您好我想运行此代码来检测使用带有OpenCV的树莓派B上的raspicam但是遇到错误的汽车。
import numpy as np
import cv2
car_cascade = cv2.CascadeClassifier('cars3.xml')
cap = cv2.VideoCapture(0)
while 1:
ret, img = cap.read()
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
cars = car_cascade.detectMultiScale(gray, 1.3, 5)
for (x,y,w,h) in cars:
cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),2)
roi_gray = gray[y:y+h, x:x+w]
roi_color = img[y:y+h, x:x+w]
cv2.imshow('img',img)
k = cv2.waitKey(30) & 0xff
if k == 27:
break
cap.release()
cv2.destroyAllWindows()
运行代码后返回
OpenCV Error: Assertion failed (scn == 3 || scn == 4) in cvtColor, file /home/pi/installopencv/opencv-3.1.0/modules/imgproc/src/color.cpp, line 8000
Traceback (most recent call last):
File "test.py", line 14, in <module>
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
cv2.error: /home/pi/installopencv/opencv-3.1.0/modules/imgproc/src/color.cpp:8000: error: (-215) scn == 3 || scn == 4 in function cvtColor
是否发生了错误,因为我使用的是raspicam和&#34; cap = cv2.VideoCapture(0)&#34;只适用于网络摄像头?我尝试启用V4L2模块,但它也没有工作
答案 0 :(得分:0)
如果您想使用Raspberry PI相机模块,请使用picamera模块获取帧,而不是OpenCV'2 videoCapture模块。特别是您希望安装具有阵列支持的模块:
pip install "picamera[array]"
这将允许您轻松地将帧传递给OpenCV。 有关如何从头开始here的非常好的教程 这是它的要点:
# import the necessary packages
from picamera.array import PiRGBArray
from picamera import PiCamera
import time
import cv2
# initialize the camera and grab a reference to the raw camera capture
camera = PiCamera()
camera.resolution = (640, 480)
camera.framerate = 32
rawCapture = PiRGBArray(camera, size=(640, 480))
# allow the camera to warmup
time.sleep(0.1)
# capture frames from the camera
for frame in camera.capture_continuous(rawCapture, format="bgr", use_video_port=True):
# grab the raw NumPy array representing the image, then initialize the timestamp
# and occupied/unoccupied text
image = frame.array
# show the frame
cv2.imshow("Frame", image)
key = cv2.waitKey(1) & 0xFF
# clear the stream in preparation for the next frame
rawCapture.truncate(0)
# if the `q` key was pressed, break from the loop
if key == ord("q"):
break
在您的情况下,您可能希望将格式从"rgb"
更改为"yuv"
。
这样,您可以直接提取y
(光度)通道,这将是您的灰度方法。希望你不需要进行色彩空间转换(从BGR到灰度)并从CSI(而不是USB)获取帧,从而获得速度的小幅提升。