imgs = get_int_imgs((img1, img2))
return {'sum': np.sum(imgs['sum']), 'pvar': np.sum(imgs['pvar'])}
def get_spatial(cam, radiance, L=50):
imgs = []
for i in range(L):
imgs.append(cam.grab(radiance))
return get_int_imgs(imgs)
data = {'temporal': {}, 'spatial': {}, 'width': None, 'height': None}
# Intialize the camera, here we can specify different image size
# or any other parameter that Camera allows
c = Camera(bit_depth=10, width=100, height=50)
# Fill the information
data['width'] = c.width
data['height'] = c.height
# Maximum exposure for test
exposure_max = 9000000
# Find the camera parameters for the test
c.exposure = exposure_max
c.blackoffset = get_emva_blackoffset(c)
c.K = get_emva_gain(c)
# Find the radiance that will saturate the camera at our maximum exposure time
saturation_radiance = c.get_radiance_for()
import numpy as np
from emva1288.camera import Camera
c = Camera()
radiances = np.linspace(0, c.get_radiance_for(mean=200), num=20)
for radiance in radiances:
img = c.grab(radiance)
print(img.mean(), img.std())
import numpy as np
from emva1288.camera import Camera
c = Camera()
radiances = np.linspace(0, c.get_radiance_for(mean=200), num=20)
for radiance in radiances:
c.set_radiance(radiance)
img = c.grab()
print(img.mean(), img.std())
def get_spatial(cam, radiance, L=50):
imgs = []
for i in range(L):
imgs.append(cam.grab(radiance))
return get_int_imgs(imgs)
data = {'temporal': {},
'spatial': {},
'width': None, 'height': None}
# Intialize the camera, here we can specify different image size
# or any other parameter that Camera allows
c = Camera(bit_depth=10,
img_x=100,
img_y=50)
# Fill the information
data['width'] = c.img_x
data['height'] = c.img_y
# Maximum exposure for test
exposure_max = 9000000
# Find the camera parameters for the test
c.exposure = exposure_max
c.blackoffset = get_emva_blackoffset(c)
c.K = get_emva_gain(c)
# Find the radiance that will saturate the camera at our maximum exposure time
