<p>所以我一直在跟随这篇关于质心追踪的教程
<a href="https://www.pyimagesearch.com/2018/07/23/simple-object-tracking-with-opencv/" rel="nofollow noreferrer">https://www.pyimagesearch.com/2018/07/23/simple-object-tracking-with-opencv/</a>
并建立了形心跟踪类,就像在教程中提到的那样。在</p>
<p>现在,当我尝试使用背景减法来检测,而不是他使用的CNN时,它不起作用,并给我这个问题从质心跟踪器.py在</p>
<pre><code>for i in range(0, inputCentroids):
TypeError: only integer scalar arrays can be converted to a scalar index
</code></pre>
<p>这是我正在使用的代码</p>
^{pr2}$
<p>密码在</p>
<pre><code>objects = ct.update(rects)
</code></pre>
<p>行。在</p>
<p>以下是本教程中的质心跟踪器的实现:</p>
<pre><code>from scipy.spatial import distance as dist
from collections import OrderedDict
import numpy as np
#Makes a the next unique object ID with
#2 ordered dictionaries
class CentroidTracker():
def __init__(self, maxDisappeared = 50):
self.nextObjectID = 0
self.objects = OrderedDict()
self.disappeared = OrderedDict()
self.maxDisappeared = maxDisappeared
def register(self, centroid):
self.objects[self.nextObjectID] = centroid
self.disappeared[self.nextObjectID] = 0
self.nextObjectID += 1
def deregister(self, objectID):
del self.objects[objectID]
del self.disappeared[objectID]
def update(self, rects):
if len(rects) == 0:
for objectID in self.disappeared.keys():
self.disappeared[objectID] += 1
if self.disappeared[objectID] > self.maxDisappeared:
self.deregister(objectID)
return self.objects
inputCentroids = np.zeros((len(rects), 2), dtype="int")
for (i, (startX, startY, endX, endY)) in enumerate(rects):
cX = int((startX + endX) / 2.0)
cY = int((startY + endY) / 2.0)
inputCentroids[i] = (cX, cY)
if len(self.objects) == 0:
for i in range(0, inputCentroids):
self.register(inputCentroids[i])
else:
objectIDs = list(self.objects.keys())
objectCentroids = list(self.objects.values())
D = dist.cdist(np.array(objectCentroids), inputCentroids)
rows = D.min(axis=1).argsort()
cols = D.argmin(axis=1)[rows]
usedRows = set()
usedCols = set()
for (row, col) in zip(rows, cols):
if row in usedRows or col in usedCols:
continue
objectID = objectIDs[row]
self.objects[objectID] = inputCentroids[col]
self.disappeared[objectID] = 0
usedRows.add(row)
usedCols.add(col)
# compute both the row and column index we have NOT yet
# examined
unusedRows = set(range(0, D.shape[0])).difference(usedRows)
unusedCols = set(range(0, D.shape[1])).difference(usedCols)
if D.shape[0] >= D.shape[1]:
# loop over the unused row indexes
for row in unusedRows:
# grab the object ID for the corresponding row
# index and increment the disappeared counter
objectID = objectIDs[row]
self.disappeared[objectID] += 1
# check to see if the number of consecutive
# frames the object has been marked "disappeared"
# for warrants deregistering the object
if self.disappeared[objectID] > self.maxDisappeared:
self.deregister(objectID)
else:
for col in unusedCols:
self.register(inputCentroids[col])
# return the set of trackable objects
return self.objects
</code></pre>
<p>我有点迷失在我做错了什么。我要做的就是把一个边界框(x,y,x+w,y+h)传入rects[]列表中,这应该会给出类似的结果,或者我是错的,不明白这是怎么回事?任何帮助都将不胜感激</p>