[Computer Vision] Homography를 이용한 파노라마 만들기(python 코드 첨부)

두 장의 이미지로 파노라마를 만드는 코드입니다. 특징점은 ORB로 잡고 opencv의 findHomography 함수를 이용해 파노라마를 만듭니다. (코드는 뒤쪽에 첨부!)

 

결과사진:

 

 

 

 

# -- coding: utf8 --
import cv2
import numpy as np

def warpImages(img1, img2, H):
    rows1, cols1 = img1.shape[:2]
    rows2, cols2 = img2.shape[:2]
    temp_points = np.float32([[0, 0], [0, rows2], [cols2, rows2], [cols2, 0]]).reshape(-1, 1, 2)

    list_of_points_2 = cv2.perspectiveTransform(temp_points, H)
    list_of_points = np.concatenate((np.float32([[0, 0], [0, rows1], [cols1, rows1], [cols1, 0]]).reshape(-1, 1, 2), list_of_points_2), axis=0)
    x_min, y_min = np.int32(list_of_points.min(axis=0).ravel() - 0.5)
    x_max, y_max = np.int32(list_of_points.max(axis=0).ravel() + 0.5)
    
    translation_dist = [-x_min, -y_min]
    H_translation = np.array([[1, 0, translation_dist[0]], [0, 1, translation_dist[1]], [0, 0, 1]])
    output_img = cv2.warpPerspective(img2, H_translation.dot(H), (x_max - x_min, y_max - y_min))
    output_img[translation_dist[1]:rows1 + translation_dist[1], translation_dist[0]:cols1 + translation_dist[0]] = img1
    return output_img


# 사진 읽어오기
img1 = '4-2.jpg'
img2 = '4-3.jpg'

img_left = cv2.imread(img1)
img_right = cv2.imread(img2)
h_left, w_left = img_left.shape[:2]
h_right, w_right = img_right.shape[:2]
cv2.imshow("left_image_original",img_left)
cv2.imshow("rigth_image_original",img_right)

# 보다 정확한 결과를 위해 gray scale로 변환
left_gray = cv2.cvtColor(img_left, cv2.COLOR_BGR2GRAY)
right_gray = cv2.cvtColor(img_right, cv2.COLOR_BGR2GRAY)

# SIFT descriptor 생성
descriptor = cv2.ORB_create()
kp_left, des_left = descriptor.detectAndCompute(img_left, None)
kp_right, des_right = descriptor.detectAndCompute(img_right, None)

# 특징점 출력
cv2.imshow("key_point_left", cv2.drawKeypoints(img_left, kp_left, None, (0,0,255)))
cv2.imshow("key_point_right", cv2.drawKeypoints(img_right, kp_right, None, (0,0,255)))

# BFmatcher 객체 생성 및 knn 매칭 (두 이미지간에 특징 일치시키기)
#bf = cv2.BFMatcher(cv2.NORM_L2, crossCheck=True)
matcher = cv2.DescriptorMatcher_create("BruteForce")
matches = matcher.knnMatch(des_left, des_right, 2)

# 좋은 매칭점 선별
good_matches = []
for m,n in matches:
    if m.distance < 0.7*n.distance:
        good_matches.append(m)

# good_matches 시각화(매칭점 연결 전시)
img_good_matches = cv2.drawMatches(img_left, kp_left, img_right, kp_right, good_matches, None, flags=cv2.DrawMatchesFlags_NOT_DRAW_SINGLE_POINTS)
cv2.imshow("Good Matches", img_good_matches)

# 매칭점 좌표 변환
src_pts = np.float32([kp_left[m.queryIdx].pt for m in good_matches]).reshape(-1,1,2)
dst_pts = np.float32([kp_right[m.trainIdx].pt for m in good_matches]).reshape(-1,1,2)

# RANSAC 알고리즘을 이용한 변환 행렬 계산 - 두 이미지 변환 행렬 계산. 임계값은 5.0 이용.
M, mask = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC, 5.0)
# 이미지 수평으로 붙이기
result = warpImages(img_right, img_left, M)

# 파노라마 이미지 출력
cv2.imshow("Panorama", result)

cv2.waitKey(0)
cv2.destroyAllWindows()

 

 

 

추가: 세 장의 이미지로 파노라마를 만드는 코드:

# -- coding: utf8 --
import cv2
import numpy as np

def warpImages(img1, img2, H):
    rows1, cols1 = img1.shape[:2]
    rows2, cols2 = img2.shape[:2]
    temp_points = np.float32([[0, 0], [0, rows2], [cols2, rows2], [cols2, 0]]).reshape(-1, 1, 2)

    list_of_points_2 = cv2.perspectiveTransform(temp_points, H)
    list_of_points = np.concatenate((np.float32([[0, 0], [0, rows1], [cols1, rows1], [cols1, 0]]).reshape(-1, 1, 2), list_of_points_2), axis=0)
    x_min, y_min = np.int32(list_of_points.min(axis=0).ravel() - 0.5)
    x_max, y_max = np.int32(list_of_points.max(axis=0).ravel() + 0.5)
    
    translation_dist = [-x_min, -y_min]
    H_translation = np.array([[1, 0, translation_dist[0]], [0, 1, translation_dist[1]], [0, 0, 1]])
    output_img = cv2.warpPerspective(img2, H_translation.dot(H), (x_max - x_min, y_max - y_min))
    output_img[translation_dist[1]:rows1 + translation_dist[1], translation_dist[0]:cols1 + translation_dist[0]] = img1
    return output_img

img_left = cv2.imread('1-1.jpg')
img_mid = cv2.imread('1-2.jpg')
img_right = cv2.imread('1-3.jpg')
cv2.imshow("left_image_original",img_left)
cv2.imshow("mid_image_original",img_mid)
cv2.imshow("right_image_original",img_right)

# 이미지를 gray scale로 변환
left_gray = cv2.cvtColor(img_left, cv2.COLOR_BGR2GRAY)
mid_gray = cv2.cvtColor(img_mid, cv2.COLOR_BGR2GRAY)

# SIFT descriptor 생성
descriptor = cv2.SIFT_create()
kp_left, des_left = descriptor.detectAndCompute(left_gray, None)
kp_mid, des_mid = descriptor.detectAndCompute(mid_gray, None)
cv2.imshow("key_point_left", cv2.drawKeypoints(img_left, kp_left, None, (0,0,255)))
cv2.imshow("key_point_mid", cv2.drawKeypoints(img_mid, kp_mid, None, (0,0,255)))
# BFMatcher 객체 생성 및 knn 매칭 (두 이미지간에 특징 일치시키기)
matcher = cv2.DescriptorMatcher_create("BruteForce")
matches1 = matcher.knnMatch(des_left, des_mid, 2)

# 좋은 매칭점 선별
good_matches1 = []
for m,n in matches1:
    if m.distance < 0.7*n.distance:
        good_matches1.append(m)

# good_matches 시각화(매칭점 연결 전시)
img_good_matches1 = cv2.drawMatches(img_left, kp_left, img_mid, kp_mid, good_matches1, None, flags=cv2.DrawMatchesFlags_NOT_DRAW_SINGLE_POINTS)
cv2.imshow("Good Matches_left+mid", img_good_matches1)


# 매칭점 좌표 변환
src_pts1 = np.float32([kp_left[m.queryIdx].pt for m in good_matches1]).reshape(-1,1,2)
dst_pts1 = np.float32([kp_mid[m.trainIdx].pt for m in good_matches1]).reshape(-1,1,2)

# RANSAC 알고리즘을 이용한 변환 행렬 계산 - 두 이미지 변환 행렬 계산. 임계값은 5.0 이용.
M1, mask1 = cv2.findHomography(src_pts1, dst_pts1, cv2.RANSAC, 5.0)
result1 = warpImages(img_mid, img_left, M1)
cv2.imshow("Merged Image_left+mid", result1)

# result1과 img_right 합치기
result1_gray = cv2.cvtColor(result1, cv2.COLOR_BGR2GRAY)
right_gray = cv2.cvtColor(img_right, cv2.COLOR_BGR2GRAY)
kp_result1, des_result1 = descriptor.detectAndCompute(result1_gray, None)
kp_right, des_right = descriptor.detectAndCompute(right_gray, None)
cv2.imshow("key_point_right", cv2.drawKeypoints(img_right, kp_right, None, (0,0,255)))


matches2 = matcher.knnMatch(des_result1, des_right, 2)
good_matches2 = []
for m,n in matches2:
    if m.distance < 0.7*n.distance:
        good_matches2.append(m)
        
img_good_matches2 = cv2.drawMatches(result1, kp_result1, img_right, kp_right, good_matches2, None, flags=cv2.DrawMatchesFlags_NOT_DRAW_SINGLE_POINTS)
cv2.imshow("Good Matches_result1+right", img_good_matches2)
src_pts2 = np.float32([kp_result1[m.queryIdx].pt for m in good_matches2]).reshape(-1,1,2)
dst_pts2 = np.float32([kp_right[m.trainIdx].pt for m in good_matches2]).reshape(-1,1,2)
M2, mask2 = cv2.findHomography(src_pts2, dst_pts2, cv2.RANSAC, 5.0) 
final_merge = warpImages(img_right, result1,M2)

cv2.imshow("Merged Image_final", final_merge)
cv2.waitKey(0)
cv2.destroyAllWindows()

 

 

 

reference:

openCV.영상 붙이기 (tistory.com)

openCV.영상 붙이기

 

#005 How to create a panorama image using OpenCV with Python (datahacker.rs)

#005 How to create a panorama image using OpenCV with Python