I am trying to perform image segmentation using scikit mean shift algorithm. I use opencv to display the segmented image.
My problem is the following: I use the code as given in different examples, and when I display the image after segmentation, I get a black image. I was wondering if someone could see what my mistake is...
Thanks a lot for the help !

Here is my code:

```
import numpy as np    
import cv2    
from sklearn.cluster import MeanShift, estimate_bandwidth

#Loading original image
originImg = cv2.imread('Swimming_Pool.jpg')

# Shape of original image    
originShape = originImg.shape

# Converting image into array of dimension [nb of pixels in originImage, 3]
# based on r g b intensities    
flatImg=np.reshape(originImg, [-1, 3])

# Estimate bandwidth for meanshift algorithm    
bandwidth = estimate_bandwidth(flatImg, quantile=0.1, n_samples=100)    
ms = MeanShift(bandwidth = bandwidth, bin_seeding=True)

# Performing meanshift on flatImg    
ms.fit(flatImg)

# (r,g,b) vectors corresponding to the different clusters after meanshift    
labels=ms.labels_

# Remaining colors after meanshift    
cluster_centers = ms.cluster_centers_    

# Finding and diplaying the number of clusters    
labels_unique = np.unique(labels)    
n_clusters_ = len(labels_unique)    
print("number of estimated clusters : %d" % n_clusters_)    

# Displaying segmented image    
segmentedImg = np.reshape(labels, originShape[:2])    
cv2.imshow('Image',segmentedImg)    
cv2.waitKey(0)    
cv2.destroyAllWindows()

```