我正在尝试使用Raspberry Pi实现lane tracking system。 因此，我正在使用OpenCV库（带有python代码）在Raspberry Pi中处理视频。但是当我拍摄视频时，它是滞后的而且没有正常播放。 相同的代码在Windows操作系统环境中正常工作，但不在Raspberry Pi内部。我很感激任何帮助。
Raspberry Pi硬件规范
Raspberry Pi 3 B型（1.2Ghz 1GB内存） 相机模块v2（800万像素）
import numpy as np
import cv2
import RPi.GPIO as GPIO
import os
import sys
import time
import picamera
import picamera.array
global motorPosition
motorPosition = 0 #1 = left, 2= right, 0= center
rightMotorPin = 17 #pin 11
leftMotorPin = 27 #pin 13
GPIO.setmode(GPIO.BCM)
GPIO.setup(rightMotorPin, GPIO.OUT) #Left
GPIO.setup(leftMotorPin, GPIO.OUT) #Right
GPIO.setwarnings(False)
motorPosition = 0
GPIO.output(leftMotorPin, GPIO.HIGH) #Left = 0
GPIO.output(rightMotorPin, GPIO.HIGH) #Right = 0
def grayscale(img):
return cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
def canny(img, low_threshold, high_threshold):
return cv2.Canny(img, low_threshold, high_threshold)
def gaussian_blur(img, kernel_size):
return cv2.GaussianBlur(img, (kernel_size, kernel_size), 0)
def region_of_interest(img, vertices):
mask = np.zeros_like(img)
if len(img.shape) > 2:
channel_count = img.shape[2]
ignore_mask_color = (255,) * channel_count
else:
ignore_mask_color = 255
cv2.fillPoly(mask, vertices, ignore_mask_color)
masked_image = cv2.bitwise_and(img, mask)
return masked_image
def get_slope(x1, y1, x2, y2):
return (y1 - y2) / (x1 - x2)
def draw_lines(img, lines, color=[255, 255, 0], thickness=20):
global motorPosition
top = 200
bottom = 540
left_x1 = []
left_y1 = []
left_x2 = []
left_y2 = []
right_x1 = []
right_y1 = []
right_x2 = []
right_y2 = []
for line in lines:
for x1, y1, x2, y2 in line:
slope = get_slope(x1, y1, x2, y2)
if slope < 0:
left_x1.append(x1)
left_y1.append(y1)
left_x2.append(x2)
left_y2.append(y2)
else:
right_x1.append(x1)
right_y1.append(y1)
right_x2.append(x2)
right_y2.append(y2)
try:
avg_right_x1 = int(np.mean(right_x1))
avg_right_y1 = int(np.mean(right_y1))
avg_right_x2 = int(np.mean(right_x2))
avg_right_y2 = int(np.mean(right_y2))
right_slope = get_slope(avg_right_x1, avg_right_y1, avg_right_x2, avg_right_y2)
right_y1 = top
right_x1 = int(avg_right_x1 + (right_y1 - avg_right_y1) / right_slope)
right_y2 = bottom
right_x2 = int(avg_right_x2 + (right_y2 - avg_right_y2) / right_slope)
cv2.line(img, (right_x1, right_y1), (right_x2, right_y2), color, thickness)
except ValueError:
pass
try:
avg_left_x1 = int(np.mean(left_x1))
avg_left_y1 = int(np.mean(left_y1))
avg_left_x2 = int(np.mean(left_x2))
avg_left_y2 = int(np.mean(left_y2))
left_slope = get_slope(avg_left_x1, avg_left_y1, avg_left_x2, avg_left_y2)
left_y1 = top
left_x1 = int(avg_left_x1 + (left_y1 - avg_left_y1) / left_slope)
left_y2 = bottom
left_x2 = int(avg_left_x2 + (left_y2 - avg_left_y2) / left_slope)
cv2.line(img, (left_x1, left_y1), (left_x2, left_y2), color, thickness)
except ValueError:
pass
left_border = (0, left_y1)
right_border = (img.shape[1], right_y1)
color3 = [0, 0, 255]
color4 = [0, 255, 0]
thickness3 = 25
left_diff = left_x1 - left_border[0]
right_diff = right_border[0] - right_x1
deviation = left_diff - right_diff
if deviation < -13 and motorPosition != 2:
motorPosition = 2
GPIO.output(leftMotorPin, GPIO.HIGH) #Left = 0
GPIO.output(rightMotorPin, GPIO.LOW) #Right = 1
print("leaning right")
cv2.line(img, (img.shape[1], 0), (img.shape[1], img.shape[0]), color3, thickness3)
elif deviation > 13 and motorPosition != 1:
motorPosition = 1
GPIO.output(leftMotorPin, GPIO.LOW) #Left = 1
GPIO.output(rightMotorPin, GPIO.HIGH) #Right = 0
print("leaning left")
cv2.line(img, (0, 0), (0, img.shape[1]), color3, thickness3)
elif (deviation >= -15 or deviation <= 15) and motorPosition != 0:
motorPosition = 0
GPIO.output(leftMotorPin, GPIO.HIGH) #Left = 0
GPIO.output(rightMotorPin, GPIO.HIGH) #Right = 0
print("center")
cv2.line(img, (0, 0), (0, img.shape[1]), color4, thickness3)
cv2.line(img, (img.shape[1], 0), (img.shape[1], img.shape[0]), color4, thickness3)
def hough_lines(img, rho, theta, threshold, min_line_len, max_line_gap):
lines = cv2.HoughLinesP(img, rho, theta, threshold, np.array([]), minLineLength=min_line_len,
maxLineGap=max_line_gap)
line_img = np.zeros((img.shape[0], img.shape[1], 3), dtype=np.uint8)
draw_lines(line_img, lines)
return line_img
def weighted_img(img, initial_img, a=0.8, b=1.0, g=0.0):
return cv2.addWeighted(initial_img, a, img, b, g)
def process_image(image):
kernel_size = 5
low_threshold = 200
high_threshold = 210
gray = grayscale(image)
blur = gaussian_blur(gray, kernel_size)
edges = canny(blur, low_threshold, high_threshold)
rho = 2
theta = np.pi / 180
threshold = 20
min_line_len = 7
max_line_gap = 10
line_image = hough_lines(edges, rho, theta, threshold, min_line_len, max_line_gap)
result = weighted_img(line_image, image)
return result
with picamera.PiCamera() as camera:
with picamera.array.PiRGBArray(camera) as output:
camera.resolution = (320, 240)
camera.framerate = 80
while(1):
print "\n\n------------------\n\n"
camera.capture(output, 'bgr')
try:
img = output.array
cv2.waitKey(1)
output.truncate(0)
if 0xFF & cv2.waitKey(5) == 27:
break
except KeyboardInterrupt:
pass
print ('KB interrupted')
print ('Process Aborted!')
break
except Exception as e:
exc_type, exc_obj, tb = sys.exc_info()
lineno = tb.tb_lineno
print ('Error : ' + str(e) + " @ line " + str(lineno))
finally:
pass
GPIO.cleanup()
cv2.destroyAllWindows()
GPIO.cleanup()
print ('Aborted')
答案 0 :(得分：0)
我降低了分辨率和帧速率，现在工作正常
camera.resolution = (176, 144)
camera.framerate = 30