) trackedFaces.append(f) mf = f #where to draw face and properties if (mf.age > 5): #draw attention line lnpt1 = (int(mf.xpt * scale), int(mf.ypt * scale - 5) - 5) if (mf.age > mf.width): lnpt2 = (int(mf.xpt * scale + mf.width), int(mf.ypt * scale - 5)) else: lnpt2 = (int(mf.xpt * scale + mf.age), int(mf.ypt * scale - 5)) cv2.Rectangle(img, lnpt1, lnpt2, RED, 4, 8, 0) ## drawing bolded attention line ### draw eyes cv2.Rectangle(img, mf.eyeLeft1, mf.eyeLeft2, MAGENTA, 3, 8, 0) cv2.Rectangle(img, mf.eyeRight1, mf.eyeRight2, MAGENTA, 3, 8, 0) # ### draw mouth cv2.Rectangle(img, mf.mouthTopLeft, mf.mouthBotRight, ORANGE, 3, 8, 0) # ### draw face cv2.Rectangle(img, pt1, pt2, getColor(mf), 3, 8, 0) #cv2.Rectangle( img, pt3, pt4, MAGENTA, 1, 8, 0 ) #forehead drawline = mf.age if (CAPTURING): saveAsJPG(img) if (osName == "nt"): cv2.Flip(img, img, 0) cv2.ShowImage('Camera', img) t = cv2.GetTickCount() - t ## counter for FPS print("%i fps." % (cv2.GetTickFrequency() * 1000000. / t)) ## print FPS
def detect_eyes(cv2_image, storage): """Detects eyes based on haar. Returns points""" return cv2.HaarDetectObjects(cv2image_grayscale(cv2_image), EYE_HAAR, storage)
def detect_mouth(cv2_image, storage): """Detects mouth based on haar. Returns points""" return cv2.HaarDetectObjects(cv2image_grayscale(cv2_image), MOUTH_HAAR, storage)
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