This function detects dominant color from an image

get_dominant_color.py

### Neda Jabbari
### Jan 8, 2020

### The get_dominant_color function detects dominant color from an image using a dictionary of basic colors, 
### k-means clustering and eucledian distance. 

def get_dominant_color(image, k, image_processing_size):
    """
    Read and prepare image in RGB color space.
    Perform kmeans clustering to get color clusters. 
    Detect dominant color based on the euclidean distance between the clusters and the basic colors. 
    Return the basic color associated with the min euclidean distance.
    """
    #Define basic colors in RGB colorspace
    basiccolors = {'red':[255,0,0],
                'blue':[0,0,255],
                'yellow':[255,255,0],
                'purple':[128,0,128],
                'dark orange':[255,140,0]}
    mincolors =[]
    #read image in BGR space
    image = cv2.imread(image)
    #convert color space from BGR to RGB
    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
    #resize the image to reduce computation time
    image = cv2.resize(image, image_processing_size, interpolation = cv2.INTER_AREA)
    #reshape array to perform clustering
    image = image.reshape((image.shape[0] * image.shape[1], 3)) 
    #make an instant from the KMeans class
    kmeans = KMeans(n_clusters = k)
    #fit kmeans 
    kmeans.fit(image)
    #Get color cluster centers 
    rgbs = kmeans.cluster_centers_.astype(int)
    #Calculate the eucledian distance 
    for rgb in rgbs:
        cluster_dist= {}
        for c,r in basiccolors.items():
            dist = distance.euclidean(rgb, r)
            cluster_dist[c] = dist                 
        for k,v in cluster_dist.items():
            if v == min(list(cluster_dist.values())):
                mincolors.append((k, v))
    #get the pair with minimum distance
    color = min(mincolors, key=lambda x:x[1])[0]
    return color