Classes¶
Three classes encapsulate the code logic:
- Imager: describes an imager and all the parameters related to it. Contains the methods dealing with lens distortions and mis-alignment correction.
- Segmentation: contains all the methods needed for the segmentation a sky/cloud image.
- Reconstruction: contains all the methods for creating a 3D reconstruction from the base height from two sky pictures taken at the same time.
Sample usage¶
The file main.py shows a sample usage for the code to generate the reconstruction of cloud base height. Here is a walkthrough the file:
First import the dependencies:
from imager import Imager
from reconstruction import Reconstruction
import numpy as np
import cv2
import datetime
Then create the first imager object. For the calibration parameters, please refer to the following publication: Geo-referencing and stereo calibration of ground-based whole sky imagers using the sun trajectory, F. M. Savoy, S. Dev, Y. H. Lee, S. Winkler, Proc. IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing, China, July 10-15, 2016.
name3 = 'wahrsis3'
center3 = [1724, 2592]
radius3 = 1470
relativePosition3 = np.array([0,0,0])
calibRot3 = np.array([[0.99555536, 0.09404159, 0.00506982], [-0.09393761, 0.99541774, -0.01786745], [-0.00672686, 0.01731178, 0.99982751]])
calibTrans3 = np.array([[ 0.00552915], [0.00141732], [0.00553584]])
longitude3 = '103:40:49.9'
lattitude3 = '1:20:35'
altitude3 = 59
wahrsis3 = Imager(name3, center3, radius3, relativePosition3, calibRot3, calibTrans3, longitude3, lattitude3, altitude3)
The second imager does not need the longitude, lattitude and altitude information.
name4 = 'wahrsis4'
center4 = [2000, 2975]
radius4 = 1665
relativePosition4 = np.array([-2.334, 101.3731, -8.04])
calibRot4 = np.array([[0.9710936, -0.23401871, 0.04703662], [0.234924, 0.97190314, -0.01466276], [-0.04228367, 0.02528894, 0.99878553]])
calibTrans4 = np.array([[-0.00274625], [-0.00316865], [0.00516088]])
wahrsis4 = Imager(name4, center4, radius4, relativePosition4, calibRot4, calibTrans4)
Then load the LDR images in two arrays:
images3 = [cv2.imread('wahrsis3/2015-10-29-12-58-01-wahrsis3-low.jpg'),
cv2.imread('wahrsis3/2015-10-29-12-58-01-wahrsis3-med.jpg'),
cv2.imread('wahrsis3/2015-10-29-12-58-01-wahrsis3-high.jpg')]
images4 = [cv2.imread('wahrsis4/2015-10-29-12-58-01-wahrsis4-low.jpg'),
cv2.imread('wahrsis4/2015-10-29-12-58-01-wahrsis4-med.jpg'),
cv2.imread('wahrsis4/2015-10-29-12-58-01-wahrsis4-high.jpg')]
Create the Reconstruction object, load the images, process and write the result to out.png.
reconst = Reconstruction(wahrsis3, wahrsis4, datetime.datetime(2015,10,29,12,58,1))
reconst.load_images(images3, images4)
reconst.process()
reconst.writeFigure("out.png")
Dependencies¶
This project was built with python 2.7.12
Furthermore, the following packages were used:
| Package | Version | Link |
|---|---|---|
| openCV | 3.1.0 | http://opencv.org/ |
| numpy | 1.12.0 | http://www.numpy.org/ |
| scipy | 0.18.1 | https://www.scipy.org/ |
| scikit-image | 0.12.3 | http://scikit-image.org/ |
| scikit-learn | 0.18.1 | http://scikit-learn.org/ |
| matplotlib | 2.0.0 | http://matplotlib.org/ |
| PyEphem | 3.7.6.0 | http://rhodesmill.org/pyephem/ |
| pycuda (optional) | 2016.1.2 | https://documen.tician.de/pycuda/ |