class FaceDetector(object):
def __init__(self):
self.dataSetFile = ''
self.successLogger = Logger(
os.path.join(FACE_DETECTION_ROOT, 'faceDetection.success'))
self.errorLogger = Logger(
os.path.join(FACE_DETECTION_ROOT, 'faceDetection.error'))
self.boundingboxFile = Logger(
os.path.join(FACE_DETECTION_ROOT, 'boundingbox.list'))
self.cc = cv2.CascadeClassifier(
os.path.join(FACE_DETECTION_ROOT,
'haarcascade_frontalface_alt.xml'))
def setDataSetFile(self, filePath):
self.dataSetFile = filePath
def run(self):
fid = open(self.dataSetFile, 'r').read().splitlines()
for src in fid:
self._detectFace(src)
def _detectFace(self, imgPath):
img = cv2.imread(imgPath)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
rects = self.cc.detectMultiScale(gray, scaleFactor=1.2, minNeighbors=2, \
minSize=(30, 30), flags = cv2.CASCADE_SCALE_IMAGE)
if not len(rects):
self.errorLogger.writeMsg("%s\n" % imgPath)
return
for rect in rects:
rect[2:] += rect[:2]
self.successLogger.writeMsg("%s\n" % imgPath)
'''
boundingbox format: left right top bottom
'''
self.boundingboxFile.writeMsg("%s %s %s %s %s\n" % \
(imgPath, str(rect[0]), str(rect[2]), str(rect[1]), str(rect[3])))
def detectLandmarks(boundingboxList):
"""
detect landmarks in `src` and store the result in `dst`
"""
#bboxes = []
#landmarks = []
fl = Landmarker()
logger = Logger(os.path.join(FACE_ALIGNMENT_ROOT, 'landmark.list'))
# create bbox list
fid = open(boundingboxList, 'r');
fLines = fid.read().splitlines()
fid.close()
for line in fLines:
word = line.split()
filename = word[0]
img = cv2.imread(filename)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
bbox = BBox([int(word[1]), int(word[2]), int(word[3]), int(word[4])])\
.subBBox(0.1, 0.9, 0.2, 1)
landmark, status = fl.detectLandmark(gray, bbox)
'''
get real landmark position
'''
landmark = bbox.reprojectLandmark(landmark)
logger.writeMsg("%s" % filename)
for x, y in landmark:
logger.writeMsg(" %s %s" % (str(x), str(y)))
logger.writeMsg('\n')
'''
free memory: force the Garbage Collector to release
'''
gc.collect()
class FaceDetector(object):
def __init__(self):
self.dataSetFile = ''
self.successLogger = Logger(os.path.join(FACE_DETECTION_ROOT, 'faceDetection.success'))
self.errorLogger = Logger(os.path.join(FACE_DETECTION_ROOT, 'faceDetection.error'))
self.boundingboxFile = Logger(os.path.join(FACE_DETECTION_ROOT, 'boundingbox.list'))
self.cc = cv2.CascadeClassifier(os.path.join(FACE_DETECTION_ROOT, 'haarcascade_frontalface_alt.xml'))
def setDataSetFile(self, filePath):
self.dataSetFile = filePath
def run(self):
fid = open(self.dataSetFile, 'r').read().splitlines()
for src in fid:
self._detectFace(src)
def _detectFace(self, imgPath):
img = cv2.imread(imgPath)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
rects = self.cc.detectMultiScale(gray, scaleFactor=1.2, minNeighbors=2, \
minSize=(30, 30), flags = cv2.CASCADE_SCALE_IMAGE)
if not len(rects):
self.errorLogger.writeMsg("%s\n" % imgPath)
return
for rect in rects:
rect[2:] += rect[:2]
self.successLogger.writeMsg("%s\n" % imgPath)
'''
boundingbox format: left right top bottom
'''
self.boundingboxFile.writeMsg("%s %s %s %s %s\n" % \
(imgPath, str(rect[0]), str(rect[2]), str(rect[1]), str(rect[3])))
