def use_camera(self):
# global cascade, capture, frame_copy
if not self.is_device_connected():
return
try:
# if camera already openned
if self.capture is not None:
return
if self.cascade is None:
self.cascade = cv.Load(
os.path.join(APPLICATION_PATH, "face.xml"))
self.capture = cv.CreateCameraCapture(0)
if self.showVideo:
cv.NamedWindow("video", 1)
self.set_resolution(640, 480)
self.frame_copy = None
except:
self.close_camera()
print "Error in use_camera"
pass
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 faceCrop(imagePattern, boxScale=1):
# Select one of the haarcascade files:
# haarcascade_frontalface_alt.xml <-- Best one?
# haarcascade_frontalface_alt2.xml
# haarcascade_frontalface_alt_tree.xml
# haarcascade_frontalface_default.xml
# haarcascade_profileface.xml
faceCascade = cv.Load('haarcascade_frontalface_alt.xml')
imgList = glob.glob(imagePattern)
if len(imgList) <= 0:
print('No Images Found')
return
for img in imgList:
pil_im = Image.open(img)
cv_im = pil2cvGrey(pil_im)
faces = DetectFace(cv_im, faceCascade)
if faces:
n = 1
for face in faces:
croppedImage = imgCrop(pil_im, face[0], boxScale=boxScale)
fname, ext = os.path.splitext(img)
croppedImage.save(fname + '_crop' + str(n) + ext)
n += 1
else:
print('No faces found:', img)
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 test(imageFilePath):
pil_im = Image.open(imageFilePath)
cv_im = pil2cvGrey(pil_im)
faceCascade = cv.Load('harr_cass.xml')
face_im = DetectFace(cv_im, faceCascade, returnImage=True)
img = cv2pil(face_im)
img.show()
img.save('test.png')
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 test(image_path):
print("testing")
print(cv2.__version__)
pil_image = Image.open(image_path)
cv_image = convert_to_greyscale(pil_image)
face_cascade = cv2.Load('haarcascade_frontalface_alt.xml')
face_image = detect_face(cv_image, face_cascade, return_image=True)
img = cv_image_to_pilimage(face_image)
img.show()
img.save('test.jpg')
def test(imageFilePath):
pil_im = Image.open(imageFilePath)
cv_im = pil2cvGrey(pil_im)
# Select one of the haarcascade files:
# haarcascade_frontalface_alt.xml <-- Best one?
# haarcascade_frontalface_alt2.xml
# haarcascade_frontalface_alt_tree.xml
# haarcascade_frontalface_default.xml
# haarcascade_profileface.xml
faceCascade = cv.Load('haarcascade_frontalface_alt.xml')
face_im = DetectFace(cv_im, faceCascade, returnImage=True)
img = cv2pil(face_im)
img.show()
img.save('test.png')
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 __init__(self, width, height, cascade_file="haarcascade_frontalface_alt.xml"): """ Detects faces in an image. @param width Width of the images that will be supplied @param height Height of the images that will be supplied @param cascade_file Haar cascade data file for fronts of faces """ # Load the cascade self.cascade = cv2.Load(cascade_file) # Storage for the algorithm to use self.storage = cv2.CreateMemStorage() # A grayscale buffer to copy images for processing into self.gray = cv2.CreateImage((width, height), 8, 1)
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 faceCrop(imagePattern, boxScale=1):
faceCascade = cv.Load('harr_cass.xml')
imgList = glob.glob(imagePattern)
if len(imgList) <= 0:
print('No Images Found')
return
for img in imgList:
pil_im = Image.open(img)
cv_im = pil2cvGrey(pil_im)
faces = DetectFace(cv_im, faceCascade)
if faces:
n = 1
for face in faces:
croppedImage = imgCrop(pil_im, face[0], boxScale=boxScale)
fname, ext = os.path.splitext(img)
croppedImage.save(fname + '_crop' + str(n) + ext)
n += 1
else:
print('No faces found:', img)
# 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
print ("#2")
def pil2cvGrey(pil_im):
#from: http://pythonpath.wordpress.com/2012/05/08/pil-to-opencv-image/
pil_im = pil_im.convert('L')
cv_im = cv2.CreateImageHeader(pil_im.size, cv2.cv.IPL_DEPTH_8U, 1)
cv2.SetData(cv_im, pil_im.tostring(), pil_im.size[0] )
return cv_im
print ("3")
def cv2pil(cv_im):
return Image.fromstring("L", cv2.GetSize(cv_im), cv_im.tostring())
print ("4")
pil_im=Image.open('C:/Users/kurnia/Desktop/barackobama.jpg')
#pil_im.show()
cv_im=pil2cvGrey(pil_im)
#the haarcascade files tells opencv what to look for.
faceCascade = cv2.Load('C:/Users/kurnia/AppData/Local/Programs/Python/Python36-32/Lib/site-packages/cv2/data/haarcascade_frontalface_default.xml')
face=DetectFace(cv_im,faceCascade)
img=cv2pil(face)
img.show()
def loadHaars(self):
faceCascade = cv.Load("haarcascades/haarcascade_frontalface_alt.xml")
eyeCascade = cv.Load("haarcascades/haarcascade_eye_tree_eyeglasses.xml")
mouthCascade = cv.Load("haarcascades/haarcascade_mcs_mouth.xml")
return (faceCascade, eyeCascade, mouthCascade)
#
### check that capture device is OK
if not capture:
print("Error opening capture device")
sys.exit(1)
#
### capture the 1st frame to get some propertie on it
frame = cv2.QueryFrame(capture)
#
### get size of the frame
frame_size = cv2.GetSize(frame)
gray = cv2.CreateImage(frame_size, 8, 1)
small_img = cv2.CreateImage(
(int(frame_size[0] / image_scale), int(frame_size[1] / image_scale)),
8, 1)
cascade = cv2.Load(cascade_name)
#
while 1: # do forever
# capture the current image
frame = cv2.QueryFrame(capture)
if frame is None:
# no image captured... end the processing
break
#
### check OS
if (osName == "nt"):
cv2.Flip(frame, frame, 0)
else:
cv2.Flip(frame, None, 1)
#
### detecting faces here
from pygame import camera
import pygame
import time
import cv2
import os
# Recognition
HAAR_PATH = "/usr/share/opencv2/haarcascades/"
# Face
FACE_HAAR = os.path.join(HAAR_PATH, "haarcascade_frontalface_default.xml")
FACE_HAAR = cv2.log(FACE_HAAR)
# Eye
EYE_HAAR = os.path.join(HAAR_PATH, "haarcascade_mcs_righteye.xml")
EYE_HAAR = cv2.Load(EYE_HAAR)
# Nose
NOSE_HAAR = os.path.join(HAAR_PATH, "haarcascade_mcs_nose.xml")
NOSE_HAAR = cv2.Load(NOSE_HAAR)
# Mouth
MOUTH_HAAR = os.path.join(HAAR_PATH, "haarcascade_mcs_mouth.xml")
MOUTH_HAAR = cv2.Load(MOUTH_HAAR)
# Screen settings
SCREEN = [640, 360]
def surface_to_string(surface):
"""Convert a pygame surface into string"""
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
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)
