kmeans image color with green mask

KMeansMaskGreen.py

def KMeansMaskGreen(img, clusters, lowHue, highHue, lowSat, highSat, loBright, hiBright):
    """
    Args:
        path2img   : (str) path to cropped player bounding box
        clusters   : (int) how many clusters to use for KMEANS
    Returns:
        rgb_array :  (tuple) Dominant colors in image in RGB format
    """
    org_img = img.copy()
    #print('Org image shape --> ',img.shape)
    green   = np.array([60,25,25])
    loGreen = np.array([lowHue, lowSat, loBright]) #low green threshold
    hiGreen = np.array([highHue, highSat, hiBright]) #Upper green threshold
    #Convert image to HSV
    hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
    #Make the mask
    mask = cv2.inRange(hsv, loGreen, hiGreen)
    mask_img = img.copy()
    mask_img[mask==255] = (255,255,255)
    #Remove white pixels from image so that they don't interfere with the process 
    mask_img = mask_img[np.all(mask_img !=  255 , axis=-1)] 
    
    #Convert image into a 1D array
    flat_img = np.reshape(mask_img,(-1,3))
    arrayLen = flat_img.shape
    #Ensure that masking didn't remove everything (Generally happens in false positives)  
    if mask_img.shape[0] <= clusters:
        #print('Cropped image has dimensions lower than number of desired clusters.Not clustering current image')
        rgb_array = np.empty((clusters,3,))
        rgb_array[:] = np.nan
        return rgb_array
    else:
        rgb_array = []
        
    #Do the clustering
    kmeans = KMeans(n_clusters = clusters, random_state=0, tol = 1e-4)
    kmeans.fit(flat_img)
    #Define the array with centroids
    dominant_colors = np.array(kmeans.cluster_centers_,dtype='uint')
    #Calculate percentages 
    percentages = (np.unique(kmeans.labels_,return_counts=True)[1])/flat_img.shape[0]
        
    #Combine centroids representing dominant colors and percentages 
    #associated with each centroid into an array
    pc = list(zip(percentages,dominant_colors))
    pc = sorted(reversed(pc), reverse = True, key = lambda x: x[0])
    
    i = 0
    for i in range(clusters):
        #dummy_array = pc[i][1]
        rgb_array.append(pc[i][1])
        i += 1
            
    return rgb_array