在视频文件中工作时，我逐帧扫描视频，直到找到使用OpenCV Haar正面级联的脸部。然后我将这些坐标传递给Camshift（使用OpenCV示例代码）从该帧开始跟踪该面。然后我在Camshift返回的跟踪框内使用Haar眼/嘴检测，假设这是我感兴趣的区域。
当我这样做时，眼/嘴检测返回很少/没有结果。
如果我只是使用相同的眼睛和嘴巴探测器进行视频的基本操作而没有Camshift，那么他们会检测到眼睛和嘴巴（虽然经常检测嘴巴是眼睛，反之亦然，但仍然比我的Camshift更好的检测 - 跟踪投资回报率方法）。
这与我的期望背道而驰 - 不应该限制已知和跟踪面部的ROI内的搜索，以便比对整个视频帧进行哑扫描时更加可靠的面部特征检测？也许我正在做一些与我的搜索坐标不合适的事情......
非常感谢任何帮助。
import numpy as np
import cv2
import cv
from common import clock, draw_str
import video
class App(object):
def __init__(self, video_src):
if video_src == "webcam":
self.cam = video.create_capture(0)
else:
self.vidFile = cv.CaptureFromFile('sources/' + video_src + '.mp4')
self.vidFrames = int(cv.GetCaptureProperty(self.vidFile, cv.CV_CAP_PROP_FRAME_COUNT))
self.cascade_fn = "haarcascades/haarcascade_frontalface_default.xml"
self.cascade = cv2.CascadeClassifier(self.cascade_fn)
self.left_eye_fn = "haarcascades/haarcascade_eye.xml"
self.left_eye = cv2.CascadeClassifier(self.left_eye_fn)
self.mouth_fn = "haarcascades/haarcascade_mcs_mouth.xml"
self.mouth = cv2.CascadeClassifier(self.mouth_fn)
self.selection = None
self.drag_start = None
self.tracking_state = 0
self.show_backproj = False
self.face_frame = 0
cv2.namedWindow('camshift')
cv2.namedWindow('source')
#cv2.namedWindow('hist')
if video_src == "webcam":
while True:
ret, img = self.cam.read()
self.rects = self.faceSearch(img)
print "Searching for face..."
if len(self.rects) != 0:
break
else:
for f in xrange(self.vidFrames):
img = cv.QueryFrame(self.vidFile)
tmp = cv.CreateImage(cv.GetSize(img), 8, 3)
cv.CvtColor(img, tmp, cv.CV_BGR2RGB)
img = np.asarray(cv.GetMat(tmp))
print "Searching frame", f+1
self.face_frame = f
self.rects = self.faceSearch(img)
if len(self.rects) != 0:
break
def faceSearch(self, img):
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
gray = cv2.equalizeHist(gray)
rects = self.detect(gray, self.cascade)
if len(rects) != 0:
print "Detected face"
sizeX = rects[0][2] - rects[0][0]
sizeY = rects[0][3] - rects[0][1]
print "Face size is", sizeX, "by", sizeY
return rects
else:
return []
def detect(self, img, cascade):
# flags = cv.CV_HAAR_SCALE_IMAGE
rects = cascade.detectMultiScale(img, scaleFactor=1.1, minNeighbors=2, minSize=(80, 80), flags = cv.CV_HAAR_SCALE_IMAGE)
if len(rects) == 0:
return []
rects[:,2:] += rects[:,:2]
return rects
def draw_rects(self, img, rects, color):
for x1, y1, x2, y2 in rects:
cv2.rectangle(img, (x1, y1), (x2, y2), color, 2)
def show_hist(self):
bin_count = self.hist.shape[0]
bin_w = 24
img = np.zeros((256, bin_count*bin_w, 3), np.uint8)
for i in xrange(bin_count):
h = int(self.hist[i])
cv2.rectangle(img, (i*bin_w+2, 255), ((i+1)*bin_w-2, 255-h), (int(180.0*i/bin_count), 255, 255), -1)
img = cv2.cvtColor(img, cv2.COLOR_HSV2BGR)
cv2.imshow('hist', img)
cv.MoveWindow('hist', 0, 440)
def faceTrack(self, img):
vis = img.copy()
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
mask = cv2.inRange(hsv, np.array((0., 60., 32.)), np.array((180., 255., 255.)))
x0, y0, x1, y1 = self.rects[0]
self.track_window = (x0, y0, x1-x0, y1-y0)
hsv_roi = hsv[y0:y1, x0:x1]
mask_roi = mask[y0:y1, x0:x1]
hist = cv2.calcHist( [hsv_roi], [0], mask_roi, [16], [0, 180] )
cv2.normalize(hist, hist, 0, 255, cv2.NORM_MINMAX);
self.hist = hist.reshape(-1)
#self.show_hist()
vis_roi = vis[y0:y1, x0:x1]
cv2.bitwise_not(vis_roi, vis_roi)
vis[mask == 0] = 0
prob = cv2.calcBackProject([hsv], [0], self.hist, [0, 180], 1)
prob &= mask
term_crit = ( cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 1 )
track_box, self.track_window = cv2.CamShift(prob, self.track_window, term_crit)
if self.show_backproj:
vis[:] = prob[...,np.newaxis]
try: cv2.ellipse(vis, track_box, (0, 0, 255), 2)
except: print track_box
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
gray = cv2.equalizeHist(gray)
xc = track_box[0][0]
yc = track_box[0][1]
xsize = track_box[1][0]
ysize = track_box[1][1]
x1 = int(xc - (xsize/2))
y1 = int(yc - (ysize/2))
x2 = int(xc + (xsize/2))
y2 = int(yc + (ysize/2))
roi_rect = y1, y2, x1, x2
roi = gray[y1:y2, x1:x2]
vis_roi = img.copy()[y1:y2, x1:x2]
subrects_left_eye = self.detect(roi.copy(), self.left_eye)
subrects_mouth = self.detect(roi.copy(), self.mouth)
if subrects_left_eye != []:
print "eye:", subrects_left_eye, "in roi:", roi_rect
self.draw_rects(vis_roi, subrects_left_eye, (255, 0, 0))
self.draw_rects(vis_roi, subrects_mouth, (0, 255, 0))
cv2.imshow('test', vis_roi)
dt = clock() - self.t
draw_str(vis, (20, 20), 'time: %.1f ms' % (dt*1000))
#draw_str(vis, (20, 35), 'frame: %d' % f)
cv2.imshow('source', img)
cv.MoveWindow('source', 500, 0)
cv2.imshow('camshift', vis)
def run(self):
if video_src == "webcam":
while True:
self.t = clock()
ret, img = self.cam.read()
self.faceTrack(img)
ch = 0xFF & cv2.waitKey(1)
if ch == 27:
break
if ch == ord('b'):
self.show_backproj = not self.show_backproj
else:
for f in xrange(self.face_frame, self.vidFrames):
self.t = clock()
img = cv.QueryFrame(self.vidFile)
if type(img) != cv2.cv.iplimage:
break
tmp = cv.CreateImage(cv.GetSize(img), 8, 3)
cv.CvtColor(img, tmp, cv.CV_BGR2RGB)
img = np.asarray(cv.GetMat(tmp))
self.faceTrack(img)
ch = 0xFF & cv2.waitKey(5)
if ch == 27:
break
if ch == ord('b'):
self.show_backproj = not self.show_backproj
cv2.destroyAllWindows()
if __name__ == '__main__':
import sys
try: video_src = sys.argv[1]
except: video_src = '1'
App(video_src).run()
答案 0 :(得分：1)
您已经将detectMultiScale的minsize提到了80像素。面部可能是真的，但眼睛和嘴巴并不那么大。这可能是不检测眼睛和嘴巴的原因之一。在呼唤眼睛和嘴巴时，尝试将其减少到20或30像素。