问题描述
我正在尝试使用opencv和python。我在opencv 2.4的C ++版本中编写了一个描述符(SIFT,SURF或ORB)匹配代码。我想用python将此代码转换为opencv。我找到了一些关于如何在c ++中使用opencv函数的文档,但是python中的许多opencv函数我都找不到如何使用它们。这是我的python代码,我目前的问题是我不知道如何在python中使用opencv c ++的drawMatches。我找到了cv2.DRAW_MATCHES_FLAGS_DEFAULT,但我不知道如何使用它。这是我使用ORB描述符进行匹配的python代码:
I am trying to use opencv with python. I wrote a descriptor (SIFT, SURF, or ORB) matching code in C++ version of opencv 2.4. I want to convert this code to opencv with python. I found some documents about how to use opencv functions in c++ but many of the opencv function in python I could not find how to use them. Here is my python code, and my current problem is that I don't know how to use "drawMatches" of opencv c++ in python. I found cv2.DRAW_MATCHES_FLAGS_DEFAULT but I have no idea how to use it. Here is my python code of matching using ORB descriptors:
im1 = cv2.imread(r'C:\boldt.jpg')
im2 = cv2.cvtColor(im1, cv2.COLOR_BGR2GRAY)
im3 = cv2.imread(r'C:\boldt_resize50.jpg')
im4 = cv2.cvtColor(im3, cv2.COLOR_BGR2GRAY)
orbDetector2 = cv2.FeatureDetector_create("ORB")
orbDescriptorExtractor2 = cv2.DescriptorExtractor_create("ORB")
orbDetector4 = cv2.FeatureDetector_create("ORB")
orbDescriptorExtractor4 = cv2.DescriptorExtractor_create("ORB")
keypoints2 = orbDetector2.detect(im2)
(keypoints2, descriptors2) = orbDescriptorExtractor2.compute(im2,keypoints2)
keypoints4 = orbDetector4.detect(im4)
(keypoints4, descriptors4) = orbDescriptorExtractor4.compute(im4,keypoints4)
matcher = cv2.DescriptorMatcher_create('BruteForce-Hamming')
raw_matches = matcher.match(descriptors2, descriptors4)
img_matches = cv2.DRAW_MATCHES_FLAGS_DEFAULT(im2, keypoints2, im4, keypoints4, raw_matches)
cv2.namedWindow("Match")
cv2.imshow( "Match", img_matches);
行img_matches = cv2.DRAW_MATCHES_FLAGS_DEFAULT(im2,keypoints2,im4,keypoints4,raw_matches)的错误消息)
Error message of the line "img_matches = cv2.DRAW_MATCHES_FLAGS_DEFAULT(im2, keypoints2, im4, keypoints4, raw_matches)"
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
TypeError: 'long' object is not callable
我花了很多时间搜索文档和示例使用opencv函数与python。但是,我非常沮丧,因为在python中使用opencv函数的信息非常少。如果有人能教我在哪里可以找到如何在python中使用opencv模块的每个函数的文档,那将是非常有帮助的。感谢您的时间和帮助。
I spent much time search documentation and examples of using opencv functions with python. However, I am very frustrated because there is very little information of using opencv functions in python. It will be extremely helpful if anyone can teach me where I can find the documentation of how to use every function of the opencv module in python. I appreciate your time and help.
推荐答案
您可以在Python中可视化功能匹配,如下所示。注意使用scipy库。
you can visualize the feature matching in Python as following. Note the use of scipy library.
# matching features of two images
import cv2
import sys
import scipy as sp
if len(sys.argv) < 3:
print 'usage: %s img1 img2' % sys.argv[0]
sys.exit(1)
img1_path = sys.argv[1]
img2_path = sys.argv[2]
img1 = cv2.imread(img1_path, cv2.CV_LOAD_IMAGE_GRAYSCALE)
img2 = cv2.imread(img2_path, cv2.CV_LOAD_IMAGE_GRAYSCALE)
detector = cv2.FeatureDetector_create("SURF")
descriptor = cv2.DescriptorExtractor_create("BRIEF")
matcher = cv2.DescriptorMatcher_create("BruteForce-Hamming")
# detect keypoints
kp1 = detector.detect(img1)
kp2 = detector.detect(img2)
print '#keypoints in image1: %d, image2: %d' % (len(kp1), len(kp2))
# descriptors
k1, d1 = descriptor.compute(img1, kp1)
k2, d2 = descriptor.compute(img2, kp2)
print '#keypoints in image1: %d, image2: %d' % (len(d1), len(d2))
# match the keypoints
matches = matcher.match(d1, d2)
# visualize the matches
print '#matches:', len(matches)
dist = [m.distance for m in matches]
print 'distance: min: %.3f' % min(dist)
print 'distance: mean: %.3f' % (sum(dist) / len(dist))
print 'distance: max: %.3f' % max(dist)
# threshold: half the mean
thres_dist = (sum(dist) / len(dist)) * 0.5
# keep only the reasonable matches
sel_matches = [m for m in matches if m.distance < thres_dist]
print '#selected matches:', len(sel_matches)
# #####################################
# visualization of the matches
h1, w1 = img1.shape[:2]
h2, w2 = img2.shape[:2]
view = sp.zeros((max(h1, h2), w1 + w2, 3), sp.uint8)
view[:h1, :w1, :] = img1
view[:h2, w1:, :] = img2
view[:, :, 1] = view[:, :, 0]
view[:, :, 2] = view[:, :, 0]
for m in sel_matches:
# draw the keypoints
# print m.queryIdx, m.trainIdx, m.distance
color = tuple([sp.random.randint(0, 255) for _ in xrange(3)])
cv2.line(view, (int(k1[m.queryIdx].pt[0]), int(k1[m.queryIdx].pt[1])) , (int(k2[m.trainIdx].pt[0] + w1), int(k2[m.trainIdx].pt[1])), color)
cv2.imshow("view", view)
cv2.waitKey()
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