Improve path detection with OpenCV (Python)

Question

I am trying to identify cards from a photograph. I managed to do what I wanted in perfect photographs, but now I am having difficulty applying the same procedure with slightly different lighting, etc. Therefore, the question is to make the following contour definition more reliable.

I need to pass most of my code so that the recipients can make images of interest, but my question only applies to the last block and image.

import numpy as np
import cv2
from matplotlib import pyplot as plt
from mpl_toolkits.axes_grid1 import ImageGrid
import math

img = cv2.imread('image.png')
img = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
plt.imshow(img)

Then the cards are discovered:

# Prepocess
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
blur = cv2.GaussianBlur(gray,(1,1),1000)
flag, thresh = cv2.threshold(blur, 120, 255, cv2.THRESH_BINARY)
# Find contours
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)
# Show image
imgcont = img.copy()
[cv2.drawContours(imgcont, [contours[i]], 0, (0,255,0), 5) for i in listindex]
plt.imshow(imgcont)

Perspective fixed:

#plt.rcParams['figure.figsize'] = (3.0, 3.0)
warp = range(numcards)
for i in range(numcards):
    card = contours[i]
    peri = cv2.arcLength(card,True)
    approx = cv2.approxPolyDP(card,0.02*peri,True)
    rect = cv2.minAreaRect(contours[i])
    r = cv2.cv.BoxPoints(rect)

    h = np.array([ [0,0],[399,0],[399,399],[0,399] ],np.float32)
    approx = np.array([item for sublist in approx for item in sublist],np.float32)
    transform = cv2.getPerspectiveTransform(approx,h)
    warp[i] = cv2.warpPerspective(img,transform,(400,400))

# Show perspective correction
fig = plt.figure(1, (10,10))
grid = ImageGrid(fig, 111, # similar to subplot(111)
                nrows_ncols = (4, 4), # creates 2x2 grid of axes
                axes_pad=0.1, # pad between axes in inch.
                aspect=True, # do not force aspect='equal'
                )

for i in range(numcards):
    grid[i].imshow(warp[i]) # The AxesGrid object work as a list of axes.

I have a problem. I want to define the outline of the shapes. The best way that I've found - to use combination bilateralFilterand AdaptativeThresholdthe image gray:

fig = plt.figure(1, (10,10))
grid = ImageGrid(fig, 111, # similar to subplot(111)
                nrows_ncols = (4, 4), # creates 2x2 grid of axes
                axes_pad=0.1, # pad between axes in inch.
                aspect=True, # do not force aspect='equal'
                )
for i in range(numcards):
    image2 = cv2.bilateralFilter(warp[i].copy(),10,100,100)
    grey = cv2.cvtColor(image2,cv2.COLOR_BGR2GRAY)
    grey2 = cv2.cv.AdaptiveThreshold(cv2.cv.fromarray(grey), cv2.cv.fromarray(grey), 255, cv2.cv.CV_ADAPTIVE_THRESH_MEAN_C, cv2.cv.CV_THRESH_BINARY, blockSize=31, param1=6)
    grid[i].imshow(grey,cmap=plt.cm.binary) 

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Answer 1

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Answer 2

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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)

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