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.