Python 在cv2.2显著性上应用k-均值时的问题
我正在做一个探测人的项目。所以我在opencv中使用显著性,并对显著性的输出应用k均值聚类 问题是应用k-means聚类后的输出是完全黑色的 代码如下:Python 在cv2.2显著性上应用k-均值时的问题,python,opencv,k-means,Python,Opencv,K Means,我正在做一个探测人的项目。所以我在opencv中使用显著性,并对显著性的输出应用k均值聚类 问题是应用k-means聚类后的输出是完全黑色的 代码如下: import cv2 import time import numpy as np cap=cv2.VideoCapture("video.avi") while(cap.isOpened()): #time.sleep(0.05) _,frame=cap.read() image=frame
import cv2
import time
import numpy as np
cap=cv2.VideoCapture("video.avi")
while(cap.isOpened()):
#time.sleep(0.05)
_,frame=cap.read()
image=frame
saliency = cv2.saliency.StaticSaliencySpectralResidual_create()
(success, saliencyMap) = saliency.computeSaliency(image)
saliencyMap = (saliencyMap * 255).astype("uint8")
#cv2.imshow("Image", image)
#cv2.imshow("Output", saliencyMap)
saliency = cv2.saliency.StaticSaliencyFineGrained_create()
(success, saliencyMap) = saliency.computeSaliency(image)
threshMap = cv2.threshold(saliencyMap.astype("uint8"), 0, 255,cv2.THRESH_BINARY | cv2.THRESH_OTSU)[1]
# show the images
#cv2.imshow("Image", image)
cv2.imshow("saliency", saliencyMap)
#cv2.imshow("Thresh", threshMap)
##############implementing k-means clustering#######################
kouts=saliencyMap
clusters=7
z=kouts.reshape((-1,3))
z=np.float32(z)
criteria= (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER,10,1.0)
ret,label,center=cv2.kmeans(z,clusters,None,criteria,10,cv2.KMEANS_RANDOM_CENTERS)
center=np.uint8(center)
res=center[label.flatten()]
kouts=res.reshape((kouts.shape))
cv2.imshow('clustered image',kouts)
k = cv2.waitKey(1) & 0xff
if k == ord('q'):
break
cap.release()
cv2.destroyAllWindows()
这是我测试算法的视频的第一部分。
有人能指出任何错误或更正吗
提前感谢。关键是将格式转换为
uint8
,并在创建地图后将强度缩放255。您对第一种类型的显著性贴图执行了此操作,但对第二种类型的显著性贴图未执行此操作:
saliency = cv2.saliency.StaticSaliencyFineGrained_create()
(success, saliencyMap) = saliency.computeSaliency(image)
### ADDED
saliencyMap = (saliencyMap * 255).astype("uint8")