v01

scripts/basalt_response_calib.py

@@ -0,0 +1,113 @@
+#!/usr/bin/env python3
+#
+# BSD 3-Clause License
+#
+# This file is part of the Basalt project.
+# https://gitlab.com/VladyslavUsenko/basalt.git
+#
+# Copyright (c) 2019-2021, Vladyslav Usenko and Nikolaus Demmel.
+# All rights reserved.
+#
+
+import sys
+import math
+import os
+import cv2
+import argparse
+
+import numpy as np
+from matplotlib import pyplot as plt
+
+parser = argparse.ArgumentParser(description='Response calibration.')
+parser.add_argument('-d', '--dataset-path', required=True, help="Path to the dataset in Euroc format")
+args = parser.parse_args()
+
+
+dataset_path = args.dataset_path
+
+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).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.
+for timestamp in timestamps:
+    path = dataset_path + '/mav0/cam0/data/' + str(timestamp)
+    img = cv2.imread(dataset_path + '/mav0/cam0/data/' + str(timestamp) + '.webp', cv2.IMREAD_GRAYSCALE)
+    if len(img.shape) == 3: img = img[:,:,0]
+    imgs.append(img)
+    pixel_avgs.append(np.mean(img))
+
+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('Error', np.sum(generated_imgs**2))
+
+for iter in range(5):
+    print('Iteration', iter)
+    irradiance = opt_irradiance()
+    print_error()
+    inv_resp = opt_inv_resp()
+    print_error()
+
+
+
+fig, (ax1, ax2) = plt.subplots(1, 2)
+ax1.plot(inv_resp[:-1])
+ax1.set(xlabel='Image Intensity', ylabel='Irradiance Value')
+ax1.set_title('Inverse Response Function')
+
+
+ax2.imshow(irradiance)
+ax2.set_title('Irradiance Image')
+plt.show()
+
+