def calculate_point(kps_left, sco_left, des_left, kps_right, sco_right,
des_right):
FLANN_INDEX_KDTREE = 1
index_params = dict(algorithm=FLANN_INDEX_KDTREE, trees=5)
search_params = dict(checks=50)
flann = cv2.FlannBasedMatcher(index_params, search_params)
matches = flann.knnMatch(des_left, des_right, k=2)
# matcher = cv2.DescriptorMatcher_create(cv2.DescriptorMatcher_FLANNBASED)
# matches = matcher.knnMatch(des_left, des_right, 2)
goodMatch = []
locations_1_to_use = []
locations_2_to_use = []
dis = []
# 匹配对筛选
min_dist = 1000
max_dist = 0
disdif_avg = 0
# 统计平均距离差
for m, n in matches:
disdif_avg += n.distance - m.distance
disdif_avg = disdif_avg / len(matches)
# print('disdif_avg:', disdif_avg)
for m, n in matches:
#自适应阈值
if n.distance > m.distance + 1.5 * disdif_avg:
# if m.distance < 0.9 * n.distance:
goodMatch.append(m)
p2 = cv2.KeyPoint(kps_right[m.trainIdx][0],
kps_right[m.trainIdx][1], 1)
p1 = cv2.KeyPoint(kps_left[m.queryIdx][0], kps_left[m.queryIdx][1],
1)
locations_1_to_use.append([p1.pt[0], p1.pt[1]])
locations_2_to_use.append([p2.pt[0], p2.pt[1]])
dis.append([n.distance, m.distance])
#goodMatch = sorted(goodMatch, key=lambda x: x.distance)
print('match num is %d' % len(goodMatch))
locations_1_to_use = np.array(locations_1_to_use)
locations_2_to_use = np.array(locations_2_to_use)
dis = np.array(dis)
# Perform geometric verification using RANSAC.
_, inliers = measure.ransac((locations_1_to_use, locations_2_to_use),
transform.AffineTransform,
min_samples=3,
residual_threshold=_RESIDUAL_THRESHOLD,
max_trials=1000)
# print('Found %d inliers' % sum(inliers))
# 筛选距离最近的前60%个数据
inlier_idxs = np.nonzero(inliers)[0]
dis_R = dis[inliers]
dis_idx = np.argsort(dis_R[:, 0] - dis_R[:, 1])
dis_sorted = dis_R[dis_idx]
l = dis_idx.shape[0]
end = int(l * 0.6)
#最终匹配结果
inlier_idxs = inlier_idxs[dis_idx[:end]]
print('sorted inliers:', end)
#最终匹配结果
matches = np.column_stack((inlier_idxs, inlier_idxs))
# print('whole time is %6.3f' % (time.perf_counter() - start0))
# Visualize correspondences, and save to file.
#1 绘制匹配连线
plt.rcParams['savefig.dpi'] = 100 #图片像素
plt.rcParams['figure.dpi'] = 100 #分辨率
plt.rcParams['figure.figsize'] = (4.0, 3.0) # 设置figure_size尺寸
_, ax = plt.subplots()
plotmatch.plot_matches(ax,
image1,
image2,
locations_1_to_use,
locations_2_to_use,
np.column_stack((inlier_idxs, inlier_idxs)),
plot_matche_points=False,
matchline=True,
matchlinewidth=0.5)
ax.axis('off')
ax.set_title('')
plt.show()
print('inlier_idxs:', len(inlier_idxs))
res = locations_2_to_use[inlier_idxs] - locations_1_to_use[inlier_idxs]
return locations_1_to_use[inlier_idxs], locations_2_to_use[inlier_idxs]
print(np.min(res[:, 0]))
print(np.max(res[:, 0]))
print(np.min(res[:, 1]))
print(np.max(res[:, 1]))
_, inliers = measure.ransac((locations_1_to_use, locations_2_to_use),
transform.AffineTransform,
min_samples=3,
residual_threshold=30,
max_trials=1000)
inlier_idxs = np.nonzero(inliers)[0]
print('Found %d inliers' % sum(inliers))
# Visualize correspondences, and save to file.
#1 绘制匹配连线, 1 일치하는 연결 그리기
plt.rcParams['savefig.dpi'] = 100 #图片像素 , 이미지 픽셀
plt.rcParams['figure.dpi'] = 100 #分辨率 , 해상도
plt.rcParams['figure.figsize'] = (4.0, 3.0
) # 设置figure_size尺寸 , figure_size 크기 설정
_, ax = plt.subplots()
plotmatch.plot_matches(
ax,
img1,
img2,
locations_1_to_use,
locations_2_to_use,
np.column_stack((inlier_idxs, inlier_idxs)),
# 매칭 안된 포인트 안 그리기
#plot_matche_points = False,
# 매칭 안된 포인트 그리기
plot_matche_points=True,
matchline=True,
matchlinewidth=0.3)
ax.axis('off')
ax.set_title('')
plt.show()
_, inliers = measure.ransac((locations_1_to_use, locations_2_to_use),
transform.AffineTransform,
min_samples=3,
residual_threshold=_RESIDUAL_THRESHOLD,
max_trials=1000)
print('Found %d inliers' % sum(inliers))
inlier_idxs = np.nonzero(inliers)[0]
#最终匹配结果
matches = np.column_stack((inlier_idxs, inlier_idxs))
print('whole time is %6.3f' % (time.perf_counter() - start0))
# Visualize correspondences, and save to file.
#1 绘制匹配连线
plt.rcParams['savefig.dpi'] = 100 #图片像素
plt.rcParams['figure.dpi'] = 100 #分辨率
plt.rcParams['figure.figsize'] = (4.0, 3.0) # 设置figure_size尺寸
_, ax = plt.subplots()
plotmatch.plot_matches(ax,
image1,
image2,
locations_1_to_use,
locations_2_to_use,
np.column_stack((inlier_idxs, inlier_idxs)),
plot_matche_points=False,
matchline=True,
matchlinewidth=0.5)
ax.axis('off')
ax.set_title('')
plt.show()
locations_2_to_use = np.array(locations_2_to_use)
# Perform geometric verification using RANSAC.
_RESIDUAL_THRESHOLD = 30
_, inliers = measure.ransac((locations_1_to_use, locations_2_to_use),
transform.AffineTransform,
min_samples=3,
residual_threshold=_RESIDUAL_THRESHOLD,
max_trials=1000)
inlier_idxs = np.nonzero(inliers)[0]
# p1 = locations_2_to_use[inlier_idxs]
# p2 = locations_1_to_use[inlier_idxs]
# Visualize correspondences, and save to file.
#1 绘制匹配连线
plt.rcParams['savefig.dpi'] = 100 #图片像素
plt.rcParams['figure.dpi'] = 100 #分辨率
plt.rcParams['figure.figsize'] = (4.0, 3.0) # 设置figure_size尺寸
_, ax = plt.subplots()
plotmatch.plot_matches(ax,
img_sar_canny,
img_optical_canny,
locations_1_to_use,
locations_2_to_use,
np.column_stack((inlier_idxs, inlier_idxs)),
plot_matche_points=False,
matchline=True,
matchlinewidth=0.5)
ax.axis('off')
ax.set_title('')
plt.show()
