How to identify different objects in an image?

Question

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 w

Answer 1

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.

1. Obtain binary image. We load the image, convert to grayscale, Gaussian blur, then adaptive threshold to obtain a binary image.

2. 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()