Python OpenCV人脸检测在Raspberry Pi上速度较慢
我正在用OpenCV和Python编码测试Raspberry Pi。视频流工作得很好(中速),但当我在流上运行人脸检测时,CPU被锁定,刷新图像的速度很慢 这是我的。如何优化我的代码Python OpenCV人脸检测在Raspberry Pi上速度较慢,python,opencv,webcam,raspberry-pi,Python,Opencv,Webcam,Raspberry Pi,我正在用OpenCV和Python编码测试Raspberry Pi。视频流工作得很好(中速),但当我在流上运行人脸检测时,CPU被锁定,刷新图像的速度很慢 这是我的。如何优化我的代码 #!/usr/bin/env python import sys import cv2.cv as cv from optparse import OptionParser min_size = (20, 20) image_scale = 2 haar_scale = 1.2 min_neighbors = 2
#!/usr/bin/env python
import sys
import cv2.cv as cv
from optparse import OptionParser
min_size = (20, 20)
image_scale = 2
haar_scale = 1.2
min_neighbors = 2
haar_flags = 0
def detect_and_draw(img, cascade):
# allocate temporary images
gray = cv.CreateImage((img.width,img.height), 8, 1)
small_img = cv.CreateImage((cv.Round(img.width / image_scale),
cv.Round (img.height / image_scale)), 8, 1)
cv.Round (img.height / image_scale)), 8, 1)
# convert color input image to grayscale
cv.CvtColor(img, gray, cv.CV_BGR2GRAY)
# scale input image for faster processing
cv.Resize(gray, small_img, cv.CV_INTER_LINEAR)
cv.EqualizeHist(small_img, small_img)
if(cascade):
t = cv.GetTickCount()
faces = cv.HaarDetectObjects(small_img, cascade, cv.CreateMemStorage(0),
haar_scale, min_neighbors, haar_flags, min_size)
t = cv.GetTickCount() - t
print "detection time = %gms" % (t/(cv.GetTickFrequency()*1000.))
if faces:
for ((x, y, w, h), n) in faces:
# the input to cv.HaarDetectObjects was resized, so scale the
# bounding box of each face and convert it to two CvPoints
pt1 = (int(x * image_scale), int(y * image_scale))
# bounding box of each face and convert it to two CvPoints
pt1 = (int(x * image_scale), int(y * image_scale))
pt2 = (int((x + w) * image_scale), int((y + h) * image_scale))
cv.Rectangle(img, pt1, pt2, cv.RGB(255, 0, 0), 3, 8, 0)
cv.ShowImage("result", img)
if __name__ == '__main__':
parser = OptionParser(usage = "usage: %prog [options] [camera_index]")
parser.add_option("-c", "--cascade", action="store", dest="cascade", type="str", help="Haar cascade file, default %default", default = "/usr/local/share/OpenCV/haarcascades")
(options, args) = parser.parse_args()
cascade = cv.Load(options.cascade)
capture = cv.CreateCameraCapture(0)
cv.NamedWindow("result", 1)
if capture:
frame_copy = None
while True:
frame = cv.QueryFrame(capture)
if not frame:
cv.WaitKey(0)
break
if not frame_copy:
frame_copy = cv.CreateImage((frame.width,frame.height),
cv.IPL_DEPTH_8U, frame.nChannels)
if frame.origin == cv.IPL_ORIGIN_TL:
cv.Copy(frame, frame_copy)
else:
cv.Copy(frame, frame_copy)
else:
cv.Flip(frame, frame_copy, 0)
detect_and_draw(frame_copy, cascade)
if cv.WaitKey(10) != -1:
break
else:
image = cv.LoadImage(input_name, 1)
detect_and_draw(image, cascade)
cv.WaitKey(0)
cv.DestroyWindow("result")
我可以建议您使用LBP级联而不是Haar。众所周知,它的检测速度高达6倍,检测率非常接近
但我不确定它是否可以在旧式python接口中访问。新包装中的
cv2.CascadeClassifier
类可以对LBP级联进行检测。您的解析器。我认为添加选项行被截断了。是的,它被截断了。但这不是我问题的重点。我从未说过这是。:-)只是给你机会去改正;我已经关闭了字符串和括号。谢谢Andrew,我在上找到了相同的答案,在上找到了一些测试代码。这给了我的树莓六足动物项目更多的希望!