import cv2
import sys
sys.path.append('..')
from cv.image_init import ImageInit
from cv.find_key_point import FindKeyPoint
from cv.hough_line_transform import HoughLines
image_p = ImageInit(320,
240,
convert_type="BINARY",
threshold=120,
bitwise_not=False)
capture = cv2.VideoCapture(0)
fkp = FindKeyPoint(True)
hl = HoughLines()
while True:
ret, frame = capture.read()
image2 = image_p.processing(frame)
_, image3 = fkp.get_key_point(image2, frame)
lines = hl.get_lines(image2, frame)
print(lines)
cv2.imshow('frame', image2)
cv2.imshow("fkp", image3)
cv2.imshow("hl", frame)
if cv2.waitKey(1) == ord('q'):
break
capture.release()
cv2.destroyAllWindows()
sys.path.append('..')
from cv.show_images import ShowImage
import time
from cv.image_init import ImageInit
LINE_CAMERA_WIDTH = 320
LINE_CAMERA_HEIGHT = 240
camera = cv2.VideoCapture('/dev/video0')
freq = cv2.getTickFrequency()
show_image = ShowImage()
init = ImageInit(320, 240)
ret, frame = camera.read()
while True:
t1 = time.perf_counter()
# 获取一帧
ret, frame = camera.read()
show_image.show(frame)
image = init.processing(frame)
show_image.show(image, window_name="image")
t2 = time.perf_counter()
frame_rate_calc = 1.0 / (t2 - t1)
print(frame_rate_calc)
if cv2.waitKey(1) == ord('q'):
break
camera.release()
cv2.destroyAllWindows()
# fl对象用于寻找引导线偏离图像中心的位置，threshold是控制连续白色的的阈值，也就是只有连续多少个白色像素点才认为已经找到引导线
# direction是开始寻找的方向，True是从左边开始寻找，False是右边。当顺时针绕圈时，引导线大概率出现在右边，所以可以选择False。
fl = FollowLine(width=320, height=240, threshold=15, direction=False)
# 串口类，此类最好不要直接使用，而是通过CarController来对车子进行控制
serial = CarSerial(SERIAL, receive=True)
# 此类并没有实现PID控制，而是简单的使用了比例这个参数。（现在这么简单的地图还无需用到PID）
# 如果需要使用PID可以直接调用car目录下的pid类，同时把此类的比例参数设置为1
ctrl = CarController(serial, proportional=0.4)
p_offset = 0
while True:
ret, frame = camera.read() # 读取每一帧
frame = init.resize(frame) # 把图像缩小，尺寸有ImageInit在初始化时指定
display.show(frame, "original")
image = init.processing(frame) # 对帧进行处理
# 偏置就是白色线的中心点距离图片中心点的距离，比如320*240的图像，中心点在160
offset, render_image = fl.get_offset(image,
frame) # 第一个参数是需要处理的图像，第二个参数是需要渲染的图像
# 直接把Offset赋值给CarController，对于一般的线没有问题。
# 但是对于急弯，或者线突然不见了，没办法处理，想稳定的巡线，需要对offset进行处理后再给CarController
# PID处理offset后再给CarController是一个选择
# 简单的可以做如下的处理，当找不到线时，会出现offset=-1000的情况，我们可以不理它当它是0.
if offset == -1000:
offset = p_offset * 1.5
else:
p_offset = offset
ctrl.follow_line(offset)
if not (self.event_function is None):
self.event_function(intersection_number=self.__intersection_number)
if __name__ == '__main__':
LINE_CAMERA = '/dev/video1'
LINE_CAMERA_WIDTH = 320
LINE_CAMERA_HEIGHT = 240
camera = cv2.VideoCapture(LINE_CAMERA)
# ret = camera.set(3, LINE_CAMERA_WIDTH)
# ret = camera.set(4, LINE_CAMERA_HEIGHT)
im_p = ImageInit(320, 240)
while True:
ret, image = camera.read()
cv2.imshow("image", image)
image2 = im_p.processing(image)
# image_processing(image, width=320, height=240, threshold=248, convert_type="BINARY")
cv2.imshow("test_one", image2)
fi = FindIntersection(150)
# data = _find(image2, (160, 230), image)
data2 = fi._find(image2, (160, 200), image)
# print(data)
print(data2)
cv2.imshow("_render_image", image)
if cv2.waitKey(1) == ord('q'):
break
camera.release()
cv2.destroyAllWindows()