From fd150bd231854160b46cec8f91495bdbb9c43e5b Mon Sep 17 00:00:00 2001 From: Vladyslav Usenko Date: Fri, 12 Jul 2019 10:31:38 +0000 Subject: [PATCH] Gamma correction --- scripts/check_gamma_correction.py | 80 +++++++++++++++++++++++++++---- 1 file changed, 72 insertions(+), 8 deletions(-) diff --git a/scripts/check_gamma_correction.py b/scripts/check_gamma_correction.py index 161f4bb..b1abc8f 100755 --- a/scripts/check_gamma_correction.py +++ b/scripts/check_gamma_correction.py @@ -3,9 +3,8 @@ import sys import math import os -import webp +import cv2 -import pandas as pd import numpy as np from matplotlib import pyplot as plt @@ -14,20 +13,85 @@ dataset_path = sys.argv[1] print(dataset_path) timestamps = np.loadtxt(dataset_path + '/mav0/cam0/data.csv', usecols=[0], delimiter=',', dtype=np.int64) -exposures = np.loadtxt(dataset_path + '/mav0/cam0/exposure.csv', usecols=[1], delimiter=',', dtype=np.int64) +exposures = np.loadtxt(dataset_path + '/mav0/cam0/exposure.csv', usecols=[1], delimiter=',', dtype=np.int64).astype(np.float64) * 1e-6 pixel_avgs = list() +if timestamps.shape[0] != exposures.shape[0]: print("timestamps and exposures do not match") + +imgs = [] # check image data. -img_extensions = ['.png', '.jpg', '.webp'] for timestamp in timestamps: path = dataset_path + '/mav0/cam0/data/' + str(timestamp) - img = webp.imread(dataset_path + '/mav0/cam0/data/' + str(timestamp) + '.webp') + img = cv2.imread(dataset_path + '/mav0/cam0/data/' + str(timestamp) + '.webp', cv2.IMREAD_GRAYSCALE)[:,:,0] + imgs.append(img) pixel_avgs.append(np.mean(img)) -plt.plot(exposures, pixel_avgs) -plt.ylabel('Img Mean') -plt.xlabel('Exposure') +imgs = np.array(imgs) +print(imgs.shape) +print(imgs.dtype) + + + +num_pixels_by_intensity = np.bincount(imgs.flat) +print('num_pixels_by_intensity', num_pixels_by_intensity) + +inv_resp = np.arange(num_pixels_by_intensity.shape[0], dtype=np.float64) +inv_resp[-1] = -1.0 # Use negative numbers to detect saturation + + +def opt_irradiance(): + corrected_imgs = inv_resp[imgs] * exposures[:, np.newaxis, np.newaxis] + times = np.ones_like(corrected_imgs) * (exposures**2)[:, np.newaxis, np.newaxis] + + times[corrected_imgs < 0] = 0 + corrected_imgs[corrected_imgs < 0] = 0 + + denom = np.sum(times, axis=0) + idx = (denom != 0) + irr = np.sum(corrected_imgs, axis=0) + irr[idx] /= denom[idx] + irr[denom == 0] = -1.0 + return irr + +def opt_inv_resp(): + generated_imgs = irradiance[np.newaxis, :, :] * exposures[:, np.newaxis, np.newaxis] + + num_pixels_by_intensity = np.bincount(imgs.flat, generated_imgs.flat >= 0) + + generated_imgs[generated_imgs < 0] = 0 + sum_by_intensity = np.bincount(imgs.flat, generated_imgs.flat) + + new_inv_resp = inv_resp + + idx = np.nonzero(num_pixels_by_intensity > 0) + new_inv_resp[idx] = sum_by_intensity[idx] / num_pixels_by_intensity[idx] + new_inv_resp[-1] = -1.0 # Use negative numbers to detect saturation + return new_inv_resp + +def print_error(): + generated_imgs = irradiance[np.newaxis, :, :] * exposures[:, np.newaxis, np.newaxis] + generated_imgs -= inv_resp[imgs] + generated_imgs[imgs == 255] = 0 + print(np.sum(generated_imgs**2)) + +for iter in range(5): + irradiance = opt_irradiance() + print_error() + inv_resp = opt_inv_resp() + print_error() + + + +plt.figure() +plt.plot(inv_resp[:-1]) +plt.ylabel('Irradiance Value') +plt.xlabel('Image Intensity') +plt.title('Inverse Responce Function') + +plt.figure() +plt.imshow(irradiance) +plt.title('Irradiance Image') plt.show()