Improve contour detection with OpenCV (Python)

Question

I am trying to identify cards from a photo. I managed to do what I wanted on ideal photos, but I am now having hard time applying the same procedure with slightly different ligh

Answer 1

Why not just use Canny and apply perspective correction after finding the contours (because it seems to blur the edges)? For example, using the small image you provided in your question (the result could be better on a bigger one):

Based on some parts of your code:

import numpy as np
import cv2

import math

img = cv2.imread('image.bmp')

# Prepocess
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
flag, thresh = cv2.threshold(gray, 120, 255, cv2.THRESH_BINARY)

# Find contours
img2, contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
contours = sorted(contours, key=cv2.contourArea, reverse=True) 

# Select long perimeters only
perimeters = [cv2.arcLength(contours[i],True) for i in range(len(contours))]
listindex=[i for i in range(15) if perimeters[i]>perimeters[0]/2]
numcards=len(listindex)

card_number = -1 #just so happened that this is the worst case
stencil = np.zeros(img.shape).astype(img.dtype)
cv2.drawContours(stencil, [contours[listindex[card_number]]], 0, (255, 255, 255), cv2.FILLED)
res = cv2.bitwise_and(img, stencil)
cv2.imwrite("out.bmp", res)
canny = cv2.Canny(res, 100, 200)
cv2.imwrite("canny.bmp", canny)

First, remove everything except a single card for simplicity, then apply Canny edge detector:

Then you can dilate/erode, correct perspective, remove the largest contour etc.

Answer 2

Except for the image in the bottom right corner, the following steps should generally work:

1. Dilate and erode the binary masks to bridge any one or two pixels gaps between contour fragments.
2. Use maximal supression to turn your thick binary masks along the boundary of your shapes into thin edges.
3. As used earlier in the pipeline, use cvFindcontours to identify closed contours. Each contour identified by the method can be tested for being closed.
4. As a general solution to such problems, I would advise you to try my algorithm to find closed contours around a given point. Check active segmentation with fixation