Perspective Transform OMR Sheet - OpenCV Python-CodePudding

I am trying to make a perspective transformation with OpenCV in python. I want to align the image and find the coordinates of top left top right and bottom left and bottom right contours. here is my code so far where I can identify all the contours.

gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
blur = cv2.GaussianBlur(gray,(3,3),0)
edges = cv2.Canny(blur,50,100)
contours, hierarchy = cv2.findContours(edges,cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) #find contours
cv2.drawContours(img,contours,-1,(0,255,0),2)
cv2.imshow('Contours',img)
cv2.waitKey(0)

Here is an image of what I get.

I'd be very thankful if you could try to help me solve this problem!

Template:

import cv2
import numpy as np

# read image
img = cv2.imread("omr_test.jpg")
hh, ww = img.shape[:2]

# read template
template = cv2.imread("omr_template.jpg")
ht, wd = template.shape[:2]

# convert img to grayscale
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

# do otsu threshold on gray image
thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY cv2.THRESH_OTSU)[1]

# pad thresh with black to preserve corners when apply morphology
pad = cv2.copyMakeBorder(thresh, 20, 20, 20, 20, borderType=cv2.BORDER_CONSTANT, value=0)

# apply morphology
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (15,15))
morph = cv2.morphologyEx(pad, cv2.MORPH_CLOSE, kernel)

# remove padding
morph = morph[20:hh 20, 20:ww 20]

# get largest external contour
contours = cv2.findContours(morph, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
contours = contours[0] if len(contours) == 2 else contours[1]
big_contour = max(contours, key=cv2.contourArea)

# get perimeter and approximate a polygon
peri = cv2.arcLength(big_contour, True)
corners = cv2.approxPolyDP(big_contour, 0.04 * peri, True)

# draw polygon on input image from detected corners
polygon = img.copy()
cv2.polylines(polygon, [corners], True, (0,255,0), 2, cv2.LINE_AA)
# Alternate: cv2.drawContours(page,[corners],0,(0,0,255),1)

# print the number of found corners and the corner coordinates
# They seem to be listed counter-clockwise from the top most corner
print(len(corners))
print(corners)

# reformat input corners to x,y list
icorners = []
for corner in corners:
    pt = [ corner[0][0],corner[0][1] ]
    icorners.append(pt)
icorners = np.float32(icorners)

# get corresponding output corners form width and height
ocorners = [ [0,0], [0,ht], [wd,ht], [wd,0] ]
ocorners = np.float32(ocorners)

# get perspective tranformation matrix
M = cv2.getPerspectiveTransform(icorners, ocorners)

# do perspective 
warped = cv2.warpPerspective(img, M, (wd, ht))

# write results
cv2.imwrite("omr_test_thresh.jpg", thresh)
cv2.imwrite("omr_test_morph.jpg", morph)
cv2.imwrite("omr_test_polygon.jpg", polygon)
cv2.imwrite("omr_test_warped.jpg", warped)

# display it
cv2.imshow("thresh", thresh)
cv2.imshow("pad", pad)
cv2.imshow("morph", morph)
cv2.imshow("polygon", polygon)
cv2.imshow("warped", warped)
cv2.waitKey(0)

Threshold Image:

Morphology Image:

Polygon Image:

Warped Input:

CodePudding user response：

Here is a correction that solves whether the input image is rotated cw or ccw in Python/OpenCV.

Read the input
Read the template (for its dimension)
Convert the input to gray and threshold
Pad the threshold (to preserve the corners when apply morphology)
Apply morphology close
Remove the padding
Get the largest external contour
Get its perimeter and approximate to 4 corners as the input corners for the warping
Sort the corners by Y and test the first two sorted points for X. If diff=X2-X1 is negative, then the output points need to be list slightly differently.
Get the output corners for the warping from the dimensions of the template and the diff of the sorted points.
Get the perspective transformation matrix
Warp the input to match the template
Save the results

Input 1 (cw rotated):

Input 2 (ccw rotated):

import cv2
import numpy as np

# read image
#img = cv2.imread("omr_test.jpg")
img = cv2.imread("omr_test2.jpg")
hh, ww = img.shape[:2]

# read template
template = cv2.imread("omr_template.jpg")
ht, wd = template.shape[:2]

# convert img to grayscale
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

# do otsu threshold on gray image
thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY cv2.THRESH_OTSU)[1]

# pad thresh with black to preserve corners when apply morphology
pad = cv2.copyMakeBorder(thresh, 20, 20, 20, 20, borderType=cv2.BORDER_CONSTANT, value=0)

# apply morphology
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (15,15))
morph = cv2.morphologyEx(pad, cv2.MORPH_CLOSE, kernel)

# remove padding
morph = morph[20:hh 20, 20:ww 20]

# get largest external contour
contours = cv2.findContours(morph, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
contours = contours[0] if len(contours) == 2 else contours[1]
big_contour = max(contours, key=cv2.contourArea)

# get perimeter and approximate a polygon
peri = cv2.arcLength(big_contour, True)
corners = cv2.approxPolyDP(big_contour, 0.04 * peri, True)

# draw polygon on input image from detected corners
polygon = img.copy()
cv2.polylines(polygon, [corners], True, (0,255,0), 2, cv2.LINE_AA)

# print the number of found corners and the corner coordinates
# They seem to be listed counter-clockwise from the top most corner
print(len(corners))
print(corners)

# reformat input corners to x,y list
sortcorners = []
for corner in corners:
    pt = [ corner[0][0],corner[0][1] ]
    sortcorners.append(pt)
icorners = np.float32(sortcorners)

# sort corners on y
def takeSecond(elem):
    return elem[1]
sortcorners.sort(key=takeSecond)

# check if second corner x is left or right of first corner x
x1 = sortcorners[0][0]
x2 = sortcorners[1][0]
diff = x2 - x1
print(x1, x2)

# get corresponding output corners form width and height
if diff >= 0:
    ocorners = [ [0,0], [0,ht], [wd,ht], [wd,0] ]
else:
    ocorners = [ [wd,0], [0,0], [0,ht], [wd,ht]]
ocorners = np.float32(ocorners)

# get perspective tranformation matrix
M = cv2.getPerspectiveTransform(icorners, ocorners)

# do perspective 
warped = cv2.warpPerspective(img, M, (wd, ht))

# write results
cv2.imwrite("omr_test2_thresh.jpg", thresh)
cv2.imwrite("omr_test2_morph.jpg", morph)
cv2.imwrite("omr_test2_polygon.jpg", polygon)
cv2.imwrite("omr_test2_warped.jpg", warped)

# display it
cv2.imshow("thresh", thresh)
cv2.imshow("pad", pad)
cv2.imshow("morph", morph)
cv2.imshow("polygon", polygon)
cv2.imshow("warped", warped)
cv2.waitKey(0)

Result for first input (cw rotated):

Result for second input (ccw rotated):