When receiving "ValueError: not enough values to unpack (expected 2, got 1)", how can I force the program to ignore and continue?

Question

我正在使用 Python (3) 和 OpenCV (3.3) 在网络摄像头上运行实时对象检测，使用样本图像，然后与视频流进行特征匹配。我已经使用 SIFT/SURF 让它工作，但我正在尝试使用 ORB 算法。

我在某些情况下收到以下错误导致程序崩溃：

for i, (m, n) in enumerate(matches):
ValueError: not enough values to unpack (expected 2, got 1)

我明白它崩溃的原因，有时图像之间有很好的匹配，有时没有，导致不匹配。

我的问题是，如何强制程序忽略并跳过没有足够值的情况并继续运行ning。

问题代码的主要区域：

    for i, (m, n) in enumerate(matches):
        if m.distance < 0.7*n.distance:
            good.append(m)

示例 'matches' 输出：

[[<DMatch 0x11bdcc030>, <DMatch 0x11bbf20b0>], [<DMatch 0x11bbf2490>, <DMatch 0x11bbf24f0>], [<DMatch 0x11bbf2750>, <DMatch 0x11bbf25d0>], [<DMatch 0x11bbf2570>, <DMatch 0x11bbf2150>], etc etc

完整代码：

import numpy as np
import cv2
from matplotlib import pyplot as plt
import matplotlib.patches as mpatches
import os, os.path
import math
import time
from datetime import datetime
startTime = datetime.now()

MIN_MATCH_COUNT = 10   # default=10

img1 = 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 SIFT
kp1, des1 = orb.detectAndCompute(img1,None)

pts_global = []
dst_global = []

position = []
heading = []
# plt.axis([0, 1280, 0, 720])

tbl_upper_horiz = 1539
tbl_lower_horiz = 343
tbl_upper_vert = 1008
tbl_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
    search_params = dict(checks = 50)

    flann = cv2.FlannBasedMatcher(index_params, search_params)

    matches = flann.knnMatch(des1,des2,k=2)

    # print (matches)

    # Calculate Frames per second (FPS)
    fps = cv2.getTickFrequency() / (cv2.getTickCount() - timer);

    # store all the good matches as per Lowe's ratio test.
    good = []

    # ratio test as per Lowe's paper
    for i, (m, n) in enumerate(matches):
        if m.distance < 0.7*n.distance:
            good.append(m)

# Do something afterwards

感谢您的帮助。

Answer 1

将matches的每个元素视为一个集合并使用异常处理：

for i, pair in enumerate(matches):
    try:
        m, n = pair
        if m.distance < 0.7*n.distance:
            good.append(m)

    except ValueError:
        pass

Answer 2

如果您这样做：

for i, (m, n) in enumerate(matches):

而且你不能保证所有的元组总是包含两个元素，那么你应该这样做：

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

上面的代码是明确的和可读的，即很明显您需要两个元素才能在处理链中进一步进行。它也不太容易出错，因为您要确保拥有正确的数据才能继续。它还允许您计算有多少元组被丢弃。

When receiving "ValueError: not enough values to unpack (expected 2, got 1)", how can I force the program to ignore and continue?

When receiving "ValueError: not enough values to unpack (expected 2, got 1)", how can I force the program to ignore and continue?

python

opencv

enumerate

orb

flann