How to align Foto using OpenCV Python-CodePudding

Need Help, I am trying to align 2 id cards using OpenCV. If I do it for 2 id cards from the same person then the result works like the picture below

Before Alignment :

The alignment work perfectly if i tried with the same person id as below picture :

But if I do for 2 id cards that come from two different person then the result is messy, need help on how to do the alignment in this case

import cv2
import numpy as np
import matplotlib.pyplot as plt


# Read reference image
refFilename = "KTP_Reza_Crop.jpeg"
print("Reading reference image : ", refFilename)
im1 = cv2.imread(refFilename, cv2.IMREAD_COLOR)
im1 = cv2.cvtColor(im1, cv2.COLOR_BGR2RGB)

# Read image to be aligned
imFilename = "KTP_Reza_No.jpeg"
print("Reading image to align : ", imFilename)
im2 = cv2.imread(imFilename, cv2.IMREAD_COLOR)
im2 = cv2.cvtColor(im2, cv2.COLOR_BGR2RGB)

plt.figure(figsize=[20,10]); 
plt.subplot(121); plt.axis('off'); plt.imshow(im1); plt.title("Original Form")
plt.subplot(122); plt.axis('off'); plt.imshow(im2); plt.title("Testing Form")

# Convert images to grayscale
im1_gray = cv2.cvtColor(im1, cv2.COLOR_BGR2GRAY)
im2_gray = cv2.cvtColor(im2, cv2.COLOR_BGR2GRAY)
  

# Detect ORB features and compute descriptors.
MAX_NUM_FEATURES = 500
orb = cv2.ORB_create(MAX_NUM_FEATURES)
keypoints1, descriptors1 = orb.detectAndCompute(im1_gray, None)
keypoints2, descriptors2 = orb.detectAndCompute(im2_gray, None)

# Display 
im1_display = cv2.drawKeypoints(im1, keypoints1, outImage=np.array([]), color=(255, 0, 0), flags=cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)
im2_display = cv2.drawKeypoints(im2, keypoints2, outImage=np.array([]), color=(255, 0, 0), flags=cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)

plt.figure(figsize=[20,10])
plt.subplot(121); plt.axis('off'); plt.imshow(im1_display); plt.title("Original Form");
plt.subplot(122); plt.axis('off'); plt.imshow(im2_display); plt.title("Testing Form");

# Match features.
matcher = cv2.DescriptorMatcher_create(cv2.DESCRIPTOR_MATCHER_BRUTEFORCE_HAMMING)
matches = matcher.match(descriptors1, descriptors2, None)
  
# Sort matches by score
matches = sorted(matches, key = lambda x:x.distance)

# Remove not so good matches
numGoodMatches = int(len(matches) * 0.1)
matches = matches[:numGoodMatches]

# Draw top matches
im_matches = cv2.drawMatches(im1, keypoints1, im2, keypoints2, matches, None)

plt.figure(figsize=[40,10])
plt.imshow(im_matches); plt.axis('off'); plt.title("Original Form");


# Extract location of good matches
points1 = np.zeros((len(matches), 2), dtype=np.float32)
points2 = np.zeros((len(matches), 2), dtype=np.float32)

for i, match in enumerate(matches):
  points1[i, :] = keypoints1[match.queryIdx].pt
  points2[i, :] = keypoints2[match.trainIdx].pt
  
# Find homography
h, mask = cv2.findHomography(points2, points1, cv2.RANSAC)

# Use homography to warp image
height, width, channels = im1.shape
im2_reg = cv2.warpPerspective(im2, h, (width, height))
# Display results 
plt.figure(figsize=[20,10]); 
plt.subplot(121); plt.imshow(im1); plt.axis('off'); plt.title("Original Form");
plt.subplot(122); plt.imshow(im2_reg); plt.axis('off'); plt.title("Testing Form");

CodePudding user response：

Two id cards with different persons may not be working well because in such a case, the two images will be similar but not exactly same, for eg: name will be different and photo would be different etc., hence the key points and descriptors would be different for both the images & your output is getting affected.

You can detect the outer edge of the id card by using edge detection and selecting the largest contour and then use perspective transform to get a top down view if that's what you are aiming for.