我有一个程序,它应该检测光源,并使用opencvlibraryforpython循环它们。该程序适用于正在捕获的相机的第一帧,然后当while循环尝试捕获第二帧时,终端会给出以下错误:
VIDEOIO ERROR: V4L2: Pixel format of incoming image is unsupported by OpenCV
Unable to stop the stream: Device or resource busy
OpenCV Error: Assertion failed (scn == 3 || scn == 4) in cvtColor, file /tmp/binarydeb/ros-kinetic-opencv3-3.3.1/modules/imgproc/src/color.cpp, line 11111
Traceback (most recent call last):
File "lazer.py", line 27, in <module>
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
cv2.error: /tmp/binarydeb/ros-kinetic-opencv3-3.3.1/modules/imgproc/src/color.cpp:11111: error: (-215) scn == 3 || scn == 4 in function cvtColor
这似乎是一个简单的修复,但我是新的打开简历,从来没有用过它。我想我必须在两帧之间停止相机,因为错误显示设备或资源正忙。另外,我想说清楚,这不是重复我以前的问题,我有一个不同的问题,因为以前我没有得到任何图像,现在我得到了一些东西。如有任何帮助,我们将不胜感激。以下是我的代码:
# import the necessary packages
from imutils import contours
from skimage import measure
import numpy as np
import argparse
import imutils
import cv2
# construct the argument parse and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-i", "--image", required=True,
help="path to the image file")
args = vars(ap.parse_args())
while(1):
camera = cv2.VideoCapture(0)
#problem is here ********************************************
ret, image = camera.read()
#image.shape
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
blurred = cv2.GaussianBlur(gray, (11, 11), 0)
#threshold the image to reveal light regions in the
# blurred image
thresh = cv2.threshold(blurred, 200, 255, cv2.THRESH_BINARY)[1]
# perform a series of erosions and dilations to remove
# any small blobs of noise from the thresholded image
thresh = cv2.erode(thresh, None, iterations=2)
thresh = cv2.dilate(thresh, None, iterations=4)
# perform a connected component analysis on the thresholded
# image, then initialize a mask to store only the "large"
# components
labels = measure.label(thresh, neighbors=8, background=0)
mask = np.zeros(thresh.shape, dtype="uint8")
# loop over the unique components
for label in np.unique(labels):
# if this is the background label, ignore it
if label == 0:
continue
# otherwise, construct the label mask and count the
# number of pixels
labelMask = np.zeros(thresh.shape, dtype="uint8")
labelMask[labels == label] = 255
numPixels = cv2.countNonZero(labelMask)
# if the number of pixels in the component is sufficiently
# large, then add it to our mask of "large blobs"
if numPixels > 300:
mask = cv2.add(mask, labelMask)
# find the contours in the mask, then sort them from left to
# right
cnts = cv2.findContours(mask.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
cnts = imutils.grab_contours(cnts)
cnts = contours.sort_contours(cnts)[0]
# loop over the contours
for (i, c) in enumerate(cnts):
# draw the bright spot on the image
(x, y, w, h) = cv2.boundingRect(c)
((cX, cY), radius) = cv2.minEnclosingCircle(c)
#x and y center are cX and cY
cv2.circle(image, (int(cX), int(cY)), int(radius),
(0, 0, 255), 3)
cv2.putText(image, "#{}".format(i + 1), (x, y - 15),
cv2.FONT_HERSHEY_SIMPLEX, 0.45, (0, 0, 255), 2)
# show the output image
cv2.imshow("Image", image)
#cv2.waitKey(1000)
if cv2.waitKey(1) == 27:
break
我知道我需要摄像头释放()课程结束时
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