def binImageCalibration(config, mask, dark, flat_struct):
""" Bin the calibration images. """
# Bin the mask
if mask is not None:
mask.img = Image.binImage(mask.img, config.detection_binning_factor,
'avg')
# Bin the dark
if dark is not None:
dark = Image.binImage(dark, config.detection_binning_factor, 'avg')
# Bin the flat
if flat_struct is not None:
flat_struct.binFlat(config.detection_binning_factor, 'avg')
return mask, dark, flat_struct
def plotStars(ff, x2, y2):
""" Plots detected stars on the input image.
"""
# Plot image with adjusted levels to better see stars
plt.imshow(Image.adjustLevels(ff.avepixel, 0, 1.3, 255), cmap='gray')
# Plot stars
for star in zip(list(y2), list(x2)):
y, x = star
c = plt.Circle((x, y), 5, fill=False, color='r')
plt.gca().add_patch(c)
plt.show()
plt.clf()
plt.close()
def plotStars(ff, x2, y2):
""" Plots detected stars on the input image.
"""
# Plot image with adjusted levels to better see stars
plt.imshow(Image.adjustLevels(ff.avepixel, 0, 1.3, 255), cmap='gray')
# Plot stars
for star in zip(list(y2), list(x2)):
y, x = star
c = plt.Circle((x, y), 5, fill=False, color='r')
plt.gca().add_patch(c)
plt.show()
plt.clf()
plt.close()
def loadImageCalibration(dir_path, config, dtype=None, byteswap=False):
""" Load the mask, dark and flat.
Arguments:
dir_path: [str] Path to the directory with calibration.
config: [ConfigStruct]
Keyword arguments:
dtype: [object] Numpy array dtype for the image. None by default, if which case it will be determined
from the input image.
byteswap: [bool] If the dark and flat should be byteswapped. False by default, and should be True for
UWO PNGs.
Return:
mask, dark, flat_struct: [tuple of ndarrays]
"""
mask_path = None
mask = None
# Try loading the mask
if os.path.exists(os.path.join(dir_path, config.mask_file)):
mask_path = os.path.join(dir_path, config.mask_file)
# Try loading the default mask
elif os.path.exists(config.mask_file):
mask_path = os.path.abspath(config.mask_file)
# Load the mask if given
if mask_path:
mask = MaskImage.loadMask(mask_path)
if mask is not None:
print('Loaded mask:', mask_path)
log.info('Loaded mask: {:s}'.format(mask_path))
# Try loading the dark frame
dark = None
if config.use_dark:
dark_path = None
# Check if dark is in the data directory
if os.path.exists(os.path.join(dir_path, config.dark_file)):
dark_path = os.path.join(dir_path, config.dark_file)
# Try loading the default dark
elif os.path.exists(config.dark_file):
dark_path = os.path.abspath(config.dark_file)
if dark_path is not None:
# Load the dark
dark = Image.loadDark(*os.path.split(dark_path),
dtype=dtype,
byteswap=byteswap)
if dark is not None:
print('Loaded dark:', dark_path)
log.info('Loaded dark: {:s}'.format(dark_path))
# Try loading a flat field image
flat_struct = None
if config.use_flat:
flat_path = None
# Check if there is flat in the data directory
if os.path.exists(os.path.join(dir_path, config.flat_file)):
flat_path = os.path.join(dir_path, config.flat_file)
# Try loading the default flat
elif os.path.exists(config.flat_file):
flat_path = os.path.abspath(config.flat_file)
if flat_path is not None:
# Load the flat
flat_struct = Image.loadFlat(*os.path.split(flat_path),
dtype=dtype,
byteswap=byteswap)
if flat_struct is not None:
print('Loaded flat:', flat_path)
log.info('Loaded flat: {:s}'.format(flat_path))
return mask, dark, flat_struct
def getThresholdedStripe3DPoints(config, img_handle, frame_min, frame_max, rho, theta, mask, flat_struct, \
dark, stripe_width_factor=1.0, centroiding=False, point1=None, point2=None, debug=False):
""" Threshold the image and get a list of pixel positions and frames of threshold passers.
This function handles all input types of data.
Arguments;
config: [config object] configuration object (loaded from the .config file).
img_handle: [FrameInterface instance] Object which has a common interface to various input files.
frame_min: [int] First frame to process.
frame_max: [int] Last frame to process.
rho: [float] Line distance from the center in HT space (pixels).
theta: [float] Angle in degrees in HT space.
mask: [ndarray] Image mask.
flat_struct: [Flat struct] Structure containing the flat field. None by default.
dark: [ndarray] Dark frame.
Keyword arguments:
stripe_width_factor: [float] Multipler by which the default stripe width will be multiplied. Default
is 1.0
centroiding: [bool] If True, the indices will be returned in the centroiding mode, which means
that point1 and point2 arguments must be given.
point1: [list] (x, y, frame) Of the first reference point of the detection.
point2: [list] (x, y, frame) Of the second reference point of the detection.
debug: [bool] If True, extra debug messages and plots will be shown.
Return:
xs, ys, zs: [tuple of lists] Indices of (x, y, frame) of threshold passers for every frame.
"""
# Get indices of stripe pixels around the line of the meteor
img_h, img_w = img_handle.ff.maxpixel.shape
stripe_indices = getStripeIndices(rho, theta, stripe_width_factor*config.stripe_width, img_h, img_w)
# If centroiding should be done, prepare everything for cutting out parts of the image for photometry
if centroiding:
# Compute the unit vector which describes the motion of the meteor in the image domain
point1 = np.array(point1)
point2 = np.array(point2)
motion_vect = point2[:2] - point1[:2]
motion_vect_unit = vectNorm(motion_vect)
# Get coordinates of 2 points that describe the line
x1, y1, z1 = point1
x2, y2, z2 = point2
# Compute the average angular velocity in px per frame
ang_vel = np.sqrt((x2 - x1)**2 + (y2 - y1)**2)/(z2 - z1)
# Compute the vector describing the length and direction of the meteor per frame
motion_vect = ang_vel*motion_vect_unit
# If the FF files is given, extract the points from FF after threshold
if img_handle.input_type == 'ff':
# Threshold the FF file
img_thres = Image.thresholdFF(img_handle.ff, config.k1_det, config.j1_det, mask=mask, \
mask_ave_bright=False)
# Extract the thresholded image by min and max frames from FF file
img = selectFFFrames(np.copy(img_thres), img_handle.ff, frame_min, frame_max)
# Remove lonely pixels
img = morph.clean(img)
# Extract the stripe from the thresholded image
stripe = np.zeros(img.shape, img.dtype)
stripe[stripe_indices] = img[stripe_indices]
# Show stripe
# show2("stripe", stripe*255)
# Show 3D could
# show3DCloud(ff, stripe)
# Get stripe positions (x, y, frame)
stripe_positions = stripe.nonzero()
xs = stripe_positions[1]
ys = stripe_positions[0]
zs = img_handle.ff.maxframe[stripe_positions]
return xs, ys, zs
# If video frames are available, extract indices on all frames in the given range
else:
xs_array = []
ys_array = []
zs_array = []
# Go through all frames in the frame range
for fr in range(frame_min, frame_max + 1):
# Break the loop if outside frame size
if fr == (img_handle.total_frames - 1):
break
# Set the frame number
img_handle.setFrame(fr)
# Load the frame
fr_img = img_handle.loadFrame()
# Apply the dark frame
if dark is not None:
fr_img = Image.applyDark(fr_img, dark)
# Apply the flat to frame
if flat_struct is not None:
fr_img = Image.applyFlat(fr_img, flat_struct)
# Mask the image
fr_img = MaskImage.applyMask(fr_img, mask)
# Threshold the frame
img_thres = Image.thresholdImg(fr_img, img_handle.ff.avepixel, img_handle.ff.stdpixel, \
config.k1_det, config.j1_det, mask=mask, mask_ave_bright=False)
# Remove lonely pixels
img_thres = morph.clean(img_thres)
# Extract the stripe from the thresholded image
stripe = np.zeros(img_thres.shape, img_thres.dtype)
stripe[stripe_indices] = img_thres[stripe_indices]
# Include more pixels for centroiding and photometry and mask out per frame pixels
if centroiding:
# Dilate the pixels in the stripe twice, to include more pixels for photometry
stripe = morph.dilate(stripe)
stripe = morph.dilate(stripe)
# Get indices of the stripe that is perpendicular to the meteor, and whose thickness is the
# length of the meteor on this particular frame - this is called stripe_indices_motion
# Compute the previous, current, and the next linear model position of the meteor on the
# image
model_pos_prev = point1[:2] + (fr - 1 - z1)*motion_vect
model_pos = point1[:2] + (fr - z1)*motion_vect
model_pos_next = point1[:2] + (fr + 1 - z1)*motion_vect
# Get the rho, theta of the line perpendicular to the meteor line
x_inters, y_inters = model_pos
# Check if the previous, current or the next centroids are outside bounds, and if so, skip the
# frame
if (not checkCentroidBounds(model_pos_prev, img_w, img_h)) or \
(not checkCentroidBounds(model_pos, img_w, img_h)) or \
(not checkCentroidBounds(model_pos_next, img_w, img_h)):
continue
# Get parameters of the perpendicular line to the meteor line
rho2, theta2 = htLinePerpendicular(rho, theta, x_inters, y_inters, img_h, img_w)
# Compute the image indices of this position which will be the intersection with the stripe
# The width of the line will be 2x the angular velocity
stripe_length = 6*ang_vel
if stripe_length < stripe_width_factor*config.stripe_width:
stripe_length = stripe_width_factor*config.stripe_width
stripe_indices_motion = getStripeIndices(rho2, theta2, stripe_length, img_h, img_w)
# Mark only those parts which overlap both lines, which effectively creates a mask for
# photometry an centroiding, excluding other influences
stripe_new = np.zeros_like(stripe)
stripe_new[stripe_indices_motion] = stripe[stripe_indices_motion]
stripe = stripe_new
if debug:
# Show the extracted stripe
img_stripe = np.zeros_like(stripe)
img_stripe[stripe_indices] = 1
final_stripe = np.zeros_like(stripe)
final_stripe[stripe_indices_motion] = img_stripe[stripe_indices_motion]
plt.imshow(final_stripe)
plt.show()
if debug and centroiding:
print(fr)
print('mean stdpixel3:', np.mean(img_handle.ff.stdpixel))
print('mean avepixel3:', np.mean(img_handle.ff.avepixel))
print('mean frame:', np.mean(fr_img))
fig, (ax1, ax2) = plt.subplots(nrows=2, sharex=True, sharey=True)
fr_img_noavg = Image.applyDark(fr_img, img_handle.ff.avepixel)
#fr_img_noavg = fr_img
# Auto levels
min_lvl = np.percentile(fr_img_noavg[2:, :], 1)
max_lvl = np.percentile(fr_img_noavg[2:, :], 99.0)
# Adjust levels
fr_img_autolevel = Image.adjustLevels(fr_img_noavg, min_lvl, 1.0, max_lvl)
ax1.imshow(stripe, cmap='gray')
ax2.imshow(fr_img_autolevel, cmap='gray')
plt.show()
pass
# Get stripe positions (x, y, frame)
stripe_positions = stripe.nonzero()
xs = stripe_positions[1]
ys = stripe_positions[0]
zs = np.zeros_like(xs) + fr
# Add the points to the list
xs_array.append(xs)
ys_array.append(ys)
zs_array.append(zs)
if debug:
print('---')
print(stripe.nonzero())
print(xs, ys, zs)
if len(xs_array) > 0:
# Flatten the arrays
xs_array = np.concatenate(xs_array)
ys_array = np.concatenate(ys_array)
zs_array = np.concatenate(zs_array)
else:
xs_array = np.array(xs_array)
ys_array = np.array(ys_array)
zs_array = np.array(zs_array)
return xs_array, ys_array, zs_array
def loadImageCalibration(dir_path, config, dtype=None, byteswap=False):
""" Load the mask, dark and flat.
Arguments:
dir_path: [str] Path to the directory with calibration.
config: [ConfigStruct]
Keyword arguments:
dtype: [object] Numpy array dtype for the image. None by default, if which case it will be determined
from the input image.
byteswap: [bool] If the dark and flat should be byteswapped. False by default, and should be True for
UWO PNGs.
Return:
mask, dark, flat_struct: [tuple of ndarrays]
"""
mask_path = None
mask = None
# Try loading the mask
if os.path.exists(os.path.join(dir_path, config.mask_file)):
mask_path = os.path.join(dir_path, config.mask_file)
# Try loading the default mask
elif os.path.exists(config.mask_file):
mask_path = os.path.abspath(config.mask_file)
# Load the mask if given
if mask_path:
mask = MaskImage.loadMask(mask_path)
if mask is not None:
print('Loaded mask:', mask_path)
log.info('Loaded mask: {:s}'.format(mask_path))
# Try loading the dark frame
dark = None
if config.use_dark:
dark_path = None
# Check if dark is in the data directory
if os.path.exists(os.path.join(dir_path, config.dark_file)):
dark_path = os.path.join(dir_path, config.dark_file)
# Try loading the default dark
elif os.path.exists(config.dark_file):
dark_path = os.path.abspath(config.dark_file)
if dark_path is not None:
# Load the dark
dark = Image.loadDark(*os.path.split(dark_path), dtype=dtype, byteswap=byteswap)
if dark is not None:
print('Loaded dark:', dark_path)
log.info('Loaded dark: {:s}'.format(dark_path))
# Try loading a flat field image
flat_struct = None
if config.use_flat:
flat_path = None
# Check if there is flat in the data directory
if os.path.exists(os.path.join(dir_path, config.flat_file)):
flat_path = os.path.join(dir_path, config.flat_file)
# Try loading the default flat
elif os.path.exists(config.flat_file):
flat_path = os.path.abspath(config.flat_file)
if flat_path is not None:
# Load the flat
flat_struct = Image.loadFlat(*os.path.split(flat_path), dtype=dtype, byteswap=byteswap)
if flat_struct is not None:
print('Loaded flat:', flat_path)
log.info('Loaded flat: {:s}'.format(flat_path))
return mask, dark, flat_struct
def detectMeteors(ff_directory, ff_name, config, flat_struct=None):
""" Detect meteors on the given FF bin image. Here are the steps in the detection:
- input image (FF bin format file) is thresholded (converted to black and white)
- several morphological operations are applied to clean the image
- image is then broken into several image "windows" (these "windows" are reconstructed from the input FF file, given
an input frame range (e.g. 64-128) which helps reduce the noise further)
- on each "window" the Kernel-based Hough transform is performed to find any lines on the image
- similar lines are joined
- stripe around the lines is extracted
- 3D line finding (third dimension is time) is applied to check if the line propagates in time
- centroiding is performed, which calculates the position and intensity of meteor on each frame
Arguments:
ff_directory: [string] an absolute path to the input FF bin file
ff_name: [string] file name of the FF bin file on which to run the detection on
config: [config object] configuration object (loaded from the .config file)
Keyword arguments:
flat_struct: [Flat struct] Structure containing the flat field. None by default.
Return:
meteor_detections: [list] a list of detected meteors, with these elements:
- rho: [float] meteor line distance from image center (polar coordinates, in pixels)
- theta: [float] meteor line angle from image center (polar coordinates, in degrees)
- centroids: [list] [frame, X, Y, level] list of meteor points
"""
t1 = time()
t_all = time()
# Load the FF bin file
ff = FFfile.read(ff_directory, ff_name)
# Load the mask file
mask = MaskImage.loadMask(config.mask_file)
# Mask the FF file
ff = MaskImage.applyMask(ff, mask, ff_flag=True)
# Apply the flat to maxpixel and avepixel
if flat_struct is not None:
ff.maxpixel = Image.applyFlat(ff.maxpixel, flat_struct)
ff.avepixel = Image.applyFlat(ff.avepixel, flat_struct)
# At the end, a check that the detection has a surface brightness above the background will be performed.
# The assumption here is that the peak of the meteor should have the intensity which is at least
# that of a patch of 4x4 pixels that are of the mean background brightness
min_patch_intensity = 4 * 4 * (np.mean(ff.maxpixel - ff.avepixel) +
config.k1_det * np.mean(ff.stdpixel) +
config.j1)
# # Show the maxpixel image
# show2(ff_name+' maxpixel', ff.maxpixel)
# Get lines on the image
line_list = getLines(ff, config.k1_det, config.j1_det, config.time_slide,
config.time_window_size, config.max_lines_det,
config.max_white_ratio, config.kht_lib_path)
logDebug('List of lines:', line_list)
# Init meteor list
meteor_detections = []
# Only if there are some lines in the image
if len(line_list):
# Join similar lines
line_list = mergeLines(line_list, config.line_min_dist, ff.ncols,
ff.nrows)
logDebug('Time for finding lines:', time() - t1)
logDebug('Number of KHT lines: ', len(line_list))
logDebug(line_list)
# Plot lines
# plotLines(ff, line_list)
# Threshold the image
img_thres = thresholdImg(ff, config.k1_det, config.j1_det)
filtered_lines = []
# Analyze stripes of each line
for line in line_list:
rho, theta, frame_min, frame_max = line
logDebug('rho, theta, frame_min, frame_max')
logDebug(rho, theta, frame_min, frame_max)
# Bounded the thresholded image by min and max frames
img = selectFrames(np.copy(img_thres), ff, frame_min, frame_max)
# Remove lonely pixels
img = morph.clean(img)
# Get indices of stripe pixels around the line
stripe_indices = getStripeIndices(rho, theta, config.stripe_width,
img.shape[0], img.shape[1])
# Extract the stripe from the thresholded image
stripe = np.zeros((ff.nrows, ff.ncols), np.uint8)
stripe[stripe_indices] = img[stripe_indices]
# Show stripe
#COMMENTED
# show2("stripe", stripe*255)
# Show 3D could
# show3DCloud(ff, stripe)
# Get stripe positions
stripe_positions = stripe.nonzero()
xs = stripe_positions[1]
ys = stripe_positions[0]
zs = ff.maxframe[stripe_positions]
# Limit the number of points to search if too large
if len(zs) > config.max_points_det:
# Extract weights of each point
maxpix_elements = ff.maxpixel[ys, xs].astype(np.float64)
weights = maxpix_elements / np.sum(maxpix_elements)
# Random sample the point, sampling is weighted by pixel intensity
indices = np.random.choice(len(zs),
config.max_points_det,
replace=False,
p=weights)
ys = ys[indices]
xs = xs[indices]
zs = zs[indices]
# Make an array to feed into the gropuing algorithm
stripe_points = np.vstack((xs, ys, zs))
stripe_points = np.swapaxes(stripe_points, 0, 1)
# Sort stripe points by frame
stripe_points = stripe_points[stripe_points[:, 2].argsort()]
t1 = time()
logDebug('finding lines...')
# Find a single line in the point cloud
detected_line = find3DLines(stripe_points,
time(),
config,
fireball_detection=False)
logDebug('time for GROUPING: ', time() - t1)
# Extract the first and only line if any
if detected_line:
detected_line = detected_line[0]
# logDebug(detected_line)
# Show 3D cloud
# show3DCloud(ff, stripe, detected_line, stripe_points, config)
# Add the line to the results list
filtered_lines.append(detected_line)
# Merge similar lines in 3D
filtered_lines = merge3DLines(filtered_lines, config.vect_angle_thresh)
logDebug('after filtering:')
logDebug(filtered_lines)
for detected_line in filtered_lines:
# Get frame range
frame_min = detected_line[4]
frame_max = detected_line[5]
# Check if the line covers a minimum frame range
if (abs(frame_max - frame_min) + 1 <
config.line_minimum_frame_range_det):
continue
# Extand the frame range for several frames, just to be sure to catch all parts of a meteor
frame_min -= config.frame_extension
frame_max += config.frame_extension
# Cap values to 0-255
frame_min = max(frame_min, 0)
frame_max = min(frame_max, 255)
logDebug(detected_line)
# Get coordinates of 2 points that describe the line
x1, y1, z1 = detected_line[0]
x2, y2, z2 = detected_line[1]
# Convert Cartesian line coordinates to polar
rho, theta = getPolarLine(x1, y1, x2, y2, ff.nrows, ff.ncols)
# Convert Cartesian line coordinate to CAMS compatible polar coordinates (flipped Y axis)
rho_cams, theta_cams = getPolarLine(x1, ff.nrows - y1, x2,
ff.nrows - y2, ff.nrows,
ff.ncols)
logDebug('converted rho, theta')
logDebug(rho, theta)
# Bounded the thresholded image by min and max frames
img = selectFrames(np.copy(img_thres), ff, frame_min, frame_max)
# Remove lonely pixels
img = morph.clean(img)
# Get indices of stripe pixels around the line
stripe_indices = getStripeIndices(rho, theta,
int(config.stripe_width * 1.5),
img.shape[0], img.shape[1])
# Extract the stripe from the thresholded image
stripe = np.zeros((ff.nrows, ff.ncols), np.uint8)
stripe[stripe_indices] = img[stripe_indices]
# Show detected line
# show('detected line: '+str(frame_min)+'-'+str(frame_max), stripe)
# Get stripe positions
stripe_positions = stripe.nonzero()
xs = stripe_positions[1]
ys = stripe_positions[0]
zs = ff.maxframe[stripe_positions]
# Make an array to feed into the centroiding algorithm
stripe_points = np.vstack((xs, ys, zs))
stripe_points = np.swapaxes(stripe_points, 0, 1)
# Sort stripe points by frame
stripe_points = stripe_points[stripe_points[:, 2].argsort()]
# Show 3D cloud
# show3DCloud(ff, stripe, detected_line, stripe_points, config)
# Get points of the given line
line_points = getAllPoints(stripe_points,
x1,
y1,
z1,
x2,
y2,
z2,
config,
fireball_detection=False)
# Skip if no points were returned
if not line_points.any():
continue
# Skip if the points cover too small a frame range
if abs(np.max(line_points[:, 2]) - np.min(line_points[:, 2])
) + 1 < config.line_minimum_frame_range_det:
continue
# Calculate centroids
centroids = []
for i in range(frame_min, frame_max + 1):
# Select pixel indicies belonging to a given frame
frame_pixels_inds = np.where(line_points[:, 2] == i)
# Get pixel positions in a given frame (pixels belonging to a found line)
frame_pixels = line_points[frame_pixels_inds].astype(np.int64)
# Get pixel positions in a given frame (pixels belonging to the whole stripe)
frame_pixels_stripe = stripe_points[np.where(
stripe_points[:, 2] == i)].astype(np.int64)
# Skip if there are no pixels in the frame
if not len(frame_pixels):
continue
# Calculate weights for centroiding
max_avg_corrected = ff.maxpixel - ff.avepixel
flattened_weights = (max_avg_corrected).astype(
np.float32) / ff.stdpixel
# Calculate centroids by half-frame
for half_frame in range(2):
# Apply deinterlacing if it is present in the video
if config.deinterlace_order >= 0:
# Deinterlace by fields (line lixels)
half_frame_pixels = frame_pixels[
frame_pixels[:, 1] %
2 == (config.deinterlace_order + half_frame) % 2]
# Deinterlace by fields (stripe pixels)
half_frame_pixels_stripe = frame_pixels_stripe[
frame_pixels_stripe[:, 1] %
2 == (config.deinterlace_order + half_frame) % 2]
# Skip if there are no pixels in the half-frame
if not len(half_frame_pixels):
continue
# Calculate half-frame value
frame_no = i + half_frame * 0.5
# No deinterlacing
else:
# Skip the second half frame
if half_frame == 1:
continue
half_frame_pixels = frame_pixels
half_frame_pixels_stripe = frame_pixels_stripe
frame_no = i
# Get maxpixel-avepixel values of given pixel indices (this will be used as weights)
max_weights = flattened_weights[half_frame_pixels[:, 1],
half_frame_pixels[:, 0]]
# Calculate weighted centroids
x_weighted = half_frame_pixels[:, 0] * np.transpose(
max_weights)
x_centroid = np.sum(x_weighted) / float(
np.sum(max_weights))
y_weighted = half_frame_pixels[:, 1] * np.transpose(
max_weights)
y_centroid = np.sum(y_weighted) / float(
np.sum(max_weights))
# Calculate intensity as the sum of threshold passer pixels on the stripe
#intensity_values = max_avg_corrected[half_frame_pixels[:,1], half_frame_pixels[:,0]]
intensity_values = max_avg_corrected[
half_frame_pixels_stripe[:, 1],
half_frame_pixels_stripe[:, 0]]
intensity = np.sum(intensity_values)
logDebug("centroid: ", frame_no, x_centroid, y_centroid,
intensity)
centroids.append(
[frame_no, x_centroid, y_centroid, intensity])
# Filter centroids
centroids = filterCentroids(centroids,
config.centroids_max_deviation,
config.centroids_max_distance)
# Convert to numpy array for easy slicing
centroids = np.array(centroids)
# Reject the solution if there are too few centroids
if len(centroids) < config.line_minimum_frame_range_det:
continue
# Check that the detection has a surface brightness above the background
# The assumption here is that the peak of the meteor should have the intensity which is at least
# that of a patch of 4x4 pixels that are of the mean background brightness
if np.max(centroids[:, 3]) < min_patch_intensity:
continue
# Check the detection if it has the proper angular velocity
if not checkAngularVelocity(centroids, config):
continue
# Append the result to the meteor detections
meteor_detections.append([rho_cams, theta_cams, centroids])
logDebug('time for processing:', time() - t_all)
# # Plot centroids to image
# fig, (ax1, ax2) = plt.subplots(nrows=2)
# ax1.imshow(ff.maxpixel - ff.avepixel, cmap='gray')
# ax1.scatter(centroids[:,1], centroids[:,2], s=5, c='r', edgecolors='none')
# # Plot lightcurve
# ax2.plot(centroids[:,0], centroids[:,3])
# # # Plot relative angular velocity
# # ang_vels = []
# # fr_prev, x_prev, y_prev, _ = centroids[0]
# # for fr, x, y, _ in centroids[1:]:
# # dx = x - x_prev
# # dy = y - y_prev
# # dfr = fr - fr_prev
# # ddist = np.sqrt(dx**2 + dy**2)
# # dt = dfr/config.fps
# # ang_vels.append(ddist/dt)
# # x_prev = x
# # y_prev = y
# # fr_prev = fr
# # ax2.plot(ang_vels)
# plt.show()
return meteor_detections
def fitPSF(ff, avepixel_mean, x2, y2, config):
""" Fit a 2D Gaussian to the star candidate cutout to check if it's a star.
Arguments:
ff: [ff bin struct] FF bin file loaded in the FF bin structure
avepixel_mean: [float] mean of the avepixel image
x2: [list] a list of estimated star position (X axis)
xy: [list] a list of estimated star position (Y axis)
config: [config object] configuration object (loaded from the .config file)
"""
# Load parameters form config if present
if config is not None:
# segment_radius: [int] radius (in pixels) of image segment around the detected star on which to
# perform the fit
# roundness_threshold: [float] minimum ratio of 2D Gaussian sigma X and sigma Y to be taken as a stars
# (hot pixels are narrow, while stars are round)
# max_feature_ratio: [float] maximum ratio between 2 sigma of the star and the image segment area
segment_radius = config.segment_radius
roundness_threshold = config.roundness_threshold
max_feature_ratio = config.max_feature_ratio
x_fitted = []
y_fitted = []
amplitude_fitted = []
intensity_fitted = []
sigma_y_fitted = []
sigma_x_fitted = []
# Set the initial guess
initial_guess = (30.0, segment_radius, segment_radius, 1.0, 1.0, 0.0, avepixel_mean)
# Go through all stars
for star in zip(list(y2), list(x2)):
y, x = star
y_min = y - segment_radius
y_max = y + segment_radius
x_min = x - segment_radius
x_max = x + segment_radius
if y_min < 0:
y_min = 0
if y_max > ff.nrows:
y_max = ff.nrows
if x_min < 0:
x_min = 0
if x_max > ff.ncols:
x_max = ff.ncols
x_min = int(x_min)
x_max = int(x_max)
y_min = int(y_min)
y_max = int(y_max)
# Extract an image segment around each star
star_seg = ff.avepixel[y_min:y_max, x_min:x_max]
# Create x and y indices
y_ind, x_ind = np.indices(star_seg.shape)
# Estimate saturation level from image type
saturation = 2**(8*star_seg.itemsize) - 1
# Fit a PSF to the star
try:
# Fit the 2D Gaussian with the limited number of iterations - this reduces the processing time
# and most of the bad star candidates take more iterations to fit
popt, pcov = opt.curve_fit(twoDGaussian, (y_ind, x_ind, saturation), star_seg.ravel(), \
p0=initial_guess, maxfev=200)
# print(popt)
except RuntimeError:
# print('Fitting failed!')
# Skip stars that can't be fitted in 200 iterations
continue
# Unpack fitted gaussian parameters
amplitude, yo, xo, sigma_y, sigma_x, theta, offset = popt
# Filter hot pixels by looking at the ratio between x and y sigmas (HPs are very narrow)
if min(sigma_y/sigma_x, sigma_x/sigma_y) < roundness_threshold:
# Skip if it is a hot pixel
continue
# Reject the star candidate if it is too large
if (4*sigma_x*sigma_y / segment_radius**2 > max_feature_ratio):
continue
### If the fitting was successfull, compute the star intensity
# Crop the star segment to take 3 sigma portion around the star
crop_y_min = int(yo - 3*sigma_y) + 1
if crop_y_min < 0: crop_y_min = 0
crop_y_max = int(yo + 3*sigma_y) + 1
if crop_y_max >= star_seg.shape[0]: crop_y_max = star_seg.shape[0] - 1
crop_x_min = int(xo - 3*sigma_x) + 1
if crop_x_min < 0: crop_x_min = 0
crop_x_max = int(xo + 3*sigma_x) + 1
if crop_x_max >= star_seg.shape[1]: crop_x_max = star_seg.shape[1] - 1
# If the segment is too small, set a fixed size
if (y_max - y_min) < 3:
crop_y_min = int(yo - 2)
crop_y_max = int(yo + 2)
if (x_max - x_min) < 3:
crop_x_min = int(xo - 2)
crop_x_max = int(xo + 2)
star_seg_crop = star_seg[crop_y_min:crop_y_max, crop_x_min:crop_x_max]
# Skip the star if the shape is too small
if (star_seg_crop.shape[0] == 0) or (star_seg_crop.shape[1] == 0):
continue
# Gamma correct the star segment
star_seg_crop = Image.gammaCorrection(star_seg_crop.astype(np.float32), config.gamma)
# Correct the background for gamma
bg_corrected = Image.gammaCorrection(offset, config.gamma)
# Subtract the background from the star segment and compute the total intensity
intensity = np.sum(star_seg_crop - bg_corrected)
# Skip stars with zero intensity
if intensity <= 0:
continue
# print(intensity)
# plt.imshow(star_seg_crop - bg_corrected, cmap='gray', vmin=0, vmax=255)
# plt.show()
###
# Calculate the intensity (as a volume under the 2D Gaussian) (OLD, before gamma correction)
# intensity = 2*np.pi*amplitude*sigma_x*sigma_y
# # Skip if the star intensity is below background level
# if intensity < offset:
# continue
# Add stars to the final list
x_fitted.append(x_min + xo)
y_fitted.append(y_min + yo)
amplitude_fitted.append(amplitude)
intensity_fitted.append(intensity)
sigma_y_fitted.append(sigma_y)
sigma_x_fitted.append(sigma_x)
# # Plot fitted stars
# data_fitted = twoDGaussian((y_ind, x_ind), *popt) - offset
# fig, ax = plt.subplots(1, 1)
# ax.hold(True)
# plt.title('Center Y: '+str(y_min[0])+', X:'+str(x_min[0]))
# ax.imshow(star_seg.reshape(segment_radius*2, segment_radius*2), cmap=plt.cm.inferno, origin='bottom',
# extent=(x_ind.min(), x_ind.max(), y_ind.min(), y_ind.max()))
# # ax.imshow(data_fitted.reshape(segment_radius*2, segment_radius*2), cmap=plt.cm.jet, origin='bottom')
# ax.contour(x_ind, y_ind, data_fitted.reshape(segment_radius*2, segment_radius*2), 8, colors='w')
# plt.show()
# plt.clf()
# plt.close()
return x_fitted, y_fitted, amplitude_fitted, intensity_fitted, sigma_y_fitted, sigma_x_fitted
def runCapture(config,
duration=None,
video_file=None,
nodetect=False,
detect_end=False,
upload_manager=None):
""" Run capture and compression for the given time.given
Arguments:
config: [config object] Configuration read from the .config file
Keyword arguments:
duration: [float] Time in seconds to capture. None by default.
video_file: [str] Path to the video file, if it was given as the video source. None by default.
nodetect: [bool] If True, detection will not be performed. False by defualt.
detect_end: [bool] If True, detection will be performed at the end of the night, when capture
finishes. False by default.
upload_manager: [UploadManager object] A handle to the UploadManager, which handles uploading files to
the central server. None by default.
"""
global STOP_CAPTURE
# Create a directory for captured files
night_data_dir_name = str(
config.stationID) + '_' + datetime.datetime.utcnow().strftime(
'%Y%m%d_%H%M%S_%f')
# Full path to the data directory
night_data_dir = os.path.join(os.path.abspath(config.data_dir),
config.captured_dir, night_data_dir_name)
# Make a directory for the night
mkdirP(night_data_dir)
log.info('Data directory: ' + night_data_dir)
# Load the default flat field image if it is available
flat_struct = None
if config.use_flat:
# Check if the flat exists
if os.path.exists(os.path.join(os.getcwd(), config.flat_file)):
flat_struct = Image.loadFlat(os.getcwd(), config.flat_file)
log.info('Loaded flat field image: ' +
os.path.join(os.getcwd(), config.flat_file))
# Get the platepar file
platepar, platepar_path, platepar_fmt = getPlatepar(config)
log.info('Initializing frame buffers...')
### For some reason, the RPi 3 does not like memory chunks which size is the multipier of its L2
### cache size (512 kB). When such a memory chunk is provided, the compression becomes 10x slower
### then usual. We are applying a dirty fix here where we just add an extra image row and column
### if such a memory chunk will be created. The compression is performed, and the image is cropped
### back to its original dimensions.
array_pad = 0
# Check if the image dimensions are divisible by RPi3 L2 cache size and add padding
if (256 * config.width * config.height) % (512 * 1024) == 0:
array_pad = 1
# Init arrays for parallel compression on 2 cores
sharedArrayBase = multiprocessing.Array(
ctypes.c_uint8,
256 * (config.width + array_pad) * (config.height + array_pad))
sharedArray = np.ctypeslib.as_array(sharedArrayBase.get_obj())
sharedArray = sharedArray.reshape(256, (config.height + array_pad),
(config.width + array_pad))
startTime = multiprocessing.Value('d', 0.0)
sharedArrayBase2 = multiprocessing.Array(
ctypes.c_uint8,
256 * (config.width + array_pad) * (config.height + array_pad))
sharedArray2 = np.ctypeslib.as_array(sharedArrayBase2.get_obj())
sharedArray2 = sharedArray2.reshape(256, (config.height + array_pad),
(config.width + array_pad))
startTime2 = multiprocessing.Value('d', 0.0)
log.info('Initializing frame buffers done!')
# Check if the detection should be performed or not
if nodetect:
detector = None
else:
if detect_end:
# Delay detection until the end of the night
delay_detection = duration
else:
# Delay the detection for 2 minutes after capture start
delay_detection = 120
# Initialize the detector
detector = QueuedPool(detectStarsAndMeteors,
cores=1,
log=log,
delay_start=delay_detection)
detector.startPool()
# Initialize buffered capture
bc = BufferedCapture(sharedArray,
startTime,
sharedArray2,
startTime2,
config,
video_file=video_file)
# Initialize the live image viewer
live_view = LiveViewer(window_name='Maxpixel')
# Initialize compression
compressor = Compressor(night_data_dir,
sharedArray,
startTime,
sharedArray2,
startTime2,
config,
detector=detector,
live_view=live_view,
flat_struct=flat_struct)
# Start buffered capture
bc.startCapture()
# Start the compression
compressor.start()
# Capture until Ctrl+C is pressed
wait(duration)
# If capture was manually stopped, end capture
if STOP_CAPTURE:
log.info('Ending capture...')
# Stop the capture
log.debug('Stopping capture...')
bc.stopCapture()
log.debug('Capture stopped')
dropped_frames = bc.dropped_frames
log.info('Total number of dropped frames: ' + str(dropped_frames))
# Stop the compressor
log.debug('Stopping compression...')
detector, live_view = compressor.stop()
log.debug('Compression stopped')
# Stop the live viewer
log.debug('Stopping live viewer...')
live_view.stop()
del live_view
log.debug('Live view stopped')
# Init data lists
star_list = []
meteor_list = []
ff_detected = []
# If detection should be performed
if not nodetect:
log.info('Finishing up the detection, ' +
str(detector.input_queue.qsize()) + ' files to process...')
# Reset the Ctrl+C to KeyboardInterrupt
resetSIGINT()
try:
# If there are some more files to process, process them on more cores
if detector.input_queue.qsize() > 0:
# Let the detector use all cores, but leave 1 free
available_cores = multiprocessing.cpu_count() - 1
if available_cores > 1:
log.info('Running the detection on {:d} cores...'.format(
available_cores))
# Start the detector
detector.updateCoreNumber(cores=available_cores)
log.info('Waiting for the detection to finish...')
# Wait for the detector to finish and close it
detector.closePool()
log.info('Detection finished!')
except KeyboardInterrupt:
log.info('Ctrl + C pressed, exiting...')
if upload_manager is not None:
# Stop the upload manager
if upload_manager.is_alive():
log.debug('Closing upload manager...')
upload_manager.stop()
del upload_manager
# Terminate the detector
if detector is not None:
del detector
sys.exit()
# Set the Ctrl+C back to 'soft' program kill
setSIGINT()
### SAVE DETECTIONS TO FILE
log.info('Collecting results...')
# Get the detection results from the queue
detection_results = detector.getResults()
# Remove all 'None' results, which were errors
detection_results = [
res for res in detection_results if res is not None
]
# Count the number of detected meteors
meteors_num = 0
for _, _, meteor_data in detection_results:
for meteor in meteor_data:
meteors_num += 1
log.info('TOTAL: ' + str(meteors_num) + ' detected meteors.')
# Save the detections to a file
for ff_name, star_data, meteor_data in detection_results:
x2, y2, background, intensity = star_data
# Skip if no stars were found
if not x2:
continue
# Construct the table of the star parameters
star_data = zip(x2, y2, background, intensity)
# Add star info to the star list
star_list.append([ff_name, star_data])
# Handle the detected meteors
meteor_No = 1
for meteor in meteor_data:
rho, theta, centroids = meteor
# Append to the results list
meteor_list.append([ff_name, meteor_No, rho, theta, centroids])
meteor_No += 1
# Add the FF file to the archive list if a meteor was detected on it
if meteor_data:
ff_detected.append(ff_name)
# Generate the name for the CALSTARS file
calstars_name = 'CALSTARS_' + "{:s}".format(str(config.stationID)) + '_' \
+ os.path.basename(night_data_dir) + '.txt'
# Write detected stars to the CALSTARS file
CALSTARS.writeCALSTARS(star_list, night_data_dir, calstars_name, config.stationID, config.height, \
config.width)
# Generate FTPdetectinfo file name
ftpdetectinfo_name = 'FTPdetectinfo_' + os.path.basename(
night_data_dir) + '.txt'
# Write FTPdetectinfo file
FTPdetectinfo.writeFTPdetectinfo(meteor_list, night_data_dir, ftpdetectinfo_name, night_data_dir, \
config.stationID, config.fps)
# Get the platepar file
platepar, platepar_path, platepar_fmt = getPlatepar(config)
# Run calibration check and auto astrometry refinement
if platepar is not None:
# Read in the CALSTARS file
calstars_list = CALSTARS.readCALSTARS(night_data_dir,
calstars_name)
# Run astrometry check and refinement
platepar, fit_status = autoCheckFit(config, platepar,
calstars_list)
# If the fit was sucessful, apply the astrometry to detected meteors
if fit_status:
log.info('Astrometric calibration SUCCESSFUL!')
# Save the refined platepar to the night directory and as default
platepar.write(os.path.join(night_data_dir,
config.platepar_name),
fmt=platepar_fmt)
platepar.write(platepar_path, fmt=platepar_fmt)
else:
log.info(
'Astrometric calibration FAILED!, Using old platepar for calibration...'
)
# Calculate astrometry for meteor detections
applyAstrometryFTPdetectinfo(night_data_dir, ftpdetectinfo_name,
platepar_path)
log.info('Plotting field sums...')
# Plot field sums to a graph
plotFieldsums(night_data_dir, config)
# Archive all fieldsums to one archive
archiveFieldsums(night_data_dir)
# List for any extra files which will be copied to the night archive directory. Full paths have to be
# given
extra_files = []
log.info('Making a flat...')
# Make a new flat field
flat_img = makeFlat(night_data_dir, config)
# If making flat was sucessfull, save it
if flat_img is not None:
# Save the flat in the root directory, to keep the operational flat updated
scipy.misc.imsave(config.flat_file, flat_img)
flat_path = os.path.join(os.getcwd(), config.flat_file)
log.info('Flat saved to: ' + flat_path)
# Copy the flat to the night's directory as well
extra_files.append(flat_path)
else:
log.info('Making flat image FAILED!')
### Add extra files to archive
# Add the platepar to the archive if it exists
if os.path.exists(platepar_path):
extra_files.append(platepar_path)
# Add the config file to the archive too
extra_files.append(os.path.join(os.getcwd(), '.config'))
### ###
night_archive_dir = os.path.join(os.path.abspath(config.data_dir),
config.archived_dir, night_data_dir_name)
log.info('Archiving detections to ' + night_archive_dir)
# Archive the detections
archive_name = archiveDetections(night_data_dir, night_archive_dir, ff_detected, config, \
extra_files=extra_files)
# Put the archive up for upload
if upload_manager is not None:
log.info('Adding file on upload list: ' + archive_name)
upload_manager.addFiles([archive_name])
# If capture was manually stopped, end program
if STOP_CAPTURE:
log.info('Ending program')
# Stop the upload manager
if upload_manager is not None:
if upload_manager.is_alive():
upload_manager.stop()
log.info('Closing upload manager...')
sys.exit()
def extractStars(ff_dir,
ff_name,
config=None,
max_global_intensity=150,
border=10,
neighborhood_size=10,
intensity_threshold=5,
flat_struct=None,
dark=None,
mask=None):
""" Extracts stars on a given FF bin by searching for local maxima and applying PSF fit for star
confirmation.
Source of one part of the code:
http://stackoverflow.com/questions/9111711/get-coordinates-of-local-maxima-in-2d-array-above-certain-value
Arguments:
ff_dir: [str] Path to directory where FF files are.
ff_name: [str] Name of the FF file.
config: [config object] configuration object (loaded from the .config file)
max_global_intensity: [int] maximum mean intensity of an image before it is discared as too bright
border: [int] apply a mask on the detections by removing all that are too close to the given image
border (in pixels)
neighborhood_size: [int] size of the neighbourhood for the maximum search (in pixels)
intensity_threshold: [float] a threshold for cutting the detections which are too faint (0-255)
flat_struct: [Flat struct] Structure containing the flat field. None by default.
dark: [ndarray] Dark frame. None by default.
mask: [ndarray] Mask image. None by default.
Return:
x2, y2, background, intensity, fwhm: [list of ndarrays]
- x2: X axis coordinates of the star
- y2: Y axis coordinates of the star
- background: background intensity
- intensity: intensity of the star
- Gaussian Full width at half maximum (FWHM) of fitted stars
"""
# This will be returned if there was an error
error_return = [[], [], [], [], [], []]
# Load parameters from config if given
if config:
max_global_intensity = config.max_global_intensity
border = config.border
neighborhood_size = config.neighborhood_size
intensity_threshold = config.intensity_threshold
# Load the FF bin file
ff = FFfile.read(ff_dir, ff_name)
# If the FF file could not be read, skip star extraction
if ff is None:
return error_return
# Apply the dark frame
if dark is not None:
ff.avepixel = Image.applyDark(ff.avepixel, dark)
# Apply the flat
if flat_struct is not None:
ff.avepixel = Image.applyFlat(ff.avepixel, flat_struct)
# Mask the FF file
if mask is not None:
ff = MaskImage.applyMask(ff, mask, ff_flag=True)
# Calculate image mean and stddev
global_mean = np.mean(ff.avepixel)
# Check if the image is too bright and skip the image
if global_mean > max_global_intensity:
return error_return
data = ff.avepixel.astype(np.float32)
# Apply a mean filter to the image to reduce noise
data = ndimage.filters.convolve(data, weights=np.full((2, 2), 1.0 / 4))
# Locate local maxima on the image
data_max = filters.maximum_filter(data, neighborhood_size)
maxima = (data == data_max)
data_min = filters.minimum_filter(data, neighborhood_size)
diff = ((data_max - data_min) > intensity_threshold)
maxima[diff == 0] = 0
# Apply a border mask
border_mask = np.ones_like(maxima) * 255
border_mask[:border, :] = 0
border_mask[-border:, :] = 0
border_mask[:, :border] = 0
border_mask[:, -border:] = 0
maxima = MaskImage.applyMask(maxima, border_mask, image=True)
# Remove all detections close to the mask image
if mask is not None:
erosion_kernel = np.ones((5, 5), mask.img.dtype)
mask_eroded = cv2.erode(mask.img, erosion_kernel, iterations=1)
maxima = MaskImage.applyMask(maxima, mask_eroded, image=True)
# Find and label the maxima
labeled, num_objects = ndimage.label(maxima)
# Skip the image if there are too many maxima to process
if num_objects > config.max_stars:
print('Too many candidate stars to process! {:d}/{:d}'.format(
num_objects, config.max_stars))
return error_return
# Find centres of mass of each labeled objects
xy = np.array(
ndimage.center_of_mass(data, labeled, range(1, num_objects + 1)))
# Remove all detection on the border
#xy = xy[np.where((xy[:, 1] > border) & (xy[:,1] < ff.ncols - border) & (xy[:,0] > border) & (xy[:,0] < ff.nrows - border))]
# Unpack star coordinates
y, x = np.hsplit(xy, 2)
# # Plot stars before the PSF fit
# plotStars(ff, x, y)
# Fit a PSF to each star
x2, y2, amplitude, intensity, sigma_y_fitted, sigma_x_fitted = fitPSF(
ff, global_mean, x, y, config)
# x2, y2, amplitude, intensity = list(x), list(y), [], [] # Skip PSF fit
# # Plot stars after PSF fit filtering
# plotStars(ff, x2, y2)
# Compute FWHM from one dimensional sigma
sigma_x_fitted = np.array(sigma_x_fitted)
sigma_y_fitted = np.array(sigma_y_fitted)
sigma_fitted = np.sqrt(sigma_x_fitted**2 + sigma_y_fitted**2)
fwhm = 2.355 * sigma_fitted
return ff_name, x2, y2, amplitude, intensity, fwhm
def fitPSF(ff, avepixel_mean, x2, y2, config):
""" Fit a 2D Gaussian to the star candidate cutout to check if it's a star.
Arguments:
ff: [ff bin struct] FF bin file loaded in the FF bin structure
avepixel_mean: [float] mean of the avepixel image
x2: [list] a list of estimated star position (X axis)
xy: [list] a list of estimated star position (Y axis)
config: [config object] configuration object (loaded from the .config file)
"""
# Load parameters form config if present
if config is not None:
# segment_radius: [int] radius (in pixels) of image segment around the detected star on which to
# perform the fit
# roundness_threshold: [float] minimum ratio of 2D Gaussian sigma X and sigma Y to be taken as a stars
# (hot pixels are narrow, while stars are round)
# max_feature_ratio: [float] maximum ratio between 2 sigma of the star and the image segment area
segment_radius = config.segment_radius
roundness_threshold = config.roundness_threshold
max_feature_ratio = config.max_feature_ratio
x_fitted = []
y_fitted = []
amplitude_fitted = []
intensity_fitted = []
sigma_y_fitted = []
sigma_x_fitted = []
# Set the initial guess
initial_guess = (30.0, segment_radius, segment_radius, 1.0, 1.0, 0.0,
avepixel_mean)
# Go through all stars
for star in zip(list(y2), list(x2)):
y, x = star
y_min = y - segment_radius
y_max = y + segment_radius
x_min = x - segment_radius
x_max = x + segment_radius
if y_min < 0:
y_min = np.array([0])
if y_max > ff.nrows:
y_max = np.array([ff.nrows])
if x_min < 0:
x_min = np.array([0])
if x_max > ff.ncols:
x_max = np.array([ff.ncols])
# Check if any of these values is NaN and skip the star
if np.any(np.isnan([x_min, x_max, y_min, y_max])):
continue
x_min = int(x_min)
x_max = int(x_max)
y_min = int(y_min)
y_max = int(y_max)
# Extract an image segment around each star
star_seg = ff.avepixel[y_min:y_max, x_min:x_max]
# Create x and y indices
y_ind, x_ind = np.indices(star_seg.shape)
# Estimate saturation level from image type
saturation = (2**(8 * star_seg.itemsize) - 1) * np.ones_like(y_ind)
# Fit a PSF to the star
try:
# Fit the 2D Gaussian with the limited number of iterations - this reduces the processing time
# and most of the bad star candidates take more iterations to fit
popt, pcov = opt.curve_fit(twoDGaussian, (y_ind, x_ind, saturation), star_seg.ravel(), \
p0=initial_guess, maxfev=200)
# print(popt)
except RuntimeError:
# print('Fitting failed!')
# Skip stars that can't be fitted in 200 iterations
continue
# Unpack fitted gaussian parameters
amplitude, yo, xo, sigma_y, sigma_x, theta, offset = popt
# Filter hot pixels by looking at the ratio between x and y sigmas (HPs are very narrow)
if min(sigma_y / sigma_x, sigma_x / sigma_y) < roundness_threshold:
# Skip if it is a hot pixel
continue
# Reject the star candidate if it is too large
if (4 * sigma_x * sigma_y / segment_radius**2 > max_feature_ratio):
continue
### If the fitting was successfull, compute the star intensity
# Crop the star segment to take 3 sigma portion around the star
crop_y_min = int(yo - 3 * sigma_y) + 1
if crop_y_min < 0: crop_y_min = 0
crop_y_max = int(yo + 3 * sigma_y) + 1
if crop_y_max >= star_seg.shape[0]: crop_y_max = star_seg.shape[0] - 1
crop_x_min = int(xo - 3 * sigma_x) + 1
if crop_x_min < 0: crop_x_min = 0
crop_x_max = int(xo + 3 * sigma_x) + 1
if crop_x_max >= star_seg.shape[1]: crop_x_max = star_seg.shape[1] - 1
# If the segment is too small, set a fixed size
if (y_max - y_min) < 3:
crop_y_min = int(yo - 2)
crop_y_max = int(yo + 2)
if (x_max - x_min) < 3:
crop_x_min = int(xo - 2)
crop_x_max = int(xo + 2)
star_seg_crop = star_seg[crop_y_min:crop_y_max, crop_x_min:crop_x_max]
# Skip the star if the shape is too small
if (star_seg_crop.shape[0] == 0) or (star_seg_crop.shape[1] == 0):
continue
# Gamma correct the star segment
star_seg_crop = Image.gammaCorrection(star_seg_crop.astype(np.float32),
config.gamma)
# Correct the background for gamma
bg_corrected = Image.gammaCorrection(offset, config.gamma)
# Subtract the background from the star segment and compute the total intensity
intensity = np.sum(star_seg_crop - bg_corrected)
# Skip stars with zero intensity
if intensity <= 0:
continue
# print(intensity)
# plt.imshow(star_seg_crop - bg_corrected, cmap='gray', vmin=0, vmax=255)
# plt.show()
###
# Calculate the intensity (as a volume under the 2D Gaussian) (OLD, before gamma correction)
# intensity = 2*np.pi*amplitude*sigma_x*sigma_y
# # Skip if the star intensity is below background level
# if intensity < offset:
# continue
# Add stars to the final list
x_fitted.append(x_min + xo)
y_fitted.append(y_min + yo)
amplitude_fitted.append(amplitude)
intensity_fitted.append(intensity)
sigma_y_fitted.append(sigma_y)
sigma_x_fitted.append(sigma_x)
# # Plot fitted stars
# data_fitted = twoDGaussian((y_ind, x_ind), *popt) - offset
# fig, ax = plt.subplots(1, 1)
# ax.hold(True)
# plt.title('Center Y: '+str(y_min[0])+', X:'+str(x_min[0]))
# ax.imshow(star_seg.reshape(segment_radius*2, segment_radius*2), cmap=plt.cm.inferno, origin='bottom',
# extent=(x_ind.min(), x_ind.max(), y_ind.min(), y_ind.max()))
# # ax.imshow(data_fitted.reshape(segment_radius*2, segment_radius*2), cmap=plt.cm.jet, origin='bottom')
# ax.contour(x_ind, y_ind, data_fitted.reshape(segment_radius*2, segment_radius*2), 8, colors='w')
# plt.show()
# plt.clf()
# plt.close()
return x_fitted, y_fitted, amplitude_fitted, intensity_fitted, sigma_y_fitted, sigma_x_fitted
def generateCalibrationReport(config, night_dir_path, match_radius=2.0, platepar=None, show_graphs=False):
""" Given the folder of the night, find the Calstars file, check the star fit and generate a report
with the quality of the calibration. The report contains information about both the astrometry and
the photometry calibration. Graphs will be saved in the given directory of the night.
Arguments:
config: [Config instance]
night_dir_path: [str] Full path to the directory of the night.
Keyword arguments:
match_radius: [float] Match radius for star matching between image and catalog stars (px).
platepar: [Platepar instance] Use this platepar instead of finding one in the folder.
show_graphs: [bool] Show the graphs on the screen. False by default.
Return:
None
"""
# Find the CALSTARS file in the given folder
calstars_file = None
for calstars_file in os.listdir(night_dir_path):
if ('CALSTARS' in calstars_file) and ('.txt' in calstars_file):
break
if calstars_file is None:
print('CALSTARS file could not be found in the given directory!')
return None
# Load the calstars file
star_list = readCALSTARS(night_dir_path, calstars_file)
### Load recalibrated platepars, if they exist ###
# Find recalibrated platepars file per FF file
platepars_recalibrated_file = None
for file_name in os.listdir(night_dir_path):
if file_name == config.platepars_recalibrated_name:
platepars_recalibrated_file = file_name
break
# Load all recalibrated platepars if the file is available
recalibrated_platepars = None
if platepars_recalibrated_file:
with open(os.path.join(night_dir_path, platepars_recalibrated_file)) as f:
recalibrated_platepars = json.load(f)
print('Loaded recalibrated platepars JSON file for the calibration report...')
### ###
### Load the platepar file ###
# Find the platepar file in the given directory if it was not given
if platepar is None:
# Find the platepar file
platepar_file = None
for file_name in os.listdir(night_dir_path):
if file_name == config.platepar_name:
platepar_file = file_name
break
if platepar_file is None:
print('The platepar cannot be found in the night directory!')
return None
# Load the platepar file
platepar = Platepar()
platepar.read(os.path.join(night_dir_path, platepar_file))
### ###
night_name = os.path.split(night_dir_path.strip(os.sep))[1]
# Go one mag deeper than in the config
lim_mag = config.catalog_mag_limit + 1
# Load catalog stars (load one magnitude deeper)
catalog_stars, mag_band_str, config.star_catalog_band_ratios = StarCatalog.readStarCatalog(\
config.star_catalog_path, config.star_catalog_file, lim_mag=lim_mag, \
mag_band_ratios=config.star_catalog_band_ratios)
### Take only those CALSTARS entires for which FF files exist in the folder ###
# Get a list of FF files in the folder\
ff_list = []
for file_name in os.listdir(night_dir_path):
if validFFName(file_name):
ff_list.append(file_name)
# Filter out calstars entries, generate a star dictionary where the keys are JDs of FFs
star_dict = {}
ff_dict = {}
for entry in star_list:
ff_name, star_data = entry
# Check if the FF from CALSTARS exists in the folder
if ff_name not in ff_list:
continue
dt = getMiddleTimeFF(ff_name, config.fps, ret_milliseconds=True)
jd = date2JD(*dt)
# Add the time and the stars to the dict
star_dict[jd] = star_data
ff_dict[jd] = ff_name
### ###
# If there are no FF files in the directory, don't generate a report
if len(star_dict) == 0:
print('No FF files from the CALSTARS file in the directory!')
return None
# If the recalibrated platepars file exists, take the one with the most stars
max_jd = 0
if recalibrated_platepars is not None:
max_stars = 0
for ff_name_temp in recalibrated_platepars:
# Compute the Julian date of the FF middle
dt = getMiddleTimeFF(ff_name_temp, config.fps, ret_milliseconds=True)
jd = date2JD(*dt)
# Check that this file exists in CALSTARS and the list of FF files
if (jd not in star_dict) or (jd not in ff_dict):
continue
# Check if the number of stars on this FF file is larger than the before
if len(star_dict[jd]) > max_stars:
max_jd = jd
max_stars = len(star_dict[jd])
# Set a flag to indicate if using recalibrated platepars has failed
if max_jd == 0:
using_recalib_platepars = False
else:
print('Using recalibrated platepars, file:', ff_dict[max_jd])
using_recalib_platepars = True
# Select the platepar where the FF file has the most stars
platepar_dict = recalibrated_platepars[ff_dict[max_jd]]
platepar = Platepar()
platepar.loadFromDict(platepar_dict)
filtered_star_dict = {max_jd: star_dict[max_jd]}
# Match stars on the image with the stars in the catalog
n_matched, avg_dist, cost, matched_stars = matchStarsResiduals(config, platepar, catalog_stars, \
filtered_star_dict, match_radius, ret_nmatch=True, lim_mag=lim_mag)
max_matched_stars = n_matched
# Otherwise take the optimal FF file for evaluation
if (recalibrated_platepars is None) or (not using_recalib_platepars):
# If there are more than a set number of FF files to evaluate, choose only the ones with most stars on
# the image
if len(star_dict) > config.calstars_files_N:
# Find JDs of FF files with most stars on them
top_nstars_indices = np.argsort([len(x) for x in star_dict.values()])[::-1][:config.calstars_files_N \
- 1]
filtered_star_dict = {}
for i in top_nstars_indices:
filtered_star_dict[list(star_dict.keys())[i]] = list(star_dict.values())[i]
star_dict = filtered_star_dict
# Match stars on the image with the stars in the catalog
n_matched, avg_dist, cost, matched_stars = matchStarsResiduals(config, platepar, catalog_stars, \
star_dict, match_radius, ret_nmatch=True, lim_mag=lim_mag)
# If no recalibrated platepars where found, find the image with the largest number of matched stars
if (not using_recalib_platepars) or (max_jd == 0):
max_jd = 0
max_matched_stars = 0
for jd in matched_stars:
_, _, distances = matched_stars[jd]
if len(distances) > max_matched_stars:
max_jd = jd
max_matched_stars = len(distances)
# If there are no matched stars, use the image with the largest number of detected stars
if max_matched_stars <= 2:
max_jd = max(star_dict, key=lambda x: len(star_dict[x]))
distances = [np.inf]
# Take the FF file with the largest number of matched stars
ff_name = ff_dict[max_jd]
# Load the FF file
ff = readFF(night_dir_path, ff_name)
img_h, img_w = ff.avepixel.shape
dpi = 200
plt.figure(figsize=(ff.avepixel.shape[1]/dpi, ff.avepixel.shape[0]/dpi), dpi=dpi)
# Take the average pixel
img = ff.avepixel
# Slightly adjust the levels
img = Image.adjustLevels(img, np.percentile(img, 1.0), 1.2, np.percentile(img, 99.99))
plt.imshow(img, cmap='gray', interpolation='nearest')
legend_handles = []
# Plot detected stars
for img_star in star_dict[max_jd]:
y, x, _, _ = img_star
rect_side = 5*match_radius
square_patch = plt.Rectangle((x - rect_side/2, y - rect_side/2), rect_side, rect_side, color='g', \
fill=False, label='Image stars')
plt.gca().add_artist(square_patch)
legend_handles.append(square_patch)
# If there are matched stars, plot them
if max_matched_stars > 2:
# Take the solution with the largest number of matched stars
image_stars, matched_catalog_stars, distances = matched_stars[max_jd]
# Plot matched stars
for img_star in image_stars:
x, y, _, _ = img_star
circle_patch = plt.Circle((y, x), radius=3*match_radius, color='y', fill=False, \
label='Matched stars')
plt.gca().add_artist(circle_patch)
legend_handles.append(circle_patch)
### Plot match residuals ###
# Compute preducted positions of matched image stars from the catalog
x_predicted, y_predicted = raDecToXYPP(matched_catalog_stars[:, 0], \
matched_catalog_stars[:, 1], max_jd, platepar)
img_y, img_x, _, _ = image_stars.T
delta_x = x_predicted - img_x
delta_y = y_predicted - img_y
# Compute image residual and angle of the error
res_angle = np.arctan2(delta_y, delta_x)
res_distance = np.sqrt(delta_x**2 + delta_y**2)
# Calculate coordinates of the beginning of the residual line
res_x_beg = img_x + 3*match_radius*np.cos(res_angle)
res_y_beg = img_y + 3*match_radius*np.sin(res_angle)
# Calculate coordinates of the end of the residual line
res_x_end = img_x + 100*np.cos(res_angle)*res_distance
res_y_end = img_y + 100*np.sin(res_angle)*res_distance
# Plot the 100x residuals
for i in range(len(x_predicted)):
res_plot = plt.plot([res_x_beg[i], res_x_end[i]], [res_y_beg[i], res_y_end[i]], color='orange', \
lw=0.5, label='100x residuals')
legend_handles.append(res_plot[0])
### ###
else:
distances = [np.inf]
# If there are no matched stars, plot large text in the middle of the screen
plt.text(img_w/2, img_h/2, "NO MATCHED STARS!", color='r', alpha=0.5, fontsize=20, ha='center',
va='center')
### Plot positions of catalog stars to the limiting magnitude of the faintest matched star + 1 mag ###
# Find the faintest magnitude among matched stars
if max_matched_stars > 2:
faintest_mag = np.max(matched_catalog_stars[:, 2]) + 1
else:
# If there are no matched stars, use the limiting magnitude from config
faintest_mag = config.catalog_mag_limit + 1
# Estimate RA,dec of the centre of the FOV
_, RA_c, dec_c, _ = xyToRaDecPP([jd2Date(max_jd)], [platepar.X_res/2], [platepar.Y_res/2], [1],
platepar)
RA_c = RA_c[0]
dec_c = dec_c[0]
fov_radius = np.hypot(*computeFOVSize(platepar))
# Get stars from the catalog around the defined center in a given radius
_, extracted_catalog = subsetCatalog(catalog_stars, RA_c, dec_c, fov_radius, faintest_mag)
ra_catalog, dec_catalog, mag_catalog = extracted_catalog.T
# Compute image positions of all catalog stars that should be on the image
x_catalog, y_catalog = raDecToXYPP(ra_catalog, dec_catalog, max_jd, platepar)
# Filter all catalog stars outside the image
temp_arr = np.c_[x_catalog, y_catalog, mag_catalog]
temp_arr = temp_arr[temp_arr[:, 0] >= 0]
temp_arr = temp_arr[temp_arr[:, 0] <= ff.avepixel.shape[1]]
temp_arr = temp_arr[temp_arr[:, 1] >= 0]
temp_arr = temp_arr[temp_arr[:, 1] <= ff.avepixel.shape[0]]
x_catalog, y_catalog, mag_catalog = temp_arr.T
# Plot catalog stars on the image
cat_stars_handle = plt.scatter(x_catalog, y_catalog, c='none', marker='D', lw=1.0, alpha=0.4, \
s=((4.0 + (faintest_mag - mag_catalog))/3.0)**(2*2.512), edgecolor='r', label='Catalog stars')
legend_handles.append(cat_stars_handle)
### ###
# Add info text
info_text = ff_dict[max_jd] + '\n' \
+ "Matched stars: {:d}/{:d}\n".format(max_matched_stars, len(star_dict[max_jd])) \
+ "Median distance: {:.2f} px\n".format(np.median(distances)) \
+ "Catalog limiting magnitude: {:.1f}".format(lim_mag)
plt.text(10, 10, info_text, bbox=dict(facecolor='black', alpha=0.5), va='top', ha='left', fontsize=4, \
color='w')
legend = plt.legend(handles=legend_handles, prop={'size': 4}, loc='upper right')
legend.get_frame().set_facecolor('k')
legend.get_frame().set_edgecolor('k')
for txt in legend.get_texts():
txt.set_color('w')
plt.axis('off')
plt.gca().get_xaxis().set_visible(False)
plt.gca().get_yaxis().set_visible(False)
plt.xlim([0, ff.avepixel.shape[1]])
plt.ylim([ff.avepixel.shape[0], 0])
# Remove the margins
plt.subplots_adjust(left=0, bottom=0, right=1, top=1, wspace=0, hspace=0)
plt.savefig(os.path.join(night_dir_path, night_name + '_calib_report_astrometry.jpg'), \
bbox_inches='tight', pad_inches=0, dpi=dpi)
if show_graphs:
plt.show()
else:
plt.clf()
plt.close()
if max_matched_stars > 2:
### Plot the photometry ###
plt.figure(dpi=dpi)
# Take only those stars which are inside the 3/4 of the shorter image axis from the center
photom_selection_radius = np.min([img_h, img_w])/3
filter_indices = ((image_stars[:, 0] - img_h/2)**2 + (image_stars[:, 1] \
- img_w/2)**2) <= photom_selection_radius**2
star_intensities = image_stars[filter_indices, 2]
catalog_mags = matched_catalog_stars[filter_indices, 2]
# Plot intensities of image stars
#star_intensities = image_stars[:, 2]
plt.scatter(-2.5*np.log10(star_intensities), catalog_mags, s=5, c='r')
# Fit the photometry on automated star intensities
photom_offset, fit_stddev, _ = photometryFit(np.log10(star_intensities), catalog_mags)
# Plot photometric offset from the platepar
x_min, x_max = plt.gca().get_xlim()
y_min, y_max = plt.gca().get_ylim()
x_min_w = x_min - 3
x_max_w = x_max + 3
y_min_w = y_min - 3
y_max_w = y_max + 3
photometry_info = 'Platepar: {:+.2f}LSP {:+.2f} +/- {:.2f} \nGamma = {:.2f}'.format(platepar.mag_0, \
platepar.mag_lev, platepar.mag_lev_stddev, platepar.gamma)
# Plot the photometry calibration from the platepar
logsum_arr = np.linspace(x_min_w, x_max_w, 10)
plt.plot(logsum_arr, logsum_arr + platepar.mag_lev, label=photometry_info, linestyle='--', \
color='k', alpha=0.5)
# Plot the fitted photometry calibration
fit_info = "Fit: {:+.2f}LSP {:+.2f} +/- {:.2f}".format(-2.5, photom_offset, fit_stddev)
plt.plot(logsum_arr, logsum_arr + photom_offset, label=fit_info, linestyle='--', color='red',
alpha=0.5)
plt.legend()
plt.ylabel("Catalog magnitude ({:s})".format(mag_band_str))
plt.xlabel("Uncalibrated magnitude")
# Set wider axis limits
plt.xlim(x_min_w, x_max_w)
plt.ylim(y_min_w, y_max_w)
plt.gca().invert_yaxis()
plt.gca().invert_xaxis()
plt.grid()
plt.savefig(os.path.join(night_dir_path, night_name + '_calib_report_photometry.png'), dpi=150)
if show_graphs:
plt.show()
else:
plt.clf()
plt.close()
def updateImage(self):
""" Updates the current plot. """
# Reset circle patches
self.circle_aperature = None
self.circle_aperature_outer = None
# Reset centroid patch
self.centroid_handle = None
# Reset photometry coloring
self.photometry_coloring_handle = None
# Save the previous zoom
if self.current_image is not None:
self.prev_xlim = plt.gca().get_xlim()
self.prev_ylim = plt.gca().get_ylim()
plt.clf()
# PNG mode
if self.png_mode:
# Get path to current PNG image
self.png_img_path = os.path.join(
self.png_dir, self.png_list[int(self.current_frame)])
# Read the image
img = scipy.misc.imread(self.png_img_path)
# FF mode
else:
# If FF is given, reconstruct frames
if self.ff is not None:
# Take the current frame from FF file
img = np.copy(self.ff.avepixel)
frame_mask = np.where(
self.ff.maxframe == int(self.current_frame))
img[frame_mask] = self.ff.maxpixel[frame_mask]
# Otherwise, create a blank background with the size enough to fit the FR bin
else:
# Get the maximum extent of the meteor frames
y_size = max(max(np.array(self.fr.yc[i]) + np.array(self.fr.size[i])//2) for i in \
range(self.fr.lines))
x_size = max(max(np.array(self.fr.xc[i]) + np.array(self.fr.size[i])//2) for i in \
range(self.fr.lines))
# Make the image square
img_size = max(y_size, x_size)
img = np.zeros((img_size, img_size), np.uint8)
# If FR is given, paste the raw frame onto the image
if self.fr is not None:
# Compute the index of the frame in the FR bin structure
frame_indx = int(
self.current_frame) - self.fr.t[self.current_line][0]
# Reconstruct the frame if it is within the bounds
if (frame_indx < self.fr.frameNum[self.current_line]) and (
frame_indx >= 0):
# Get the center position of the detection on the current frame
yc = self.fr.yc[self.current_line][frame_indx]
xc = self.fr.xc[self.current_line][frame_indx]
# # Get the frame number
# t = self.fr.t[self.current_line][frame_indx]
# Get the size of the window
size = self.fr.size[self.current_line][frame_indx]
# Paste the frames onto the big image
y_img = np.arange(yc - size // 2, yc + size // 2)
x_img = np.arange(xc - size // 2, xc + size // 2)
Y_img, X_img = np.meshgrid(y_img, x_img)
y_frame = np.arange(len(y_img))
x_frame = np.arange(len(x_img))
Y_frame, X_frame = np.meshgrid(y_frame, x_frame)
img[Y_img, X_img] = self.fr.frames[
self.current_line][frame_indx][Y_frame, X_frame]
# Save the limits of the FR
self.fr_xmin = np.min(x_img)
self.fr_xmax = np.max(x_img)
self.fr_ymin = np.max(y_img)
self.fr_ymax = np.min(y_img)
# Draw a red rectangle around the pasted frame
rect_x = np.min(x_img)
rect_y = np.max(y_img)
rect_w = np.max(x_img) - rect_x
rect_h = np.min(y_img) - rect_y
plt.gca().add_patch(mpatches.Rectangle((rect_x, rect_y), rect_w, rect_h, fill=None, \
edgecolor='red', alpha=0.5))
# Apply the deinterlace
if self.deinterlace_mode > -1:
# Set the deinterlace index to handle proper deinterlacing order
if self.deinterlace_mode == 0:
deinter_indx = 0
else:
deinter_indx = 1
# Deinterlace the image using the appropriate method
if (self.current_frame + deinter_indx * 0.5) % 1 == 0:
img = Image.deinterlaceOdd(img)
else:
img = Image.deinterlaceEven(img)
# Current image without adjustments
self.current_image = np.copy(img)
### Adjust image levels
# Guess the bit depth from the array type
bit_depth = 8 * img.itemsize
img = Image.adjustLevels(img, 0, self.img_gamma, (2**bit_depth - 1),
bit_depth)
###
plt.imshow(img, cmap='gray', vmin=0, vmax=255)
if (self.prev_xlim is not None) and (self.prev_ylim is not None):
# Restore previous zoom
plt.xlim(self.prev_xlim)
plt.ylim(self.prev_ylim)
self.drawText()
# Don't draw the picks in the photometry coloring more
if not self.photometry_coloring_mode:
# Plot image pick
self.drawPicks(update_plot=False)
# Plot the photometry coloring
self.drawPhotometryColoring(update_plot=False)
plt.gcf().canvas.draw()
def extractStars(ff_dir, ff_name, config=None, max_global_intensity=150, border=10, neighborhood_size=10,
intensity_threshold=5, flat_struct=None, dark=None, mask=None):
""" Extracts stars on a given FF bin by searching for local maxima and applying PSF fit for star
confirmation.
Source of one part of the code:
http://stackoverflow.com/questions/9111711/get-coordinates-of-local-maxima-in-2d-array-above-certain-value
Arguments:
ff: [ff bin struct] FF bin file loaded in the FF bin structure
config: [config object] configuration object (loaded from the .config file)
max_global_intensity: [int] maximum mean intensity of an image before it is discared as too bright
border: [int] apply a mask on the detections by removing all that are too close to the given image
border (in pixels)
neighborhood_size: [int] size of the neighbourhood for the maximum search (in pixels)
intensity_threshold: [float] a threshold for cutting the detections which are too faint (0-255)
flat_struct: [Flat struct] Structure containing the flat field. None by default.
dark: [ndarray] Dark frame. None by default.
mask: [ndarray] Mask image. None by default.
Return:
x2, y2, background, intensity, sigma_fitted: [list of ndarrays]
- x2: X axis coordinates of the star
- y2: Y axis coordinates of the star
- background: background intensity
- intensity: intensity of the star
- Gaussian stddev of fitted stars
"""
# This will be returned if there was an error
error_return = [[], [], [], []]
# Load parameters from config if given
if config:
max_global_intensity = config.max_global_intensity
border = config.border
neighborhood_size = config.neighborhood_size
intensity_threshold = config.intensity_threshold
# Load the FF bin file
ff = FFfile.read(ff_dir, ff_name)
# If the FF file could not be read, skip star extraction
if ff is None:
return error_return
# Apply the dark frame
if dark is not None:
ff.avepixel = Image.applyDark(ff.avepixel, dark)
# Apply the flat
if flat_struct is not None:
ff.avepixel = Image.applyFlat(ff.avepixel, flat_struct)
# Mask the FF file
if mask is not None:
ff = MaskImage.applyMask(ff, mask, ff_flag=True)
# Calculate image mean and stddev
global_mean = np.mean(ff.avepixel)
# Check if the image is too bright and skip the image
if global_mean > max_global_intensity:
return error_return
data = ff.avepixel.astype(np.float32)
# Apply a mean filter to the image to reduce noise
data = ndimage.filters.convolve(data, weights=np.full((2, 2), 1.0/4))
# Locate local maxima on the image
data_max = filters.maximum_filter(data, neighborhood_size)
maxima = (data == data_max)
data_min = filters.minimum_filter(data, neighborhood_size)
diff = ((data_max - data_min) > intensity_threshold)
maxima[diff == 0] = 0
# Apply a border mask
border_mask = np.ones_like(maxima)*255
border_mask[:border,:] = 0
border_mask[-border:,:] = 0
border_mask[:,:border] = 0
border_mask[:,-border:] = 0
maxima = MaskImage.applyMask(maxima, border_mask, image=True)
# Find and label the maxima
labeled, num_objects = ndimage.label(maxima)
# Skip the image if there are too many maxima to process
if num_objects > config.max_stars:
print('Too many candidate stars to process! {:d}/{:d}'.format(num_objects, config.max_stars))
return error_return
# Find centres of mass of each labeled objects
xy = np.array(ndimage.center_of_mass(data, labeled, range(1, num_objects+1)))
# Remove all detection on the border
#xy = xy[np.where((xy[:, 1] > border) & (xy[:,1] < ff.ncols - border) & (xy[:,0] > border) & (xy[:,0] < ff.nrows - border))]
# Unpack star coordinates
y, x = np.hsplit(xy, 2)
# # Plot stars before the PSF fit
# plotStars(ff, x, y)
# Fit a PSF to each star
x2, y2, amplitude, intensity, sigma_y_fitted, sigma_x_fitted = fitPSF(ff, global_mean, x, y, config)
# x2, y2, amplitude, intensity = list(x), list(y), [], [] # Skip PSF fit
# # Plot stars after PSF fit filtering
# plotStars(ff, x2, y2)
# Compute one dimensional sigma
sigma_x_fitted = np.array(sigma_x_fitted)
sigma_y_fitted = np.array(sigma_y_fitted)
sigma_fitted = np.sqrt(sigma_x_fitted**2 + sigma_y_fitted**2)
return ff_name, x2, y2, amplitude, intensity, sigma_fitted
def getThresholdedStripe3DPoints(config, img_handle, frame_min, frame_max, rho, theta, mask, flat_struct, \
dark, stripe_width_factor=1.0, centroiding=False, point1=None, point2=None, debug=False):
""" Threshold the image and get a list of pixel positions and frames of threshold passers.
This function handles all input types of data.
Arguments;
config: [config object] configuration object (loaded from the .config file).
img_handle: [FrameInterface instance] Object which has a common interface to various input files.
frame_min: [int] First frame to process.
frame_max: [int] Last frame to process.
rho: [float] Line distance from the center in HT space (pixels).
theta: [float] Angle in degrees in HT space.
mask: [ndarray] Image mask.
flat_struct: [Flat struct] Structure containing the flat field. None by default.
dark: [ndarray] Dark frame.
Keyword arguments:
stripe_width_factor: [float] Multipler by which the default stripe width will be multiplied. Default
is 1.0
centroiding: [bool] If True, the indices will be returned in the centroiding mode, which means
that point1 and point2 arguments must be given.
point1: [list] (x, y, frame) Of the first reference point of the detection.
point2: [list] (x, y, frame) Of the second reference point of the detection.
debug: [bool] If True, extra debug messages and plots will be shown.
Return:
xs, ys, zs: [tuple of lists] Indices of (x, y, frame) of threshold passers for every frame.
"""
# Get indices of stripe pixels around the line of the meteor
img_h, img_w = img_handle.ff.maxpixel.shape
stripe_indices = getStripeIndices(
rho, theta, stripe_width_factor * config.stripe_width, img_h, img_w)
# If centroiding should be done, prepare everything for cutting out parts of the image for photometry
if centroiding:
# Compute the unit vector which describes the motion of the meteor in the image domain
point1 = np.array(point1)
point2 = np.array(point2)
motion_vect = point2[:2] - point1[:2]
motion_vect_unit = vectNorm(motion_vect)
# Get coordinates of 2 points that describe the line
x1, y1, z1 = point1
x2, y2, z2 = point2
# Compute the average angular velocity in px per frame
ang_vel = np.sqrt((x2 - x1)**2 + (y2 - y1)**2) / (z2 - z1)
# Compute the vector describing the length and direction of the meteor per frame
motion_vect = ang_vel * motion_vect_unit
# If the FF files is given, extract the points from FF after threshold
if img_handle.input_type == 'ff':
# Threshold the FF file
img_thres = Image.thresholdFF(img_handle.ff, config.k1_det, config.j1_det, mask=mask, \
mask_ave_bright=False)
# Extract the thresholded image by min and max frames from FF file
img = selectFFFrames(np.copy(img_thres), img_handle.ff, frame_min,
frame_max)
# Remove lonely pixels
img = morph.clean(img)
# Extract the stripe from the thresholded image
stripe = np.zeros(img.shape, img.dtype)
stripe[stripe_indices] = img[stripe_indices]
# Show stripe
# show2("stripe", stripe*255)
# Show 3D could
# show3DCloud(ff, stripe)
# Get stripe positions (x, y, frame)
stripe_positions = stripe.nonzero()
xs = stripe_positions[1]
ys = stripe_positions[0]
zs = img_handle.ff.maxframe[stripe_positions]
return xs, ys, zs
# If video frames are available, extract indices on all frames in the given range
else:
xs_array = []
ys_array = []
zs_array = []
# Go through all frames in the frame range
for fr in range(frame_min, frame_max + 1):
# Break the loop if outside frame size
if fr == (img_handle.total_frames - 1):
break
# Set the frame number
img_handle.setFrame(fr)
# Load the frame
fr_img = img_handle.loadFrame()
# Apply the dark frame
if dark is not None:
fr_img = Image.applyDark(fr_img, dark)
# Apply the flat to frame
if flat_struct is not None:
fr_img = Image.applyFlat(fr_img, flat_struct)
# Mask the image
fr_img = MaskImage.applyMask(fr_img, mask)
# Threshold the frame
img_thres = Image.thresholdImg(fr_img, img_handle.ff.avepixel, img_handle.ff.stdpixel, \
config.k1_det, config.j1_det, mask=mask, mask_ave_bright=False)
# Remove lonely pixels
img_thres = morph.clean(img_thres)
# Extract the stripe from the thresholded image
stripe = np.zeros(img_thres.shape, img_thres.dtype)
stripe[stripe_indices] = img_thres[stripe_indices]
# Include more pixels for centroiding and photometry and mask out per frame pixels
if centroiding:
# Dilate the pixels in the stripe twice, to include more pixels for photometry
stripe = morph.dilate(stripe)
stripe = morph.dilate(stripe)
# Get indices of the stripe that is perpendicular to the meteor, and whose thickness is the
# length of the meteor on this particular frame - this is called stripe_indices_motion
# Compute the previous, current, and the next linear model position of the meteor on the
# image
model_pos_prev = point1[:2] + (fr - 1 - z1) * motion_vect
model_pos = point1[:2] + (fr - z1) * motion_vect
model_pos_next = point1[:2] + (fr + 1 - z1) * motion_vect
# Get the rho, theta of the line perpendicular to the meteor line
x_inters, y_inters = model_pos
# Check if the previous, current or the next centroids are outside bounds, and if so, skip the
# frame
if (not checkCentroidBounds(model_pos_prev, img_w, img_h)) or \
(not checkCentroidBounds(model_pos, img_w, img_h)) or \
(not checkCentroidBounds(model_pos_next, img_w, img_h)):
continue
# Get parameters of the perpendicular line to the meteor line
rho2, theta2 = htLinePerpendicular(rho, theta, x_inters,
y_inters, img_h, img_w)
# Compute the image indices of this position which will be the intersection with the stripe
# The width of the line will be 2x the angular velocity
stripe_length = 6 * ang_vel
if stripe_length < stripe_width_factor * config.stripe_width:
stripe_length = stripe_width_factor * config.stripe_width
stripe_indices_motion = getStripeIndices(
rho2, theta2, stripe_length, img_h, img_w)
# Mark only those parts which overlap both lines, which effectively creates a mask for
# photometry an centroiding, excluding other influences
stripe_new = np.zeros_like(stripe)
stripe_new[stripe_indices_motion] = stripe[
stripe_indices_motion]
stripe = stripe_new
if debug:
# Show the extracted stripe
img_stripe = np.zeros_like(stripe)
img_stripe[stripe_indices] = 1
final_stripe = np.zeros_like(stripe)
final_stripe[stripe_indices_motion] = img_stripe[
stripe_indices_motion]
plt.imshow(final_stripe)
plt.show()
if debug and centroiding:
print(fr)
print('mean stdpixel3:', np.mean(img_handle.ff.stdpixel))
print('mean avepixel3:', np.mean(img_handle.ff.avepixel))
print('mean frame:', np.mean(fr_img))
fig, (ax1, ax2) = plt.subplots(nrows=2,
sharex=True,
sharey=True)
fr_img_noavg = Image.applyDark(fr_img, img_handle.ff.avepixel)
#fr_img_noavg = fr_img
# Auto levels
min_lvl = np.percentile(fr_img_noavg[2:, :], 1)
max_lvl = np.percentile(fr_img_noavg[2:, :], 99.0)
# Adjust levels
fr_img_autolevel = Image.adjustLevels(fr_img_noavg, min_lvl,
1.0, max_lvl)
ax1.imshow(stripe, cmap='gray')
ax2.imshow(fr_img_autolevel, cmap='gray')
plt.show()
pass
# Get stripe positions (x, y, frame)
stripe_positions = stripe.nonzero()
xs = stripe_positions[1]
ys = stripe_positions[0]
zs = np.zeros_like(xs) + fr
# Add the points to the list
xs_array.append(xs)
ys_array.append(ys)
zs_array.append(zs)
if debug:
print('---')
print(stripe.nonzero())
print(xs, ys, zs)
if len(xs_array) > 0:
# Flatten the arrays
xs_array = np.concatenate(xs_array)
ys_array = np.concatenate(ys_array)
zs_array = np.concatenate(zs_array)
else:
xs_array = np.array(xs_array)
ys_array = np.array(ys_array)
zs_array = np.array(zs_array)
return xs_array, ys_array, zs_array
def extractStars(ff_dir,
ff_name,
config=None,
max_global_intensity=150,
border=10,
neighborhood_size=10,
intensity_threshold=5,
flat_struct=None):
""" Extracts stars on a given FF bin by searching for local maxima and applying PSF fit for star
confirmation.
Source of one part of the code:
http://stackoverflow.com/questions/9111711/get-coordinates-of-local-maxima-in-2d-array-above-certain-value
Arguments:
ff: [ff bin struct] FF bin file loaded in the FF bin structure
config: [config object] configuration object (loaded from the .config file)
max_global_intensity: [int] maximum mean intensity of an image before it is discared as too bright
border: [int] apply a mask on the detections by removing all that are too close to the given image
border (in pixels)
neighborhood_size: [int] size of the neighbourhood for the maximum search (in pixels)
intensity_threshold: [float] a threshold for cutting the detections which are too faint (0-255)
flat_struct: [Flat struct] Structure containing the flat field. None by default.
Return:
x2, y2, background, intensity: [list of ndarrays]
- x2: X axis coordinates of the star
- y2: Y axis coordinates of the star
- background: background intensity
- intensity: intensity of the star
"""
# Load parameters from config if given
if config:
max_global_intensity = config.max_global_intensity
border = config.border
neighborhood_size = config.neighborhood_size
intensity_threshold = config.intensity_threshold
# Load the FF bin file
ff = FFfile.read(ff_dir, ff_name)
# Load the mask file
mask = MaskImage.loadMask(config.mask_file)
# Mask the FF file
ff = MaskImage.applyMask(ff, mask, ff_flag=True)
# Apply the flat to maxpixel and avepixel
if flat_struct is not None:
ff.maxpixel = Image.applyFlat(ff.maxpixel, flat_struct)
ff.avepixel = Image.applyFlat(ff.avepixel, flat_struct)
# Calculate image mean and stddev
global_mean = np.mean(ff.avepixel)
# Check if the image is too bright and skip the image
if global_mean > max_global_intensity:
return [[], [], [], []]
data = ff.avepixel.astype(np.float32)
# Apply a mean filter to the image to reduce noise
data = ndimage.filters.convolve(data, weights=np.full((2, 2), 1.0 / 4))
# Locate local maxima on the image
data_max = filters.maximum_filter(data, neighborhood_size)
maxima = (data == data_max)
data_min = filters.minimum_filter(data, neighborhood_size)
diff = ((data_max - data_min) > intensity_threshold)
maxima[diff == 0] = 0
# Apply a border mask
border_mask = np.ones_like(maxima) * 255
border_mask[:border, :] = 0
border_mask[-border:, :] = 0
border_mask[:, :border] = 0
border_mask[:, -border:] = 0
maxima = MaskImage.applyMask(maxima, (True, border_mask))
# Find and label the maxima
labeled, num_objects = ndimage.label(maxima)
# Skip the image if there are too many maxima to process
if num_objects > config.max_stars:
print('Too many candidate stars to process! {:d}/{:d}'.format(
num_objects, config.max_stars))
return [[], [], [], []]
# Find centres of mass of each labeled objects
xy = np.array(
ndimage.center_of_mass(data, labeled, range(1, num_objects + 1)))
# Remove all detection on the border
#xy = xy[np.where((xy[:, 1] > border) & (xy[:,1] < ff.ncols - border) & (xy[:,0] > border) & (xy[:,0] < ff.nrows - border))]
# Unpack star coordinates
y, x = np.hsplit(xy, 2)
# # Plot stars before the PSF fit
# plotStars(ff, x, y)
# Fit a PSF to each star
x2, y2, amplitude, intensity = fitPSF(ff, global_mean, x, y, config=config)
# x2, y2, amplitude, intensity = list(x), list(y), [], [] # Skip PSF fit
# # Plot stars after PSF fit filtering
# plotStars(ff, x2, y2)
return x2, y2, amplitude, intensity
# Check if there are any file in the directory
if(len(ff_list) == None):
print("No files found!")
sys.exit()
# Try loading a flat field image
flat_struct = None
if config.use_flat:
# Check if there is flat in the data directory
if os.path.exists(os.path.join(ff_dir, config.flat_file)):
flat_struct = Image.loadFlat(ff_dir, config.flat_file)
# Try loading the default flat
elif os.path.exists(config.flat_file):
flat_struct = Image.loadFlat(os.getcwd(), config.flat_file)
# Initialize the detector
detector = QueuedPool(detectStarsAndMeteors, cores=-1, log=log)
# Give detector jobs
for ff_name in ff_list:
detector.addJob([ff_dir, ff_name, config, flat_struct])
# Start the detection
# Get paths to every FF bin file in a directory
ff_list = [ff for ff in os.listdir(dir_path) if FFfile.validFFName(ff)]
# Check if there are any file in the directory
if (len(ff_list) == None):
print("No files found!")
sys.exit()
# Try loading a flat field image
flat_struct = None
if config.use_flat:
# Check if there is flat in the data directory
if os.path.exists(os.path.join(dir_path, config.flat_file)):
flat_struct = Image.loadFlat(dir_path, config.flat_file)
# Try loading the default flat
elif os.path.exists(config.flat_file):
flat_struct = Image.loadFlat(os.getcwd(), config.flat_file)
# Init results list
results_list = []
# Open a file for results
results_path = os.path.abspath(dir_path) + os.sep
results_name = results_path.split(os.sep)[-2]
results_file = open(results_path + results_name + '_results.txt', 'w')
total_meteors = 0
def generateCalibrationReport(config,
night_dir_path,
match_radius=2.0,
platepar=None,
show_graphs=False):
""" Given the folder of the night, find the Calstars file, check the star fit and generate a report
with the quality of the calibration. The report contains information about both the astrometry and
the photometry calibration. Graphs will be saved in the given directory of the night.
Arguments:
config: [Config instance]
night_dir_path: [str] Full path to the directory of the night.
Keyword arguments:
match_radius: [float] Match radius for star matching between image and catalog stars (px).
platepar: [Platepar instance] Use this platepar instead of finding one in the folder.
show_graphs: [bool] Show the graphs on the screen. False by default.
Return:
None
"""
# Find the CALSTARS file in the given folder
calstars_file = None
for calstars_file in os.listdir(night_dir_path):
if ('CALSTARS' in calstars_file) and ('.txt' in calstars_file):
break
if calstars_file is None:
print('CALSTARS file could not be found in the given directory!')
return None
# Load the calstars file
star_list = readCALSTARS(night_dir_path, calstars_file)
### Load recalibrated platepars, if they exist ###
# Find recalibrated platepars file per FF file
platepars_recalibrated_file = None
for file_name in os.listdir(night_dir_path):
if file_name == config.platepars_recalibrated_name:
platepars_recalibrated_file = file_name
break
# Load all recalibrated platepars if the file is available
recalibrated_platepars = None
if platepars_recalibrated_file:
with open(os.path.join(night_dir_path,
platepars_recalibrated_file)) as f:
recalibrated_platepars = json.load(f)
print(
'Loaded recalibrated platepars JSON file for the calibration report...'
)
### ###
### Load the platepar file ###
# Find the platepar file in the given directory if it was not given
if platepar is None:
# Find the platepar file
platepar_file = None
for file_name in os.listdir(night_dir_path):
if file_name == config.platepar_name:
platepar_file = file_name
break
if platepar_file is None:
print('The platepar cannot be found in the night directory!')
return None
# Load the platepar file
platepar = Platepar()
platepar.read(os.path.join(night_dir_path, platepar_file),
use_flat=config.use_flat)
### ###
night_name = os.path.split(night_dir_path.strip(os.sep))[1]
# Go one mag deeper than in the config
lim_mag = config.catalog_mag_limit + 1
# Load catalog stars (load one magnitude deeper)
catalog_stars, mag_band_str, config.star_catalog_band_ratios = StarCatalog.readStarCatalog(\
config.star_catalog_path, config.star_catalog_file, lim_mag=lim_mag, \
mag_band_ratios=config.star_catalog_band_ratios)
### Take only those CALSTARS entires for which FF files exist in the folder ###
# Get a list of FF files in the folder
ff_list = []
for file_name in os.listdir(night_dir_path):
if validFFName(file_name):
ff_list.append(file_name)
# Filter out calstars entries, generate a star dictionary where the keys are JDs of FFs
star_dict = {}
ff_dict = {}
for entry in star_list:
ff_name, star_data = entry
# Check if the FF from CALSTARS exists in the folder
if ff_name not in ff_list:
continue
dt = getMiddleTimeFF(ff_name, config.fps, ret_milliseconds=True)
jd = date2JD(*dt)
# Add the time and the stars to the dict
star_dict[jd] = star_data
ff_dict[jd] = ff_name
### ###
# If there are no FF files in the directory, don't generate a report
if len(star_dict) == 0:
print('No FF files from the CALSTARS file in the directory!')
return None
# If the recalibrated platepars file exists, take the one with the most stars
max_jd = 0
using_recalib_platepars = False
if recalibrated_platepars is not None:
max_stars = 0
for ff_name_temp in recalibrated_platepars:
# Compute the Julian date of the FF middle
dt = getMiddleTimeFF(ff_name_temp,
config.fps,
ret_milliseconds=True)
jd = date2JD(*dt)
# Check that this file exists in CALSTARS and the list of FF files
if (jd not in star_dict) or (jd not in ff_dict):
continue
# Check if the number of stars on this FF file is larger than the before
if len(star_dict[jd]) > max_stars:
max_jd = jd
max_stars = len(star_dict[jd])
# Set a flag to indicate if using recalibrated platepars has failed
if max_jd == 0:
using_recalib_platepars = False
else:
print('Using recalibrated platepars, file:', ff_dict[max_jd])
using_recalib_platepars = True
# Select the platepar where the FF file has the most stars
platepar_dict = recalibrated_platepars[ff_dict[max_jd]]
platepar = Platepar()
platepar.loadFromDict(platepar_dict, use_flat=config.use_flat)
filtered_star_dict = {max_jd: star_dict[max_jd]}
# Match stars on the image with the stars in the catalog
n_matched, avg_dist, cost, matched_stars = matchStarsResiduals(config, platepar, catalog_stars, \
filtered_star_dict, match_radius, ret_nmatch=True, lim_mag=lim_mag)
max_matched_stars = n_matched
# Otherwise take the optimal FF file for evaluation
if (recalibrated_platepars is None) or (not using_recalib_platepars):
# If there are more than a set number of FF files to evaluate, choose only the ones with most stars on
# the image
if len(star_dict) > config.calstars_files_N:
# Find JDs of FF files with most stars on them
top_nstars_indices = np.argsort([len(x) for x in star_dict.values()])[::-1][:config.calstars_files_N \
- 1]
filtered_star_dict = {}
for i in top_nstars_indices:
filtered_star_dict[list(star_dict.keys())[i]] = list(
star_dict.values())[i]
star_dict = filtered_star_dict
# Match stars on the image with the stars in the catalog
n_matched, avg_dist, cost, matched_stars = matchStarsResiduals(config, platepar, catalog_stars, \
star_dict, match_radius, ret_nmatch=True, lim_mag=lim_mag)
# If no recalibrated platepars where found, find the image with the largest number of matched stars
if (not using_recalib_platepars) or (max_jd == 0):
max_jd = 0
max_matched_stars = 0
for jd in matched_stars:
_, _, distances = matched_stars[jd]
if len(distances) > max_matched_stars:
max_jd = jd
max_matched_stars = len(distances)
# If there are no matched stars, use the image with the largest number of detected stars
if max_matched_stars <= 2:
max_jd = max(star_dict, key=lambda x: len(star_dict[x]))
distances = [np.inf]
# Take the FF file with the largest number of matched stars
ff_name = ff_dict[max_jd]
# Load the FF file
ff = readFF(night_dir_path, ff_name)
img_h, img_w = ff.avepixel.shape
dpi = 200
plt.figure(figsize=(ff.avepixel.shape[1] / dpi,
ff.avepixel.shape[0] / dpi),
dpi=dpi)
# Take the average pixel
img = ff.avepixel
# Slightly adjust the levels
img = Image.adjustLevels(img, np.percentile(img, 1.0), 1.3,
np.percentile(img, 99.99))
plt.imshow(img, cmap='gray', interpolation='nearest')
legend_handles = []
# Plot detected stars
for img_star in star_dict[max_jd]:
y, x, _, _ = img_star
rect_side = 5 * match_radius
square_patch = plt.Rectangle((x - rect_side/2, y - rect_side/2), rect_side, rect_side, color='g', \
fill=False, label='Image stars')
plt.gca().add_artist(square_patch)
legend_handles.append(square_patch)
# If there are matched stars, plot them
if max_matched_stars > 2:
# Take the solution with the largest number of matched stars
image_stars, matched_catalog_stars, distances = matched_stars[max_jd]
# Plot matched stars
for img_star in image_stars:
x, y, _, _ = img_star
circle_patch = plt.Circle((y, x), radius=3*match_radius, color='y', fill=False, \
label='Matched stars')
plt.gca().add_artist(circle_patch)
legend_handles.append(circle_patch)
### Plot match residuals ###
# Compute preducted positions of matched image stars from the catalog
x_predicted, y_predicted = raDecToXYPP(matched_catalog_stars[:, 0], \
matched_catalog_stars[:, 1], max_jd, platepar)
img_y, img_x, _, _ = image_stars.T
delta_x = x_predicted - img_x
delta_y = y_predicted - img_y
# Compute image residual and angle of the error
res_angle = np.arctan2(delta_y, delta_x)
res_distance = np.sqrt(delta_x**2 + delta_y**2)
# Calculate coordinates of the beginning of the residual line
res_x_beg = img_x + 3 * match_radius * np.cos(res_angle)
res_y_beg = img_y + 3 * match_radius * np.sin(res_angle)
# Calculate coordinates of the end of the residual line
res_x_end = img_x + 100 * np.cos(res_angle) * res_distance
res_y_end = img_y + 100 * np.sin(res_angle) * res_distance
# Plot the 100x residuals
for i in range(len(x_predicted)):
res_plot = plt.plot([res_x_beg[i], res_x_end[i]], [res_y_beg[i], res_y_end[i]], color='orange', \
lw=0.5, label='100x residuals')
legend_handles.append(res_plot[0])
### ###
else:
distances = [np.inf]
# If there are no matched stars, plot large text in the middle of the screen
plt.text(img_w / 2,
img_h / 2,
"NO MATCHED STARS!",
color='r',
alpha=0.5,
fontsize=20,
ha='center',
va='center')
### Plot positions of catalog stars to the limiting magnitude of the faintest matched star + 1 mag ###
# Find the faintest magnitude among matched stars
if max_matched_stars > 2:
faintest_mag = np.max(matched_catalog_stars[:, 2]) + 1
else:
# If there are no matched stars, use the limiting magnitude from config
faintest_mag = config.catalog_mag_limit + 1
# Estimate RA,dec of the centre of the FOV
_, RA_c, dec_c, _ = xyToRaDecPP([jd2Date(max_jd)], [platepar.X_res / 2],
[platepar.Y_res / 2], [1], platepar)
RA_c = RA_c[0]
dec_c = dec_c[0]
fov_radius = np.hypot(*computeFOVSize(platepar))
# Get stars from the catalog around the defined center in a given radius
_, extracted_catalog = subsetCatalog(catalog_stars, RA_c, dec_c,
fov_radius, faintest_mag)
ra_catalog, dec_catalog, mag_catalog = extracted_catalog.T
# Compute image positions of all catalog stars that should be on the image
x_catalog, y_catalog = raDecToXYPP(ra_catalog, dec_catalog, max_jd,
platepar)
# Filter all catalog stars outside the image
temp_arr = np.c_[x_catalog, y_catalog, mag_catalog]
temp_arr = temp_arr[temp_arr[:, 0] >= 0]
temp_arr = temp_arr[temp_arr[:, 0] <= ff.avepixel.shape[1]]
temp_arr = temp_arr[temp_arr[:, 1] >= 0]
temp_arr = temp_arr[temp_arr[:, 1] <= ff.avepixel.shape[0]]
x_catalog, y_catalog, mag_catalog = temp_arr.T
# Plot catalog stars on the image
cat_stars_handle = plt.scatter(x_catalog, y_catalog, c='none', marker='D', lw=1.0, alpha=0.4, \
s=((4.0 + (faintest_mag - mag_catalog))/3.0)**(2*2.512), edgecolor='r', label='Catalog stars')
legend_handles.append(cat_stars_handle)
### ###
# Add info text in the corner
info_text = ff_dict[max_jd] + '\n' \
+ "Matched stars within {:.1f} px radius: {:d}/{:d} \n".format(match_radius, max_matched_stars, \
len(star_dict[max_jd])) \
+ "Median distance = {:.2f} px\n".format(np.median(distances)) \
+ "Catalog lim mag = {:.1f}".format(lim_mag)
plt.text(10, 10, info_text, bbox=dict(facecolor='black', alpha=0.5), va='top', ha='left', fontsize=4, \
color='w', family='monospace')
legend = plt.legend(handles=legend_handles,
prop={'size': 4},
loc='upper right')
legend.get_frame().set_facecolor('k')
legend.get_frame().set_edgecolor('k')
for txt in legend.get_texts():
txt.set_color('w')
### Add FOV info (centre, size) ###
# Mark FOV centre
plt.scatter(platepar.X_res / 2,
platepar.Y_res / 2,
marker='+',
s=20,
c='r',
zorder=4)
# Compute FOV centre alt/az
azim_centre, alt_centre = raDec2AltAz(max_jd, platepar.lon, platepar.lat,
RA_c, dec_c)
# Compute FOV size
fov_h, fov_v = computeFOVSize(platepar)
# Compute the rotation wrt. horizon
rot_horizon = rotationWrtHorizon(platepar)
fov_centre_text = "Azim = {:6.2f}$\\degree$\n".format(azim_centre) \
+ "Alt = {:6.2f}$\\degree$\n".format(alt_centre) \
+ "Rot h = {:6.2f}$\\degree$\n".format(rot_horizon) \
+ "FOV h = {:6.2f}$\\degree$\n".format(fov_h) \
+ "FOV v = {:6.2f}$\\degree$".format(fov_v) \
plt.text(10, platepar.Y_res - 10, fov_centre_text, bbox=dict(facecolor='black', alpha=0.5), \
va='bottom', ha='left', fontsize=4, color='w', family='monospace')
### ###
# Plot RA/Dec gridlines #
addEquatorialGrid(plt, platepar, max_jd)
plt.axis('off')
plt.gca().get_xaxis().set_visible(False)
plt.gca().get_yaxis().set_visible(False)
plt.xlim([0, ff.avepixel.shape[1]])
plt.ylim([ff.avepixel.shape[0], 0])
# Remove the margins
plt.subplots_adjust(left=0, bottom=0, right=1, top=1, wspace=0, hspace=0)
plt.savefig(os.path.join(night_dir_path, night_name + '_calib_report_astrometry.jpg'), \
bbox_inches='tight', pad_inches=0, dpi=dpi)
if show_graphs:
plt.show()
else:
plt.clf()
plt.close()
if max_matched_stars > 2:
### PHOTOMETRY FIT ###
# If a flat is used, set the vignetting coeff to 0
if config.use_flat:
platepar.vignetting_coeff = 0.0
# Extact intensities and mangitudes
star_intensities = image_stars[:, 2]
catalog_mags = matched_catalog_stars[:, 2]
# Compute radius of every star from image centre
radius_arr = np.hypot(image_stars[:, 0] - img_h / 2,
image_stars[:, 1] - img_w / 2)
# Fit the photometry on automated star intensities (use the fixed vignetting coeff, use robust fit)
photom_params, fit_stddev, fit_resid, star_intensities, radius_arr, catalog_mags = \
photometryFitRobust(star_intensities, radius_arr, catalog_mags, \
fixed_vignetting=platepar.vignetting_coeff)
photom_offset, _ = photom_params
### ###
### PLOT PHOTOMETRY ###
# Note: An almost identical code exists in RMS.Astrometry.SkyFit in the PlateTool.photometry function
dpi = 130
fig_p, (ax_p, ax_r) = plt.subplots(nrows=2, facecolor=None, figsize=(6.0, 7.0), dpi=dpi, \
gridspec_kw={'height_ratios':[2, 1]})
# Plot raw star intensities
ax_p.scatter(-2.5 * np.log10(star_intensities),
catalog_mags,
s=5,
c='r',
alpha=0.5,
label="Raw")
# If a flat is used, disregard the vignetting
if not config.use_flat:
# Plot intensities of image stars corrected for vignetting
lsp_corr_arr = np.log10(correctVignetting(star_intensities, radius_arr, \
platepar.vignetting_coeff))
ax_p.scatter(-2.5*lsp_corr_arr, catalog_mags, s=5, c='b', alpha=0.5, \
label="Corrected for vignetting")
# Plot photometric offset from the platepar
x_min, x_max = ax_p.get_xlim()
y_min, y_max = ax_p.get_ylim()
x_min_w = x_min - 3
x_max_w = x_max + 3
y_min_w = y_min - 3
y_max_w = y_max + 3
photometry_info = "Platepar: {:+.1f}*LSP + {:.2f} +/- {:.2f}".format(platepar.mag_0, \
platepar.mag_lev, platepar.mag_lev_stddev) \
+ "\nVignetting coeff = {:.5f}".format(platepar.vignetting_coeff) \
+ "\nGamma = {:.2f}".format(platepar.gamma)
# Plot the photometry calibration from the platepar
logsum_arr = np.linspace(x_min_w, x_max_w, 10)
ax_p.plot(logsum_arr, logsum_arr + platepar.mag_lev, label=photometry_info, linestyle='--', \
color='k', alpha=0.5)
# Plot the fitted photometry calibration
fit_info = "Fit: {:+.1f}*LSP + {:.2f} +/- {:.2f}".format(
-2.5, photom_offset, fit_stddev)
ax_p.plot(logsum_arr,
logsum_arr + photom_offset,
label=fit_info,
linestyle='--',
color='b',
alpha=0.75)
ax_p.legend()
ax_p.set_ylabel("Catalog magnitude ({:s})".format(mag_band_str))
ax_p.set_xlabel("Uncalibrated magnitude")
# Set wider axis limits
ax_p.set_xlim(x_min_w, x_max_w)
ax_p.set_ylim(y_min_w, y_max_w)
ax_p.invert_yaxis()
ax_p.invert_xaxis()
ax_p.grid()
### Plot photometry vs radius ###
img_diagonal = np.hypot(img_h / 2, img_w / 2)
# Plot photometry residuals (including vignetting)
ax_r.scatter(radius_arr, fit_resid, c='b', alpha=0.75, s=5, zorder=3)
# Plot a zero line
ax_r.plot(np.linspace(0, img_diagonal, 10), np.zeros(10), linestyle='dashed', alpha=0.5, \
color='k')
# Plot only when no flat is used
if not config.use_flat:
# Plot radius from centre vs. fit residual
fit_resids_novignetting = catalog_mags - photomLine((np.array(star_intensities), \
np.array(radius_arr)), photom_offset, 0.0)
ax_r.scatter(radius_arr,
fit_resids_novignetting,
s=5,
c='r',
alpha=0.5,
zorder=3)
px_sum_tmp = 1000
radius_arr_tmp = np.linspace(0, img_diagonal, 50)
# Plot vignetting loss curve
vignetting_loss = 2.5*np.log10(px_sum_tmp) \
- 2.5*np.log10(correctVignetting(px_sum_tmp, radius_arr_tmp, \
platepar.vignetting_coeff))
ax_r.plot(radius_arr_tmp,
vignetting_loss,
linestyle='dotted',
alpha=0.5,
color='k')
ax_r.grid()
ax_r.set_ylabel("Fit residuals (mag)")
ax_r.set_xlabel("Radius from centre (px)")
ax_r.set_xlim(0, img_diagonal)
### ###
plt.tight_layout()
plt.savefig(os.path.join(night_dir_path,
night_name + '_calib_report_photometry.png'),
dpi=150)
if show_graphs:
plt.show()
else:
plt.clf()
plt.close()
# Check if there are any file in the directory
if(len(ff_list) == None):
print("No files found!")
sys.exit()
# Try loading a flat field image
flat_struct = None
if config.use_flat:
# Check if there is flat in the data directory
if os.path.exists(os.path.join(ff_dir, config.flat_file)):
flat_struct = Image.loadFlat(ff_dir, config.flat_file)
# Try loading the default flat
elif os.path.exists(config.flat_file):
flat_struct = Image.loadFlat(os.getcwd(), config.flat_file)
extraction_list = []
# Go through all files in the directory and add them to the detection list
for ff_name in sorted(ff_list):
# Check if the given file is a valid FF file
if not FFfile.validFFName(ff_name):