def capture_background(self):
""" Capture an image into a 2d-array using current exposure settings"""
if self.col == 'Interpolated':
self.interpolate = True
else:
self.interpolate = False
st = time.time()
BayerArray = camarray.PiBayerArray(self)
self.capture(BayerArray, 'jpeg', bayer=True)
# bayer ordering
((ry, rx), (gy, gx), (Gy, Gx),
(by, bx)) = BayerArray.BAYER_OFFSETS[BayerArray._header.bayer_order]
et1 = time.time() - st
print('elapsed time (capture):', et1)
if self.interpolate:
## Use full resolution by interpolating the bayer data back to the full sensor resolution
## demosaic - postprocess ccd data back to full resolution rgb array
self.background = BayerArray.demosaic()
et2 = time.time() - st
print('elapsed time (demosaic):', et2)
##red pixels only:
self.background = np.asarray(self.background[:, :, 0], dtype=int)
else:
## Lose a factor of 2 in resolution, but without interpolating
## Use only 1 color of pixel in the bayer pattern - much faster than demosaic!
## red pixels only:
if self.col == 'Red':
print(BayerArray.array)
self.background = np.asarray(BayerArray.array[ry::2, rx::2, 0],
dtype=int)
elif self.col == 'Green':
## green pixels:
self.background = np.asarray(BayerArray.array[gy::2, gx::2, 1],
dtype=int)
else:
## blue pixels:
self.background = np.asarray(BayerArray.array[by::2, bx::2, 2],
dtype=int)
def capture_image(self):
""" Capture an image into a 2d-array using current exposure settings"""
if self.col == 'Interpolated':
self.interpolate = True
else:
self.interpolate = False
if self.auto_exp == 'auto':
self.shutter_speed = self.autoexpose()
st = time.time()
BayerArray = camarray.PiBayerArray(self)
self.capture(BayerArray, 'jpeg', bayer=True)
# bayer ordering
((ry, rx), (gy, gx), (Gy, Gx),
(by, bx)) = BayerArray.BAYER_OFFSETS[BayerArray._header.bayer_order]
et1 = time.time() - st
print('elapsed time (capture):', et1)
#print BayerArray.array.shape
#self.image = BayerArray.array ## full BGGR bayer mosaic array
if self.interpolate:
## Use full resolution by interpolating the bayer data back to the full sensor resolution
##demosaic - postprocess ccd data back to full resolution rgb array
self.image = np.asarray(BayerArray.demosaic(), dtype=int)
et2 = time.time() - st
print('elapsed time (demosaic):', et2)
##red pixel values:
self.image = self.image[:, :, 0]
else:
## Lose a factor of 2 in resolution, but without interpolating - i.e. use only 1 color of pixel
# red pixels:
## red pixels only:
if self.col == 'Red':
print(BayerArray.array)
self.image = np.asarray(BayerArray.array[ry::2, rx::2, 0],
dtype=int)
elif self.col == 'Green':
## green pixels:
self.image = np.asarray(BayerArray.array[gy::2, gx::2, 1],
dtype=int)
else:
## blue pixels:
self.image = np.asarray(BayerArray.array[by::2, bx::2, 2],
dtype=int)
# dtype=int >> converts to signed integers - otherwise background subtraction can fail
## apply crops for region of interest here
h, w = self.image.shape
self.roi_frac = [int(self.roi[0]/self.ccd_xsize),int(self.roi[1]/self.ccd_xsize),\
1-int(self.roi[2]/self.ccd_ysize),1-int(self.roi[3]/self.ccd_ysize)]
self.cropped_image = self.image[h * self.roi_frac[3]:h *
self.roi_frac[2], w *
self.roi_frac[0]:w * self.roi_frac[1]]
if self.background is not None:
cropped_bg = self.background[h * self.roi_frac[3]:h *
self.roi_frac[2], w *
self.roi_frac[0]:w * self.roi_frac[1]]
# remove dark frame
if self.bg_subtract:
if self.background is None:
print('\t !! WARNING :: No dark frame image to subtract ')
else:
self.image = self.image - self.background
self.cropped_image = self.cropped_image - cropped_bg
#print self.roi_frac
#print 'Cropped shape:', cropped_image.shape
ch, cw = self.cropped_image.shape
# only fit to cropped part of the image
self.imageX = self.cropped_image.sum(axis=0).astype(np.float) / ch
self.imageY = self.cropped_image.sum(axis=1).astype(np.float) / cw
# Approximate position - should use neareset integer to Ntimes pixel pitch
self.Xs = np.linspace(self.roi[0], self.roi[1], cw)
self.Ys = np.linspace(self.roi[3], self.roi[2], ch)
et2 = time.time() - st
print('elapsed time (capture + processing):', et2)
self.et2 = et2