tesla_001.jpg
读取图像
- 使用 python-opencv 读取图像
- 使用 pygame 显示图像，这是一贯的套路
from __future__ import print_function
import os
import cv2
import pygame
from pygame.locals import *
class Display(object):
def __init__(self,W,H):
self.W = W
self.H = H
pygame.init()
pygame.display.set_caption("self Drive")
self.screen = pygame.display.set_mode((self.W,self.H),0,32)
def paint(self,frame):
self.screen.fill([0,0,0])
img = cv2.cvtColor(frame,cv2.COLOR_BGR2RGB)
img = cv2.transpose(img)
img = pygame.surfarray.make_surface(img)
self.screen.blit(img,(0,0))
pygame.display.update()
binary_warped = img.astype('uint8')
# coding=utf-8
import numpy as np
import cv2
import glob
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
from display import Display
import pygame
from pygame.locals import *
if __name__ == "__main__":
cap = cv2.VideoCapture("project_video.mp4")
Width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
Height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
Width = Width // 2
Height = Height // 2
src = np.float32([
[678, 440],[1279, 720],
[0, 720],[595, 440]])//2
dst = np.float32([
[1180, 0],[1180, 720],
[100, 720],[100, 0]])//2
display = Display(Width,Height)
def sliding_windows_search(img):
binary_warped = img.astype('uint8')
return binary_warped
while cap.isOpened():
ret, frame = cap.read()
frame = cv2.resize(frame,(Width,Height))
# print(frame)
M = cv2.getPerspectiveTransform(src, dst)
img_size = (frame.shape[1], frame.shape[0])
warped = cv2.warpPerspective(frame, M, img_size, flags=cv2.INTER_NEAREST)
display.paint(warped)
for event in pygame.event.get():
if event.type == QUIT:
sys.exit()
if event.type == pygame.KEYUP:
if event.key == ord('s'):
cv2.imwrite("wrapper_image_001.jpg",warped)
if event.key == ord('q'):
pygame.quit()
sys.exit()
# M = cv2.getPerspectiveTransform(src, dst)
# Minv = cv2.getPerspectiveTransform(dst, src)
bird-eye 图效果
现在思路与上一次思路略有不同，上一次我们同颜色和区域选择后得到图进行 canny 边缘检测，然后在 canny 边缘检测的基础上进行霍夫变换来检测车道线，
<img src="./wrapper_image_001.jpg">
M = cv2.getPerspectiveTransform(src, dst)
img_size = (frame.shape[1], frame.shape[0])
warped = cv2.warpPerspective(frame, M, img_size, flags=cv2.INTER_NEAREST)
重点我们是说如何通过滑动窗口来实现车道线检测的，首先我们将经过处理灰度图读取，并将类型转换为uint8。
def sliding_windows_search(img):
binary_warped = img.astype('uint8')
return binary_warped
def sliding_windows_search(img):
binary_warped = img.astype('uint8')
out_img = np.dstack((binary_warped, binary_warped, binary_warped))
return out_img
HLS(Hue, Saturation, Lightness) 色彩空间
- 色相(Hue): 是色彩的基本属性，就是平常所说的颜色名称，如红色、黄色等。
- 饱和度(Saturation):是指色彩的纯度，越高色彩越纯，低则逐渐变灰，取0-100%的数值。
- 亮度(Lightness):亮度（L），取0-100%。
通过将图像转换到特定色彩空间边缘我们将
def color_thresh(img, s_thresh=(0,255), v_thresh=(0,255)):
# 将颜色转换为 HLS 颜色空间然后提取(分离) S 通道
hls = cv2.cvtColor(img, cv2.COLOR_RGB2HLS)
s_channel = hls[:,:,2]
s_binary = np.zeros_like(s_channel)
s_binary[(s_channel>=s_thresh[0]) & (s_channel<=s_thresh[1])] = 1
# 将颜色转换为 HSV 颜色空间然后提取(分离)出 V 通道
hsv = cv2.cvtColor(img, cv2.COLOR_RGB2HSV)
v_channel = hsv[:,:,2]
v_binary = np.zeros_like(v_channel)
v_binary[(v_channel>=v_thresh[0]) & (v_channel<=v_thresh[1])] = 1
c_binary = np.zeros_like(s_channel)
c_binary[(s_binary==1) & (v_binary==1)] = 1
这次与上一次简单地在 RGB 3 个通道分别做阈值不同，我们通过颜色空间变换然后分别在 HSV 和 HSL 空间表示亮度的值 value 和 light 上来进行过滤，提取车道线位置。
c_binary[(s_binary==1) & (v_binary==1)] = 1
滑动窗口进行车道线搜索，
def sliding_windows_search(img):
# 将输入图像类型转换为 uint8
binary_warped = img.astype('uint8')
# 然后将输入 binary_warped 高度截取一半，对每一个列像素值求和
histogram = np.sum(binary_warped[binary_warped.shape[0]//2:,:], axis=0)
# midpoint = np.int(histogram.shape[0]/2)
out_img = np.dstack((binary_warped, binary_warped, binary_warped))
# print(type(histogram[1]))
# print(len(histogram))
for xv, hv in enumerate(histogram):
# print(xv,hv)
cv2.circle(out_img,(int(xv),int(hv//255)),1,(255,0,0),2)
midpoint = np.int(histogram.shape[0]/2)
# print(midpoint)
return out_img
- 首先将图片转换为灰度图，像素类型转换为 uinit8
- 然后对图片取颜色直方图
- 计算直方图的中点位置
midpoint = np.int(histogram.shape[0]/2)
leftx_base = np.argmax(histogram[:midpoint])
rightx_base = np.argmax(histogram[midpoint:]) + midpoint
cv2.circle(out_img,(leftx_base,0),5,(0,255,0),5)
cv2.circle(out_img,(rightx_base,0),5,(0,255,0),5)
histgram_screen_capture_01.jpg
如图两个绿色点位置表示出根据颜色直方图检测出车道线起始位置。
for xv, hv in enumerate(histogram):
print(xv,hv)
cv2.circle(out_img,(int(xv),int(hv//255)),1,(255,0,0),1)
histgram_screen_capture.jpg
a = np.array([
[1,2,0],
[2,0,1]
])
b = a.nonzero()
print(b)
(array([0, 0, 1, 1]), array([0, 1, 0, 2]))
margin = 100
# Set minimum number of pixels found to recenter window
minpix = 50
# Create empty lists to receive left and right lane pixel indices
left_lane_inds = []
right_lane_inds = []
# Step through the windows one by one
for window in range(nwindows):
# Identify window boundaries in x and y (and right and left)
win_y_low = binary_warped.shape[0] - (window+1)*window_height
win_y_high = binary_warped.shape[0] - window*window_height
win_xleft_low = leftx_current - margin
win_xleft_high = leftx_current + margin
win_xright_low = rightx_current - margin
win_xright_high = rightx_current + margin
# 将检测窗口可视化
cv2.rectangle(out_img,(win_xleft_low,win_y_low),(win_xleft_high,win_y_high),(0,255,0), 2)
cv2.rectangle(out_img,(win_xright_low,win_y_low),(win_xright_high,win_y_high),(0,255,0), 2)
for window in range(nwindows):
# 因为车道线是 x 轴(width)方向移动，在 y 轴上进行 nwindows 等分，每一个滑动窗口上边和下边位置
win_y_low = binary_warped.shape[0] - (window+1)*window_height
win_y_high = binary_warped.shape[0] - window*window_height
# 计算出检测窗口位置，margin 对滑动检测窗口左右空间扩展，margin 这里还是翻译为扩展比较好
win_xleft_low = leftx_current - margin
win_xleft_high = leftx_current + margin
win_xright_low = rightx_current - margin
win_xright_high = rightx_current + margin
# 将每一个滑动窗口绘制出来
cv2.rectangle(out_img,(win_xleft_low,win_y_low),(win_xleft_high,win_y_high),(0,255,0), 2)
cv2.rectangle(out_img,(win_xright_low,win_y_low),(win_xright_high,win_y_high),(0,255,0), 2)
# 识别出所有在窗口内的非 0 点的像素位置
good_left_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) & (nonzerox >= win_xleft_low) & (nonzerox < win_xleft_high)).nonzero()[0]
good_right_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) & (nonzerox >= win_xright_low) & (nonzerox < win_xright_high)).nonzero()[0]
# 将这点放到对应 list 中
left_lane_inds.append(good_left_inds)
right_lane_inds.append(good_right_inds)
# If you found > minpix pixels, recenter next window on their mean position
if len(good_left_inds) > minpix:
leftx_current = np.int(np.mean(nonzerox[good_left_inds]))
if len(good_right_inds) > minpix:
rightx_current = np.int(np.mean(nonzerox[good_right_inds]))
img_array = np.array([[1,2,3],
[2,3,1],
[2,3,2]])
print(img_array.shape)
histogram = np.sum(img_array[:,:],axis=0)
print(histogram)
(3, 3)
[5 8 6]
img_col_arr = np.dstack((img_array,img_array,img_array))
print(img_col_arr.shape)
(3, 3, 3)
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