从视频文件开始，我逐帧扫描视频，直到找到使用 OpenCV Haar 正面面部级联的面部。然后，我将这些坐标传递给 Camshift（使用 OpenCV 示例代码）以从该帧开始跟踪该面部。然后，我在 Camshift 返回的跟踪框中使用 Haar 眼睛/嘴巴检测，假设这是我感兴趣的区域。
当我这样做时，眼睛/嘴巴检测返回很少/没有结果。
如果我只是在没有 Camshift 的情况下使用相同的眼睛和嘴巴检测器对视频进行基本运行，那么它们会检测到眼睛和嘴巴（尽管经常将嘴巴检测为眼睛，反之亦然，但仍然比我的 Camshift 跟踪 ROI 方法更好的检测）。
这与我的期望背道而驰——不应该将搜索限制在已知和跟踪的人脸的 ROI 内，从而实现比对整个视频帧进行愚蠢扫描更可靠的人脸特征检测吗？也许我对搜索坐标做了一些不恰当的事情……</p>
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()