Get external contour using opencv (python)

Question

I am trying to get the external contour of an image using opencv and python.

I found a solution to this problem here ( Process image to find external contour ) but the solution does not work for me - instead of the contour image it opens two new images (one which is all black and the other one black and white).

This is the code I am using:

import cv2 # Import OpenCV
import numpy as np # Import NumPy

# Read in the image as grayscale - Note the 0 flag
im = cv2.imread("img.jpg", 0)

# Run findContours - Note the RETR_EXTERNAL flag
# Also, we want to find the best contour possible with CHAIN_APPROX_NONE
_ ,contours, hierarchy = cv2.findContours(im.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)

# Create an output of all zeroes that has the same shape as the input
# image
out = np.zeros_like(im)

# On this output, draw all of the contours that we have detected
# in white, and set the thickness to be 3 pixels
cv2.drawContours(out, contours, -1, 255, 3)

# Spawn new windows that shows us the donut
# (in grayscale) and the detected contour
cv2.imshow('Donut', im) 
cv2.imshow('Output Contour', out)

# Wait indefinitely until you push a key.  Once you do, close the windows
cv2.waitKey(0)
cv2.destroyAllWindows()

The illustration shows the two windows I get instead of the contour.

Answer 1

You are doing some mistakes that compromise your result. Reading from the documentation it says that:

• For better accuracy, use binary images (see step 3).
• finding contours is like finding white object from black background (see step 2).

You don't stick with these rules so you don't get good results. Also you are plotting your results to a black image and they are not visible.

Below is the full solution for your case.

I am also using an adaptive threshold for better results.

# Step 1: Read in the image as grayscale - Note the 0 flag
im = cv2.imread("/home/jorge/Downloads/input.jpg", 0)
cv2.imshow('Original', im)
cv2.waitKey(0)
cv2.destroyAllWindows()

# Step 2: Inverse the image to get black background
im2 = im.copy()
im2 = 255 - im2
cv2.imshow('Inverse', im2)
cv2.waitKey(0)
cv2.destroyAllWindows()

# Step 3: Get an adaptive binary image
im3 = cv2.adaptiveThreshold(im2, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY_INV, 11, 2)
cv2.imshow('Inverse_binary', im3)
cv2.waitKey(0)
cv2.destroyAllWindows()

# Step 4: find contours
_, contours, hierarchy = cv2.findContours(im3.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)

# Step 5: This creates a white image instead of a black one to plot contours being black
out = 255*np.ones_like(im)
cv2.drawContours(out, contours, -1, (0, 255, 0), 3)
cv2.drawContours(im, contours, -1, (0, 255, 0))