OpenCV and matplotlib driven script to display histogram for variety of file formats including raw like DNG, high dynamic range like EXR etc.

histogram.py

import cv2
import numpy as np
import matplotlib.pyplot as plt
import sys
import os
import argparse

os.environ["OPENCV_IO_ENABLE_OPENEXR"] = "true"

def generate_histogram(image_path, num_bins, y_scale, percentile, x_min, x_max):
    # Load the image
    image = cv2.imread(image_path, cv2.IMREAD_ANYCOLOR | cv2.IMREAD_ANYDEPTH)
    
    if image is None:
        print("Error: Unable to load the image.")
        return
    
    # Flatten the image into a 1D array
    pixels = image.flatten()
    print("%d values" % len(pixels))
    min_value = np.nanmin(pixels)
    max_value = np.nanmax(pixels)
    print("%f - %f range of values" % (min_value, max_value))
    
    # If x_min and x_max are not provided, use the actual min and max values
    if x_min is None:
        x_min = min_value
    if x_max is None:
        x_max = max_value
    
    # Ensure x_min and x_max are within the valid range
    x_min = max(x_min, min_value)
    x_max = min(x_max, max_value)
    
    print("X-axis range: %f to %f" % (x_min, x_max))
    
    # BGR channels
    blue_pixels = pixels[0::3]
    green_pixels = pixels[1::3]
    red_pixels = pixels[2::3]

    # Create the histogram using NumPy
    red_hist, bins = np.histogram(red_pixels, bins=num_bins, range=(x_min, x_max))
    green_hist, bins = np.histogram(green_pixels, bins=num_bins, range=(x_min, x_max))
    blue_hist, bins = np.histogram(blue_pixels, bins=num_bins, range=(x_min, x_max))    

    # Plot the histogram using Matplotlib
    plt.figure(figsize=(14, 9))
    plt.bar(range(num_bins), red_hist, width=1.0, align='edge', color='red', alpha=0.5)
    plt.bar(range(num_bins), green_hist, width=1.0, align='edge', color='green', alpha=0.5)
    plt.bar(range(num_bins), blue_hist, width=1.0, align='edge', color='blue', alpha=0.5)
    plt.title('Color distribution histogram')
    plt.xlabel('Pixel Value')
    plt.yscale(y_scale)

    if percentile is not None:
        y_limit = int(max(np.percentile(red_hist, percentile), np.percentile(blue_hist, percentile), np.percentile(green_hist, percentile)))
        plt.ylim(0, y_limit)
    
    bin_width = (x_max - x_min) / num_bins
    plt.ylabel('Num of pixels per %f wide bin' % bin_width)

    step = int(num_bins / 25)
    x_ticks = np.arange(0, num_bins, step=step)
    x_ticks = np.append(x_ticks, num_bins)
    x_tick_labels = [x_min + i * bin_width for i in x_ticks]
    plt.xticks(x_ticks, x_tick_labels, rotation=90)

    # Add text annotations for parameters
    info_text = f'# Bins: {num_bins}\nScale: {y_scale}\nX-range: {x_min} to {x_max}\n'
    if percentile is not None:
        info_text += f'Percentile: {percentile}%\n'
        
    plt.text(0.95, 0.95, info_text,
             verticalalignment='top', horizontalalignment='right',
             transform=plt.gca().transAxes, bbox=dict(facecolor='white', alpha=0.8))

    # Extract the directory path from the input image path
    input_dir = os.path.dirname(image_path)
    
    # Generate the output image path in the same directory as the input TIFF file
    image_name, image_extension = os.path.splitext(os.path.basename(image_path))
    output_image_path = os.path.join(input_dir, f"histogram-{image_name}_{image_extension[1:]}-{y_scale}-bins-{num_bins}-xrange-{x_min}-{x_max}-percentile-{percentile}.png")
    print("saving histogram into file: %s" % output_image_path)
    
    # Save the plot as an image
    plt.savefig(output_image_path, bbox_inches='tight')

if __name__ == "__main__":
    parser = argparse.ArgumentParser(description='Generate a histogram from an image.')

    # Positional argument for image_path
    parser.add_argument('image_path', help='Path to the image file')

    # Optional named arguments
    parser.add_argument('--bins', type=int, default=255, help='Number of bins in the histogram')
    parser.add_argument('--scale', choices=['linear', 'log'], default='linear', help='Y-axis scale for the histogram')
    parser.add_argument('--percentile', type=float, default=None, help='What percent of the data to display (useful, when some buckets go way out)')

    # New arguments for x-axis range
    parser.add_argument('--x_min', type=float, default=None, help='Minimum value of the x-axis range')
    parser.add_argument('--x_max', type=float, default=None, help='Maximum value of the x-axis range')

    args = parser.parse_args()

    # Call your function with the parsed arguments
    generate_histogram(args.image_path, args.bins, args.scale, args.percentile, args.x_min, args.x_max)