def _read_fit_image(path: Path):
"""
read FIT image from filesystem
:param path: path to image file to load from
:type path: pathlib.Path
:return: the loaded image, with data and headers parsed or None if a known error occurred
:rtype: Image or None
"""
try:
with fits.open(str(path.resolve())) as fit:
# pylint: disable=E1101
data = fit[0].data
header = fit[0].header
image = Image(data)
if 'BAYERPAT' in header:
image.bayer_pattern = header['BAYERPAT']
_set_image_file_origin(image, path)
except OSError as error:
_report_fs_error(path, error)
return None
return image
def process_image(self, image: Image):
if image.needs_debayering():
bayer_pattern = image.bayer_pattern
cv2_debayer_dict = {
"BG": cv2.COLOR_BAYER_BG2RGB,
"GB": cv2.COLOR_BAYER_GB2RGB,
"RG": cv2.COLOR_BAYER_RG2RGB,
"GR": cv2.COLOR_BAYER_GR2RGB
}
cv_debay = bayer_pattern[3] + bayer_pattern[2]
# ugly temp fix for GBRG CFA patterns poorly handled by openCV
if cv_debay == "GR":
cv_debay = "BG"
try:
debayered_data = cv2.cvtColor(image.data,
cv2_debayer_dict[cv_debay])
except KeyError:
raise ProcessingError(
f"unsupported bayer pattern : {bayer_pattern}")
image.data = debayered_data
return image
def process_image(self, image: Image):
if image.is_color():
image.set_color_axis_as(0)
image.data = np.float32(image.data)
return image
def process_image(self, image: Image):
if image.is_color():
image.set_color_axis_as(2)
image.data = np.uint16(np.clip(image.data, 0, 2**16 - 1))
return image
def process_image(self, image: Image):
for param in self._parameters:
_LOGGER.debug(f"Autostretch param {param.name} = {param.value}")
active = self._parameters[0]
stretch_method = self._parameters[1]
stretch_strength = self._parameters[2]
if active.value:
_LOGGER.debug("Performing Autostretch...")
image.data = np.interp(image.data,
(image.data.min(), image.data.max()),
(0, _16_BITS_MAX_VALUE))
@log
def histo_adpative_equalization(data):
# special case for autostretch value == 0
strength = stretch_strength.value if stretch_strength.value != 0 else 0.1
return exposure.equalize_adapthist(np.uint16(data),
nbins=_16_BITS_MAX_VALUE +
1,
clip_limit=.01 * strength)
@log
def contrast_stretching(data):
low, high = np.percentile(
data,
(stretch_strength.value, 100 - stretch_strength.value))
return exposure.rescale_intensity(data, in_range=(low, high))
available_stretches = [
contrast_stretching, histo_adpative_equalization
]
chosen_stretch = available_stretches[stretch_method.choices.index(
stretch_method.value)]
if image.is_color():
for channel in range(3):
image.data[channel] = chosen_stretch(image.data[channel])
else:
image.data = chosen_stretch(image.data)
_LOGGER.debug("Autostretch Done")
# autostretch output range is [0, 1]
# so we remap values to our range [0, Levels._UPPER_LIMIT]
image.data *= _16_BITS_MAX_VALUE
# final interpolation
image.data = np.float32(
np.interp(image.data, (image.data.min(), image.data.max()),
(0, _16_BITS_MAX_VALUE)))
return image
def _apply_transformation(self, image: Image,
transformation: SimilarityTransform):
"""
Apply a transformation to an image.
If image is color, channels are processed using multiprocessing, allowing global operation to take less time on
a multi core CPU
Image is modified in place by this function
:param image: the image to apply transformation to
:type image: Image
:param transformation: the transformation to apply
:type transformation: skimage.transform._geometric.SimilarityTransform
"""
if image.is_color():
_LOGGER.debug(f"Aligning color image...")
manager = Manager()
results_dict = manager.dict()
channel_processors = []
for channel in range(3):
processor = Process(
target=Stacker._apply_single_channel_transformation,
args=[
image, self._last_stacking_result, transformation,
results_dict, channel
])
processor.start()
channel_processors.append(processor)
for processor in channel_processors:
processor.join()
_LOGGER.debug(
"Color channel processes are done. Fetching results and storing results..."
)
for channel, data in results_dict.items():
image.data[channel] = data
_LOGGER.debug(f"Aligning color image DONE")
else:
_LOGGER.debug(f"Aligning b&w image...")
result_dict = dict()
Stacker._apply_single_channel_transformation(
image, self._last_stacking_result, transformation, result_dict)
image.data = result_dict[0]
_LOGGER.debug(f"Aligning b&w image : DONE")
def on_new_stack_result(self, image: Image):
"""
A new image has been stacked
:param image: the result of the stack
:type image: Image
"""
image.origin = "Stacking result"
self._last_stacking_result = image.clone()
self.purge_queue(self._post_process_queue)
self._post_process_queue.put(image.clone())
def _save_image_as_jpg(image: Image, target_path: str):
"""
Saves image as jpg.
:param image: the image to save
:type image: Image
:param target_path: the absolute path of the image file to save to
:type target_path: str
:return: a tuple with 2 values :
- True if save succeeded, False otherwise
- Details on cause of save failure, if occurs
As we are using cv2.imwrite, we won't get any details on failures. So failure details will always
be the empty string.
"""
# here we are sure that image data type us unsigned 16 bits. We need to downscale to 8 bits
image.data = (image.data / (((2**16) - 1) /
((2**8) - 1))).astype('uint8')
return cv2.imwrite(target_path,
cv2.cvtColor(image.data, cv2.COLOR_RGB2BGR),
[int(cv2.IMWRITE_JPEG_QUALITY), 90]), ''
def save_image(self,
image: Image,
file_extension: str,
dest_folder_path: str,
filename_base: str,
add_timestamp: bool = False):
"""
Save an image to disk.
:param image: the image to save
:type image: Image
:param file_extension: The image save file format extension
:type file_extension: str
:param dest_folder_path: The path of the folder image will be saved to
:type dest_folder_path: str
:param filename_base: The name of the file to save to (without extension)
:type filename_base: str
:param add_timestamp: Do we add a timestamp to image name
:type add_timestamp: bool
"""
filename_base = filename_base
if add_timestamp:
filename_base += '-' + Controller.get_timestamp()
image_to_save = image.clone()
image_to_save.destination = dest_folder_path + "/" + filename_base + '.' + file_extension
self._saver_queue.put(image_to_save)
def process_image(self, image: Image):
# pylint: disable=R0914
active = self._parameters[0]
black = self._parameters[1]
midtones = self._parameters[2]
white = self._parameters[3]
for param in self._parameters:
_LOGGER.debug(f"Levels param {param.name} = {param.value}")
if active.value:
# midtones correction
do_midtones = not midtones.is_default()
_LOGGER.debug(f"Levels : do midtones adjustments : {do_midtones}")
if do_midtones:
_LOGGER.debug("Performing midtones adjustments...")
midtones_value = midtones.value if midtones.value > 0 else 0.1
image.data = _16_BITS_MAX_VALUE * image.data**(
1 / midtones_value) / _16_BITS_MAX_VALUE**(1 /
midtones_value)
_LOGGER.debug("Midtones level adjustments Done")
# black / white levels
do_black_white_levels = not black.is_default(
) or not white.is_default()
_LOGGER.debug(
f"Levels : do black and white adjustments : {do_black_white_levels}"
)
if do_black_white_levels:
_LOGGER.debug("Performing black / white level adjustments...")
image.data = np.clip(image.data, black.value, white.value)
_LOGGER.debug("Black / white level adjustments Done")
# final interpolation
image.data = np.float32(
np.interp(image.data, (image.data.min(), image.data.max()),
(0, _16_BITS_MAX_VALUE)))
return image
def _stack_image(self, image: Image):
"""
Compute stacking according to user defined stacking mode
the image data is modified in place by this function
:param image: the image to be stacked
:type image: Image
"""
_LOGGER.debug(f"Stacking in {self._stacking_mode} mode...")
if self._stacking_mode == STACKING_MODE_SUM:
image.data = image.data + self._last_stacking_result.data
elif self._stacking_mode == STACKING_MODE_MEAN:
image.data = (self.size * self._last_stacking_result.data +
image.data) / (self.size + 1)
else:
raise StackingError(
f"Unsupported stacking mode : {self._stacking_mode}")
_LOGGER.debug(f"Stacking in {self._stacking_mode} done.")
def on_new_post_processor_result(self, image: Image):
"""
A new image processing result is here
:param image: the new processing result
:type image: Image
"""
image.origin = "Process result"
DYNAMIC_DATA.histogram_container = compute_histograms_for_display(
image, Controller._BIN_COUNT)
DYNAMIC_DATA.post_processor_result = image
self._notify_model_observers(image_only=True)
self.save_post_process_result()
def process_image(self, image: Image):
"""
Performs RGB balance
:param image: the image to process
:type image: Image
"""
for param in self._parameters:
_LOGGER.debug(f"Color balance param {param.name} = {param.value}")
active = self._parameters[0]
red = self._parameters[1]
green = self._parameters[2]
blue = self._parameters[3]
if active.value:
red_value = red.value if red.value > 0 else 0.1
green_value = green.value if green.value > 0 else 0.1
blue_value = blue.value if blue.value > 0 else 0.1
processed = False
if not red.is_default():
image.data[0] = image.data[0] * red_value
processed = True
if not green.is_default():
image.data[1] = image.data[1] * green_value
processed = True
if not blue.is_default():
image.data[2] = image.data[2] * blue_value
processed = True
if processed:
image.data = np.clip(image.data, 0, _16_BITS_MAX_VALUE)
return image
def _handle_image(self, image: Image):
if self.size == 0:
_LOGGER.debug(
"This is the first image for this stack. Publishing right away"
)
self._publish_stacking_result(image)
self._align_reference = image
else:
try:
if not image.is_same_shape_as(self._last_stacking_result):
raise StackingError(
"Image dimensions or color don't match stack content. "
f"New image shape : {image.data.shape} <=> "
f"Reference shape : {self._last_stacking_result.data.shape}"
)
try:
if self._align_before_stack:
# alignment is a memory greedy process, we take special care of such errors
try:
self._align_image(image)
except OSError as os_error:
raise StackingError(os_error)
self._stack_image(image)
except AttributeError:
raise StackingError("Our reference images are gone.")
self._publish_stacking_result(image)
except StackingError as stacking_error:
_LOGGER.warning(
f"Could not stack image {image.origin} : {stacking_error}. Image is DISCARDED"
)
def _set_image_file_origin(image: Image, path: Path):
image.origin = f"FILE : {str(path.resolve())}"
def _read_raw_image(path: Path):
"""
Reads a RAW DLSR image from file
:param path: path to the file to read from
:type path: pathlib.Path
:return: the image or None if a known error occurred
:rtype: Image or None
"""
try:
with imread(str(path.resolve())) as raw_image:
# in here, we make sure we store the bayer pattern as it would be advertised if image was a FITS image.
#
# lets assume image comes from a DSLR sensor with the most common bayer pattern.
#
# The actual/physical bayer pattern would look like a repetition of :
#
# +---+---+
# | R | G |
# +---+---+
# | G | B |
# +---+---+
#
# RawPy will report the bayer pattern description as 2 discrete values :
#
# 1) raw_image.raw_pattern : a 2x2 numpy array representing the indices used to express the bayer patten
#
# in our example, its value is :
#
# +---+---+
# | 0 | 1 |
# +---+---+
# | 3 | 2 |
# +---+---+
#
# and its flatten version is :
#
# [0, 1, 3, 2]
#
# 2) raw_image.color_desc : a bytes literal formed of the color of each pixel of the bayer pattern, in
# ascending index order from raw_image.raw_pattern
#
# in our example, its value is : b'RGBG'
#
# We need to express/store this pattern in a more common way, i.e. as it would be described in a FITS
# header. Or put simply, we want to express the bayer pattern as it would be described if
# raw_image.raw_pattern was :
#
# +---+---+
# | 0 | 1 |
# +---+---+
# | 2 | 3 |
# +---+---+
bayer_pattern_indices = raw_image.raw_pattern.flatten()
bayer_pattern_desc = raw_image.color_desc.decode()
_LOGGER.debug(f"Bayer pattern indices = {bayer_pattern_indices}")
_LOGGER.debug(f"Bayer pattern description = {bayer_pattern_desc}")
assert len(bayer_pattern_indices) == len(bayer_pattern_desc)
bayer_pattern = ""
for i, index in enumerate(bayer_pattern_indices):
assert bayer_pattern_indices[i] < len(bayer_pattern_indices)
bayer_pattern += bayer_pattern_desc[index]
_LOGGER.debug(
f"Computed, FITS-compatible bayer pattern = {bayer_pattern}")
new_image = Image(raw_image.raw_image_visible.copy())
new_image.bayer_pattern = bayer_pattern
_set_image_file_origin(new_image, path)
return new_image
except LibRawNonFatalError as non_fatal_error:
_report_fs_error(path, non_fatal_error)
return None
except LibRawFatalError as fatal_error:
_report_fs_error(path, fatal_error)
return None
def _find_transformation(self, image: Image):
"""
Iteratively try and find a valid transformation to align image with stored align reference.
We perform 3 tries with growing image sizes of a centered image subset : 10%, 30% and 100% of image size
:param image: the image to be aligned
:type image: Image
:return: the found transformation
:raises: StackingError when no transformation is found using the whole image
"""
for ratio in [.1, .33, 1.]:
top, bottom, left, right = self._get_image_subset_boundaries(ratio)
# pick green channel if image has color
if image.is_color():
new_subset = image.data[1][top:bottom, left:right]
ref_subset = self._align_reference.data[1][top:bottom,
left:right]
else:
new_subset = image.data[top:bottom, left:right]
ref_subset = self._align_reference.data[top:bottom, left:right]
try:
_LOGGER.debug(
f"Searching valid transformation on subset "
f"with ratio:{ratio} and shape: {new_subset.shape}")
transformation, matches = al.find_transform(
new_subset, ref_subset)
_LOGGER.debug(
f"Found transformation with subset ratio = {ratio}")
_LOGGER.debug(f"rotation : {transformation.rotation}")
_LOGGER.debug(f"translation : {transformation.translation}")
_LOGGER.debug(f"scale : {transformation.scale}")
matches_count = len(matches[0])
_LOGGER.debug(
f"image matched features count : {matches_count}")
if matches_count < _MINIMUM_MATCHES_FOR_VALID_TRANSFORM:
_LOGGER.debug(
f"Found transformation but matches count is too low : "
f"{matches_count} < {_MINIMUM_MATCHES_FOR_VALID_TRANSFORM}. "
"Discarding transformation")
raise StackingError("Too few matches")
return transformation
# pylint: disable=W0703
except Exception as alignment_error:
# we have no choice but catching Exception, here. That's what AstroAlign raises in some cases
# this will catch MaxIterError as well...
if ratio == 1.:
raise StackingError(alignment_error)
_LOGGER.debug(
f"Could not find valid transformation on subset with ratio = {ratio}."
)
continue
