通过opencv
如果对象是单行（较小的图像），则此程序识别对象。
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
分类器 - https://github.com/abhi-kumar/CAR-DETECTION/blob/master/cas1.xml
用单行标识图像-1中的汽车。
但无法用2行识别图像中的对象？
1 个答案:
答案 0 :(得分：4)
我可以通过两个解决方案找到第二个图像的矩形。我通过c ++解决了这个问题，但你应该能够轻松地将它转换为python
解决方案1：阈值和计数。
1：在图像上应用otsu阈值
2：扩大图像
3：找到轮廓
4：找到有效的矩形
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
但是，如果不能看到所有的线路，那么这不起作用，解决方案二的时间。
2：使用HoughLinesP和轮廓找到矩形
/**
* 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：
此解决方案没有绘制一个矩形，如果您将相机拉得更远，可以修复此问题。如果图像1没有隐藏水平线，则解决方案2也适用于图像1，我认为在正常情况下，这条线不会被隐藏起来。如果是这样，您可以自己测量距离并绘制线条。
我建议您尝试dlib，dlib的对象检测器非常棒。
源代码位于github。
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