How to determine if two pixels belong to the same line - c++

I have two white lines on a black background. I need to input the coordinates of two white pixels and get an answer whether these pixels belong to the same line. Lines are one pixel wide.

Given the constraints of this problem, you can just traverse the path between the two white pixels, summing how many white pixels there are between that path. If they are on the same line, you will sum a lot, otherwise, most of the path will be on the black. Also, you should normalize the sum by the path length, and compare the result to a given threshold.
//x1, y1, x2, y2 and img given as input
double len = sqrt((x1-x2)*(x1-x2)+(y1-y2)*(y1-y2) + 0.0001;
double dx = (x2-x1)/len; // Division by zero safe
double dy = (y2-y1)/len;
int sum = 0;
for (int i=0; i<len; ++i) {
int x = x1 + (dx*i + 0.5); // 0.5 for rounding when truncating
int y = y1 + (dy*i + 0.5);
sum += img.at<uchar>(y,x) > 200; // White value higher than 200
}
if (sum > len * 0.5) { // 0.5 is the threshold
return 1; // They are on the same line
}
return 0; // Else they are on different lines
Beware of the rounding parameter that I choose as 0.5, it depends on how you are generating that lines.

Here's what I was suggesting in the comments.... starting at either white pixel, floodfill with black. Check colour of other starting pixel. If it's still white, it can't be on the same line we just filled with black.
import cv2
# Load image as greyscale
im = cv2.imread('lines.png', cv2.IMREAD_GRAYSCALE)
w1x, w1y = 171, 108 # bottom line, right end
w2x, w2y = 86, 124 # bottom line. left end
w3x, w3y = 44, 74 # top line, left end
w4x, w4y = 143,25 # top line, right end
# Floodfill with black starting at white px 1
cv2.floodFill(im, None, (w1x,w1y), newVal=0, loDiff=50, upDiff=50, flags=8)
# Check white px 2
print(im[w2y, w2x]) # prints 0 because white px 2 is on same line as white px 1
# Check white px 3
print(im[w3y, w3x]) # prints 255, because white px 3 is on other line
# Check white px 4
print(im[w4y, w4x]) # prints 255, because white px 4 is on other line
Here's the other approach I was suggesting in the comments... join the two points with a white line and see how many objects are now in the image. If there are still 2, we drew along one of the lines. If there is now 1 object, the line we constructed must have joined the other two objects together into a single, larger object:
import cv2
im = cv2.imread('lines.png', cv2.IMREAD_GRAYSCALE)
# Count objects - initially 2
nObjects = len(cv2.findContours(im, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)[0])
# Draw white line connecting white px 1 and white px 2
cv2.line(im,(w1x,w1y),(w2x,w2y),255,1)
# Count objects - still 2
nObjects = len(cv2.findContours(im, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)[0])
# Draw white line connecting white px 1 and white px 3
cv2.line(im,(w1x,w1y),(w3x,w3y),255,1)
# Count objects - now just 1
nObjects = len(cv2.findContours(im, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)[0])

Related

How to detect randomly curved non-continuous fixed colored lines from an image?

I am very new in the python openCV. I wanted to find the 7 fixed colored randomly curved lines from an image. The result should be boolean which will give me if the image contains the 7 fixed colored randomly curved lines or not. The sample input image is as below:
I also wanted to find out the non-continuous faint green colored trapezium from the image.
I have written the below code to filter out the specific color from the image but unable to detect the lines & unable to conclude if the image is containing the 7 different lines & trapezium. Below is my sample code for the same:
import cv2
import numpy as np
boundaries = [
(32, 230, 32), # 2 Green lines
(10, 230, 230), # 1 Yellow line
(230, 72, 32), # 1 Blue line
(255, 255, 255), # 2 White lines
(32, 72, 230) # 1 Red line
]
box = [(0, 100, 0), (100, 255, 100)]
image = cv2.imread('testImage5.png')
image = removeBlackBands(image)
# cv2.imshow('Cropped Image', image)
# cv2.waitKey(0)
for row in boundaries:
# create NumPy arrays from the boundaries
row = np.array(row, dtype="uint8")
mask = cv2.inRange(image, row, row)
cv2.GaussianBlur(mask, (5,5), 0)
cv2.imshow('Filtered', mask)
cv2.waitKey(0)
lines = cv2.HoughLinesP(mask, cv2.HOUGH_PROBABILISTIC, np.pi / 180, 50, 50, 100)
if lines is not None:
for x in range(0, len(lines)):
print("line ", x)
for x1, y1, x2, y2 in lines[x]:
print("x1 = {}, y1 = {}, x2 = {}, y2 = {}".format(x1, y1, x2, y2))
cv2.line(image,(x1,y1),(x2,y2),(0,0, 255),2, cv2.LINE_AA)
pts = np.array([[x1, y1], [x2, y2]], np.int32)
cv2.polylines(image, [pts], True, (0, 255, 0))
cv2.imshow('Processed.jpg', image)
cv2.waitKey(0)
# create NumPy arrays from the boundaries
lower = np.array(box[0], dtype="uint8")
upper = np.array(box[1], dtype="uint8")
# find the colors within the specified boundaries and apply
# the mask
mask = cv2.inRange(image, lower, upper)
output = cv2.bitwise_and(image, image, mask=mask)
output = cv2.cvtColor(output, cv2.COLOR_BGR2GRAY)
output = cv2.Canny(output, 100, 150)
# show the images
# cv2.imshow("output", output)
# cv2.waitKey(0)
cv2.destroyAllWindows()
Can somebody help me? Thanks in Advance..!!!
This is the function I wrote for the same.
def detectNodes(self, image, tolerance=0):
"""
Detect the nodes in the image
Algorithm used:
1. Pre-process the image to filter out the required color
2. Convert the pre-processed image to binary image
Args:
image(np.ndarray): Numpy Nd array of image
tolerance: (int): Margin of consideration while extracting color from image. Default: 0
Returns
True upon success, False otherwise
"""
noOfNodesDetected = 0
curveWidth = 2
noOfNodeDetectThreshold = 5
cropH = self.testData["nodalROI"].get("height")
cropW = self.testData["nodalROI"].get("width")
roiImage = ppu.crop(image, cropH, cropW) # Crop node ROI
for color in self.nodalColorBoundaries.keys():
filtered = ImageProc.colorFilter(roiImage, colors=self.nodalColorBoundaries[color], tolerance=tolerance)
bgrImage = ppu.convertColorSpace(filtered, "bgr_to_gray")
thresh = ppu.threshold(bgrImage, 1, "thresh_binary")
logging.info("The shape of image is [{}]".format((thresh.shape)))
height, width = thresh.shape
pointFraction = self.testData.get("pointsToFormEquationOfCurve", None)
points = [int(fraction * height) for fraction in pointFraction]
logging.info("Point using for formulating the equation are [{}]".format(points))
pointFractionEvaluation = self.testData.get("pointsForEvaluationOfCurve", None)
pointsForEvaluation_h = [int(fraction * height) for fraction in pointFractionEvaluation]
logging.info("Point using for Evaluating the equation are [{}]".format(pointsForEvaluation_h))
curve1 = []
curve2 = []
for point in points:
prevX = 0
flag = 0
for w in range(0, width):
if thresh[point][w] == 255:
if (abs(prevX - w)) > curveWidth:
if flag == 0:
curve1.append((point, w))
flag = 1
else:
curve2.append((point, w))
prevX = w
fitter = CurveFitter1D()
if curve2:
logging.error("Second curve detected with color {} having points {}".format(color, curve2))
if curve1:
x1 = [point[0] for point in curve1]
y1 = [point[1] for point in curve1]
logging.qvsdebug("Points using to find the Polynomial with color {} are {} ".format(color, curve1))
fitter._fit_polyfit(x1, y1, 4)
logging.qvsdebug("Coefficient of the Polynomial with color {} are {} ".format(
color, fitter._fitterNamespace.coefs))
else:
logging.error("Points not found with {}".format(color))
return False
pointsForEvaluation_w = [int(round(fitter._predY_polyfit(point))) for point in pointsForEvaluation_h]
logging.qvsdebug(
"Points using for the verification of Polynomial representing curve with color {} are {} ".format(
color, zip(pointsForEvaluation_h, pointsForEvaluation_w)))
counter = 0
for i in range(len(pointsForEvaluation_h)):
if pointsForEvaluation_w[i] + 2 >= width:
continue # Continue if control is reaching to width of iamge - 2
if any(thresh[pointsForEvaluation_h[i]][pointsForEvaluation_w[i] - 2:pointsForEvaluation_w[i] + 3]):
counter += 1
logging.info(
"Out of {} points {} points are detected on the curve for color {}".format(len(pointsForEvaluation_h),
counter, color))
nodeDetectThreshold = int(len(pointsForEvaluation_h) * 0.6)
if counter >= nodeDetectThreshold:
noOfNodesDetected += 1
if noOfNodesDetected >= noOfNodeDetectThreshold:
logging.info("Nodes found in this frame are [%d], minimum expected [%d]" % (
noOfNodesDetected, noOfNodeDetectThreshold))
return True
else:
logging.error("Nodes found in this frame are [%d], minimum expected is [%d]" % (
noOfNodesDetected, noOfNodeDetectThreshold))
return False

algorithm that finds the "mean positioned" white pixel in a column and repeats the process for each column

I am attempting to create an algorithm that locates the white pixels in a column of a binary image, and then adds the y co-ordinates/column number of each white pixel and divides this value by the number of white pixels in the column, in order to get the "mean/middle positioned" white pixel in the column. And this returns an (x,y) co-ordinate that can be plotted. This process repeats for each column in the image and each time sy sets back to 0.
The end goal is instead of having a lines that are multiple pixels thick/wide, as shown in the image's numpy arraycurrent line multiple thicks wide array, I have lines that are just one pixel wide, whilst mantaining the original shape. I planned on doing this by selecting the "mean positioned white pixel in each column". I will then use these pixels to obtain x and y co-ordinates to plot.
Here is what I have
sx = x = img.shape[1]
sy = 0
whitec = cv2.countNonZero(img.shape[1])
arrayOfMeanY = [] #array to place (x,y) co-ordinate in
#Select column to iterate
for x in range(img.shape[1]):
# iterating through individual items in the column
for y in range(img.shape[0]):
# Checking for white pixels
pixel = img[x,y]
if pixel == 255:
# Then we check the y values of the white pixels in the column and add them all up
sy = sy+y
whitec +=1
# Doing the calculation for the mean and putting it into the meanY list
sy = sy/whitec
y = sy
print img[x,y]
array.append(y)
cv2.waitKey(0)
# reset sy to 0 for the next column
sy = 0
My issue is I recieve this error when I run the code:
File "<ipython-input-6-e4c2225ff632>", line 27, in <module>
whitec = cv2.countNonZero(img.shape[1]) #n= number of white pixels
in the column
TypeError: src is not a numpy array, neither a scalar
How do I rectify this issue, and once once this issue is rectified will my coding do what I described above.
No need for loops here. With numpy you hardly ever need to loop over individual pixels.
Instead, create a function which takes the mean of the locations of the non-zero pixels for each column (I converted to np.intp to index the image; you could just cast with int() but np.intp is what Numpy uses for indexing arrays so, it's slightly more appropriate).
def avgWhiteLocOverCol(col):
return np.intp(np.mean(np.where(col)))
Then you can simply apply the function along all columns with np.apply_along_axis().
avgRows = np.apply_along_axis(avgWhiteLocOverCol, 0, img)
For example, let's create an image with white pixels on the middle row and on the diagonal:
import numpy as np
import cv2
img = np.eye(500)*255
img[249,:] = 255
cv2.imshow('',img)
cv2.waitKey(0)
Then we can apply the function over each column, which should give a line with half the slope:
def avgWhiteLocOverCol(col):
return int(np.mean(np.where(col)))
avgRows = np.apply_along_axis(avgWhiteLocOverCol, 0, img)
avgIndImg = np.zeros_like(img)
avgIndImg[avgRows,range(img.shape[1])] = 255
cv2.imshow('',avgIndImg)
cv2.waitKey(0)

Fine Tuning Hough Line function parameters OpenCV

I've been trying to get 4 lines around the square so that I can obtain the vertices of the square. I'm going with this approach rather than finding corners directly using Harris or contours method due to accuracy. Using houghlines in built function in opencv I'm unable to get full length lines to get intersection points and I'm also getting too many irrelevant lines. I'd like to know if the parameters can be fine tuned to obtain my requirements? If yes how do I go about it? My question is exactly the same as this one here. However I'm not getting those lines itself even after changing those parameters. I've attached the original image along with the code and output:
Original Image:
Code:
#include <Windows.h>
#include "opencv2\highgui.hpp"
#include "opencv2\imgproc.hpp"
#include "opencv2/imgcodecs/imgcodecs.hpp"
#include "opencv2/videoio/videoio.hpp"
using namespace cv;
using namespace std;
int main(int argc, const char** argv)
{
Mat image,src;
image = imread("c:/pics/output2_1.bmp");
src = image.clone();
cvtColor(image, image, CV_BGR2GRAY);
threshold(image, image, 0, 255, CV_THRESH_OTSU + CV_THRESH_BINARY_INV);
namedWindow("thresh", WINDOW_NORMAL);
resizeWindow("thresh", 600, 400);
imshow("thresh", image);
cv::Mat edges;
cv::Canny(image, edges, 0, 255);
vector<Vec2f> lines;
HoughLines(edges, lines, 1, CV_PI / 180, 100, 0, 0);
for (size_t i = 0; i < lines.size(); i++)
{
float rho = lines[i][0], theta = lines[i][1];
Point pt1, pt2;
double a = cos(theta), b = sin(theta);
double x0 = a*rho, y0 = b*rho;
pt1.x = cvRound(x0 + 1000 * (-b));
pt1.y = cvRound(y0 + 1000 * (a));
pt2.x = cvRound(x0 - 1000 * (-b));
pt2.y = cvRound(y0 - 1000 * (a));
line(src, pt1, pt2, Scalar(0, 0, 255), 3, CV_AA);
}
namedWindow("Edges Structure", WINDOW_NORMAL);
resizeWindow("Edges Structure", 600, 400);
imshow("Edges Structure", src);
waitKey(0);
return(0);
}
Output Image:
Update: There is a frame on this image, so I was able to reduce the irrelevant lines in the border of the image by removing that frame, however I'm still not getting complete lines covering the square.
There are many ways to do this, I will give an example of just one. However, I'm quickest in python, so my code example will be in that language. Should not be hard to translate it, though (please feel free to edit your post with your C++ solution after you've finished it for others).
For preprocessing, I highly suggest dilate()ing your edge image. This will make the lines thicker which will help fit the Hough lines better. What the Hough lines function does in the abstract is basically make a grid of lines passing through a ton of angles and distances, and if the lines go over any white pixels from Canny, then it gives that line a score for each point it goes through. However, the lines from Canny won't be perfectly straight, so you'll get a few different lines scoring. Making those Canny lines thicker will mean each line that is really close to fitting well will have better chances of scoring higher.
If you're going to use HoughLinesP, then your output will be line segments, where all you have is two points on the line.
Since the lines are mostly vertical and horizontal, you can easily split the lines based on their position. If the two y-coordinates of one line are near each other, then the line is mostly horizontal. If the two x-coordinates are near each other, then the line is mostly vertical. So you can segment your lines into vertical lines and horizontal lines that way.
def segment_lines(lines, delta):
h_lines = []
v_lines = []
for line in lines:
for x1, y1, x2, y2 in line:
if abs(x2-x1) < delta: # x-values are near; line is vertical
v_lines.append(line)
elif abs(y2-y1) < delta: # y-values are near; line is horizontal
h_lines.append(line)
return h_lines, v_lines
Then, you can obtain intersection points of two line segments from their endpoints using determinants.
def find_intersection(line1, line2):
# extract points
x1, y1, x2, y2 = line1[0]
x3, y3, x4, y4 = line2[0]
# compute determinant
Px = ((x1*y2 - y1*x2)*(x3-x4) - (x1-x2)*(x3*y4 - y3*x4))/ \
((x1-x2)*(y3-y4) - (y1-y2)*(x3-x4))
Py = ((x1*y2 - y1*x2)*(y3-y4) - (y1-y2)*(x3*y4 - y3*x4))/ \
((x1-x2)*(y3-y4) - (y1-y2)*(x3-x4))
return Px, Py
So now if you loop through all your lines, you'll have intersection points from all your horizontal and vertical lines, but you have many lines, so you'll have many intersection points for the same corner of the box.
However, these are all in one vector, so not only do you need to average the points in each corner, you need to actually group them together, too. You can achieve this with k-means clustering, which is implemented in OpenCV as kmeans().
def cluster_points(points, nclusters):
criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 10, 1.0)
_, _, centers = cv2.kmeans(points, nclusters, None, criteria, 10, cv2.KMEANS_PP_CENTERS)
return centers
Finally, we can simply plot those centers (making sure we round first---since so far everything is a float) onto the image with circle() to make sure we've done it right.
And we have it; four points, at the corners of the box.
Here's my full code in python, including the code to generate the figures above:
import cv2
import numpy as np
def find_intersection(line1, line2):
# extract points
x1, y1, x2, y2 = line1[0]
x3, y3, x4, y4 = line2[0]
# compute determinant
Px = ((x1*y2 - y1*x2)*(x3-x4) - (x1-x2)*(x3*y4 - y3*x4))/ \
((x1-x2)*(y3-y4) - (y1-y2)*(x3-x4))
Py = ((x1*y2 - y1*x2)*(y3-y4) - (y1-y2)*(x3*y4 - y3*x4))/ \
((x1-x2)*(y3-y4) - (y1-y2)*(x3-x4))
return Px, Py
def segment_lines(lines, delta):
h_lines = []
v_lines = []
for line in lines:
for x1, y1, x2, y2 in line:
if abs(x2-x1) < delta: # x-values are near; line is vertical
v_lines.append(line)
elif abs(y2-y1) < delta: # y-values are near; line is horizontal
h_lines.append(line)
return h_lines, v_lines
def cluster_points(points, nclusters):
criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 10, 1.0)
_, _, centers = cv2.kmeans(points, nclusters, None, criteria, 10, cv2.KMEANS_PP_CENTERS)
return centers
img = cv2.imread('image.png')
# preprocessing
img = cv2.resize(img, None, fx=.5, fy=.5)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
edges = cv2.Canny(gray, 50, 150)
dilated = cv2.dilate(edges, np.ones((3,3), dtype=np.uint8))
cv2.imshow("Dilated", dilated)
cv2.waitKey(0)
cv2.imwrite('dilated.png', dilated)
# run the Hough transform
lines = cv2.HoughLinesP(dilated, rho=1, theta=np.pi/180, threshold=100, maxLineGap=20, minLineLength=50)
# segment the lines
delta = 10
h_lines, v_lines = segment_lines(lines, delta)
# draw the segmented lines
houghimg = img.copy()
for line in h_lines:
for x1, y1, x2, y2 in line:
color = [0,0,255] # color hoz lines red
cv2.line(houghimg, (x1, y1), (x2, y2), color=color, thickness=1)
for line in v_lines:
for x1, y1, x2, y2 in line:
color = [255,0,0] # color vert lines blue
cv2.line(houghimg, (x1, y1), (x2, y2), color=color, thickness=1)
cv2.imshow("Segmented Hough Lines", houghimg)
cv2.waitKey(0)
cv2.imwrite('hough.png', houghimg)
# find the line intersection points
Px = []
Py = []
for h_line in h_lines:
for v_line in v_lines:
px, py = find_intersection(h_line, v_line)
Px.append(px)
Py.append(py)
# draw the intersection points
intersectsimg = img.copy()
for cx, cy in zip(Px, Py):
cx = np.round(cx).astype(int)
cy = np.round(cy).astype(int)
color = np.random.randint(0,255,3).tolist() # random colors
cv2.circle(intersectsimg, (cx, cy), radius=2, color=color, thickness=-1) # -1: filled circle
cv2.imshow("Intersections", intersectsimg)
cv2.waitKey(0)
cv2.imwrite('intersections.png', intersectsimg)
# use clustering to find the centers of the data clusters
P = np.float32(np.column_stack((Px, Py)))
nclusters = 4
centers = cluster_points(P, nclusters)
print(centers)
# draw the center of the clusters
for cx, cy in centers:
cx = np.round(cx).astype(int)
cy = np.round(cy).astype(int)
cv2.circle(img, (cx, cy), radius=4, color=[0,0,255], thickness=-1) # -1: filled circle
cv2.imshow("Center of intersection clusters", img)
cv2.waitKey(0)
cv2.imwrite('corners.png', img)
Finally, just one question...why not use the Harris corner detector implemented in OpenCV as cornerHarris()? Because it works really well with very minimal code. I thresholded the grayscale image, and then gave a little blur to remove spurious corners, and, well...
This was produced with the following code:
import cv2
import numpy as np
img = cv2.imread('image.png')
# preprocessing
img = cv2.resize(img, None, fx=.5, fy=.5)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
r, gray = cv2.threshold(gray, 120, 255, type=cv2.THRESH_BINARY)
gray = cv2.GaussianBlur(gray, (3,3), 3)
# run harris
gray = np.float32(gray)
dst = cv2.cornerHarris(gray,2,3,0.04)
# dilate the corner points for marking
dst = cv2.dilate(dst,None)
dst = cv2.dilate(dst,None)
# threshold
img[dst>0.01*dst.max()]=[0,0,255]
cv2.imshow('dst',img)
cv2.waitKey(0)
cv2.imwrite('harris.png', img)
I think with some minor adjustments the Harris corner detector can probably be much more accurate than extrapolating Hough line intersections.

Draw all lines obtained with HoughLines in OpenCV

I'm using OpenCV 3.2.
I'd like to extract and draw all lines in this image.
For this, I first obtain the contours of the image. For example, I'm using the Canny algorithm, with a double threshold 100 (low) and 200 (high).
Mat image = cv::imread(<image_path>, cv::IMREAD_GRAYSCALE);
cv::Mat contours;
cv::Canny(image, contours, 100, 200);
Then, I call the HoughLines function with a resolution of 1 pixel and π / 45 radians. I just want those lines which have a length of at least 60 pixels.
std::vector<cv::Vec2f> lines;
cv::HoughLines(canny, lines, 1, CV_PI/45, 60);
This returns me a vector lines with the rho p and theta θ parameters in the Hough space of the desired lines. As we know, the line going through a contour pixel (x_i, y_i) is:
p = x_i cos(θ) + y_i sin(θ)
We know p and θ, so we know all the pixels in this line. Two easy points to calculate are A with x_i = 0 and B with y_i = 0.
A = (0, p / sin(θ))
B = (p / cos(θ), 0)
Let's draw them with the line function in blue color.
cv::cvtColor(image, image, CV_GRAY2BGR);
for (unsigned int i = 0; i < lines.size(); ++i) {
float p = lines[i][0];
float theta = lines[i][1];
cv::Point a(0, static_cast<int>(p / std::sin(theta)));
cv::Point b(static_cast<int>(p / std::cos(theta)), 0);
cv::line(image, a, b, cv::Scalar(255, 0, 0));
}
The result is that it only draws me 6 lines, of a total of 14 obtained. As you can see, only those lines that intersect the row 0 and column 0 of the image are drawn. What is the same, those lines which have A and B points in the image boundary. The rest of the lines have these points outside the image.
How can I achieve to draw all the lines in an easy way? I can calculate all the pixels of the obtained lines and draw them (we know them), but I'd like to draw them by minimizing lines of code and using OpenCV api.

Counting the point which intercept in a line with opencv python

I am working in vehicle counting with opencv and python programming, I already complete step:
1. Detect moving vehicle with BackgroundSubtractorMOG2
2. Draw rectangle on it, then poin a centroid of that
3. Draw a line (to indicate of the counting)
if that centroid accros/intercept with the line I want count that 1. but in my code sometime it add sometime no. Here the line code:
cv2.line(frame,(0,170),(300,170),(200,200,0),2)
and there the centroid:
if w > 20 and h > 25:
cv2.rectangle(frame, (x,y), (x+w,y+h), (180, 1, 0), 1)
x1=w/2
y1=h/2
cx=x+x1
cy=y+y1
centroid=(cx,cy)
cv2.circle(frame,(int(cx),int(cy)),4,(0,255,0),-1)
my counting code:
if cy==170:
counter=counter+1
Can anyone help me. please. for your advice thankyou!
Here is my approach that would work independently of the video frame rate. Assuming that you are able to track a car's centroid at each frame, I would save the last two centroids' position (last_centroid and centroid in my code) and process as follows:
compute the intercepting line equation's parameters ( (a,b,c) from aX + bY + c = 0)
compute the equation's parameters of the segment line between last_centroid and centroid
find if the two lines are intersecting
if so, increment your counter
Here is how I implemented it in OpenCV (Python):
import cv2
import numpy as np
import collections
Params = collections.namedtuple('Params', ['a','b','c']) #to store equation of a line
def calcParams(point1, point2): #line's equation Params computation
if point2[1] - point1[1] == 0:
a = 0
b = -1.0
elif point2[0] - point1[0] == 0:
a = -1.0
b = 0
else:
a = (point2[1] - point1[1]) / (point2[0] - point1[0])
b = -1.0
c = (-a * point1[0]) - b * point1[1]
return Params(a,b,c)
def areLinesIntersecting(params1, params2, point1, point2):
det = params1.a * params2.b - params2.a * params1.b
if det == 0:
return False #lines are parallel
else:
x = (params2.b * -params1.c - params1.b * -params2.c)/det
y = (params1.a * -params2.c - params2.a * -params1.c)/det
if x <= max(point1[0],point2[0]) and x >= min(point1[0],point2[0]) and y <= max(point1[1],point2[1]) and y >= min(point1[1],point2[1]):
print("intersecting in:", x,y)
cv2.circle(frame,(int(x),int(y)),4,(0,0,255), -1) #intersecting point
return True #lines are intersecting inside the line segment
else:
return False #lines are intersecting but outside of the line segment
cv2.namedWindow('frame')
frame = np.zeros((240,320,3), np.uint8)
last_centroid = (200,200) #centroid of a car at t-1
centroid = (210,180) #centroid of a car at t
line_params = calcParams(last_centroid, centroid)
intercept_line_params = calcParams((0,170), (300,170))
print("Params:", line_params.a,line_params.b,line_params.c)
while(1):
cv2.circle(frame,last_centroid,4,(0,255,0), -1) #last_centroid
cv2.circle(frame,centroid,4,(0,255,0), -1) #current centroid
cv2.line(frame,last_centroid,centroid,(0,0,255),1) #segment line between car centroid at t-1 and t
cv2.line(frame,(0,170),(300,170),(200,200,0),2) #intercepting line
print("AreLinesIntersecting: ",areLinesIntersecting(intercept_line_params,line_params,last_centroid,centroid))
cv2.imshow('frame',frame)
if cv2.waitKey(15) & 0xFF == ord('q'):
break
cv2.destroyAllWindows()
And here are some results:
Fig1. Segment is intersecting the line (intercepting line in blue - segment line between last_centroid and centroid in red)
Fig2. Segment is NOT intersecting the line
N.B. I found the formulas to calculate the intersection point here.
I hope my approach will help to address your problem.
To assume that the centroid will assume a position 170 (in x or y) is wrong, because videos generally works at 30 fps, that mean you will get 30 centroid locations per second which means even if there the object crosses the line, it may never be 170!
To counter this, one method that can be used is defining a line margin. This means now you have a line margin x before actual line (y = 170) and x after the line margin.
So if your object falls anywhere in the margin, you can increment the counter. Now the next big part would be to make a tracking mechanism wherein you collect the list of point for each object and check if it fell in the margin.