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 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 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 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
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 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