How to find the distance between two concentric contours, for different angles?

Question

I have an image with two contours, where one contour is always 'inside' another. I want to find the distance between the two contours for 90 different angles (meaning, distance at every 4 degrees). How do I go about doing it?

Here's an example image:

Thank you!

Answer 1

In the following code, I have just given you the example for the vertical line, the rest can be obtained by rotating the line. Result looks like this, instead of drawing you can use the coordinates for distance calculation.

import shapely.geometry as shapgeo
import numpy as np
import cv2


img = cv2.imread('image.jpg', 0)
ret, img =cv2.threshold(img, 128, 255, cv2.THRESH_BINARY)

#Fit the ellipses
_, contours0, hierarchy = cv2.findContours( img.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
outer_ellipse = [cv2.approxPolyDP(contours0[0], 0.1, True)]
inner_ellipse = [cv2.approxPolyDP(contours0[2], 0.1, True)]

h, w = img.shape[:2]
vis = np.zeros((h, w, 3), np.uint8)
cv2.drawContours( vis, outer_ellipse, -1, (255,0,0), 1)
cv2.drawContours( vis, inner_ellipse, -1, (0,0,255), 1)

##Extract contour of ellipses
cnt_outer = np.vstack(outer_ellipse).squeeze()
cnt_inner = np.vstack(inner_ellipse).squeeze()

#Determine centroid
M = cv2.moments(cnt_inner)
cx = int(M['m10']/M['m00'])
cy = int(M['m01']/M['m00'])
print cx, cy

#Draw full segment lines 
cv2.line(vis,(cx,0),(cx,w),(150,0,0),1)

# Calculate intersections using Shapely
# http://toblerity.org/shapely/manual.html
PolygonEllipse_outer= shapgeo.asLineString(cnt_outer)
PolygonEllipse_inner= shapgeo.asLineString(cnt_inner)
PolygonVerticalLine=shapgeo.LineString([(cx,0),(cx,w)])


insecouter= np.array(PolygonEllipse_outer.intersection(PolygonVerticalLine)).astype(np.int)
insecinner= np.array(PolygonEllipse_inner.intersection(PolygonVerticalLine)).astype(np.int)
cv2.line(vis,(insecouter[0,0], insecinner[1,1]),(insecouter[1,0], insecouter[1,1]),(0,255,0),2)
cv2.line(vis,(insecouter[0,0], insecinner[0,1]),(insecouter[1,0], insecouter[0,1]),(0,255,0),2)

cv2.imshow('contours', vis)

0xFF & cv2.waitKey()
cv2.destroyAllWindows()  

Answer 2

Take this image of two sets of two shapes:

We want to find the distance between the edges of each set of shapes, including where the edges overlap.

1. First things first, we import the necessary modules:

import cv2
import numpy as np

2. To do that, we will first need to retrieve every shape in the image as lists of contours. In the above particular example, there are 4 shapes that need to be detected. To retrieve each shape, we will need to use a mask to mask out every color besides the color of the shape of interest:

def get_masked(img, lower, upper):
    img_hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
    mask = cv2.inRange(img_hsv, np.array(lower), np.array(upper))
    img_mask = cv2.bitwise_and(img, img, mask=mask)
    return img_mask

The lower and upper parameters will determine the minimum HVS values and the maximum HSV values that will not be masked out of the image. Given the right lower and upper parameters, you will be able to extract one image with only the green shapes, and one image with only the blue shapes:

3. With the masked images, you can then proceed to process them into more clean contours. Here is the preprocess function, with values that can be tweaked whenever necessary:

def get_processed(img):
    img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    img_blur = cv2.GaussianBlur(img_gray, (7, 7), 7)
    img_canny = cv2.Canny(img_blur, 50, 50)
    kernel = np.ones((7, 7))
    img_dilate = cv2.dilate(img_canny, kernel, iterations=2)
    img_erode = cv2.erode(img_dilate, kernel, iterations=2)
    return img_erode

Passing in the masked images will give you

4. With the images masked and processed, they will be ready for opencv to detect their contours:

def get_contours(img):
    contours, hierarchy = cv2.findContours(img, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
    return [cnt for cnt in contours if cv2.contourArea(cnt) > 500]

The list comprehension at the return statement is there to filter out noise by specifying that every contour must have an area that is greater than 500.

5. Now, we will define some basic functions that we will later use:

def get_centeroid(cnt):
    length = len(cnt)
    sum_x = np.sum(cnt[..., 0])
    sum_y = np.sum(cnt[..., 1])
    return int(sum_x / length), int(sum_y / length)

def get_pt_at_angle(pts, pt, ang):
    angles = np.rad2deg(np.arctan2(*(pt - pts).T))
    angles = np.where(angles < -90, angles + 450, angles + 90)
    found= np.rint(angles) == ang
    if np.any(found):
        return pts[found][0]

The names of the functions are pretty self-explanatory; the first one returns the center point of a contour, and the second one returns a point in a given array of points, pts , that is at a given angle, ang , relative to a given point, pt . The np.where in the get_pt_at_angle function is there to shift the starting angle, 0 , to the positive x axis, as it by default will be at the positive y axis.

6. Time to define the function that will return the distances. First, define it so that these five parameters can be passed in:

def get_distances(img, cnt1, cnt2, center, step):

A brief explanation on each parameter:

• img , the image array
• cnt1 , the first shape
• cnt2 , the second shape
• center , the origin for the distance calculations
• step , the number of degrees to be jumped per value

7. Define a dictionary to store the distances, with the angles as key and the distances as values:

    angles = dict()

8. Loop through each angle you want to retrieve the distance of the edges of the two shapes, and find the coordinate of the two contours that are the ct angle of the iterations, angle , relative to the origin point, center , using the get_pt_at_angle function we defined earlier.

    for angle in range(0, 360, step):
        pt1 = get_pt_at_angle(cnt1, center, angle)
        pt2 = get_pt_at_angle(cnt2, center, angle)

9. Check if a point exists in both contours that is at the specific angle relative to the origin:

        if np.any(pt1) and np.any(pt2):

10. You can use the np.linalg.norm method to get the distance between the two points. I also made it draw the text and connecting lines for visualization. Don't forget to add the angle and value to the angles dictionary, and you can then break out of the inner for loop. At the end of the function, return the image that has the text and lines drawn on it:

            d = round(np.linalg.norm(pt1 - pt2))
            cv2.putText(img, str(d), tuple(pt1), cv2.FONT_HERSHEY_PLAIN, 0.8, (0, 0, 0))
            cv2.drawContours(img, np.array([[center, pt1]]), -1, (255, 0, 255), 1)
            angles[angle] = d

    return img, angles

11. Finally, you can utilize the function defined on an image:

img = cv2.imread("shapes1.png")

img_green = get_masked(img, [10, 0, 0], [70, 255, 255])
img_blue = get_masked(img, [70, 0, 0], [179, 255, 255])

img_green_processed = get_processed(img_green)
img_blue_processed = get_processed(img_blue)

img_green_contours = get_contours(img_green_processed)
img_blue_contours = get_contours(img_blue_processed)

Using the image of four shapes, you can tell that the img_green_contours and img_blue_contours will each contain two contours. But you might be wondering: how did I choose the minimum and maximum HSV values? Well, I used a trackbar code. You can run the below code, adjusting the HSV values using the trackbars until you find a range where everything in the image is masked out (in black) except for the shape you want to retrieve:

import cv2
import numpy as np

def empty(a):
    pass
    
cv2.namedWindow("TrackBars")
cv2.createTrackbar("Hue Min", "TrackBars", 0, 179, empty)
cv2.createTrackbar("Hue Max", "TrackBars", 179, 179, empty)
cv2.createTrackbar("Sat Min", "TrackBars", 0, 255, empty)
cv2.createTrackbar("Sat Max", "TrackBars", 255, 255, empty)
cv2.createTrackbar("Val Min", "TrackBars", 0, 255, empty)
cv2.createTrackbar("Val Max", "TrackBars", 255, 255, empty)

img = cv2.imread("shapes0.png")

while True:
    h_min = cv2.getTrackbarPos("Hue Min", "TrackBars")
    h_max = cv2.getTrackbarPos("Hue Max", "TrackBars")
    s_min = cv2.getTrackbarPos("Sat Min", "TrackBars")
    s_max = cv2.getTrackbarPos("Sat Max", "TrackBars")
    v_min = cv2.getTrackbarPos("Val Min", "TrackBars")
    v_max = cv2.getTrackbarPos("Val Max", "TrackBars")
    
    img_hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)

    lower = np.array([h_min, s_min, v_min])
    upper = np.array([h_max, s_max, v_max])
    
    mask = cv2.inRange(img_hsv, lower, upper)
    img_masked = cv2.bitwise_and(img, img, mask=mask)

    cv2.imshow("Image", img_masked)
    if cv2.waitKey(1) & 0xFF == ord("q"): # If you press the q key
        break

With the values I chose, I got:

12. Loop through the blue shape contours and green shape contours in parallel, and depending on which color shape you want the origin to be at the center of, you can pass that color contour into the get_centeroid function we defined earlier:

for cnt_blue, cnt_green in zip(img_blue_contours, img_green_contours[::-1]):
    center = get_centeroid(cnt_blue)
    img, angles = get_distances(img, cnt_green.squeeze(), cnt_blue.squeeze(), center, 30)
    print(angles)

Notice that I used 30 as the step; that number can be changed to 4 , I used 30 so the visualization would be more clear.

13. Finally, we can display the image:

cv2.imshow("Image", img)
cv2.waitKey(0)

Altogether:

import cv2
import numpy as np

def get_masked(img, lower, upper):
    img_hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
    mask = cv2.inRange(img_hsv, np.array(lower), np.array(upper))
    img_mask = cv2.bitwise_and(img, img, mask=mask)
    return img_mask

def get_processed(img):
    img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    img_blur = cv2.GaussianBlur(img_gray, (7, 7), 7)
    img_canny = cv2.Canny(img_blur, 50, 50)
    kernel = np.ones((7, 7))
    img_dilate = cv2.dilate(img_canny, kernel, iterations=2)
    img_erode = cv2.erode(img_dilate, kernel, iterations=2)
    return img_erode

def get_contours(img):
    contours, hierarchy = cv2.findContours(img, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
    return [cnt for cnt in contours if cv2.contourArea(cnt) > 500]

def get_centeroid(cnt):
    length = len(cnt)
    sum_x = np.sum(cnt[..., 0])
    sum_y = np.sum(cnt[..., 1])
    return int(sum_x / length), int(sum_y / length)

def get_pt_at_angle(pts, pt, ang):
    angles = np.rad2deg(np.arctan2(*(pt - pts).T))
    angles = np.where(angles < -90, angles + 450, angles + 90)
    found= np.rint(angles) == ang
    if np.any(found):
        return pts[found][0]
        
def get_distances(img, cnt1, cnt2, center, step):
    angles = dict()
    for angle in range(0, 360, step):
        pt1 = get_pt_at_angle(cnt1, center, angle)
        pt2 = get_pt_at_angle(cnt2, center, angle)
        if np.any(pt1) and np.any(pt2):
            d = round(np.linalg.norm(pt1 - pt2))
            cv2.putText(img, str(d), tuple(pt1), cv2.FONT_HERSHEY_PLAIN, 0.8, (0, 0, 0))
            cv2.drawContours(img, np.array([[center, pt1]]), -1, (255, 0, 255), 1)
            angles[angle] = d
            
    return img, angles

img = cv2.imread("shapes1.png")

img_green = get_masked(img, [10, 0, 0], [70, 255, 255])
img_blue = get_masked(img, [70, 0, 0], [179, 255, 255])

img_green_processed = get_processed(img_green)
img_blue_processed = get_processed(img_blue)

img_green_contours = get_contours(img_green_processed)
img_blue_contours = get_contours(img_blue_processed)

for cnt_blue, cnt_green in zip(img_blue_contours, img_green_contours[::-1]):
    center = get_centeroid(cnt_blue)
    img, angles = get_distances(img, cnt_green.squeeze(), cnt_blue.squeeze(), center, 30)
    print(angles)

cv2.imshow("Image", img)
cv2.waitKey(0)

Output:

{0: 5, 30: 4, 60: 29, 90: 25, 120: 31, 150: 8, 180: 5, 210: 7, 240: 14, 270: 12, 300: 14, 330: 21}
{0: 10, 30: 9, 60: 6, 90: 0, 120: 11, 150: 7, 180: 5, 210: 6, 240: 6, 270: 4, 300: 0, 330: 16}

Note: For certain shapes, some angles might be absent in the dictionary. That would be caused by the process function; you would get more accurate results if you turn down some of the values, like the blur sigma

Answer 3

I borrowed the general idea using Shapely and the basic code from tfv's answer . Nevertheless, iterating the desired angles, calculating the needed end points for the correct lines to be intersected with the shapes, calculating and storing the distances, etc. were missing, so I added all that.

That'd be my full code:

import cv2
import numpy as np
import shapely.geometry as shapgeo

# Read image, and binarize
img = cv2.imread('G48xu.jpg', cv2.IMREAD_GRAYSCALE)
img = cv2.threshold(img, 128, 255, cv2.THRESH_BINARY)[1]

# Find (approximated) contours of inner and outer shape
cnts, hier = cv2.findContours(img.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)
outer = [cv2.approxPolyDP(cnts[0], 0.1, True)]
inner = [cv2.approxPolyDP(cnts[2], 0.1, True)]

# Just for visualization purposes: Draw contours of inner and outer shape
h, w = img.shape[:2]
vis = np.zeros((h, w, 3), np.uint8)
cv2.drawContours(vis, outer, -1, (255, 0, 0), 1)
cv2.drawContours(vis, inner, -1, (0, 0, 255), 1)

# Squeeze contours for further processing
outer = np.vstack(outer).squeeze()
inner = np.vstack(inner).squeeze()

# Calculate centroid of inner contour
M = cv2.moments(inner)
cx = int(M['m10'] / M['m00'])
cy = int(M['m01'] / M['m00'])

# Calculate maximum needed radius for later line intersections
r_max = np.min([cx, w - cx, cy, h - cy])

# Set up angles (in degrees)
angles = np.arange(0, 360, 4)

# Initialize distances
dists = np.zeros_like(angles)

# Prepare calculating the intersections using Shapely
poly_outer = shapgeo.asLineString(outer)
poly_inner = shapgeo.asLineString(inner)

# Iterate angles and calculate distances between inner and outer shape
for i, angle in enumerate(angles):

    # Convert angle from degrees to radians
    angle = angle / 180 * np.pi

    # Calculate end points of line from centroid in angle's direction
    x = np.cos(angle) * r_max + cx
    y = np.sin(angle) * r_max + cy
    points = [(cx, cy), (x, y)]

    # Calculate intersections using Shapely
    poly_line = shapgeo.LineString(points)
    insec_outer = np.array(poly_outer.intersection(poly_line))
    insec_inner = np.array(poly_inner.intersection(poly_line))

    # Calculate distance between intersections using L2 norm
    dists[i] = np.linalg.norm(insec_outer - insec_inner)

    # Just for visualization purposes: Draw lines for some examples
    if (i == 10) or (i == 40) or (i == 75):

        # Line from centroid to end points
        cv2.line(vis, (cx, cy), (int(x), int(y)), (128, 128, 128), 1)

        # Line between both shapes
        cv2.line(vis,
                 (int(insec_inner[0]), int(insec_inner[1])),
                 (int(insec_outer[0]), int(insec_outer[1])), (0, 255, 0), 2)

        # Distance
        cv2.putText(vis, str(dists[i]), (int(x), int(y)),
                    cv2.FONT_HERSHEY_COMPLEX, 0.75, (0, 255, 0), 2)

# Output angles and distances
print(np.vstack([angles, dists]).T)

# Just for visualization purposes: Output image
cv2.imshow('Output', vis)
cv2.waitKey(0)
cv2.destroyAllWindows()

I generated some examplary output for visualization purposes:

And, here's an excerpt from the output, showing angle and the corresponding distance:

[[  0  70]
 [  4  71]
 [  8  73]
 [ 12  76]
 [ 16  77]
 ...
 [340  56]
 [344  59]
 [348  62]
 [352  65]
 [356  67]]

Hopefully, the code is self-explanatory. If not, please don't hesitate to ask questions. I'll gladly provide further information.

----------------------------------------
System information
----------------------------------------
Platform:      Windows-10-10.0.16299-SP0
Python:        3.9.1
NumPy:         1.20.2
OpenCV:        4.5.1
Shapely:       1.7.1
----------------------------------------