Raspberry Pi 中的 OpenCV 视频捕获和播放问题
OpenCV Video Capturing & playing issue in Raspberry Pi
我正在尝试使用 Raspberry Pi 实现 lane tracking system。
因此，我正在使用 OpenCV 库（使用 python 代码）在 Raspberry Pi 中处理视频。但是当我捕捉视频时，它会滞后并且无法正常播放。
相同的代码在 Windows OS 环境中运行良好，但在 Raspberry Pi 环境中运行不正常。我将不胜感激任何帮助。
Raspberry Pi 硬件规格
Raspberry Pi 3 型号 B（1.2Ghz 1GB 内存）
摄像头模块 v2（8 百万像素）
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')
我降低了分辨率和帧率，现在可以正常工作了
camera.resolution = (176, 144)
camera.framerate = 30
我正在尝试使用 Raspberry Pi 实现 lane tracking system。 因此，我正在使用 OpenCV 库（使用 python 代码）在 Raspberry Pi 中处理视频。但是当我捕捉视频时，它会滞后并且无法正常播放。 相同的代码在 Windows OS 环境中运行良好，但在 Raspberry Pi 环境中运行不正常。我将不胜感激任何帮助。
Raspberry Pi 硬件规格
Raspberry Pi 3 型号 B（1.2Ghz 1GB 内存） 摄像头模块 v2（8 百万像素）
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')
我降低了分辨率和帧率，现在可以正常工作了
camera.resolution = (176, 144)
camera.framerate = 30