0
我正在嘗試使用Raspberry Pi實現lane tracking system。 因此，我正在使用OpenCV庫（使用Python代碼）處理Raspberry Pi內部的視頻。但是當我捕捉視頻時，它滯後，不能正常播放。 相同的代碼在Windows操作系統環境下工作正常，但不在Raspberry Pi內。我會很感激任何幫助。OpenCV視頻樹莓派中的捕獲和播放問題
樹莓裨硬件規格
樹莓裨3 B型（1.2GHz的1GB RAM） 相機模塊V2（800萬像素）
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')
覆盆子pi的計算能力不是那麼好。嘗試測量每個算法子組件所需的時間。 – Micka