Python OpenCV / PyQt5 车道检测与可视化
OpenCV C++版本 车道检测 https://blog.csdn.net/francislucien2017/article/details/83443639
lane detection.py
import os
import sys
import cv2
import numpy as np
from PyQt5.QtWidgets import QApplication
import GUI
# pre-define function
def grayscale(img):
return cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
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)
# crop the region of interest
def ROI(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_img = cv2.bitwise_and(img, mask)
return masked_img
def draw_lines(img, lines, color=[255,0,0], thickness=2):
for line in lines:
for x1, y1, x2, y2 in line:
cv2.line(img, (x1, y1), (x2, y2), color, thickness)
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)
return lines
# angle filter
def bypass_angle_filter(lines, low_thres, hi_thres):
filtered_lines = []
for line in lines:
for x1, y1, x2, y2 in line:
angle = abs(np.arctan((y2-y1)/(x2-x1+0.0001))*180/np.pi)
if low_thres < angle < hi_thres:
filtered_lines.append([[x1, y1, x2, y2]])
return filtered_lines
def yellow_enhance(img_rgb):
img_hsv = cv2.cvtColor(img_rgb, cv2.COLOR_BGR2HSV)
lower_yellow = np.array([40, 100, 20])
upper_yellow = np.array([100, 255, 255])
mask = cv2.inRange(img_hsv, lower_yellow, upper_yellow)
gray = grayscale(img_rgb)
return cv2.addWeighted(mask, 1., gray, 1., 0.)# merge the right / left line
def average_lines(img, lines, y_min, y_max):
# m: 斜率 b: 截距 norm: 长度
hough_pts = {'m_left': [], 'b_left': [], 'norm_left': [],
'm_right': [], 'b_right': [], 'norm_right': []}
if lines:
for line in lines:
for x1, y1, x2, y2 in line:
X, Y = [x1, x2], [y1, y2]
m, b = np.polyfit(X, Y, 1)
norm = ((x2-x1)**2 + (y2-y1)**2)**0.5
if m < 0: # right lane
hough_pts['m_right'].append(m)
hough_pts['b_right'].append(b)
hough_pts['norm_right'].append(norm)
if m > 0: # left lane
hough_pts['m_left'].append(m)
hough_pts['b_left'].append(b)
hough_pts['norm_left'].append(norm)
if len(hough_pts['norm_left']):
b_avg_left = np.mean(np.array(hough_pts['b_left']))
m_avg_left = np.mean(np.array(hough_pts['m_left']))
xmin_left = int((y_min-b_avg_left)/m_avg_left)
xmax_left = int((y_max-b_avg_left)/m_avg_left)
left_lane = [[xmin_left, y_min, xmax_left, y_max]]
else:
left_lane = [[0, 0, 0, 0]]
if len(hough_pts['norm_right']):
b_avg_right = np.mean(np.array(hough_pts['b_right']))
m_avg_right = np.mean(np.array(hough_pts['m_right']))
xmin_right = int((y_min - b_avg_right)/m_avg_right)
xmax_right = int((y_max-b_avg_right)/m_avg_right)
right_lane = [[xmin_right, y_min, xmax_right, y_max]]
else:
right_lane = [[0, 0, 0, 0]]
return [left_lane, right_lane]
# major process
def pipeline(img, vertices, low_thres, hi_thres):
gray = yellow_enhance(img)
gray_blur = gaussian_blur(gray,3)
gray_dilate = cv2.dilate(gray_blur, (3,3), iterations=10)
edges = canny(gray_dilate, 50, 180)
h, w = img.shape[:2]
masked = ROI(edges, vertices)
h_lines = hough_lines(masked,
rho=1,
theta=np.pi/180,
threshold=26,
min_line_len=5,
max_line_gap=50
)
# Angle High Pass filter
h_lines = bypass_angle_filter(h_lines, low_thres, hi_thres)
# averaging lines
avg_lines = average_lines(img, h_lines, int(h * 0.65), h)
avg_img = np.zeros(img.shape, dtype=np.uint8)
draw_lines(avg_img, avg_lines, color=[255, 0, 0], thickness=10)
img_all_lines = cv2.addWeighted(avg_img, 1., img, 1., 0.)
return img_all_lines# process the frame / video
def process_img(img):
h, w = img.shape[:2]
vertices = np.array([[(200, h-80), (490, h*0.65),
(820, h*0.65), (w-150, w-80),
(100, h-80)]], dtype=np.int32)
low_thres, hi_thres = [30, 80]
return pipeline(img, vertices, low_thres, hi_thres)
def process_video(dir):
cap = cv2.VideoCapture(dir)
ret, frame = cap.read()
h, w = frame.shape[:2]
fourcc = cv2.VideoWriter_fourcc(*'XVID')
output = cv2.VideoWriter('./test_videos/output.avi', fourcc, 30.0, (w//2, h//2))
while cap.isOpened():
ret, frame = cap.read()
if ret:
processed_img = process_img(frame)
frame = cv2.resize(frame, (w//2, h//2), cv2.INTER_LINEAR)
processed_img = cv2.resize(processed_img, (w//2, h//2), cv2.INTER_LINEAR)
cv2.imshow('frame', frame)
cv2.imshow('result', processed_img)
output.write(processed_img)
if cv2.waitKey(10) == 27:
break
else:
break
cap.release()
output.release()
cv2.destroyAllWindows()# main function
if __name__ == '__main__':
isProcessed = True
if not isProcessed:
process_video('./test_videos/challenge.mp4') # show and save the video
else:
app = QApplication(sys.argv)
v_dir = './test_videos/challenge.mp4'
v_dir2 = './test_videos/output.avi'
gui = GUI.GUI(v_dir, v_dir2)
gui.show()
sys.exit(app.exec_())
GUI.py
from PyQt5.QtCore import *
from PyQt5.QtWidgets import *
from PyQt5.QtGui import *
import cv2
import sys
class GUI(QWidget):
def __init__(self, video_dir, video_dir2):
super().__init__()
self.timer = QTimer(self)
self.timer.timeout.connect(self.show_pic) # 计时结束调用 show_pic 函数
self.timer.start(30)
self.cap = cv2.VideoCapture(video_dir)
self.cap2 = cv2.VideoCapture(video_dir2)
self.label = QLabel()
self.label2 = QLabel()
h_box = QHBoxLayout(self)
h_box.addWidget(self.label)
h_box.addWidget(self.label2)
self.setLayout(h_box)
self.move(300, 300)
self.setWindowTitle('Lane Detection')
def show_pic(self):
ret, frame = self.cap.read()
ret2, frame2 = self.cap2.read()
if ret and ret2:
show = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
h, w = show.shape[:2]
show = cv2.resize(show, (w//2, h//2), cv2.INTER_LINEAR)
show_image = QImage(show.data, show.shape[1], show.shape[0], QImage.Format_RGB888)
self.label.setPixmap(QPixmap.fromImage(show_image))
show2 = cv2.cvtColor(frame2, cv2.COLOR_BGR2RGB)
show2 = cv2.resize(show2, (w//2, h//2), cv2.INTER_LINEAR)
show_image2 = QImage(show2.data, show2.shape[1], show2.shape[0], QImage.Format_RGB888)
self.label2.setPixmap(QPixmap.fromImage(show_image2))
self.timer.start(30)
# # test part
# if __name__=='__main__':
# app = QApplication(sys.argv)
# v_dir = './test_videos/challenge.mp4'
# v_dir2 = './test_videos/white.mp4'
# gui = GUI(v_dir, v_dir2)
# gui.show()
# sys.exit(app.exec_())
最终效果
process image：
GUI：
总结：
测试视频中表现还可以，然而鲁棒性并不是很好，很多预设参数，一旦图像质量、尺寸有变化，效果就会比较差；
GUI部分做的很随意，其实有很多地方可以改进，比如加几个button，connect一个文件对话框可以选取视频进行处理或对比，以及可以设置用cv2.VideoCapture打开摄像头录制视频，边录制边对每一帧处理再显示在Qt的label上；
总的来说就是重新熟悉一下opencv 和 pyqt 中的一些函数，练手还可以，作为小项目的话还有很多地方可以扩充和改进。