utils.py

"""
Version: 1.0
Summary: color clustering alogrithm, designed for analysis color distributation in plant image
Author: suxing liu
Author-email: suxingliu@gmail.com

USAGE

import utils
# using as a library

"""
#!/usr/bin/python


# import the necessary packages
import numpy as np
import cv2
from scipy import ndimage
import pylab as P
from matplotlib import pyplot as plt
import matplotlib.colors as colors
from matplotlib.ticker import FormatStrFormatter


# Function of rgb to hex color space
def rgb_to_hex(rgb_tuple):
    return colors.rgb2hex([1.0*x/255 for x in rgb_tuple])

# Function for generating the histogram using centered labels
def centroid_histogram(clt):
    # grab the number of different clusters and create a histogram 
    # based on the number of pixels assigned to each cluster
    numLabels = np.arange(0, len(np.unique(clt.labels_)) + 1)
    (hist, _) = np.histogram(clt.labels_, bins = numLabels)

    # normalize the histogram, such that it sums to one
    hist = hist.astype("float")
    hist /= hist.sum()

    # return the histogram
    return hist
    
# Function for plotting the histogram using centered labels    
def plot_centroid_histogram(path, clt):
    # grab the number of different clusters and create a histogram
    # based on the number of pixels assigned to each cluster
    numLabels = np.arange(0, len(np.unique(clt.labels_)) + 1)
    #(hist, _) = np.histogram(clt.labels_, bins = numLabels)

    #prepare a new figure
    fig, ax = plt.subplots()
    
    #setting the titles and lables, style
    plt.title("Dominant Color Distribution Histogram")
    plt.xlabel("Color Value")
    plt.ylabel("Percentage")
    counts, bins, patches = ax.hist(clt.labels_, numLabels, normed=1, histtype='bar', stacked=True, color='gray',alpha=0.8,edgecolor='gray')

    # Set the ticks to be at the edges of the bins.
    ax.set_xticks(bins)
    # Set the xaxis's tick labels to be formatted with 1 decimal place...
    ax.xaxis.set_major_formatter(FormatStrFormatter('%0.1f'))
    
    #centroids = sorted(clt.cluster_centers_, reverse = True)
    
    centroids = clt.cluster_centers_

    # Setting each cluster's value
    for bin_size, cbin, patch, color in zip(counts, bins, patches, centroids):
        bin_color = color.astype("uint8").tolist()
        bin_color_val = rgb_to_hex(bin_color)
        patch.set_facecolor(bin_color_val)
        patch.set_label(bin_color)
        #print (bin_color_val)
        #print len(centroids)
        #print len(counts)
    
    # add legend to a plot
    plt.legend()

        
    # Label the raw counts and the percentages below the x-axis...
    bin_centers = 0.5 * np.diff(bins) + bins[:-1]
    for count, x in zip(counts, bin_centers):
        
        # Label the percentages
        percent = '%0.000f%%' % (100 * float(count) / counts.sum())
        ax.annotate(percent, xy=(x, 0), xycoords=('data', 'axes fraction'),xytext=(0, -32), textcoords='offset points', va='top', ha='center')
        
    # Give more room at the bottom of the plot
    plt.subplots_adjust(bottom=0.15)
    
    #set the clutser distributation figure name
    #define result path for labeled images
    fig_name = path + 'color_distribution.png'
    #fig_name = 'Color_Distribution.png'
    
    #save plot as an image
    plt.savefig(fig_name)
    
    plt.close(fig)
    
        
    '''
    # Save the cluster results into text file
    file_name = 'color_data.txt'
    
    # save each clustre result as one line with space 
    counts_sorted = sorted(counts, reverse = True)
    with open(file_name, 'a') as f:
        #for idx in min(range(len(centroids)),range(len(counts))):
        for idx in range(len(counts_sorted)):
            #bin_color = color.astype("uint8").tolist()
            #bin_color_val = rgb_to_hex(bin_color)
            bin_color_val = 'Level'
            percent = '%0.000f' % (100 * float(counts_sorted[idx]) / counts.sum())
            #f.write('%s\n' % ((bin_color_val) + '   ' + str(percent)))
            f.write('%s\t' % (str(percent)))
        f.write('\n')
    '''
    
    '''
    fig = plt.figure(5)
    #clustered = np.hstack([image, quant])
    clustered = quant
    plt.imshow(clustered)
    #plt.show()

    
    #save clustered image
    filename = args["image"]
    fig_name = (str(filename[0:-4]) + '_' +'cluster_out.png')
    fig_path_save = save_path + fig_name
    mpimg.imsave(fig_path_save, clustered)
    '''
    #plt.show()
            
    return numLabels
    
# Function for plotting the color distributation as a bar figure 
def plot_colors(hist, centroids):
    # initialize the bar chart representing the relative frequency
    # of each of the colors
    bar = np.zeros((10, 100, 3), dtype = "uint8")
    startX = 0

    
    # loop over the percentage of each cluster and the color of
    # each cluster
    for (percent, color) in zip(hist, centroids):
        # plot the relative percentage of each cluster
        endX = startX + (percent * 100)
        cv2.rectangle(bar, (int(startX), 0), (int(endX), 10), color.astype("uint8").tolist(), -1)
        startX = endX
    
    # return the bar chart
    return bar

# Function for plotting the histogram using centered labels    
def plot_labeled_histogram(pixels,bins_num):
    # grab the number of different clusters and create a histogram
    # based on the number of pixels assigned to each cluster
    numLabels = bins_num
    
    #prepare a new figure
    fig, ax = plt.subplots()
    plt.title("Color Distributation Histogram")
    plt.xlabel("Color Value")
    plt.ylabel("Frequency")
    counts, bins, patches = ax.hist(pixels, numLabels, normed=1, histtype='bar', stacked=True, color='gray',alpha=0.8,edgecolor='gray')

    # Set the ticks to be at the edges of the bins.
    ax.set_xticks(bins)
    # Set the xaxis's tick labels to be formatted with 1 decimal place...
    ax.xaxis.set_major_formatter(FormatStrFormatter('%0.1f'))
    
    
    color_lable = clt.cluster_centers_

    # Setting each cluster's value
    for bin_size, cbin, patch, color in zip(counts, bins, patches, color_lable):
        bin_color = color.astype("uint8").tolist()
        bin_color_val = rgb_to_hex(bin_color)
        patch.set_facecolor(bin_color_val)
        patch.set_label(bin_color)
        #print bin_color_val
    
    # add legend to a plot
    plt.legend()
        
    # Label the raw counts and the percentages below the x-axis...
    bin_centers = 0.5 * np.diff(bins) + bins[:-1]
    for count, x in zip(counts, bin_centers):
        # Label the raw counts
        #ax.annotate(str(count), xy=(x, 0), xycoords=('data', 'axes fraction'), xytext=(0, -18), textcoords='offset points', va='top', ha='center')
        
        # Label the percentages
        percent = '%0.000f%%' % (100 * float(count) / counts.sum())
        ax.annotate(percent, xy=(x, 0), xycoords=('data', 'axes fraction'),xytext=(0, -32), textcoords='offset points', va='top', ha='center')
        
    # Give ourselves some more room at the bottom of the plot
    plt.subplots_adjust(bottom=0.15)
    
    # save plot as an image
    #plt.savefig("Color_Distribution.png")

    #plt.show()
            
    return numLabels
    

def plot_color_bar(path, bar):
    
    #show color bart and save bar figure
    fig = plt.figure(0)
    plt.title("Color Distributation Histogram")
    plt.imshow(bar)
    plt.xlabel("Percentage")
    plt.ylabel("Color category")
    frame = plt.gca()
    #frame.axes.get_xaxis().set_visible(False)
    frame.axes.get_yaxis().set_visible(False)

    #save bar image
    complete_path = path + 'color_bar.png'       
    plt.savefig(complete_path)
    plt.close(fig)