привет, я хочу работать с opencv в raspberry pi
, но у меня есть ошибка
Traceback (последний вызов был последним): файл "testface.py", строка 76, в
c = max(cnts, key=cv2.contourArea)
cv2.error: OpenCV (4.0.0) /home/pi/opencv-4.0.0/modules/imgproc/src/shapedescr.cpp:272: ошибка: (-215: подтверждение не выполнено)npoints> = 0 && (глубина == CV_32F || глубина == CV_32S) в функции 'contourArea'
см. мой код:
# import the necessary packages
from __future__ import print_function
from imutils.video import VideoStream
import argparse
import imutils
import time
import cv2
import RPi.GPIO as GPIO
# initialize GPIO
redLed = 21
GPIO.setmode(GPIO.BCM)
GPIO.setwarnings(False)
GPIO.setup(redLed, GPIO.OUT)
# construct the argument parse and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-p", "--picamera", type=int, default=-1,
help="whether or not the Raspberry Pi camera should be used")
args = vars(ap.parse_args())
# initialize the video stream and allow the camera sensor to warmup
print("[INFO] waiting for camera to warmup...")
vs = VideoStream(usePiCamera=args["picamera"] > 0).start()
time.sleep(2.0)
# define the lower and upper boundaries of the object
# to be detected in the HSV color space
colorLower = (24, 100, 100)
colorUpper = (44, 255, 255)
# Start with LED off
print("\n Starting..... ==> Press 'q' to quit Program \n")
GPIO.output(redLed, GPIO.LOW)
ledOn = False
# loop over the frames from the video stream
while True:
# grab the next frame from the video stream, Invert 180o, resize the
# frame, and convert it to the HSV color space
frame = vs.read()
frame = imutils.resize(frame, width=500)
frame = imutils.rotate(frame, angle=180)
hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
# construct a mask for the obect color, then perform
# a series of dilations and erosions to remove any small
# blobs left in the mask
mask = cv2.inRange(hsv, colorLower, colorUpper)
mask = cv2.erode(mask, None, iterations=2)
mask = cv2.dilate(mask, None, iterations=2)
# find contours in the mask and initialize the current
# (x, y) center of the object
cnts = cv2.findContours(mask.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if imutils.is_cv2() else cnts[1]
center = None
#print("hey")
if cnts is None:
cnts = [0]
# only proceed if at least one contour was found
if len(cnts) > 0:
# find the largest contour in the mask, then use
# it to compute the minimum enclosing circle and
# centroid
c = max(cnts, key=cv2.contourArea)
((x, y), radius) = cv2.minEnclosingCircle(c)
M = cv2.moments(c)
center = (int(M["m10"] / M["m00"]), int(M["m01"] / M["m00"]))
# only proceed if the radius meets a minimum size
if radius > 10:
# draw the circle and centroid on the frame,
# then update the list of tracked points
cv2.circle(frame, (int(x), int(y)), int(radius),
(0, 255, 255), 2)
cv2.circle(frame, center, 5, (0, 0, 255), -1)
# if the led is not already on, turn the LED on
if not ledOn:
GPIO.output(redLed, GPIO.HIGH)
ledOn = True
print("fined")
# if the object is not detected, turn the LED off
elif ledOn:
GPIO.output(redLed, GPIO.LOW)
ledOn = False
# show the frame to our screen
cv2.imshow("Frame", frame)
key = cv2.waitKey(1) & 0xFF
# if the 'q' key is pressed, stop the loop
if key == ord("q"):
break
# do a bit of cleanup
print("\n Exiting Program and cleanup stuff \n")
GPIO.cleanup()
cv2.destroyAllWindows()
vs.stop()