python 计算机视觉 opencv——实现智能小车巡线

代码如下：

import cv2

import numpy as np

screen_height = 480

screen_width = 640

def readImg(path):

img = cv2.imread(path, 1)

img = cv2.resize(img, (screen_width, screen_height))

return img

def createWait():

img = np.ones((screen_height, screen_width), dtype=np.uint8)

img[:, :] = 225

return img

def calculate(dst2):

points = []

sum_x = 0

sum_y = 0

for y in range(screen_height):

for x in range(screen_width):

if dst2[y][x] == 0:

points.append([x, y])

for point in points:

sum_x += point[0]

sum_y += point[1]

points_len = len(points)

average_x = sum_x / points_len

average_y = sum_y / points_len

sum_up = 0

sum_down = 0

for point in points:

sum_up += point[0] * point[1]

sum_down += point[0] ** 2

sum_up -= points_len * average_x * average_y

sum_down -= points_len * average_x ** 2

b = sum_up / sum_down

a = average_y - b * average_x

end = {'b': b, 'a': a}

return end

def getLine(path):

frame = readImg(path)

# 灰度化

gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

cv2.imwrite('./gray.jpg', gray)

# 二值化

threshold_image = cv2.threshold(gray, 60, 225, cv2.THRESH_BINARY)

binary = threshold_image[1]

cv2.imwrite('./binary.jpg', binary)

# 取反

subtract = cv2.subtract(createWait(), binary)

cv2.imwrite('./dst2.jpg', subtract)

# 腐蚀

kernel = np.ones((5, 5), np.uint8)

erode = cv2.erode(subtract, kernel, iterations=3)

#　膨胀

dilate = cv2.dilate(erode, kernel)

# 寻找轮廓

contours = cv2.findContours(dilate, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)

contour = contours[0]

area = []

for k in range(len(contour)):

area.append((cv2.contourArea(contour[k])))

max_idx = np.argmax(np.array(area))

tatImg = cv2.drawContours(createWait(), contour, max_idx, (0, 0, 225), -1)

cv2.imwrite('./tatImg.jpg', tatImg)

return calculate(tatImg)

def showResult(path):

img = readImg(path)

end = getLine(path)

for i in range(screen_width):

cv2.circle(img,

(int(i), int(end['b'] * i + end['a'])),

2,

(0, 0, 225),

0,

1)

cv2.imshow(path[6:], img)

cv2.imwrite('./img.jpg', img)

if __name__ == '__main__':

path = r'path/to/your/img'

showResult(path=path)

识别效果：

a)原图 b)轨迹轮廓

c)边缘检测 d)轨迹绘制

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