通过opencv检测停车场
问题描述：
该程序识别对象是否为单排（较小的图像）。通过opencv检测停车场
from __future__ import division
from collections import defaultdict
from collections import OrderedDict
from cv2 import line
import cv2
from matplotlib import pyplot as plt
from networkx.algorithms import swap
from numpy import mat
from skimage.exposure import exposure
import numpy as np
from org import imutils
from numpy.core.defchararray import rindex
import sys
def line(p1, p2):
A = (p1[1] - p2[1])
B = (p2[0] - p1[0])
C = (p1[0]*p2[1] - p2[0]*p1[1])
return A, B, -C
def intersection(L1, L2):
D = L1[0] * L2[1] - L1[1] * L2[0]
Dx = L1[2] * L2[1] - L1[1] * L2[2]
Dy = L1[0] * L2[2] - L1[2] * L2[0]
if D != 0:
x = Dx/D
y = Dy/D
return x,y
else:
return False
def comupteIntersect(hline,vline):
hx1=hline[0];hy1=hline[1];hx2=hline[2];hy2=hline[3];
vx3=vline[0];vy3=vline[1];vx4=vline[2];vy4=vline[3];
return 0;
input = sys.argv[1]
# CascadeClassifier class to detect objects. cas1.xml will have the trained data
face_cascade = cv2.CascadeClassifier(sys.argv[2])
# im will have the input in image format
im = cv2.imread(input)
im2=im
# cvtColor Converts an image from one color space to another.
gray=cv2.cvtColor(im,cv2.COLOR_BGR2GRAY)
# apply diverse linear filters to smooth images using GaussianBlur
blur = cv2.GaussianBlur(gray,(5,15),0)
# apply segmentation
# Application example: Separate out regions of an image corresponding to objects which we want to analyze. This separation is based on the variation of intensity between the object pixels and the background pixels.
# To differentiate the pixels we are interested in from the rest (which will eventually be rejected), we perform a comparison of each pixel intensity value with respect to a threshold (determined according to the problem to solve).
# Once we have separated properly the important pixels, we can set them with a determined value to identify them (i.e. we can assign them a value of 0 (black), 255 (white) or any value that suits your needs).
ret3,th3 = cv2.threshold(blur,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)
# Contours can be explained simply as a curve joining all the continuous points (along the boundary), having same color or intensity. The contours are a useful tool for shape analysis and object detection and recognition.
#
# For better accuracy, use binary images. So before finding contours, apply threshold or canny edge detection.
# findContours function modifies the source image. So if you want source image even after finding contours, already store it to some other variables.
# In OpenCV, finding contours is like finding white object from black background. So remember, object to be found should be white and background should be black.
contours, hierarchy = cv2.findContours(th3,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
# by here skeleton would have been drawn
#to draw the contour in the image enable the below line
#img = cv2.drawContours(im, contours, -1, (0,255,0), 1)
idx =0
for cnt in contours:
x,y,w,h = cv2.boundingRect(cnt)
if w-x>900 and h-y>100:
roi=im[y:y+h,x:x+w]
crop_rect=im[y:y+h,x:x+w]
# cv2.imshow('crop_rect',crop_rect)
# cv2.waitKey(0)
idx+=1
cv2.imwrite('crp_contour'+str(idx) + '.jpg', crop_rect)
im4=crop_rect
im3=crop_rect
gray=cv2.cvtColor(crop_rect,cv2.COLOR_BGR2GRAY)
blur = cv2.GaussianBlur(gray,(5,15),0)
ret3,th3 = cv2.threshold(blur,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)
contours, hierarchy = cv2.findContours(th3,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
rect=None
for cnt in contours:
x1=[]
y1=[]
rect = cv2.minAreaRect(cnt)
box = cv2.cv.BoxPoints(rect)
box = np.int0(box)
x1.append(box[0][0]);
x1.append(box[1][0]);
x1.append(box[2][0]);
x1.append(box[3][0]);
y1.append(box[0][1]);
y1.append(box[1][1]);
y1.append(box[2][1]);
y1.append(box[3][1]);
x=np.amin(x1)
y=np.amin(y1)
w=np.amax(x1)
h=np.amax(y1)
# re = cv2.rectangle([box])
# x,y,w,h = cv2.boundingRect(cnt)
if w-x>900 and h-y>100:
rect = cv2.minAreaRect(cnt)
box = cv2.cv.BoxPoints(rect)
box = np.int0(box)
x,y,w,h = cv2.boundingRect(cnt)
# crop_rect1=crop_rect[y:y+h,x:x+w]
# cv2.imshow('crop_rect',crop_rect1)
# cv2.waitKey(0)
break
#(top-left corner(x,y), (width, height), angle of rotation)
x=rect[0][0]
y=rect[0][1]
w=rect[1][0]
h=rect[1][1]
angle=rect[2]
if rect[2]<-45:
angle += 90.0;
temp=w
w=h
h=temp
center=(x+w)/2,(y+h)/2
img=crop_rect.copy()
rot_mat = cv2.getRotationMatrix2D(center, angle, 1);
dst=cv2.warpAffine(crop_rect,rot_mat, (int(w),int(h)));
# cv2.imshow('Rotated and Cropped Image',dst)
# cv2.waitKey(0)
horizontal = []
im6=dst
im4=im6
im3=im6
gray=cv2.cvtColor(im6,cv2.COLOR_BGR2GRAY)
edges = cv2.Canny(gray,50,150,apertureSize = 3)
# cv2.imshow('edges Image',edges)
# cv2.waitKey(0)
# Find the edge of the image
# lines = cv2.HoughLines(edges,1,np.pi/95,40)
lines = cv2.HoughLines(edges,1,np.pi/180,40)
for rho,theta in lines[0]:
pt1 = []
im5=im6
if (theta<np.pi/180*95 and theta>np.pi/180*88):
if (rho==78.0):
a = np.cos(theta)
b = np.sin(theta)
x0 = a*rho
y0 = b*rho
x1 = int(x0 + 1000*(-b))
y1 = int(y0 + 1000*(a))
x2 = int(x0 - 1000*(-b))
y2 = int(y0 - 1000*(a))
pt1.append(x1)
pt1.append(y1)
pt1.append(x2)
pt1.append(y2)
horizontal.append(pt1)
cv2.line(im5,(x1,y1),(x2,y2),(0,0,255),2)
# cv2.imshow('for',im5)
# cv2.waitKey(0)
break
#
diff = h-y
toty1 = diff+y1+20.0
toty2 = diff+y2+20.0
#cv2.line(im5,(int(x1),int(toty1)),(int(x2),int(toty2)),(0,0,255),2)
pt1 = []
pt1.append(int(x1))
pt1.append(int(toty1))
pt1.append(int(x2))
pt1.append(int(toty2))
horizontal.append(pt1)
minLineLength = 50
maxLineGap = 10
im7=im3
gray = cv2.cvtColor(im5, cv2.COLOR_BGR2GRAY)
gray = cv2.bilateralFilter(gray, 11, 17, 17)
edged = cv2.Canny(gray, 30, 200)
m,n = gray.shape
L=[]
lines = cv2.HoughLines(edged, 2, np.pi/180,10,0,0)[0]
# or theta>np.pi/180*80 and theta<np.pi/180*100 or theta>np.pi/180*170 or theta<np.pi/180*10
i=0
d = defaultdict(list)
for (rho,theta) in lines:
if(i<1000):
if(theta>np.pi/180*170 or theta<np.pi/180*10):
if(theta!=0 and rho!=-795.0 and rho!=-745.0 and rho!=-749.0 and rho!=425.0 and rho!=251.0 and rho!=253.0):
l=[]
x0 = np.cos(theta)*rho
y0 = np.sin(theta)*rho
pt1 = (int(x0 + (m+n)*(-np.sin(theta))), int(y0 + (m+n)*np.cos(theta)))
pt2 = (int(x0 - (m+n)*(-np.sin(theta))), int(y0 - (m+n)*np.cos(theta)))
if (pt1[0]==-92 or pt1[0]==-27 or pt1[0]==65 or pt1[0]==154 or pt1[0]==315 or pt1[0]==409 or
pt1[0]==469 or pt1[0]==519 or pt1[0]==549 or pt1[0]==573 or pt1[0]==592):
# cv2.line(im3, pt1,pt2 ,(255,0,0), 2,cv2.cv.CV_AA)
# cv2.imshow('img44',im3)
# cv2.waitKey(0)
#b=str(pt1)+","+str(pt2)
l.append(pt1)
l.append(pt2)
L.append(l)
d[pt1[0]].append(l)
i+=1
else:
break
sdict=OrderedDict(sorted(d.items(), key=lambda t: t[0]))
vertical = []
xcoordinates=[]
ycoordinates=[]
i=0;j=0;
p=[]
pt=[]
for t in range(0,6):
p.append(t)
pt.append(p)
ncars = 0
sub_image_point=[];
# process each full parking slot image
for a in sdict:
vx3=sdict[a][0][0][0];vy3=sdict[a][0][0][1];vx4=sdict[a][0][1][0];vy4=sdict[a][0][1][1];
pt[0]=[];pt[4]=[]
pt[0].append(vx3);pt[0].append(vy3);
pt[4].append(vx4);pt[4].append(vy4);
j+=1;
if (j!=1):
for k in range(0,2):
i+=1
pt1=pt[k+k*k]
pt2=pt[k+2*2]
L1=line(pt1,pt2)
for hline in horizontal:
pt3=[];pt4=[]
hx1=hline[0];hy1=hline[1];hx2=hline[2];hy2=hline[3];
pt3.append(hx1);pt3.append(hy1);
pt4.append(hx2);pt4.append(hy2);
L2=line(pt3,pt4)
R = intersection(L1, L2)
if R:
xcoordinates.append(R.__getitem__(0))
ycoordinates.append(R.__getitem__(1))
else:
print "\n","No single intersection point detected"
if i==2:
i=0;
pt[2]=pt[0];pt[5]=pt[4];p=[];
p.append(np.amin(ycoordinates));p.append(np.amax(ycoordinates));
p.append(np.amin(xcoordinates));p.append(np.amax(xcoordinates));
sub_image_point.append(p)
# crop_rect=im3[np.amin(ycoordinates):np.amax(ycoordinates),np.amin(xcoordinates):np.amax(xcoordinates)]
# cv2.imshow('Crop_Rect',crop_rect)
# cv2.waitKey(0)
xcoordinates=[]
ycoordinates=[]
else:
pt[2]=[];pt[5]=[]
pt[2]=pt[0];pt[5]=pt[4];
cv2.destroyAllWindows()
i=0;
pt=[]
# process slice of each full parking slot image
for p in sub_image_point:
i+=1
x1=p[0];y1=p[1];x2=p[2];y2=p[3];
crop_rect=im3[x1:y1,x2:y2]
cars = face_cascade.detectMultiScale(crop_rect, 1.1,5)
for (x,y,w,h) in cars:
cv2.rectangle(crop_rect,(x,y),(x+w,y+h),(0,0,255),2)
ncars = ncars + 1
print "\n",ncars, "Car is detected in ",i," slot"
pt.append(i)
# show result
# cv2.imshow("Result",crop_rect)
# cv2.waitKey(0);
i=0;
pt1=[]
print "\n","occupied slots: ",pt1
for p in pt:
print " ",p
分类 - https://github.com/abhi-kumar/CAR-DETECTION/blob/master/cas1.xml
识别图像-1单排汽车。
但无法识别2行图像中的对象？
答
我能找到第二图像的两个solutions.I矩形解决了C++的问题，但你应该能够放心
解决方案1将其转换到Python：阈值和countours。
1：图像
2上应用大津的阈值：扩张图像
3：发现轮廓
4：找到有效矩形
的代码是
void identify_ob_by_edges(cv::Mat const &img)
{
cv::Mat gray;
cv::cvtColor(img, gray, CV_BGR2GRAY);
cv::threshold(gray, gray, 0, 255,
cv::THRESH_BINARY | cv::THRESH_OTSU);
auto const kernel =
cv::getStructuringElement(cv::MORPH_RECT, {7,7});
cv::dilate(gray, gray, kernel);
std::vector<std::vector<cv::Point>> contours;
cv::findContours(gray.clone(), contours, cv::RETR_TREE,
cv::CHAIN_APPROX_SIMPLE);
cv::Mat img_copy = img.clone();
for(auto const &contour : contours){
auto const rect = cv::boundingRect(contour);
if(rect.area() >= 2000 &&
(rect.height/static_cast<double>(rect.width)) > 1.0){
cv::rectangle(img_copy, rect, {255, 0, 0}, 3);
}
}
cv::imshow("binarize", gray);
cv::imshow("color", img_copy);
cv::waitKey();
cv::imwrite("result.jpg", img_copy);
}
结果为
但如果不是所有的线可以看出，次溶液中的两种本不工作。
2：使用HoughLinesP和轮廓找出长方形
/**
* Work if no critical lines are completely hide
*/
void identify_ob_by_lines(cv::Mat const &img)
{
cv::Mat gray;
cv::cvtColor(img, gray, CV_BGR2GRAY);
cv::threshold(gray, gray, 0, 255,
cv::THRESH_BINARY | cv::THRESH_OTSU);
cv::Mat edges;
cv::Canny(gray, edges, 30, 90);
std::vector<cv::Vec4i> lines;
cv::HoughLinesP(edges, lines, 1,
CV_PI/180, 50, 50, 10);
std::vector<cv::Vec4i> hor_lines;
std::vector<cv::Vec4i> vec_lines;
//remove lines with invalid angle
for(auto const &l : lines)
{
auto const p1 = cv::Point(l[0], l[1]);
auto const p2 = cv::Point(l[2], l[3]);
auto const angle = abs_line_angle(p1, p2);
if(angle >= 76){
vec_lines.emplace_back(l);
}else if(angle <= 5){
hor_lines.emplace_back(l);
}
}
//remove_adjacent_lines(hor_lines, 1, 400);
remove_adjacent_lines(vec_lines, 0, 30);
//draw lines on blank image
cv::Mat blank = cv::Mat::zeros(img.size(), CV_8U);
draw_lines(blank, hor_lines, {255});
draw_lines(blank, vec_lines, {255});
//find the contours of blank image
std::vector<std::vector<cv::Point>> contours;
cv::findContours(blank.clone(), contours, cv::RETR_TREE,
cv::CHAIN_APPROX_SIMPLE);
for(auto const &contour : contours){
auto const rect = cv::boundingRect(contour);
if(rect.area() >= 2000 &&
(rect.height/static_cast<double>(rect.width)) > 1.0){
//cv::rectangle(img_copy, rect, {255, 0, 0}, 3);
auto const min_rect = cv::minAreaRect(contour);
cv::Point2f rect_points[4];
min_rect.points(rect_points);
for(size_t j = 0; j < 4; ++j){
cv::line(img, rect_points[j],
rect_points[(j+1)%4], {255, 0, 0}, 2, 8);
}
}
}
cv::imshow("img copy", img);
cv::waitKey();
cv::imwrite("result.jpg", blank);
}
结果：
有一个矩形不通过这个解决方案绘制的，这可能是固定的，如果你将相机拉得更远。如果图像1不隐藏水平线，解决方案2也应该适用于图像1，我认为在正常情况下，线条不会像这样隐藏。如果是这样，您可以测量距离并自行绘制线条。
我建议你给dlib一试，dlib的物体检测器非常棒。
源代码位于github。
您正在使用哪些版本的Python和OpenCV？除了你的标签指示，cv2.cv.BoxPoints（rect）不是OpenCV 3.0。 – tfv