我跟随这个网站练习对象跟踪。
https://www.hackster.io/mjrobot/automatic-vision-object-tracking-5575c4
所有功能均正常运行。但是有一个小问题。
当我运行objectDetectCoord.py时，您可以看到视频流非常流畅。
objectDetectCoord.py
from __future__ import print_function
from imutils.video import VideoStream
import imutils
import time
import cv2
import os
import RPi.GPIO as GPIO
# initialize LED GPIO
redLed = 21
GPIO.setwarnings(False)
GPIO.setmode(GPIO.BCM)
GPIO.setup(redLed, GPIO.OUT)
# print object coordinates
def mapObjectPosition (x, y):
print ("[INFO] Object Center coordenates at X0 = {0} and Y0 = {1}".format(x, y))
# initialize the video stream and allow the camera sensor to warmup
print("[INFO] waiting for camera to warmup...")
vs = VideoStream(0).start()
time.sleep(2.0)
# define the lower and upper boundaries of the object
# to be tracked in the HSV color space
colorLower = (24, 100, 100)
colorUpper = (44, 255, 255)
# Start with LED off
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 object 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
# 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)
# position Servo at center of circle
mapObjectPosition(int(x), int(y))
# if the led is not already on, turn the LED on
if not ledOn:
GPIO.output(redLed, GPIO.HIGH)
ledOn = True
# if the ball 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)
# if [ESC] key is pressed, stop the loop
key = cv2.waitKey(1) & 0xFF
if key == 27:
break
# do a bit of cleanup
print("\n [INFO] Exiting Program and cleanup stuff \n")
GPIO.cleanup()
cv2.destroyAllWindows()
vs.stop()
但是，当我使用平移和倾斜并运行objectDetectTrack.py时，视频流变得非常滞后。
我想知道这是正常情况还是错误，以及如何 改进它。谢谢！
objectDetectTrack.py
from __future__ import print_function
from imutils.video import VideoStream
import argparse
import imutils
import time
import cv2
import os
import RPi.GPIO as GPIO
#define Servos GPIOs
panServo = 27
tiltServo = 17
# initialize LED GPIO
redLed = 21
GPIO.setwarnings(False)
GPIO.setmode(GPIO.BCM)
GPIO.setup(redLed, GPIO.OUT)
#position servos
def positionServo (servo, angle):
os.system("python angleServoCtrl.py " + str(servo) + " " + str(angle))
print("[INFO] Positioning servo at GPIO {0} to {1} degrees\n".format(servo, angle))
# position servos to present object at center of the frame
def mapServoPosition (x, y):
global panAngle
global tiltAngle
if (x < 220):
panAngle += 10
if panAngle > 140:
panAngle = 140
positionServo (panServo, panAngle)
if (x > 280):
panAngle -= 10
if panAngle < 40:
panAngle = 40
positionServo (panServo, panAngle)
if (y < 160):
tiltAngle += 10
if tiltAngle > 140:
tiltAngle = 140
positionServo (tiltServo, tiltAngle)
if (y > 210):
tiltAngle -= 10
if tiltAngle < 40:
tiltAngle = 40
positionServo (tiltServo, tiltAngle)
# initialize the video stream and allow the camera sensor to warmup
print("[INFO] waiting for camera to warmup...")
vs = VideoStream(0).start()
time.sleep(2.0)
# define the lower and upper boundaries of the object
# to be tracked in the HSV color space
colorLower = (24, 100, 100)
colorUpper = (44, 255, 255)
# Start with LED off
GPIO.output(redLed, GPIO.LOW)
ledOn = False
# Initialize angle servos at 90-90 position
global panAngle
panAngle = 90
global tiltAngle
tiltAngle =90
# positioning Pan/Tilt servos at initial position
positionServo (panServo, panAngle)
positionServo (tiltServo, tiltAngle)
# 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 object 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
# 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)
# position Servo at center of circle
mapServoPosition(int(x), int(y))
# if the led is not already on, turn the LED on
if not ledOn:
GPIO.output(redLed, GPIO.HIGH)
ledOn = True
# if the ball 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)
# if [ESC] key is pressed, stop the loop
key = cv2.waitKey(1) & 0xFF
if key == 27:
break
# do a bit of cleanup
print("\n [INFO] Exiting Program and cleanup stuff \n")
positionServo (panServo, 90)
positionServo (tiltServo, 90)
GPIO.cleanup()
cv2.destroyAllWindows()
vs.stop()