def _detectOneObject(self, classifier, image, rect,
imageSizeToMinSizeRatio):
"""
探索領域の中で目や鼻や口を探す
:param classifier: 使う分類器
:type classifier: cv2.CascadeClassifier
:param image: 全体画像
:param rect: 対象を探す領域
:param imageSizeToMinSizeRatio: 無視するサイズの割合
:return: 対象の(x, y, w, h)
:rtype : tuple
"""
x, y, w, h = rect
minSize = utils.widthHeightDividedBy(image, imageSizeToMinSizeRatio)
subImage = image[y:y + h, x:x + w]
""":type : numpy.ndarray"""
subRects = classifier.detectMultiScale(subImage, self.scaleFactor,
self.minNeighbors, self.flags,
minSize)
""":type : list[tuple]"""
if len(subRects) == 0:
return None
subX, subY, subW, subH = subRects[0]
return (x + subX, y + subY, subW, subH)
def update(self, image):
self._faces = []
if utils.isGray(image):
image = cv2.equalizeHist(image)
else:
image = cv2.cvtColor(image, cv2.cv.CV_BGR2GRAY)
minSize = utils.widthHeightDividedBy(image, 8)
faceRects = self._faceClassifier.detectMultiScale(image, self.scaleFactor, self.minNeighbors, self.flags, minSize)
if faceRects is not None:
for faceRect in faceRects:
face = Face()
face.faceRect = faceRect
x, y, w, h = faceRect
searchRect = (x+w/7, y, w*2/7, h/2)
face.leftEyeRect = self._detectOneObject(self._eyeClassifier, image, searchRect, 64)
searchRect = (x+w*4/7, y, w*2/7, h/2)
face.rightEyeRect = self._detectOneObject(self._eyeClassifier, image, searchRect, 64)
searchRect = (x+w/4, y+h/4, w/2, h/2)
face.noseRect = self._detectOneObject(self._noseClassifier, image, searchRect, 32)
searchRect = (x+w/6, y+h*2/3, w*2/3, h/3)
face.mouthRect = self._detectOneObject(self._mouthClassifier, image, searchRect, 16)
self._faces.append(face)
def _detectOneObject(self, classifier, image, rect,
imageSizeToMinSizeRatio):
x, y, w, h = rect
minSize = utils.widthHeightDividedBy(image, imageSizeToMinSizeRatio)
#dgb = 'x : {} , y : {} , w : {} , h : {}'.format(x,y,w,h)
#print(dgb)
y = int(y)
x = int(x)
w = int(w)
h = int(h)
subImage = image[y:y + h, x:x + w]
subRects = classifier.detectMultiScale(subImage, self.scaleFactor,
self.minNeighbors, self.flags,
minSize)
if len(subRects) == 0:
return None
subX, subY, subW, subH = subRects[0]
return (x + subX, y + subY, subW, subH)
def _detect_one_object(self, classifier, image, search_rect, image_size_to_min_size_ratio):
"""
detects object in region sepcified by search_rect
will only detect image > image_size_to_min_size_ratio
classifier = haarscascade
search_rect = [x,y,w,h]
"""
x, y, w, h = search_rect
min_size = utils.widthHeightDividedBy(
image, image_size_to_min_size_ratio)
sub_image = image[y:y+h, x:x+w]
sub_rects = []
try:
sub_rects = classifier.detectMultiScale(
sub_image, self.scale_factor, self.min_neighbors, self.flags, min_size)
except Exception as e:
print("classifer", classifier)
print(e)
if (len(sub_rects) == 0):
return None
sub_x, sub_y, sub_w, sub_h = sub_rects[0]
return (x+sub_x, y+sub_y, sub_w, sub_h)
def update(self, image):
"""Update the tracked facial features."""
self._faces = []
if utils.isGray(image):
image = cv2.equalizeHist(image)
else:
#image = cv2.cvtColor(image, cv2.cv.CV_BGR2GRAY)
image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
cv2.equalizeHist(image, image)
minSize = utils.widthHeightDividedBy(image, 8)
faceRects = self._faceClassifier.detectMultiScale(image,
self.scaleFactor,
self.minNeighbors,
self.flags,
minSize)
if faceRects is not None:
for faceRect in faceRects:
face = Face()
face.faceRect = faceRect
x, y, w, h = faceRect
# Seek an eye in the upper-left part of the face.
searchRect = (int(x+w/7), y, int(w*2/7), int(h/2))
face.leftEyeRect = self._detectOneObject(self._eyeClassifier, image, searchRect, 64)
# Seek an eye in the upper-right part of the face.
searchRect = (int(x+w*4/7), y, int(w*2/7), int(h/2))
face.rightEyeRect = self._detectOneObject(self._eyeClassifier, image, searchRect, 64)
# Seek a nose in the middle part of the face.
searchRect = (int(x+w/4), int(y+h/4), int(w/2), int(h/2))
face.noseRect = self._detectOneObject(self._noseClassifier, image, searchRect, 32)
# Seek a mouth in the lower-middle part of the face.
searchRect = (int(x+w/6), int(y+h*2/3), int(w*2/3), int(h/3))
face.mouthRect = self._detectOneObject(self._mouthClassifier, image, searchRect, 16)
# Seek a smile in the lower-middle part of the face.
searchRect = (int(x+w/6), int(y+h*2/3), int(w*2/3), int(h/3))
face.smileRect = self._detectOneObject(self._smileClassifier, image, searchRect, 16)
# Seek a eyepair_small in the left-middle part of the face.
# searchRect = (x+w/7, y, w*5/7, h/2)
# face.eyepair_smallRect = self._detectOneObject(self._eyepair_smallClassifier, image, searchRect, 16)
self._faces.append(face)
def _detectOneObject(self, classifier, image, rect, imageSizeToMinSizeRatio): x, y, w, h = rect minSize = utils.widthHeightDividedBy(image, imageSizeToMinSizeRatio) subImage = image[y:y+h, x:x+w] subRects = classifier.detectMultiScale(subImage, self.scaleFactor, self.minNeighbors, self.flags, minSize) if len(subRects) == 0: return None subX, subY, subW, subH = subRects[0] return (x + subX, y + subY, subW, subH)
def _detectOneObject(self, classifier, image, rect,
imageSizeToMinSizeRatio):
x, y, w, h = rect
minSize = utils.widthHeightDividedBy(image, imageSizeToMinSizeRatio)
subImage = image[y:y + h, x:x + w]
subRects = classifier.detectMultiScale(subImage, self.scaleFactor,
self.minNeighbors, self.flags,
minSize)
if len(subRects) == 0:
return None
subX, subY, subW, subH = subRects[0]
return (x + subX, y + subY, subW, subH)
def update(self, image):
""" Update the tracked facial features """
# reset the features list
self._faces = []
# Equalize the variant. This makes the tracker more robust to variations in lighting
# Create greyscale variant of the image, if it isn't already, to improve performance
if utils.isGray(image):
image = cv2.equalizeHist(image)
else:
image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
cv2.equalizeHist(image,image)
# Classify the image
minSize = utils.widthHeightDividedBy(image,8)
faceRects = self._faceClassifier.detectMultiScale(
image, self.scaleFactor, self.minNeighbours, self.flags, minSize)
# If we find viable matches, place them in the faces list
if faceRects is not None:
for faceRect in faceRects:
face = Face()
face.faceRect = faceRect
# Decompose the target feature
x,y,w,h = faceRect
# Seek an eye in the upper LHS of the face
searchRect = (x+w/7,y,w*2/7,h/2)
face.leftEyeRect = self._detectOneObject(
self._eyeClassifier, image, searchRect, 64
)
# Seek an eye in the upper RHS of the face
searchRect = (x+w*4/7,y,w*2/7,h/2)
face.rightEyeRect = self._detectOneObject(
self._eyeClassifier, image, searchRect, 64
)
# Seek a nose in the middle of the face
searchRect = (x+w/4,y+h/4,w/2,h/2)
face.noseRect = self._detectOneObject(
self._noseClassifier, image, searchRect, 32
)
# Seek a mouth in the lower third of the face
searchRect = (x+w/6,y+h*2/3,w*2/3,h/3)
face.mouthRect = self._detectOneObject(
self._eyeClassifier, image, searchRect, 16
)
self._faces.append(face)
def update(self, image):
"""Update the tracked facial features."""
self._faces = []
if utils.isGray(image):
image = cv2.equalizeHist(image)
else:
image = cv2.cvtColor(image, cv2.cv.CV_BGR2GRAY)
cv2.equalizeHist(image, image)
minSize = utils.widthHeightDividedBy(image, 8)
faceRects = self._faceClassifier.detectMultiScale(
image, self.scaleFactor, self.minNeighbors, self.flags,
minSize)
if faceRects is not None:
for faceRect in faceRects:
face = Face()
face.faceRect = faceRect
face.logging_faceZone(image)
x, y, w, h = faceRect
# Seek an eye in the upper-left part of the face.
searchRect = (x+w/7, y, w*2/7, h/2)
face.leftEyeRect = self._detectOneObject(
self._eyeClassifier, image, searchRect, 64)
# Seek an eye in the upper-right part of the face.
searchRect = (x+w*4/7, y, w*2/7, h/2)
face.rightEyeRect = self._detectOneObject(
self._eyeClassifier, image, searchRect, 64)
# Seek a nose in the middle part of the face.
searchRect = (x+w/4, y+h/4, w/2, h/2)
face.noseRect = self._detectOneObject(
self._noseClassifier, image, searchRect, 32)
# Seek a mouth in the lower-middle part of the face.
searchRect = (x+w/6, y+h*2/3, w*2/3, h/3)
face.mouthRect = self._detectOneObject(
self._mouthClassifier, image, searchRect, 16)
if(face.detectOrganCount>=1):
self._faces.append(face)
def update(self, image):
"""updates tracked face features"""
self._faces = []
if (utils.is_gray(image)):
image = cv2.equalizeHist(image)
else:
image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
image = cv2.equalizeHist(image)
min_size = utils.widthHeightDividedBy(image, 8)
face_rects = self._face_classifier.detectMultiScale(
image, self.scale_factor, self.min_neighbors, self.flags, min_size)
if (face_rects is not None):
for face_rect in face_rects:
face = Face()
face.face_rect = face_rect
x, y, w, h = face_rect
# look for an eye in the upper-left part of the face
search_rect = (x+w//7, y, w*2//7, h//2)
face.left_eye_rect = self._detect_one_object(
self._eye_classifier, image, search_rect, 64
)
# look for an eye in the upper-right part of the face
search_rect = (x + (w*4)//7, y, w*2//7, h//2)
face.right_eye_rect = self._detect_one_object(
self._eye_classifier, image, search_rect, 64
)
# look for an node in the middle part of the face
search_rect = (x+w//4, y+h//4, w*2, h//2)
face.nose_rect = self._detect_one_object(
self._nose_classifier, image, search_rect, 32
)
# look for an mouth in the lower-middle part of the face
search_rect = (x+w//6, y + (h*2)//3, w*2//3, h//3)
face.mouth_rect = self._detect_one_object(
self._mouth_classifier, image, search_rect, 16
)
self._faces.append(face)
def update(self, image):
"""Update the tracked facial features."""
self._faces = []
if utils.isGray(image):
image = cv2.equalizeHist(image)
else:
image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
cv2.equalizeHist(image, image)
minSize = utils.widthHeightDividedBy(image, 8)
print(minSize)
faceRects = self._faceClassifier.detectMultiScale(
image, self.scaleFactor, self.minNeighbors, self.flags, minSize)
print(faceRects)
if faceRects is not None:
for faceRect in faceRects:
face = Face()
face.faceRect = faceRect
x, y, w, h = faceRect
# Seek an eye in the upper-left part of the face.
searchRect = (x + w / 7, y, w * 2 / 7, h / 2)
face.leftEyeRect = self._detectOneObject(
self._eyeClassifier, image, searchRect, 64)
# Seek an eye in the upper-right part of the face.
searchRect = (x + w * 4 / 7, y, w * 2 / 7, h / 2)
face.rightEyeRect = self._detectOneObject(
self._eyeClassifier, image, searchRect, 64)
# Seek a nose in the middle part of the face.
searchRect = (x + w / 4, y + h / 4, w / 2, h / 2)
face.noseRect = self._detectOneObject(self._noseClassifier,
image, searchRect, 32)
# Seek a mouth in the lower-middle part of the face.
searchRect = (x + w / 6, y + h * 2 / 3, w * 2 / 3, h / 3)
face.mouthRect = self._detectOneObject(self._mouthClassifier,
image, searchRect, 16)
self._faces.append(face)
def update(self, image):
self._faces = []
if utils.isGray(image):
image = cv2.equalizeHist(image)
else:
image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
cv2.equalizeHist(image, image)
minSize = utils.widthHeightDividedBy(image, 8)
minSize = (int(minSize[0]), int(minSize[1]))
faceRects = self._faceClassifier.detectMultiScale(
image, self.scaleFactor, self.minNeighbors, self.flags, minSize)
if faceRects is not None:
for faceRect in faceRects:
face = Face()
face.faceRect = faceRect
x, y, w, h = faceRect
#buscar o olho e atualizar o lado esquerdo da face
searchRect = (x + w / 7, y, w * 2 / 7, h / 2)
face.leftEyeRect = self._detectOneObject(
self._eyeClassifier, image, searchRect, 64)
#buscar o olho e atualizar o lado direito da face
searchRect = (x + w * 4 / 7, y, w * 2 / 7, h / 2)
face.rightEyeRect = self._detectOneObject(
self._eyeClassifier, image, searchRect, 64)
#buscar nariz
searchRect = (x + w / 4, y + h / 4, w / 2, h / 2)
face.noseRect = self._detectOneObject(self._noseClassifier,
image, searchRect, 32)
#buscar boca
searchRect = (x + w / 6, y + h * 2 / 3, w * 2 / 3, h / 3)
face.mouthRect = self._detectOneObject(self._mouthClassifier,
image, searchRect, 16)
self._faces.append(face)
def update(self, image):
"""
:param image:
:return:
"""
self._faces = []
if utils.isGray(image):
image = cv2.equalizeHist(image)
# The algorithm normalizes the brightness and increases the contrast of the image.
# 明るさをノーマライズしコントラストを強める
else:
# グレースケール画像に変換する
image = cv2.cvtColor(image, cv2.cv.CV_BGR2GRAY)
image = cv2.equalizeHist(image)
# まず顔を検出する
# 画像の1/8 * 1/8 = 1/64より小さい顔は認識しない
minSize = utils.widthHeightDividedBy(image, 8) # => (w, h)
faceRects = self._faceClassifier.detectMultiScale(
image, self.scaleFactor, self.minNeighbors, self.flags, minSize)
""":type : list[tuple]"""
# => (x, y, w, h)
#
# void CascadeClassifier::detectMultiScale(
# const Mat& image, vector<Rect>& objects, double scaleFactor=1.1, int minNeighbors=3,
# int flags=0, Size minSize=Size()
# )
# 入力画像中から異なるサイズのオブジェクトを検出します．検出されたオブジェクトは，矩形のリストとして返されます．
#
# パラメタ:
# image – CV_8U 型の行列．ここに格納されている画像中からオブジェクトが検出されます
# objects – 矩形を要素とするベクトル．それぞれの矩形は，検出したオブジェクトを含みます
# scaleFactor – 各画像スケールにおける縮小量を表します
# minNeighbors – オブジェクト候補となる矩形は，最低でもこの数だけの近傍矩形を含む必要があります
# flags – このパラメータは，新しいカスケードでは利用されません．
# 古いカスケードに対しては，cvHaarDetectObjects 関数の場合と同じ意味を持ちます
# minSize – オブジェクトが取り得る最小サイズ．これよりも小さいオブジェクトは無視されます
# 顔を検出できたら・・・
if faceRects is not None:
# 顔ごとに・・・
for faceRect in faceRects:
# faceオブジェクトをつくる
face = Face()
face.faceRect = faceRect
x, y, w, h = faceRect
# 左目を探す
searchRect = (x + w / 7, y, w * 2 / 7, h / 2)
# □■■□□□□
# □■■□□□□
# □□□□□□□
# □□□□□□□
face.leftEyeRect = self._detectOneObject(
self._eyeClassifier, image, searchRect, 64)
# 1/64 * 1/64より小さいオブジェクトを無視する
# if face.leftEyeRect == None:
# print("Left Eye detect failed")
# 右目を探す
searchRect = (x + w * 4 / 7, y, w * 2 / 7, h / 2)
# □□□□■■□
# □□□□■■□
# □□□□□□□
# □□□□□□□
face.rightEyeRect = self._detectOneObject(
self._eyeClassifier, image, searchRect, 64)
# 鼻を探す
searchRect = (x + w / 4, y + h / 4, w / 2, h / 2)
# □□□□□□□
# □□■■■□□
# □□■■■□□
# □□□□□□□
face.noseRect = self._detectOneObject(self._noseClassifier,
image, searchRect, 32)
# 1/32 * 1/32より小さいオブジェクトを無視する
# 口を探す
searchRect = (x + w / 6, y + h * 2 / 3, w * 2 / 3, h / 3)
# □□□□□□□
# □□□□□□□
# □■■■■■□
# □■■■■■□
face.mouthRect = self._detectOneObject(self._mouthClassifier,
image, searchRect, 16)
# 1/16 * 1/16より小さいオブジェクトを無視する
self._faces.append(face)
