我正在使用 Python (3) 和 OpenCV (3.3) 在網(wǎng)絡(luò)攝像頭上運(yùn)行實(shí)時(shí)對(duì)象檢測(cè)，使用樣本圖像，然后與視頻流進(jìn)行特征匹配。我已經(jīng)使用 SIFT/SURF 讓它工作，但我正在嘗試使用 ORB 算法。在某些情況下，我收到以下錯(cuò)誤導(dǎo)致程序崩潰：for i, (m, n) in enumerate(matches):ValueError: not enough values to unpack (expected 2, got 1)我理解崩潰背后的原因，有時(shí)圖像之間有很好的匹配，有時(shí)沒有，導(dǎo)致不匹配。我的問題是，如何強(qiáng)制程序忽略并跳過沒有足夠值的情況并繼續(xù)運(yùn)行。有問題的主要代碼區(qū)域： for i, (m, n) in enumerate(matches): if m.distance < 0.7*n.distance: good.append(m)“匹配”輸出示例：[[<DMatch 0x11bdcc030>, <DMatch 0x11bbf20b0>], [<DMatch 0x11bbf2490>, <DMatch 0x11bbf24f0>], [<DMatch 0x11bbf2750>, <DMatch 0x11bbf25d0>], [<DMatch 0x11bbf2570>, <DMatch 0x11bbf2150>], etc etc完整代碼：import numpy as npimport cv2from matplotlib import pyplot as pltimport matplotlib.patches as mpatchesimport os, os.pathimport mathimport timefrom datetime import datetimestartTime = datetime.now()MIN_MATCH_COUNT = 10 # default=10img1 = cv2.imread('Pattern3_small.jpg',0) # queryImage# Create ORB object. You can specify params here or later.orb = cv2.ORB_create()cap = cv2.VideoCapture(0)# cap = cv2.VideoCapture("output_H264_30.mov")# find the keypoints and descriptors with SIFTkp1, des1 = orb.detectAndCompute(img1,None)pts_global = []dst_global = []position = []heading = []# plt.axis([0, 1280, 0, 720])tbl_upper_horiz = 1539tbl_lower_horiz = 343tbl_upper_vert = 1008tbl_lower_vert = 110# cv2.namedWindow("Frame", cv2.WINDOW_NORMAL)# cv2.resizeWindow("Frame", 600,350)while True: _, img2 = cap.read() # Start timer timer = cv2.getTickCount() # find the keypoints and descriptors with SIFT # kp1, des1 = sift.detectAndCompute(img1,None) kp2, des2 = orb.detectAndCompute(img2,None) FLANN_INDEX_KDTREE = 0 FLANN_INDEX_LSH = 6 # index_params = dict(algorithm = FLANN_INDEX_KDTREE, trees = 5) index_params= dict(algorithm = FLANN_INDEX_LSH, table_number = 6, # 12, 6 key_size = 12, # 20, 12 multi_probe_level = 1) #2, 1
2 回答
滄海一幻覺
TA貢獻(xiàn)1824條經(jīng)驗(yàn) 獲得超5個(gè)贊
將 的每個(gè)元素matches視為一個(gè)集合并使用異常處理：
for i, pair in enumerate(matches):
try:
m, n = pair
if m.distance < 0.7*n.distance:
good.append(m)
except ValueError:
pass
當(dāng)年話下
TA貢獻(xiàn)1890條經(jīng)驗(yàn) 獲得超9個(gè)贊
如果你這樣做：
for i, (m, n) in enumerate(matches):
并且您不能保證所有元組始終包含兩個(gè)元素，那么您應(yīng)該執(zhí)行以下操作：
for i, values in enumerate(matches):
if len(values) < 2:
continue # you don't have the second element to compare against
...
# Do your usual processing here
上面的代碼是明確的和可讀的，即很明顯，您需要兩個(gè)元素才能在處理鏈中進(jìn)一步進(jìn)行。它也不太容易出錯(cuò)，因?yàn)槟_保有正確的數(shù)據(jù)可以繼續(xù)。它還允許您計(jì)算丟棄了多少元組。
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