histogram_analyser.py

import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import ImageGrid
import numpy as np
import PIL
import cv2
import glob
import os

def apply_plot_style(xlim, ylim, xlabel, ylabel, grid):
    plt.clf()
    plt.xlim(xlim)
    plt.ylim(ylim)
    plt.xlabel(xlabel)
    plt.ylabel(ylabel)
    plt.grid(grid)

def save_histogram(hist, xlim, ylim, fname_out, title, xlabel, ylabel, grid=True, color='k'):
    apply_plot_style(xlim, ylim, xlabel, ylabel, grid)
    plt.title(title)
    hist_size = len(np.array(hist).shape)
    if hist_size == 2:
        colors = {'b': 'r', 'g': 'g', 'r': 'b'}
        plt.plot(hist, color=colors[color[0]] if color in ['blue', 'green', 'red'] else 'k')
    elif hist_size == 3:
        for c, h in zip(list(color), hist):
            plt.plot(h, color=c)
    else:
        raise 'ERROR: hist shape must be equals to 2 or 3'
    plt.savefig(fname_out, dpi=300)

def thumb_grid(im_list, grid_shape, fname_out, scale=1.0, axes_pad=0.07, figsize=(7, 7), dpi=900):
    assert len(grid_shape) == 2 # Grid must be 2D:
    assert np.prod(grid_shape) >= len(im_list) # Make sure all images can fit in grid:
    plt.figure(figsize=figsize, dpi=dpi)

    grid = ImageGrid(plt.gcf(), 111, grid_shape, axes_pad=axes_pad)
    shape_index = np.argmin([np.prod(np.array(im.shape)) for im in im_list])
    thumb_shape = np.array(np.array(im_list[shape_index]).shape) * scale
    for i, im in enumerate(im_list):
        data_orig = im.copy()

        # Scale image:
        im = PIL.Image.fromarray(data_orig)
        im.thumbnail(thumb_shape, PIL.Image.ANTIALIAS)
        data_thumb = np.array(im)
        grid[i].imshow(data_thumb)

        # Turn off axes:
        grid[i].axes.get_xaxis().set_visible(False)
        grid[i].axes.get_yaxis().set_visible(False)

    plt.gca().set_axis_off()
    plt.subplots_adjust(top = 1, bottom = 0, right = 1, left = 0, hspace = 0, wspace = 0)
    plt.margins(0,0)
    plt.gca().xaxis.set_major_locator(plt.NullLocator())
    plt.gca().yaxis.set_major_locator(plt.NullLocator())
    plt.savefig(fname_out, bbox_inches = 'tight', pad_inches = 0.06)

def get_histogram(fname_in, color, normalized=True):
    image = cv2.imread(fname_in)
    channel_index = ['red', 'green', 'blue', 'gray', 'bgr'].index(color)
    if channel_index < 3: # color
        channel = cv2.split(image)[channel_index]
        hist = cv2.calcHist(channel, [channel_index], None, [256], [0, 256])
    elif channel_index == 3: # gray
        image_gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
        hist = cv2.calcHist(image_gray, [0], None, [256], [0, 256])
    elif channel_index == 4: # bgr
        hist = [] 
        channels = cv2.split(image)
        for i, ch in enumerate(channels):
            h = cv2.calcHist(ch, [i], None, [256], [0, 256])
            hist.append(h)
    else:
        raise 'ERROR: color values must be one of: red, green, blue, gray or bgr'

    if normalized:
        if channel_index == 4:
            for i in range(len(hist)):
                hist[i] = hist[i] / np.linalg.norm(hist[i])
        else:
            hist = hist / np.linalg.norm(hist)
    return hist
        
if __name__ == '__main__':
    base_path = 'histograms'
    try:
        os.system(f'cd {base_path}/; mv 0* ../; rm *; mv ../0* .')
    except:
        os.system(f'cd {base_path}/; mv 0* ../; rm *; mv ../0* .')

    filename = f'{base_path}/original_image.jpeg'
    for i, color in enumerate(['bgr', 'gray', 'blue', 'green', 'red']):
        hist = get_histogram(filename, color)
        save_histogram(
            hist, 
            xlim=[0, 256],
            ylim=[0, 1], 
            fname_out=f'{base_path}/{i + 1}_{color}.jpeg', 
            title=f'Histogram - Channel {color.capitalize()}',
            xlabel='Pixel Value', 
            ylabel='Density',
            color=color
        )

    filenames = glob.glob(f'{base_path}/*.jpeg')
    filenames = sorted(filenames)
    images = [cv2.imread(f'{f}') for f in filenames for d in f if d.isdigit()]
    thumb_grid(images, (2, 3), f'{base_path}/histograms.jpeg')
    
# files
# histogram_analyser.py
# histograms/original_image.jpeg