I am trying to identify a rectangle underwater in a noisy environment. I implemented Canny to find the edges, and drew the found edges using cv2.circle. From here, I am trying to identify the imperfect rectangle in the image (the black one below the long rectangle that covers the top of the frame)
I have attempted multiple solutions, including thresholds, blurs and resizing the image to detect the rectangle. Below is the barebones code with just drawing the identified edges.
import numpy as np
import cv2
import imutils
img_text = 'img5.png'
img = cv2.imread(img_text)
original = img.copy()
min_value = 50
max_value = 100
# draw image and return coordinates of drawn pixels
image = cv2.Canny(img, min_value, max_value)
indices = np.where(image != 0)
coordinates = zip(indices[1], indices[0])
for point in coordinates:
cv2.circle(original, point, 1, (0, 0, 255), -1)
cv2.imshow('original', original)
cv2.waitKey(0)
cv2.destroyAllWindows()
Where the output displays this:
output
From here I want to be able to separately detect just the rectangle and draw another rectangle on top of the output in green, but I haven't been able to find a way to detect the original rectangle on its own.
For your specific image, I obtained quite good results with a simple thresholding on the blue channel.
image = cv2.imread("test.png")
t, img = cv2.threshold(image[:,:,0], 80, 255, cv2.THRESH_BINARY)
In order to adapt the threshold, I propose a simple way of varying the threshold until you get one component. I have also implemented the rectangle drawing:
def find_square(image):
markers = 0
threshold = 10
while np.amax(markers) == 0:
threshold += 5
t, img = cv2.threshold(image[:,:,0], threshold, 255, cv2.THRESH_BINARY_INV)
_, markers = cv2.connectedComponents(img)
kernel = np.ones((5,5),np.uint8)
img = cv2.morphologyEx(img, cv2.MORPH_OPEN, kernel)
img = cv2.morphologyEx(img, cv2.MORPH_DILATE, kernel)
nonzero = cv2.findNonZero(img)
x, y, w, h = cv2.boundingRect(nonzero)
cv2.rectangle(image, (x, y), (x+w, y+h), (0, 255, 0), 2)
cv2.imshow("image", image)
And the results on the provided example images:
The idea behind this approach is based on the observation that the most information is in the blue channel. If you separate the images in the channels, you will see that in the blue channel, the dark square has the best contrast. It is also the darkest region on this channel, which is why thresholding works. The problem remains the threshold setting. Based on the above intuition, we are looking for the lowest threshold that will bring up something (and hope that it will be the square). What I did is to simply increase gradually the threshold until something appears.
Then, I applied some morphology operations to eliminate other small points that may appear after thresholding and to make the square look a bit bigger (the edges of the square are lighter, and therefore not the entire square is captured). Then is was a matter of drawing the rectangle.
The code can be made much nicer (and more efficient) by doing some statistical analysis on the histogram. Simply compute the threshold such that 5% (or some percent) of the pixels are darker. You may require do so a connected component analysis to keep the biggest blob.
Also, my usage of connectedComponents is very poor and inefficient. Again, code written in a hurry to prove the concept.
Related
I am trying to define the coordinates of multiple rectangles appearing randomly in the screen. The width of the rectangles is defined (even if with the contour method i noticed there is a bit of inaccuracy in determine it).
With my python code:
yellow = (5,242,206)
while True:
isFrameValid, frame = capture.read()
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
roi = gray [0:300, 0:1920]
threshold, thresh_image = cv2.threshold(roi, 30, 255, cv2.THRESH_BINARY)
#Select contours
contours, _ =cv2.findContours(thresh_image,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
cv2.drawContours(frame, contours, -1,yellow,1)
I can only detect the entire block, even trying with different options instead of RETR_EXTERNAL.Looking at my example images, what I'd like to achive is to detect the 3 rectangles (appearing in random position in the screen) so I can correctly determine their coordinates. Are there any ideas or methods I dont know about since im new with Opencv?
)
Example to reproduce the problem with an image
import cv2
img= cv2.imread('./IwOXW.png')
yellow = (5,242,206)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
roi = gray [0:300, 0:1920]
threshold, thresh_image = cv2.threshold(roi, 30, 255, cv2.THRESH_BINARY)
#Select contours
contours, _ = cv2.findContours(thresh_image,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
cv2.drawContours(img, contours, -1,yellow,1)
cv2.imshow('Frame',img)
cv2.waitKey(0)
with this image
The reason findCountours can't detect the entire block is because there is no line on the inside for it to detect.
I can think of two options for you to try:
Use the contours that you have, and write some smarts to find 90 degree bends, and thus build your rectangles
You could investigate using HoughLines to detect the lines. You would probably still have to write some code to take the detected lines and figure out what are rectangles, but it might be simpler with HoughLines as it will give you straight lines to work with. Look for HoughLines in the docs: https://docs.opencv.org/4.x/
I was trying to detect billboard images on a random background. I was able to localize the billboard using SSD, this give me approximate bounding box around the billboard. Now I want to find the exact corners of the billboard for my application. I tried using different strategies which I came across such as Harris corner detection (using Opencv), finding intersections of lines using, Canny + morphological operations + contours. The details on the output is given below.
Harris corner detection
The pseudocode for the harris corner detection is as follows:
img_patch_gray = np.float32(img_patch_gray)
harris_point = cv2.cornerHarris(img_patch_gray,2,3,0.04)
img_patch[harris_point>0.01*harris_point.max()]=[255,0,0]
plt.figure(figsize=IMAGE_SIZE)
plt.imshow(img_patch)
Here the red dots are the corners detected by the Harris corner detection algorithm and the points of interest are encircled in green.
Using Hough line detection
Here I was trying to find the intersection of the lines and then choosing the points. Something similar to stackoverflow link, but it is very difficult to get the exact lines since billboards have text and graphics in it.
Contour based
In this approach I have used canny edge detector, followed by dilation(3*3 kernel), followed by contour.
bin_img = cv2.Canny(gray_img_patch,100,250)
bin_img = dilate(bin_img, 3)
plt.imshow(bin_img, cmap='gray')
(_,cnts, _) = cv2.findContours(bin_img.copy(),
cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
cnts = sorted(cnts, key = cv2.contourArea, reverse = True)[:10]
cv2.drawContours(img_patch, [cnts[0]],0, (0,255,0), 1)
, . I had tried using approxPolyDp function from openCV but it was not as expected since it can also approximate larger or smaller contours by four points and in some images it might not form contours around the billboard frame.
I have used openCV 3.4 for all the image processing operations. used can be found here. Please note that the image discussed here is just for the illustration purpose and in general image can be of any billboard.
Thanks in advance, any help is appreciated.
This is a very difficult task because the image containes a lot of noise. You can get an approximation of the contour but specific corners would be very hard. I have made an example on how I would make an approximation. It may not work on other images. Maybe it will help a bit or give you a new idea. Cheers!
import cv2
import numpy as np
# Read the image
img = cv2.imread('billboard.png')
# Blur the image with a big kernel and then transform to gray colorspace
blur = cv2.GaussianBlur(img,(19,19),0)
gray = cv2.cvtColor(blur,cv2.COLOR_BGR2GRAY)
# Perform histogram equalization on the blur and then perform Otsu threshold
equ = cv2.equalizeHist(gray)
_, thresh = cv2.threshold(equ,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)
# Perform opening on threshold with a big kernel (erosion followed by dilation)
kernel = np.ones((20,20),np.uint8)
opening = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel)
# Search for contours and select the biggest one
_, contours, hierarchy = cv2.findContours(opening,cv2.RETR_TREE,cv2.CHAIN_APPROX_NONE)
cnt = max(contours, key=cv2.contourArea)
# Make a hull arround the contour and draw it on the original image
mask = np.zeros((img.shape[:2]), np.uint8)
hull = cv2.convexHull(cnt)
cv2.drawContours(mask, [hull], 0, (255,255,255),-1)
# Search for contours and select the biggest one again
_, thresh = cv2.threshold(mask,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)
_, contours, hierarchy = cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_NONE)
cnt = max(contours, key=cv2.contourArea)
# Draw approxPolyDP on the image
epsilon = 0.008*cv2.arcLength(cnt,True)
approx = cv2.approxPolyDP(cnt,epsilon,True)
cv2.drawContours(img, [cnt], 0, (0,255,0), 5)
# Display the image
cv2.imshow('img', img)
cv2.waitKey(0)
cv2.destroyAllWindows()
Result:
Lets say I have the following image where there is a folder image with a white label on it.
What I want is to detect the coordinates of end points of the folder and the white paper on it (both rectangles).
Using the coordinates, I want to know the exact place of the paper on the folder.
GIVEN :
The inner white paper rectangle is always going to be of the fixed size, so may be we can use this knowledge somewhere?
I am new to opencv and trying to find some guidance around how should I approach this problem?
Problem Statement : We cannot rely on color based solution since this is just an example and color of both the folder as well as the rectangular paper can change.
There can be other noisy papers too but one thing is given, The overall folder and the big rectangular paper would always be the biggest two rectangles at any given time.
I have tried opencv canny for edge detection and it looks like this image.
Now how can I find the coordinates of outer rectangle and inner rectangle.
For this image, there are three domain colors: (1) the background-yellow (2) the folder-blue (3) the paper-white. Use the color info may help, I analysis it in RGB and HSV like this:
As you can see(the second row, the third cell), the regions can be easily seperated in H(HSV) if you find the folder mask first.
We can choose
My steps:
(1) find the folder region mask in HSV using inRange(hsv, (80, 10, 20), (150, 255, 255))
(2) find contours on the mask and filter them by width and height
Here is the result:
Related:
Choosing the correct upper and lower HSV boundaries for color detection with`cv::inRange` (OpenCV)
How to define a threshold value to detect only green colour objects in an image :Opencv
You can opt for (Adaptive Threshold)[https://docs.opencv.org/3.4/d7/d4d/tutorial_py_thresholding.html]
Obtain the hue channel of the image.
Perform adaptive threshold with a certain block size. I used size of 15 for half the size of the image.
This is invariant to color as you expected. Now you can go ahead and extract what you need!!
This solution helps to identify the white paper region of the image.
This is the full code for the solution:
import cv2
import numpy as np
image = cv2.imread('stack2.jpg',-1)
paper = cv2.resize(image,(500,500))
ret, thresh_gray = cv2.threshold(cv2.cvtColor(paper, cv2.COLOR_BGR2GRAY),
200, 255, cv2.THRESH_BINARY)
image, contours, hier = cv2.findContours(thresh_gray, cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_NONE)
for c in contours:
area = cv2.contourArea(c)
rect = cv2.minAreaRect(c)
box = cv2.boxPoints(rect)
# convert all coordinates floating point values to int
box = np.int0(box)
# draw a green 'nghien' rectangle
if area>500:
cv2.drawContours(paper, [box], 0, (0, 255, 0),1)
print([box])
cv2.imshow('paper', paper)
cv2.imwrite('paper.jpg',paper)
cv2.waitKey(0)
First using a manual threshold(200) you can detect paper in the image.
ret, thresh_gray = cv2.threshold(cv2.cvtColor(paper, cv2.COLOR_BGR2GRAY), 200, 255, cv2.THRESH_BINARY)
After that you should find contours and get the minAreaRect(). Then you should get coordinates for that rectangle(box) and draw it.
rect = cv2.minAreaRect(c)
box = cv2.boxPoints(rect)
box = np.int0(box)
cv2.drawContours(paper, [box], 0, (0, 255, 0),1)
In order to avoid small white regions of the image you can use area = cv2.contourArea(c) and check if area>500 and drawContours().
final output:
Console output gives coordinates for the white paper.
console output:
[array([[438, 267],
[199, 256],
[209, 60],
[447, 71]], dtype=int64)]
I have the following image
I'm trying to find the pixel coordinates of main rectangles (those between white lines). I tried few things but I can't obtain good enough solution. The solution doesn't have to be perfect and it's ok if not all rectangles are detected (especially those really small ones). Though corners location will have to be as much exact as possible, especially with those bigger blurry (I'm trying to write some simple AR engine).
I can clarify there are only 4 levels of grayscale: 0, 110, 180 and 255 (when printing, no screen it will vary because of lightning and shadows)
So far I tried few things:
manual multilevel thresholding (because of shadows and different lightning it didn't work)
adaptive thresholding : 2 problems:
it combines 180 and 255 colors into white, and 0, 110 into black
edge/corner location of blurred(bigger) rectangles is not exact (it adds blur to rectangle area)
sobel edge detection (corners of blurred rectangles are more sharp, but it detects also inner edges in rectangles, also those edge contours are not always closed
Looks like combining those two thinks somehow would yield better results. Or maybe somebody have different idea?
I was also thinking about doing floodfill, but it was hard to find for sure good seed point and threshold automatically (there might be some other white objects in the background). Besides I will want to optimize later this for GPU and floodfill algorithm is rather not a good fit for this.
Below is some sample code I tried so far:
image = cv2.imread('data/image.jpg');
gray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)
cv2.imshow('image', gray)
adaptive = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 601, 0)
cv2.imshow('adaptive', adaptive)
gradx = cv2.Sobel(gray, ddepth=cv2.CV_32F, dx=1, dy=0, ksize=3)
grady = cv2.Sobel(gray, ddepth=cv2.CV_32F, dx=0, dy=1, ksize=3)
abs_gradx = cv2.convertScaleAbs(grady)
abs_grady = cv2.convertScaleAbs(grady)
grad = cv2.addWeighted(abs_gradx, 0.5, abs_grady, 0.5, 0)
kernel = cv2.getStructuringElement(cv2.MORPH_CROSS,(5,5))
grad = cv2.morphologyEx(grad, cv2.MORPH_OPEN, kernel)
grad = cv2.morphologyEx(grad, cv2.MORPH_CLOSE, kernel)
cv2.imshow('sobel',grad)
#kernel = cv2.getStructuringElement(cv2.MORPH_CROSS,(7,7))
#grad = cv2.morphologyEx(grad, cv2.MORPH_OPEN, kernel)
retval, grad = cv2.threshold(grad, 10, 255, cv2.THRESH_BINARY)
cv2.imshow('sobel+morph+thrs',grad)
cv2.waitKey()
I believe your answer lies in using the Hough transform to detect lines, extending these lines to span the breaks between the darker squares, and then looking for intersects marking out corners. I've had a quick play in Matlab and have come up with the following, it's not perfect but should show the potential:
% Open image
i = imread('http://i.stack.imgur.com/kwcXm.jpg');
% Use a sharpening filter to enhance some of the edges
H = fspecial('unsharp');
i = imfilter(i, H, 'replicate');
% Detect edge segments using canny
BW = edge(i, 'canny');
% Apply hough transform to edges
[H, T, R] = hough(BW, 'RhoResolution', 0.5, 'Theta', -90:0.5:89.5);
% Find peaks in hough transform
P = houghpeaks(H, 5, 'threshold', ceil(0.1*max(H(:))));
% Extract lines from peaks, extending partial lines
lines = houghlines(BW, T, R, P, 'FillGap', 100, 'MinLength', 5);
% Plot detected lines on image
imshow(i); hold on;
for k = 1:length(lines)
xy = [lines(k).point1; lines(k).point2];
plot(xy(:,1),xy(:,2),'LineWidth',2,'Color','green');
end
With the final result:
Obviously there's room for improvement, with a number of lines still to detect, but if tweaking the various parameters doesn't work you could take the initial result and the search for more lines with similar angles to obtain a more complete set. The corners can then be found from the intersects which should be simple enough to extract.
I would try the following:
Hough transform to detect all straight lines
Look for sets of parallel lines that are:
A sufficient distance apart
Separated by the lighter color
There are a couple of things that make your problem trickier than it needs to be:
Perspective distortion
Changes in lighting, minor shadows
If you could minimize the above, it may help solving the problem.
I'm intending to write a program to detect and differentiate certain objects from a nearly solid background. The foreground and the background have a high contrast difference which I would further increase to aid in the object identification process. I'm planning to use Hough transform technique and OpenCV.
Sample image
As seen in the above image, I would want to separately identify the circular objects and the square objects (or any other shape out of a finite set of shapes). Since I'm quite new to image processing I do not have an idea whether such a situation needs a neural network to be implemented and each shape to be learned beforehand. Would a technique such as template matching let me do this without a neural network?
These posts will get you started:
How to detect circles
How to detect squares
How to detect a sheet of paper (advanced square detection)
You will probably have to adjust some parameters in these codes to match your circles/squares, but the core of the technique is shown on these examples.
If you intend to detect shapes other than just circles, (and from the image I assume you do), I would recommend the Chamfer matching for a quick start, especially as you have a good contrast.
The basic premise, explained in simple terms, is following:
You do an edge detection (for example, cvCanny in opencv)
You create a distance image, where the value of each pixel means the distance fom the nearest edge.
You take the shapes you would like to detect, define sample points along the edges of the shape, and try to match these points on the distance image. Basically you just add the values on the distance image which are "under" the coordinates of your sample points, given a specific position of your objects.
Find a good minimization algorithm, the effectiveness of this depends on your application.
This basic approach is a general solution, usually works well, but without further advancements, it is very slow.
Usually it's a good idea to first separate the objects of interest, so you don't have to always do the full search on the whole image. Find a good threshold, so you can separate objects. You still don't know which object it is, but you only have to do the matching itself in close proximity of this object.
Another good idea is, instead of doing the full search on the high resolution image, first do it on a very low resolution. The result will not be very accurate, but you can know the general areas where it's worth to do a search on a higher resolution, so you don't waste your time on areas where there is nothing of interest.
There are a number of more advanced techniques, but it's still worth to take a look at the basic chamfer matching, as it is the base of a large number of techniques.
With the assumption that the objects are simple shapes, here's an approach using thresholding + contour approximation. Contour approximation is based on the assumption that a curve can be approximated by a series of short line segments which can be used to determine the shape of a contour. For instance, a triangle has three vertices, a square/rectangle has four vertices, a pentagon has five vertices, and so on.
Obtain binary image. We load the image, convert to grayscale, Gaussian blur, then adaptive threshold to obtain a binary image.
Detect shapes. Find contours and identify the shape of each contour using contour approximation filtering. This can be done using arcLength to compute the perimeter of the contour and approxPolyDP to obtain the actual contour approximation.
Input image
Detected objects highlighted in green
Labeled contours
Code
import cv2
def detect_shape(c):
# Compute perimeter of contour and perform contour approximation
shape = ""
peri = cv2.arcLength(c, True)
approx = cv2.approxPolyDP(c, 0.04 * peri, True)
# Triangle
if len(approx) == 3:
shape = "triangle"
# Square or rectangle
elif len(approx) == 4:
(x, y, w, h) = cv2.boundingRect(approx)
ar = w / float(h)
# A square will have an aspect ratio that is approximately
# equal to one, otherwise, the shape is a rectangle
shape = "square" if ar >= 0.95 and ar <= 1.05 else "rectangle"
# Star
elif len(approx) == 10:
shape = "star"
# Otherwise assume as circle or oval
else:
shape = "circle"
return shape
# Load image, grayscale, Gaussian blur, and adaptive threshold
image = cv2.imread('1.jpg')
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
blur = cv2.GaussianBlur(gray, (7,7), 0)
thresh = cv2.adaptiveThreshold(blur,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY_INV,31,3)
# Find contours and detect shape
cnts = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
for c in cnts:
# Identify shape
shape = detect_shape(c)
# Find centroid and label shape name
M = cv2.moments(c)
cX = int(M["m10"] / M["m00"])
cY = int(M["m01"] / M["m00"])
cv2.putText(image, shape, (cX - 20, cY), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (36,255,12), 2)
cv2.imshow('thresh', thresh)
cv2.imshow('image', image)
cv2.waitKey()