如何使用 OpenCV 对齐照片 Python

Question

需要帮助，我正在尝试使用 OpenCV 对齐 2 张身份证。如果我对同一个人的 2 张身份证执行此操作，则结果如下图所示

对齐前：

如果我尝试使用与下图相同的人 ID，则对齐效果非常好：

但是如果我对来自两个不同人的 2 张身份证进行处理，那么结果会很乱，在这种情况下需要有关如何对齐的帮助

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");

Answer 1

两张不同人的身份证可能效果不佳，因为在这种情况下，两张图像将相似但不完全相同，例如：姓名不同，照片不同等，因此图像的关键点和描述符会有所不同，并且您的输出会受到影响。

您可以使用边缘检测并选择最大的轮廓来检测身份证的外边缘，然后使用 perspective transform 获得自上而下的视图（如果这是您的目标）。

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