opencv+树莓派+c语言,实时图像处理与OpenCV的和树莓派
最近我在OpenCV的加速图像处理上我的树莓派3平台上运行的挣扎。我做了脸部识别应用程序，但它运行速度非常缓慢。我读线程，多处理等诸多课题，但我仍然感到困惑了。我只是用面部检测来测试它，以使其更简单。这里是我的代码：实时图像处理与OpenCV的和树莓派
pivideostream.py - 在线程更新帧
from picamera.array import PiRGBArray
from picamera import PiCamera
from threading import Thread
import cv2
class PiVideoStream:
def __init__(self, resolution=(640, 480), framerate=30):
self.camera = PiCamera()
self.camera.resolution = resolution
self.camera.framerate = framerate
self.rawCapture = PiRGBArray(self.camera, size=resolution)
self.stream = self.camera.capture_continuous(self.rawCapture,format='bgr', use_video_port=True)
self.image = None
self.stopped = False
def start(self):
t = Thread(target=self.update)
t.daemon = True
t.start()
return self
def update(self):
for frame in self.stream:
self.image = frame.array
self.rawCapture.truncate(0)
if self.stopped:
self.stream.close()
self.rawCapture.close()
self.camera.close()
return
def read(self):
return self.image
def stop(self):
self.stopped = True
process_img_thread.py - 主程序
from pivideostream import PiVideoStream
import cv2
from picamera.array import PiRGBArray
from picamera import PiCamera
import time
def detect_in_thread():
# Start updating frames in threaded manner
face_cascade = cv2.CascadeClassifier('./haarcascades/haarcascade_frontalface_default.xml')
eye_cascade = cv2.CascadeClassifier('./haarcascades/haarcascade_eye.xml')
thread_stream = PiVideoStream()
thread_stream.start()
time.sleep(2)
# Read frames
while True:
# Original image
image = thread_stream.read()
# Full image - face detection
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(gray,1.3,5)
for (x,y,w,h) in faces:
detected_face = cv2.rectangle(image,(x,y),(x+w,y+h),(0,255,0),2)
# Region of interest - eyes detection
roi_color = image[y:y+h,x:x+w]
roi_gray = gray[y:y+h,x:x+w]
eyes = eye_cascade.detectMultiScale(roi_gray,1.03,5,0,(40,40))
for (ex,ey,ew,eh) in eyes:
cv2.rectangle(roi_color,(ex,ey),(ex+ew,ey+eh),(0,255,255),2)
# Show computed image
cv2.imshow('Threaded Camera OpenCV Preview',image)
if cv2.waitKey(1) & 0xFF == ord("q"):
break
# Close image window and thread
cv2.destroyAllWindows()
thread_stream.stop()
if __name__ == "__main__":
detect_in_thread()
当我显示来自相机它的工作原始帧伟大的，但是当我仅仅是为了处理图像添加主程序的东西，视频速度大约是1 FPS :(。 有人能帮助我吗？
2017-02-01
Kordian
+1
脸部检测是一种昂贵的任务和覆盆子pi是一个缓慢的decice 。也许试试TK1 SoC，但不能保证足够快。 –
+1
如果你知道面的近似大小在您的图像，你可以调整图像或限制检测尺寸。 –