# Need to draw only good matches, so create a mask
matchesMask = [[0, 0] for i in range(len(matches))]
good = []
pts1 = []
pts2 = []
# ratio test as per Lowe's paper
for i, (m, n) in enumerate(matches):
if m.distance < 0.7*n.distance:
good.append(m)
pts2.append(kp2[m.trainIdx].pt)
pts1.append(kp1[m.queryIdx].pt)
matchesMask = [1, 0]
rows, cols = srcImg.shape[:2]
MIN_MATCH_COUNT = 10
if len(good) > MIN_MATCH_COUNT:
src_pts = np.float32([kp1[m.queryIdx].pt for m in good]).reshape(-1, 1, 2)
dst_pts = np.float32([kp2[m.trainIdx].pt for m in good]).reshape(-1, 1, 2)
M, mask = cv.findHomography(src_pts, dst_pts, cv.RANSAC, 5.0)
warpImg = cv.warpPerspective(testImg, np.array(M), (testImg.shape[1], testImg.shape[0]), flags=cv.WARP_INVERSE_MAP)
for col in range(0, cols):
if srcImg[:, col].any() and warpImg[:, col].any():
left = col
break
for col in range(cols-1, 0, -1):
if srcImg[:, col].any() and warpImg[:, col].any():
right = col
break
res = np.zeros([rows, cols, 3], np.uint8)
for row in range(0, rows):
for col in range(0, cols):
if not srcImg[row, col].any():
res[row, col] = warpImg[row, col]
elif not warpImg[row, col].any():
res[row, col] = srcImg[row, col]
else:
srcImgLen = float(abs(col - left))
testImgLen = float(abs(col - right))
alpha = srcImgLen / (srcImgLen + testImgLen)
res[row, col] = np.clip(srcImg[row, col] * (1-alpha) + warpImg[row, col] * alpha, 0, 255)
# opencv is bgr, matplotlib is rgb
res = cv.cvtColor(res, cv.COLOR_BGR2RGB)
# show the result
plt.figure()
plt.imshow(res)
plt.show()
else:
print("Not enough matches are found - {}/{}".format(len(good), MIN_MATCH_COUNT))
matchesMask = None
