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使用視頻文件，我逐幀掃描視頻，直到使用OpenCV Haar正面人臉級聯找到人臉。然後，我將這些座標傳遞給Camshift（使用OpenCV示例代碼）從該框架開始跟蹤該面部。然後，我使用Camshift返回的跟蹤框中的哈爾眼/嘴檢測，假設這是我感興趣的區域。OpenCV Haar特徵檢測，僅限於Camshift跟蹤區域
當我這樣做時，眼睛/嘴巴檢測返回很少/沒有結果。
如果我只是通過視頻和沒有攝像頭的相同眼睛和嘴巴探測器進行基本運行，然後他們會檢測到眼睛和嘴巴（儘管經常將嘴巴視爲眼睛，反之亦然，但仍然比我的Camshift追蹤投資回報率方法）。
這與我的期望相反 - 不應該限制在已知跟蹤面部的ROI範圍內進行搜索，以便比對整個視頻幀進行啞掃描更可靠的面部特徵檢測？也許我正在做一些不適合我的搜索座標...
任何幫助非常感謝。
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()