I have an image which contains lot of boxes in it. My task is to find by how many degree that image is rotated. I am using open-cv for solving that task: first finding lines in the image, and then finding their angles and returning angles by taking mean or median as result.
img_gray = cv2.cvtColor(img_before, cv2.COLOR_BGR2GRAY)
img_edges = cv2.Canny(img_gray, 100, 100, apertureSize=3)
lines = cv2.HoughLinesP(img_edges, 1, math.pi / 180.0, 100, minLineLength=1000, maxLineGap=100)
angles = []
for [[x1, y1, x2, y2]] in lines:
angle = math.degrees(math.atan2(double(y2) - y1, double(x2) - x1))
This code I am using for finding the angle and returning the result, and for part 2:
(h, w) = img_before.shape[:2]
(cX, cY) = (w // 2, h // 2)
M = cv2.getRotationMatrix2D((cX, cY),angle, 1.0)
img_before = cv2.warpAffine(img_before, M, (w, h))
I am using this part for rotating the image by myself and checking whether I am getting the correct answer or not.
So, now, here comes the problem. My code is working correctly: I am getting an error less than 0.01 degree.
But when I am rotating an image with an integer angle - always I am getting the correct answer. But, when I am rotating it with some floating number (eg: 1.3, 1.5), I am getting worse results. Sometimes, it don't deduct a single line where for integer angle it deduct more than 2000 lines. Sometimes, I get 45 degree as resultant angle of line though I am rotating the image which is 2.5, but when I do it 2 or 3 I am getting the correct result.
Kindly help me with this. Alternatively, is there any other solution that I can use for my problem?
Related
I am trying to find the direction of triangles in an image. below is the image:
These triangles are pointing upward/downward/leftward/rightward. This is not the actual image. I have already used canny edge detection to find edges then contours and then the dilated image is shown below.
My logic to find the direction:
The logic I am thinking to use is that among the three corner coordinates If I can identify the base coordinates of the triangle (having the same abscissa or ordinates values coordinates), I can make a base vector. Then angle between unit vectors and base vectors can be used to identify the direction. But this method can only determine if it is up/down or left/right but cannot differentiate between up and down or right and left. I tried to find the corners using cv2.goodFeaturesToTrack but as I know it's giving only the 3 most effective points in the entire image. So I am wondering if there is other way to find the direction of triangles.
Here is my code in python to differentiate between the triangle/square and circle:
#blue_masking
mask_blue=np.copy(img1)
row,columns=mask_blue.shape
for i in range(0,row):
for j in range(0,columns):
if (mask_blue[i][j]==25):
mask_blue[i][j]=255
else:
mask_blue[i][j]=0
blue_edges = cv2.Canny(mask_blue,10,10)
kernel_blue = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(2,2))
dilated_blue = cv2.dilate(blue_edges, kernel)
blue_contours,hierarchy =
cv2.findContours(dilated_blue,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
for cnt in blue_contours:
area = cv2.contourArea(cnt)
perimeter = cv2.arcLength(cnt,True)
M = cv2.moments(cnt)
cx = int(M['m10']/M['m00'])
cy = int(M['m01']/M['m00'])
if(12<(perimeter*perimeter)/area<14.8):
shape="circle"
elif(14.8<(perimeter*perimeter)/area<18):
shape="squarer"
elif(18<(perimeter*perimeter)/area and area>200):
shape="triangle"
print(shape)
print(area)
print((perimeter*perimeter)/area,"\n")
cv2.imshow('mask_blue',dilated_blue)
cv2.waitKey(0)
cv2.destroyAllWindows()
Source image can be found here: img1
Please help, how can I found the direction of triangles?
Thank you.
Assuming that you only have four cases: [up, down, left, right], this code should work well for you.
The idea is simple:
Get the bounding rectangle for your contour. Use: box = cv2.boundingRect(contour_pnts)
Crop the image using the bounding rectangle.
Reduce the image vertically and horizontally using the Sum option. Now you have the sum of pixels along each axis. The axis with the largest sum determines whether the triangle base is vertical or horizontal.
To identify whether the triangle is pointing left/right or up/down: you need to check whether the bounding rectangle center is before or after the max col/row:
The code (assumes you start from the cropped image):
ver_reduce = cv2.reduce(img, 0, cv2.REDUCE_SUM, None, cv2.CV_32F)
hor_reduce = cv2.reduce(img, 1, cv2.REDUCE_SUM, None, cv2.CV_32F)
#For smoothing the reduced vector, could be removed
ver_reduce = cv2.GaussianBlur(ver_reduce, (3, 1), 0)
hor_reduce = cv2.GaussianBlur(hor_reduce, (1, 3), 0)
_,ver_max, _, ver_col = cv2.minMaxLoc(ver_reduce)
_,hor_max, _, hor_row = cv2.minMaxLoc(hor_reduce)
ver_col = ver_col[0]
hor_row = hor_row[1]
contour_pnts = cv2.findNonZero(img) #in my code I do not have the original contour points
rect_center, size, angle = cv2.minAreaRect(contour_pnts )
print(rect_center)
if ver_max > hor_max:
if rect_center[0] > ver_col:
print ('right')
else:
print ('left')
else:
if rect_center[1] > hor_row:
print ('down')
else:
print ('up')
Photos:
Well, Mark has mentioned a solution that may not be as efficient but perhaps more accurate. I think this one should be equally efficient but perhaps less accurate. But since you already have a code that finds triangles, try adding the following code after you have found triangle contour:
hull = cv2.convexHull(cnt) # convex hull of contour
hull = cv2.approxPolyDP(hull,0.1*cv2.arcLength(hull,True),True)
# You can double check if the contour is a triangle here
# by something like len(hull) == 3
You should get 3 hull points for a triangle, these should be the 3 vertices of your triangles. Given your triangles always 'face' only in 4 directions; Y coordinate of the hull will have close value to the Y coordinate of the centroid for triangle facing left or right and whether it's pointing left or right will depend on whether hull X is less than or greater than centroid X. Similarly use hull and centroid X and Y for triangle pointing up or down.
I would like to rotate an image based on a second image. Both images are satellite images, however, they are not rotated in the same direction(in one image top is in the north direction and in the other the rotation is not known). But, I have at least three pixel pairs in each of the images (x1,y1,x2,y2). So my idea is to figure out their relative position and get the rotation angle from that.
Currently, I estimate the angle like this:
def angle_between(v1, v2):
""" Returns the angle in radians between vectors 'v1' and 'v2'::
>>> angle_between((1, 0, 0), (0, 1, 0))
1.5707963267948966
>>> angle_between((1, 0, 0), (1, 0, 0))
0.0
>>> angle_between((1, 0, 0), (-1, 0, 0))
3.141592653589793
"""
v1_u = unit_vector(v1)
v2_u = unit_vector(v2)
angle_rad = np.arccos(np.clip(np.dot(v1_u, v2_u), -1.0, 1.0))
return (angle_rad*180)/math.pi
with the inputs like this:
v1 = [points[0][0] - points[1][0], points[0][1] - points[1][1]] #hist
v2 = [points[0][2] - points[1][2], points[0][3] - points[1][3]] #ref
However, this only uses two pixel pairs instead of the three. Therefore, the rotation is some times incorrect. Could anybody show me how to use all three pixels?
My first attempt was to check on which side of the straight the third pixel lies in the image and based on that negate the angle. But, this does not work for all images.
EDIT:
I cannot add the original images, as they are copyrighted, however, as the image content is not really important I added whitened images. The first is the input image with the three points drawn in, the second is the rotated image (where additionally the (wrong, due to rotation) cutout area is marked with a rectangle) and third the historical image.
The points are the following:
567.01,144,1544.4,4581.8
1182.6,1568.1,2934.1,3724.3
938.97,1398.1,2795.8,4002.5
with:
x_historical, y_historical, x_presentday, y_presentday
So, I have ellipses given - they are defined by their midpoint, an horizontal radius(rh) and an vertical radius(rv). I'm drawing them using sin/cos and the result looks fairly good to me(just making sure this isn't an error source).
Now say I have an angle(or a direction vector) given and I want to have the point on the ellipse's outline with that angle/direction. My intuitive approach was to simply use the direction vector, normalise it and multiply its x-component with rh, its y-component with rv. Now both my written program AND all the calculations I did on a paper give me not the point I want but another one, though it's still on the ellipse's outline. However, this method works just fine if the direction is one of (1,0), (0, 1), (-1, 0), (0, -1), (so it works for 0°, 90°, 180°, 270°).
Although there is a farily big amount of data about ellipses themselves on the internet, I couldn't find any information about my particular problem - and I couldn't come up with any better solution than the above one.
So, any idea how to achieve this?
If I understand what you are asking then I think that what you need is polar form of an ellipse where the angle is measured from the ellipse centre. Using this form of the ellipse, you will be able to evaulate your elliptic radius value for a given choice of theta and then plot your point.
If you take a look at this gif image you will see why using the parametric angle give you the correct result only at theta = 90, 180, 270 and 360 degrees http://en.wikipedia.org/wiki/File:Parametric_ellipse.gif . Use the polar form for an ellipse and you should get the points that you want.
You are correct - the parametric angle is not the same as the angle between the desired point and the X axis. However, their tangents are proportional (with a factor of rh/rv) so you can use this approach:
Get the tangent of the desired angle
Multiply this tangent by rh/rv
Use trigonometric identities to compute the sine and cosine from the tangent
Scale/position the point according to the parameters (midpoint, rh, rv)
In Python:
from math import copysign, cos, sin, sqrt
class Ellipse:
def __init__(self, mx, my, rh, rv):
self.mx = mx
self.my = my
self.rh = rh
self.rv = rv
def pointFromAngle(self, a):
c = cos(a)
s = sin(a)
ta = s / c ## tan(a)
tt = ta * self.rh / self.rv ## tan(t)
d = 1. / sqrt(1. + tt * tt)
x = self.mx + copysign(self.rh * d, c)
y = self.my + copysign(self.rv * tt * d, s)
return x, y
EDIT - Thanks for all the answers everyone. I think I accidentally led you slightly wrong as the square in the picture below should be a rectangle (I see most of you are referencing squares which seems like it would make my life a lot easier). Also, the x/y lines could go in any direction, so the red dot won't always be at the top y boundary. I was originally going for a y = mx + b solution, but then I got stuck trying to figure out how I know whether to plug in the x or the y (one of them has to be known, obviously).
I have a very simple question (I think) that I'm currently struggling with for some reason. I'm trying to have a type of minimap in my game which shows symbols around the perimeter of the view, pointing towards objectives off-screen.
Anyway, I'm trying to find the value of the red point (while the black borders and everything in green is known):
It seems like simple trigonometry, but for some reason I can't wrap my head around it. I just need to find the "new" x value from the green point to the red point, then I can utilize basic math to get the red point, but how I go about finding that new x is puzzling me.
Thanks in advance!
scale = max(abs(x), abs(y))
x = x / scale
y = y / scale
This is the simple case, for a square from (-1, -1) to (1, 1). If you want a different sized square, multiply the coordinates by sidelen / 2.
If you want a rectangle instead of a square, use the following formula. (This is another solution to the arbitrarily-sized square version)
scale = max(abs(x) / (width / 2), abs(y) / (height / 2))
x = x / scale
y = y / scale
Let's call the length of one side of the square l. The slope of the line is -y/x. That means, if you move along the line and rise a distance y toward the top of the square, then you'll move a distance x to the left. But since the green point is at the center of the square, you can rise only l/2. You can express this as a ratio:
-y -l/2
——— = ———
x d
Where d is the distance you'll move to the left. Solving for d, we have
d = xl/2y
So if the green dot is at (0, 0), the red dot is at (-l/2, xl/2y).
All you need is the angle and the width of the square w.
If the green dot is at (0,0), then the angle is a = atan(y/x), the y-coordinate of the dot is w/2, and therefore the x-coordinate of the dot is tan(1/a) * (w/2). Note that tan(1/a) == pi/2 - tan(a), or in other words the angle you really want to plug into tan is the one outside the box.
Edit: yes, this can be done without trig, too. All you need is to interpolate the x-coordinate of the dot on the line. So you know the y-coordinate is w/2, then the x-coordinate is (w/2) * x/y. But, be careful which quadrant of the square you're working with. That formula is only valid for -y<x<y, otherwise you want to reverse x and y.
I am trying to make a program that automatically corrects the perspective of a rectangle. I have managed to get the silhouette of the rectangle, and have the code to correct the perspective, but I can't find the corners. The biggest problem is that, because it has been deformed, I can't use the following "code":
c1 = min(x), min(y)
c2 = max(x), min(y)
c3 = min(x), max(y)
c4 = max(x), max(y)
This wouldn't work with this situation (X represents a corner):
X0000000000X
.00000000000
..X000000000
.....0000000
........0000
...........X
Does anyone know how to do this?
Farthest point from the center will give you one corner.
Farthest point from the first corner will give you another corner, which may be either adjacent or opposite to the first.
Farthest point from the line between those two corners (a bit more math intensitive) will give you a third corner. I'd use distance from center as a tie breaker.
For finding the 4th corner, it'll be the point outside the triangle formed by the first 3 corners you found, farthest from the nearest line between those corners.
This is a very time consuming way to do it, and I've never tried it, but it ought to work.
You could try to use a scanline algorithm - For every line of the polygon (so y = min(y)..max(y)), get l = min(x) and r = max(x). Calculate the left/right slope (deltax) and compare it with the slope the line before. If it changed (use some tolerance here), you are at a corner of the rectangle (or close to it). That won't work for all cases, as the slope can't be that exact because of low resolution, but for large rectangles and slopes not too similar, this should work.
At least, it works well for your example:
X0000000000X l = 0, r = 11
.00000000000 l = 1, r = 11, deltaxl = 1, deltaxr = 0
..X000000000 l = 2, r = 11, deltaxl = 1, deltaxr = 0
.....0000000 l = 5, r = 11, deltaxl = 3, deltaxr = 0
........0000 l = 8, r = 11, deltaxl = 3, deltaxr = 0
...........X l = 11, r = 11, deltaxl = 3, deltaxr = 0
You start with the top of the rectangle where you get two different values for l and r, so you already have two of the corners. On the left side, for the first three lines you'll get deltax = 1, but after it, you'll get deltax = 3, so there is a corner at (3, 3). On the right side, nothing changes, deltax = 0, so you only get the point at the end.
Note that you're "collecting" corners here, so if you don't have 4 corners at the end, the slopes were too similar (or you have a picture of a triangle) and you can switch to a different (more exact) algorithm or just give an error. The same if you have more than 4 corners or some other strange things like holes in the rectangle. It seems some kind of image detection is involved, so these cases can occur, right?
There are cases in which a simple deltax = (x - lastx) won't work good, see this example for the left side of a rectangle:
xxxxxx
xxxxx deltax = 1 dy/dx = 1/1 = 1
xxxxx deltax = 0 dy/dx = 2/1 = 2
xxxx deltax = 1 dy/dx = 3/2 = 1.5
xxxx deltax = 0 dy/dx = 4/2 = 2
xxx deltax = 1 dy/dx = 5/3 = 1.66
Sometimes deltax is 0, sometimes is 1. It's better to use the slope of the line from the actual point to the top left/right point (deltay / deltax). Using it, you'll still have to stick with a tolerance, but your values will get more exact with each new line.
You could use a hough transform to find the 4 most prominent lines in the masked image. These lines will be the sides of the quadrangle.
The lines will intersect in up to 6 points, which are the 4 corners and the 2 perspective vanishing points.
These are easy to distinguish: pick any point inside the quadrangle, and check if the line from this point to each of the 6 intersection points intersects any of the lines. If not, then that intersection point is a corner.
This has the advantage that it works well even for noisy or partially obstructed images, or if your segmentation is not exact.
en.wikipedia.org/wiki/Hough_transform
Example CImg Code
I would be very interested in your results. I have been thinking about writing something like this myself, to correct photos of paper sheets taken at an angle. I am currently struggling to think of a way to correct the perspective if the 4 points are known
p.s.
Also check out
Zhengyou Zhang , Li-Wei He, "Whiteboard scanning and image enhancement"
http://research.microsoft.com/en-us/um/people/zhang/papers/tr03-39.pdf
for a more advanced solution for quadrangle detection
I have asked a related question, which tries to solve the perspective transform:
proportions of a perspective-deformed rectangle
This looks like a convex hull problem.
http://en.wikipedia.org/wiki/Convex_hull
Your problem is simpler but the same solution should work.