From 7919878582d61d0268e2abd7b2f60a4d8f2a7f9e Mon Sep 17 00:00:00 2001
From: Vladyslav Usenko <vlad.usenko@tum.de>
Date: Thu, 11 Jul 2019 19:13:03 +0200
Subject: [PATCH] fix bug

---
 scripts/check_gamma_correction.py | 16 +++++++++-------
 1 file changed, 9 insertions(+), 7 deletions(-)

diff --git a/scripts/check_gamma_correction.py b/scripts/check_gamma_correction.py
index 192dff1..bb3ce65 100755
--- a/scripts/check_gamma_correction.py
+++ b/scripts/check_gamma_correction.py
@@ -13,7 +13,7 @@ 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).astype(np.float64) * 1e-9
+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")
@@ -34,21 +34,23 @@ print(imgs.dtype)
 inv_resp = np.arange(256, dtype=np.float64)
 inv_resp[250:] = -1.0 # Use negative numbers to detect oversaturation 
 
-irradiance = imgs[0] / exposures[0]
 
 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
 
-    irr = np.sum(corrected_imgs, axis=0) / np.sum(times, axis=0)
+    times[corrected_imgs < 0] = 0
+    corrected_imgs[corrected_imgs < 0] = 0
+
+    denom = np.sum(times, axis=0)
+    irr = np.sum(corrected_imgs, axis=0) / denom
+    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)
+    num_pixels_by_intensity = np.bincount(imgs.flat, generated_imgs.flat >= 0)
     sum_by_intensity = np.bincount(imgs.flat, generated_imgs.flat)
     
     new_inv_resp = inv_resp
@@ -64,7 +66,6 @@ def print_error():
     generated_imgs[imgs == 255] = 0
     print(np.sum(generated_imgs**2))
 
-print_error()
 for iter in range(3):
     irradiance = opt_irradiance()
     print_error()
@@ -72,6 +73,7 @@ for iter in range(3):
     print_error()
 
 
+
 plt.figure()
 plt.plot(inv_resp)
 plt.ylabel('Img Mean')