CODEWITHSUNDEEP
pythonimageopencvimage-processingcomputer-vision
Gautham
Gautham

Reputation: 109

OpenCV: shape detection

I have .jpg file of different shapes. I am trying to detect the shape of each figure and print it besides it. I am using the below code to create and draw contours.

contours, _ = cv2.findContours(threshold, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)
for con in contours:
    approx = cv2.approxPolyDP(con, 0.01*cv2.arcLength(con, True), True)
    cv2.drawContours(img, [approx], 0, (0,0,0), 5)

if len(approx) == 4:
        cv2.putText(img, "Quadrilateral", (x,y), font, 1, (0))

Now that I have figured out cv2.approxPolyDp() joins the contour points and create a boundary definite to the shape, How do I determine the exact shape, i.e whether it is square or rectangle? As in the above code if len(approx) == 4

This is the Image:

e-p

Upvotes: 3

Views: 3346

Answers (1)

nathancy
nathancy

Reputation: 46580

You can use aspect ratio to distinguish between a square and a rectangle. By observation, a square has equal length and width whereas a rectangle has one side longer. This same logic can be applied to identify a circle vs oval. Here's the results:

enter image description here

import cv2

def detect_shape(c):
    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"

    # Pentagon
    elif len(approx) == 5:
        shape = "pentagon"

    # Otherwise assume as circle or oval
    else:
        (x, y, w, h) = cv2.boundingRect(approx)
        ar = w / float(h)
        shape = "circle" if ar >= 0.95 and ar <= 1.05 else "oval"

    return shape

image = cv2.imread('1.png')
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
thresh = cv2.adaptiveThreshold(gray,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY_INV,51,7)

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:
    shape = detect_shape(c)
    x,y,w,h = cv2.boundingRect(c)
    cv2.putText(image, shape, (x, y - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (36,255,12), 2)

cv2.imshow('thresh', thresh)
cv2.imshow('image', image)
cv2.waitKey()

Upvotes: 4

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