def main():
opt = process_command_line()
print opt
ensure_dir(opt.work_dir)
basenames = [os.path.basename(fi) for fi in opt.files]
# Initiate SIFT detector
sift = cv2.xfeatures2d.SIFT_create(
contrastThreshold=opt.contrast_threshold, sigma=opt.sigma)
# queryImage
img1_subg = cvt.color_to_gray(cvt.file_to_cv2(opt.files[0]))
target_size = img1_subg.shape[::-1]
# trainImage
img2 = cvt.file_to_cv2(opt.files[1])
img2_subg = cvt.color_to_gray(img2)
"""
debug_render_bands(img1_subg,
os.path.join(opt.work_dir, "banded-" + basenames[0]),
strip_width=2000)
debug_render_bands(img2_subg,
os.path.join(opt.work_dir, "banded-" + basenames[1]),
strip_width=2000)
"""
img1_bands = get_image_bands(img1_subg, opt.strip_width)
img2_bands = get_image_bands(img2_subg, opt.strip_width)
# find the keypoints and descriptors
debug_log("Gather features of images")
#kp1, des1 = get_features_on_bands(img1_subg, img1_bands, sift, basenames[0])
kp1, des1 = get_features_on_bands(img1_subg, img1_bands, sift)
kp2, des2 = get_features_on_bands(img2_subg, img2_bands, sift)
""" Debug keypoint shifting in bands
file_out = "/dev/shm/{}-banded.jpg".format(basenames[0])
draw_keypoints(img1_subg, kp1, 80, (0, 255, 0), file_out)
"""
ref_package = (kp1, des1)
target_package = (kp2, des2)
warp_matrix = match_get_transf(ref_package, target_package,
opt.min_matches)
print("Warp matrix:")
print(warp_matrix)
success = (warp_matrix is not None)
if success:
img2_aligned = align_image(img2, warp_matrix, target_size)
aligned_file = os.path.join(opt.work_dir, "reg-" + basenames[1])
cvt.cv2_to_file(img2_aligned, aligned_file)
result = "done" if success else "failed"
debug_log("Alignment of", opt.files[1], result)
def get_rgb_average(images_file_list):
avg = cvt.file_to_cv2(os.path.expanduser(
images_file_list[0])).astype(float)
for img_file in images_file_list[1:]:
avg += cvt.file_to_cv2(os.path.expanduser(img_file))
avg /= len(images_file_list)
return avg.astype(np.uint8)
def main():
opt = process_command_line()
print opt
ensure_dir(opt.work_dir)
basenames = [os.path.basename(fi) for fi in opt.files]
# Initiate SIFT detector
sift = cv2.xfeatures2d.SIFT_create(
contrastThreshold=opt.contrast_threshold, sigma=opt.sigma)
# queryImage
#img1 = cvt.image_load_resize(opt.files[0], opt.reduction)
img1 = cvt.file_to_cv2(opt.files[0])
img1_subg = cvt.image_resize(cvt.color_to_gray(img1), opt.reduction)
# trainImage
#img2 = cvt.image_load_resize(opt.files[1], opt.reduction)
img2 = cvt.file_to_cv2(opt.files[1])
img2_subg = cvt.image_resize(cvt.color_to_gray(img2), opt.reduction)
# find the keypoints and descriptors
debug_log("Gather features of images")
kp1, des1 = sift.detectAndCompute(img1_subg, None)
kp2, des2 = sift.detectAndCompute(img2_subg, None)
ref_package = (kp1, des1)
target_package = (kp2, des2)
""" Direct
img2_aligned = match_and_align(ref_package, target_package,
opt.min_matches)
success = (img2_aligned is not None)
if success:
cvt.cv2_to_file(img1, os.path.join(opt.work_dir, basenames[0]))
cvt.cv2_to_file(img2, os.path.join(opt.work_dir, basenames[1]))
aligned_file = os.path.join(opt.work_dir, "reg-" + basenames[1])
cvt.cv2_to_file(img2_aligned, aligned_file)
"""
warp_matrix = match_get_transf(ref_package, target_package,
opt.min_matches)
print("Warp matrix:")
print(warp_matrix)
success = (warp_matrix is not None)
if success:
matrix_scaling = get_size_scaling_matrix(opt.reduction)
scaled_wm = matrix_scaling * warp_matrix
#scaled_wm = np.dot(matrix_scaling, warp_matrix)
img2_aligned = align_image(img2, scaled_wm, img1.shape[:2][::-1])
aligned_file = os.path.join(opt.work_dir, "reg-" + basenames[1])
cvt.cv2_to_file(img2_aligned, aligned_file)
result = "done" if success else "failed"
debug_log("Alignment of", opt.files[1], result)
def opaque_builder(kind, file_list, threshold=12):
img_max = cvt.file_to_cv2(file_list[0]).max(axis=2)
img_opaque = (img_max < threshold)
for img_file in file_list:
img_max = cvt.file_to_cv2(img_file).max(axis=2)
img_opaque &= (img_max < threshold)
return (kind, img_opaque)
def main():
opt = process_command_line()
print opt
avg = cvt.file_to_cv2(os.path.expanduser(opt.files[0])).astype(float)
for img_file in opt.files[1:]:
avg += cvt.file_to_cv2(os.path.expanduser(img_file))
avg /= len(opt.files)
success = cvt.cv2_to_file(avg.astype(np.uint8), opt.output)
result = "done" if success else "failed"
debug_log("Average image construction into", opt.output, result)
def apply_displacements(target_dir, angle, displacements, work_dirs, kind,
do_register, blur, files_in):
file_out = os.path.join(target_dir,
"full-{}.{}".format(angle, img_data_fmt))
if not pth.isfile(file_out):
kind_dir = ensure_dir(pth.join(work_dirs['temp'], kind))
my_work_dirs = setup_directories(pth.join(kind_dir, str(angle)))
if blur is not None:
new_dir = blankfield_guard(files_in, kind, my_work_dirs['in'],
blur)
else:
new_dir = pth.dirname(files_in[0])
coords_fi = pth.join(new_dir, "tiles.txt")
TileConfigurator().generate(displacements, coords_fi)
success, img, _ = fiji_grid_fuse(my_work_dirs,
coords_fi,
file_out,
do_crop=False)
shutil.rmtree(my_work_dirs['in'])
else:
debug_log("Ensemble", file_out, "already exists.")
img = None
cropped_fpath = os.path.join(target_dir,
"center-crop-{}.jpg".format(angle))
if not pth.isfile(cropped_fpath):
load_img = img if img is not None else file_to_cv2(file_out)
cropped_img = crop_center_chunk(load_img, 1024)
cv2_to_file(cropped_img, cropped_fpath)
else:
debug_log("Center-cropped chunk", cropped_fpath, "already exists.")
reduced_fpath = os.path.join(target_dir, "full-{}.small.jpg".format(angle))
if not pth.isfile(reduced_fpath):
load_img = img if img is not None else file_to_cv2(file_out)
reduced_img = image_resize(load_img, 30)
cv2_to_file(reduced_img, reduced_fpath)
else:
debug_log("Reduced size image", reduced_fpath, "already exists.")
if not do_register:
pyramid_archive = pth.join(target_dir, "pyramid-{}.tar".format(angle))
if not pth.isfile(pyramid_archive):
make_pyramid(img, pyramid_archive, "{}-{}.dzi".format(kind, angle))
else:
debug_log("Pyramid archive", pyramid_archive, "already exists.")
return file_out
def image_list_grabber(kind, img_file, index, crop_box):
# Get working area and original bounding box
img = cvt.file_to_cv2(img_file)
img_crop = cvt.color_to_gray(crop_to_bounding_box(img, crop_box))
return (kind, index, img_crop, get_bounding_box(img.shape))
def image_list_grabber(img_file, index, crop_box, use_borders=False):
img = cvt.file_to_cv2(img_file)
img_crop = crop_to_bounding_box(img, crop_box)
img_crop_tgt = (img_crop if not use_borders else
get_borders(img_crop))
return (index, img_crop_tgt, get_bounding_box(img.shape))
def main():
image_file = sys.argv[1]
bfield_file = sys.argv[2]
image = file_to_cv2(image_file)
bfield = file_to_cv2(bfield_file)
bfield_dark = is_image_dark(bfield)
print("Is bfield dark: {}".format(bfield_dark))
corrct = (blankfield_linear_correct(image, bfield)
if bfield_dark else blankfield_dark_correct(image, bfield))
img_out = os.path.abspath(
os.path.splitext(os.path.basename(image_file))[0] + "-bfield.jpg")
cv2_to_file(corrct, img_out, 99)
print("corrected image written to: {}".format(img_out))
def main():
args = process_command_line()
in_image = cvt.file_to_cv2(args['file-in'])
border_h = args['border-horizontal']
border_v = args['border-vertical']
out_image = in_image[border_h:-border_h, border_v:-border_v, :]
cvt.cv2_to_file(out_image, args['file-out'])
def main():
opt = process_command_line()
print opt
targets = sorted(set(opt.files) - set([
opt.reference,
]))
image0 = cvt.file_to_cv2(opt.reference)
for image_fi in targets:
basename = os.path.basename(image_fi)
debug_log("Matching", os.path.basename(opt.reference), "with",
basename)
image = cvt.file_to_cv2(image_fi)
output_file = os.path.join(opt.work_dir, "pre-" + basename)
register_image_pair(image0, image, opt.reduction, output_file)
def gather_images_data(file_list, crop_size, max_threads, use_borders=False,
reference_crop_box_corner=None, assume_zero_displacements=False):
# Pick the first file as the non-moving reference
ref_file = file_list[0]
img_ref = cvt.file_to_cv2(ref_file)
img_ref_bb = get_bounding_box(img_ref.shape)
# If not given, determine reference crop box corner from the reference file
img_ref_br = (img_ref_bb[1] if reference_crop_box_corner is None else
reference_crop_box_corner)
crop_box = [(img_ref_br - crop_size)/2, (img_ref_br + crop_size)/2]
img_crop = crop_to_bounding_box(img_ref, crop_box)
img_ref_crop = (img_crop if not use_borders else get_borders(img_crop))
# Read the file lists with few threads as each image is potentially huge
job_args = ( (img_file, i, crop_box, use_borders) for i, img_file
in enumerate(file_list[1:]) )
if max_threads > 1:
pool = mp.Pool(processes=max_threads)
jobs = [pool.apply_async(image_list_grabber, args) for args in job_args]
pool.close()
pool.join()
grab_iter = ( job.get() for job in jobs )
else:
grab_iter = ( image_list_grabber(*args) for args in job_args )
data_list = sorted(grab_iter)
data_dict = {'crops': [img_ref_crop,] + [ elem[1] for elem in data_list ],
'bboxes': [img_ref_bb,] + [ elem[2] for elem in data_list ],
'targets': file_list}
if not assume_zero_displacements:
# Compute displacements w.r.t reference via the image crops just obtained
job_args = ( (i, data_dict) for i in range(1, len(data_dict['crops'])) )
if max_threads > 1:
pool = mp.Pool(processes=max_threads)
jobs = [ pool.apply_async(displacement_compute_worker, args)
for args in job_args ]
pool.close()
pool.join()
disp_iter = ( job.get() for job in jobs )
else:
disp_iter = ( displacement_compute_worker(*args) for args in job_args )
deltas_idx = dict(disp_iter)
deltas = [delta for i, delta in sorted(deltas_idx.items())]
deltas_ref = add_pairwise_displacements(deltas, 0)
else:
deltas_ref = [ np.r_[0, 0] ]*len(data_dict['crops'])
data_dict['deltas'] = deltas_ref
basename_ref = pth.basename(ref_file)
debug_log("Displacements relative to", basename_ref, deltas_ref)
return data_dict
def align_images(work_dir, ppl_files, xpl_files, warp_matrix):
# Get reference bounding box
for xpl_file in xpl_files:
metadata_json = xpl_file.replace(".png", ".metadata.json")
if os.path.isfile(metadata_json):
xpl_h, xpl_w = json.load(open(metadata_json))
else:
xpl_image = cvt.file_to_cv2(xpl_file)
xpl_h, xpl_w = xpl_image.shape[:2]
break
target_size = (xpl_w, xpl_h)
# Finally, align image files and crop to common box
for ppl_file in ppl_files:
img = cvt.file_to_cv2(ppl_file)
img_aligned = warp_image(img, warp_matrix, target_size)
img_bbox = get_bounding_box(img.shape)
new_bbox = transform_points(img_bbox, warp_matrix)
print "Start bbox:", img_bbox[0], img_bbox[-1]
print "New bbox:", new_bbox
#cropped = crop_to_bounding_box(img_aligned, common_box)
cropped = img_aligned
basename = os.path.basename(ppl_file)
cf_name = "reg-" + basename
cropped_file = os.path.join(work_dir, cf_name)
success = cvt.cv2_to_file(cropped, cropped_file)
if success:
center_crop = crop_center_chunk(cropped, 1024)
center_crop_name = "crop-" + cf_name.replace(".png", ".jpg")
center_crop_file = os.path.join(work_dir, center_crop_name)
cvt.cv2_to_file(center_crop, center_crop_file)
result = "done" if success else "failed"
debug_log("Alignment of", ppl_file, "into", cropped_file, result)
def reference_grabber(kind, img_file, crop_size):
# Get working area (center crop) and bounding box
img_ref = cvt.file_to_cv2(img_file)
img_ref_bb = get_bounding_box(img_ref.shape)
crop_box = get_center_crop_bbox(img_ref, crop_size)
img_ref_crop = cvt.color_to_gray(crop_to_bounding_box(img_ref, crop_box))
return (kind, {
'crop-ref': img_ref_crop,
'bbox-ref': img_ref_bb,
'cropbox': crop_box
})
def file_image_preprocess(source_file,
target_file,
reduction_percentage=100,
blankfield=None):
if reduction_percentage == 100 and blankfield is None:
shutil.copyfile(source_file, target_file)
else:
image = image_resize(file_to_cv2(source_file), reduction_percentage)
corrected = (image if blankfield is None else
blankfield_linear_correct(image, blankfield))
cv2_to_file(corrected, target_file, 99)
return target_file
def image_align(img_file,
delta,
padding_shift,
target_size,
crop_box,
work_dir,
is_reference,
correct_shift_ref_crop=None):
basename = os.path.basename(img_file)
cf_name = "reg-" + basename
cropped_file = os.path.join(work_dir, cf_name)
shift_total = delta + padding_shift[::-1] if not is_reference else delta
debug_log("Shift to apply (with padding) to", basename + ":", shift_total)
warp_matrix = get_translation_matrix(shift_total)[:2]
img = cvt.file_to_cv2(img_file)
img_pre_aligned = warp_image(img, warp_matrix, target_size)
if correct_shift_ref_crop is None:
img_aligned = img_pre_aligned
else:
align_corr_bbox, align_corr_crop = correct_shift_ref_crop
this_crop = cvt.color_to_gray(
crop_to_bounding_box(img_pre_aligned, align_corr_bbox))
delta_pair = [align_corr_crop, this_crop]
delta_align = compute_displacements_direct(delta_pair, 0)[1]
warp_matrix = get_translation_matrix(delta_align)[:2]
img_aligned = warp_image(img_pre_aligned, warp_matrix, target_size)
#cropped = crop_to_bounding_box(img_aligned, crop_box)
cropped = img_aligned
success = cvt.cv2_to_file(cropped, cropped_file)
if success:
center_crop_box = get_center_crop_bbox(cropped, 1024)
center_crop = crop_to_bounding_box(cropped, center_crop_box)
center_crop_name = "crop-" + cf_name.replace(".png", ".jpg")
center_crop_file = os.path.join(work_dir, center_crop_name)
cvt.cv2_to_file(center_crop, center_crop_file)
result = "done" if success else "failed"
debug_log("Alignment of", img_file, "into", cropped_file, result)
#center_crop_box = get_center_crop_bbox(cropped, 4096)
#center_crop = crop_to_bounding_box(cropped, center_crop_box)
#return success, (center_crop_box, cvt.color_to_gray(center_crop))
return success
def align_images(file_list, delta_xp, work_dir):
body_sets = { kind: sorted(fi for fi in file_list
if kind in fi and "crop" not in fi)
for kind in image_kinds }
# Get image target size
xpl_image = cvt.file_to_cv2(body_sets['xpl'][0])
target_size = xpl_image.shape[:2][::-1]
# Then, align all image files (except the reference) and crop to common box
pool = mp.Pool(processes=4)
jobs = []
for target in body_sets['ppl']:
jobs.append(pool.apply_async(image_align,
(target, delta_xp, target_size, work_dir)))
pool.close()
pool.join()
success = all(job.get() for job in jobs)
return success
def make_pyramid(image_in, pyramid_archive_path, descriptor_name):
temp_mount = tempfile.mkdtemp(dir='/tmp')
with ArchiveMount(pyramid_archive_path, temp_mount) as pyramid_contain_dir:
pyramid_descriptor = pth.join(pyramid_contain_dir, descriptor_name)
creator = deepzoom.ImageCreator(tile_size=128,
tile_overlap=2,
tile_format="jpg",
image_quality=0.95,
resize_filter="bicubic")
load_img = (image_in if isinstance(image_in, np.ndarray) else
file_to_cv2(image_in))
creator.create(load_img, pyramid_descriptor)
debug_log("Pyramid archive", pyramid_archive_path, "created.")
#os.rmdir(temp_mount)
shutil.rmtree(temp_mount)
return True
def image_align(img_file, delta, target_size, work_dir):
basename = os.path.basename(img_file)
cf_name = "reg-" + basename
aligned_file = os.path.join(work_dir, cf_name)
warp_matrix = get_translation_matrix(delta)[:2]
img = cvt.file_to_cv2(img_file)
img_aligned = warp_image(img, warp_matrix, target_size)
success = cvt.cv2_to_file(img_aligned, aligned_file)
if success:
center_crop_box = get_center_crop_bbox(img_aligned, 1024)
center_crop = crop_to_bounding_box(img_aligned, center_crop_box)
center_crop_name = "crop-" + cf_name.replace(".png", ".jpg")
center_crop_file = os.path.join(work_dir, center_crop_name)
cvt.cv2_to_file(center_crop, center_crop_file)
result = "done" if success else "failed"
debug_log("Alignment of", img_file, "into", aligned_file, result)
return success
def image_align_worker(img_file, target_file, delta, padding_shift, target_size,
crop_box, make_center_chunk, make_jpeg=False):
img = cvt.file_to_cv2(img_file)
total_shift = delta + padding_shift[::-1]
cropped = translate_crop(img, total_shift, crop_box, target_size)
success = cvt.cv2_to_file(cropped, target_file)
if success:
registration_file = pth.splitext(target_file)[0] + ".json"
registration_save(total_shift, crop_box, target_size, registration_file)
if success and make_center_chunk:
gen_center_small_chunk(cropped, target_file, 1024)
if success and make_jpeg:
reduced_fpath = pth.splitext(target_file)[0] + '.jpg'
cvt.cv2_to_file(cropped, reduced_fpath)
result = "done" if success else "failed"
debug_log("Alignment of", img_file, "into", target_file, result)
return success
def assemble_twostep(directories, files, out_file_name, corner_dim_min=[2, 3]):
n_rows, n_cols, row_cells, img_type, _, _, _ = parse_image_grid_list(files)
body_dir = ensure_dir(os.path.join(directories['out'], "body"))
debug_log("Assembling of body into", body_dir, "started.")
body_work_dir = setup_directories(body_dir)
body_simple_file = os.path.join(body_dir, "body-straight.png")
(success, tiles_file, body_matrix, body_simple,
pre_crop_file) = fiji_grid_stitch(body_work_dir,
files,
body_simple_file,
True,
keep_uncropped=True,
pre_copy_files=False,
threshold_reg=0.5,
threshold_maxavg=10,
threshold_abs=50)
if not success:
debug_log("Could not assemble body")
return False
c_h, c_w = body_simple.shape[:2]
grey_chunk = color_to_gray(body_simple[:c_h / 2, :c_w / 2, :])
left_black = find_left_border(grey_chunk, c_w / 2)
top_black = find_top_border(grey_chunk, c_h / 2)
pre_corner = body_simple[:top_black, :left_black]
corner_is_black = (pre_corner.max() == 0)
debug_log("Corner void size:", left_black, "x", top_black, corner_is_black)
if (left_black == 0 and top_black == 0) or (not corner_is_black):
debug_log("Apparently no corner stitching is necessary!")
shutil.move(body_simple_file, out_file_name)
return True
dim_h, dim_w = file_to_cv2(files[0]).shape[:2]
corner_dim = [
max(corner_dim_min[0], 1 + 2 * int(ceil(float(top_black) / dim_h))),
max(corner_dim_min[1], 1 + 2 * int(ceil(float(left_black) / dim_w)))
]
debug_log("Corner dimensions: {}".format(corner_dim))
corner_dir = ensure_dir(os.path.join(directories['out'], "corner"))
corner_work_dir = setup_directories(corner_dir)
corner_file = os.path.join(corner_dir, "corner.png")
corner_input_files = [
cell for row in row_cells[:corner_dim[0]]
for cell in row[:corner_dim[1]]
]
debug_log("Assembling of corner into", corner_dir, "started.")
success, _, corner_matrix, corner, _ = fiji_grid_stitch(
corner_work_dir,
corner_input_files,
corner_file,
True,
pre_copy_files=False)
if not success:
debug_log("Could not assemble corner")
return False
debug_log("Generating Laplacian blending weights for corner")
body = patch_body_corner_inmem(body_simple, body_matrix, corner,
corner_matrix)
debug_log("Saving blended image to", out_file_name)
success = cv2_to_file(body, out_file_name)
result_name = "done" if success else "failed"
debug_log("Saving blended image", out_file_name, result_name)
return success
def assemble_multigrid(directories,
files,
out_file_name,
pre_resize=100,
base_cell_size=4,
max_threads=cpu_count(),
keep_uncropped=False,
blankfield_file=None):
n_rows, n_cols, row_cells, img_type, _, _, _ = parse_image_grid_list(files)
pool = Pool(processes=max_threads)
cs = float(base_cell_size)
base_cells_dims = np.ceil(np.r_[n_rows, n_cols] / cs).astype(int)
cells_dir = ensure_dir(os.path.join(directories['out'], "cells"))
cell_jobs = []
cell_files = []
blankfield = (None if blankfield_file is None else image_resize(
file_to_cv2(blankfield_file), pre_resize))
digits_row, digits_col = map(get_digits, base_cells_dims)
for b_row in range(base_cells_dims[0]):
for b_col in range(base_cells_dims[1]):
base_name = "{:0{w_r}d}_{:0{w_c}d}".format(b_row,
b_col,
w_r=digits_row,
w_c=digits_col)
temp_dirs = setup_directories(os.path.join(cells_dir, base_name))
files = [
file_image_preprocess(
row_cells[i][j],
os.path.join(temp_dirs['in'],
os.path.basename(row_cells[i][j])),
pre_resize, blankfield)
for i in range(base_cell_size *
b_row, min(base_cell_size *
(b_row + 1), n_rows))
for j in range(base_cell_size *
b_col, min(base_cell_size *
(b_col + 1), n_cols))
]
out_file = os.path.join(cells_dir,
"{}.{}".format(base_name, img_data_fmt))
cell_jobs.append(
pool.apply_async(fiji_grid_stitch,
(temp_dirs, files, out_file), {
'pre_copy_files': False,
'keep_uncropped': keep_uncropped
}))
cell_files.append(out_file)
pool.close()
pool.join()
cell_successes = [job.get() for job in cell_jobs]
if all(cell_successes):
debug_log("Assembling of base cells into", cells_dir, "is complete.",
"Assembling image...")
return fiji_grid_stitch(directories,
cell_files,
out_file_name,
keep_uncropped=keep_uncropped)
else:
return False
def main():
opt = process_command_line()
print opt
ensure_dir(opt.work_dir)
band = opt.crop_size
basename_ref = os.path.basename(opt.reference)
img1 = cvt.file_to_cv2(opt.reference)
img1_bb = get_bounding_box(img1.shape)
img1_br = img1_bb[1]
img1_crop_bb = [(img1_br - band) / 2, (img1_br + band) / 2]
target_size = get_bounding_box_size(img1_bb)
img1_crop = cvt.color_to_gray(crop_to_bounding_box(img1, img1_crop_bb))
"""
Using crop chunks centered at each images' center impedes resolving
the image-to-image displacement
"""
#img1_crop = cvt.color_to_gray(crop_center_chunk(img1, band))
all_bboxes = [
img1_bb.tolist(),
]
pre_aligned_files = [
opt.reference,
]
# trainImages
targets = sorted(set(opt.files) - set([
opt.reference,
]))
for img_file in targets:
basename = os.path.basename(img_file)
img2 = cvt.file_to_cv2(img_file)
img2_crop = cvt.color_to_gray(crop_to_bounding_box(img2, img1_crop_bb))
#img2_crop = cvt.color_to_gray(crop_center_chunk(img2, band))
debug_log("Computing translation of", basename, "relative to",
basename_ref)
peak_loc, peak_val = get_phasecorr_peak(img1_crop, img2_crop, 100)
debug_log("Translation is", peak_loc, "value:", peak_val)
warp_matrix = get_translation_matrix(peak_loc)[:2]
img2_aligned = warp_image(img2, warp_matrix, target_size)
img2_adj_bb = get_adjusted_bounding_box(img1_bb,
get_bounding_box(img2.shape),
warp_matrix)
aligned_file = os.path.join(opt.work_dir, "pre-" + basename)
cvt.cv2_to_file(img2_aligned, aligned_file)
all_bboxes.append(img2_adj_bb)
pre_aligned_files.append(aligned_file)
debug_log("Alignment of", img_file, "done")
common_box = intersect_bounding_boxes(all_bboxes)
for fi_aligned in pre_aligned_files:
debug_log("Cropping", fi_aligned, newline=False)
aligned = cvt.file_to_cv2(fi_aligned)
cropped = crop_to_bounding_box(aligned, common_box)
cf_name = (("reg-" + basename_ref) if fi_aligned == opt.reference else
os.path.basename(fi_aligned).replace("pre-", "reg-"))
cropped_file = os.path.join(opt.work_dir, cf_name)
success = cvt.cv2_to_file(cropped, cropped_file)
if success:
center_crop = crop_center_chunk(cropped, 1024)
center_crop_name = "crop-" + cf_name.replace(".png", ".jpg")
center_crop_file = os.path.join(opt.work_dir, center_crop_name)
cvt.cv2_to_file(center_crop, center_crop_file)
if not opt.keep_uncropped and fi_aligned != opt.reference:
os.remove(fi_aligned)
result = "done" if success else "failed"
print(result)
def main():
opt = process_command_line()
print opt
ensure_dir(opt.work_dir)
# Feature detector
sift = cv2.xfeatures2d.SIFT_create(
contrastThreshold=opt.contrast_threshold, sigma=opt.sigma)
initial_w = opt.strip_width
s_width = initial_w
max_width = 3700
# queryImage
img1_subg = cvt.color_to_gray(cvt.file_to_cv2(opt.reference))
target_size = img1_subg.shape[::-1]
img1_bb = get_bounding_box(img1_subg.shape)
basename_ref = os.path.basename(opt.reference)
img1_bands = get_image_bands(img1_subg, max_width)
debug_log("Gather features of reference image", basename_ref, "with w =",
max_width)
kp1, des1 = get_features_on_bands(img1_subg, img1_bands, sift)
ref_package = (kp1, des1)
# objects cached by strip width, with initial element
#img1_features = {}
match_stats = {'fail': {s_width: 0}, 'success': {s_width: 0}}
all_bboxes = [
img1_bb.tolist(),
]
pre_aligned_files = [
opt.reference,
]
# trainImages
targets = sorted(set(opt.files) - set([
opt.reference,
]))
for img_file in targets:
basename = os.path.basename(img_file)
img2 = cvt.file_to_cv2(img_file)
img2_subg = cvt.color_to_gray(img2)
keypoints = []
descriptors = []
s_width = initial_w
s_clearance = 1000
converged = False
while not converged:
debug_log("Gather features of image", basename, "with w =",
s_width, "and c =", s_clearance)
img2_bands = get_image_bands(img2_subg, s_width)
kp2, des2 = get_features_on_bands(img2_subg, img2_bands, sift)
keypoints.extend(kp2)
descriptors.append(des2)
target_package = (keypoints, np.vstack(descriptors))
warp_matrix = match_get_transf(ref_package, target_package,
opt.min_matches)
print("Warp matrix {}->{}:".format(basename_ref, basename))
print(warp_matrix)
success = (warp_matrix is not None
and is_good_matrix(warp_matrix, 3e-3))
converged = success
if not success:
debug_log("Not good enough matching achieved.",
"Increasing bands width")
iter_step = 500
s_width = iter_step
s_clearance += s_width
continue
mark_successful_iter(s_width, match_stats)
img2_aligned = align_image(img2, warp_matrix, target_size)
img2_adj_bb = get_adjusted_bounding_box(
img1_bb, get_bounding_box(img2.shape), warp_matrix)
aligned_file = os.path.join(opt.work_dir, "pre-" + basename)
cvt.cv2_to_file(img2_aligned, aligned_file)
all_bboxes.append(img2_adj_bb)
pre_aligned_files.append(aligned_file)
result = "done" if success else "failed"
debug_log("Alignment of", img_file, result)
common_box = intersect_bounding_boxes(target_size, all_bboxes)
for fi_aligned in pre_aligned_files:
debug_log("Cropping", fi_aligned, newline=False)
aligned = cvt.file_to_cv2(fi_aligned)
cropped = crop_to_bounding_box(aligned, common_box)
cf_name = (("reg-" + basename_ref) if fi_aligned == opt.reference else
os.path.basename(fi_aligned).replace("pre-", "reg-"))
cropped_file = os.path.join(opt.work_dir, cf_name)
success = cvt.cv2_to_file(cropped, cropped_file)
if success and not opt.keep_uncropped and fi_aligned != opt.reference:
os.remove(fi_aligned)
result = "done" if success else "failed"
print(result)
def main():
opt = process_command_line()
print opt
ensure_dir(opt.work_dir)
# Feature detector
sift_options = dict(sigma=opt.sigma,
contrastThreshold=opt.contrast_threshold)
sift = cv2.xfeatures2d.SIFT_create(**sift_options)
strip_width = opt.strip_width
# Reference image
basename_ref = os.path.basename(opt.reference)
image_ref = cvt.file_to_cv2(opt.reference)
imgref_subg = cvt.color_to_gray(image_ref)
target_size = imgref_subg.shape[::-1]
imgref_clearance = -1
kp_ref, des_ref = [], None
ref_max_clearance = min(target_size) - 2 * strip_width
# Target images
targets = sorted(set(opt.files) - set([
opt.reference,
]))
for img_file in targets:
basename = os.path.basename(img_file)
img2 = cvt.file_to_cv2(img_file)
img2_subg = cvt.color_to_gray(img2)
keypoints = []
descriptors = []
s_clearance = opt.clearance
tgt_max_clearance = min(img2_subg.shape) - 2 * strip_width
converged = False
while not converged:
if s_clearance > imgref_clearance:
imgref_clearance = s_clearance
if ref_max_clearance < imgref_clearance:
debug_log("Cannot expand feature extraction for reference",
basename_ref, "; using current data")
else:
imgref_bands = get_image_bands(imgref_subg, strip_width,
imgref_clearance)
debug_log("Gather features of reference image",
basename_ref, "with w =", strip_width, "and c =",
imgref_clearance)
kp_, des_ = get_features_on_bands(imgref_subg,
imgref_bands, sift)
kp_ref.extend(kp_)
des_ref = (des_ if des_ref is None else np.vstack(
[des_ref, des_]))
ref_package = (kp_ref, des_ref)
if tgt_max_clearance < s_clearance:
debug_log("Cannot expand feature extraction for target",
basename_ref, "; Aborting")
success = False
break
debug_log("Gather features of image", basename, "with w =",
strip_width, "and c =", s_clearance)
img2_bands = get_image_bands(img2_subg, strip_width, s_clearance)
kp_tgt, des_tgt = get_features_on_bands(img2_subg, img2_bands,
sift)
keypoints.extend(kp_tgt)
descriptors.append(des_tgt)
target_package = (keypoints, np.vstack(descriptors))
src_pts_flann, dst_pts_flann = match_points_flann(
ref_package, target_package, opt.min_matches)
similarity = get_transf_similarity(src_pts_flann, dst_pts_flann)
scale_change_pct = 100 * abs(similarity.scale - 1)
success = (scale_change_pct < 1)
converged = success
debug_log("Similarity transform: scale change pct.:",
scale_change_pct, "Trl:", similarity.translation, "Rot:",
similarity.rotation)
if not success:
debug_log("Not good enough matching achieved.",
"Increasing bands width")
s_clearance += strip_width
continue
transform = SimilarityTransform(scale=1,
rotation=similarity.rotation,
translation=similarity.translation)
warp_matrix = get_transf_homography(src_pts_flann, dst_pts_flann)
print(
"Compare Flann matrices:\n Homography:\n{}\n Similarity:\n{}".
format(warp_matrix, transform.params))
""" BFmatcher test
src_pts_bf, dst_pts_bf = match_points_bf(ref_package, target_package,
opt.min_matches)
warp_matrix_bf = get_transf_homography(src_pts_bf, dst_pts_bf)
similarity = get_transf_similarity(src_pts_bf, dst_pts_bf, min_samples=2)
transform_bf = SimilarityTransform(scale=1, rotation=similarity.rotation,
translation=similarity.translation)
print("Compare BF matrices:\n Homography:\n{}\n Similarity:\n{}".format(warp_matrix_bf, transform_bf.params))
"""
img2_aligned = align_image(img2, transform.params, target_size)
aligned_file = os.path.join(opt.work_dir, "reg-" + basename)
success = cvt.cv2_to_file(img2_aligned, aligned_file)
if success:
center_crop = crop_center_chunk(img2_aligned, 1024)
center_crop_name = "crop-" + basename.replace(".png", ".jpg")
center_crop_file = os.path.join(opt.work_dir, center_crop_name)
cvt.cv2_to_file(center_crop, center_crop_file)
if opt.small_images:
small = cvt.image_resize(img2_aligned, 30)
small_name = "small-" + basename.replace(".png", ".jpg")
small_file = os.path.join(opt.work_dir, small_name)
cvt.cv2_to_file(small, small_file)
result = "done" if success else "failed"
debug_log("Alignment of", img_file, result)
def main():
opt = process_command_line()
print opt
ensure_dir(opt.work_dir)
targets = opt.files
first_img = opt.files[0]
basename_ref = os.path.basename(first_img)
img1 = cvt.file_to_cv2(first_img)
img1_crop = cvt.color_to_gray(crop_center_chunk(img1, opt.crop_size))
img1_bb = get_bounding_box(img1.shape[:2])
all_bboxes = [
img1_bb,
]
relative_displacements = []
# Get pairwise relative displacements
for img_file in targets[1:]:
basename = os.path.basename(img_file)
img2 = cvt.file_to_cv2(img_file)
img2_crop = cvt.color_to_gray(crop_center_chunk(img2, opt.crop_size))
debug_log("Computing translation of", basename, "relative to",
basename_ref)
peak_loc, peak_val = get_phasecorr_peak(img1_crop, img2_crop, 100)
debug_log("Translation is", peak_loc, "value:", peak_val)
relative_displacements.append(peak_loc)
all_bboxes.append(get_bounding_box(img2.shape[:2]))
img1, img1_crop, basename_ref = img2, img2_crop, basename
del img1, img2, img1_crop, img2_crop
# Determine largest bounding box
bboxes_area = np.array(
[get_bounding_box_area(bbox) for bbox in all_bboxes])
largest_area = np.argmax(bboxes_area)
largest_bbox = all_bboxes[largest_area]
target_size = get_bounding_box_size(largest_bbox)
reference = targets[largest_area]
basename_ref = os.path.basename(reference)
print "disps:", relative_displacements
debug_log("Largest area image is", reference, "({})".format(largest_area))
# Propagate displacements
pre_aligned_files = []
for i, img_file in enumerate(targets):
# Displacements are applied relative to largest bbox
if i == largest_area:
pre_aligned_files.append(reference)
continue
basename = os.path.basename(img_file)
img = cvt.file_to_cv2(img_file)
# displacement[i] = pos[i+1] - pos[i]
if largest_area < i:
disp_chain = range(largest_area, i)
direction = 1
else:
disp_chain = range(i, largest_area)
direction = -1
total_displacement = direction * sum(relative_displacements[j]
for j in disp_chain)
debug_log("Displacement from", reference, "to", img_file, "is",
total_displacement)
print "dir", direction, "; chain", disp_chain
warp_matrix = get_translation_matrix(total_displacement)[:2]
img_aligned = align_image(img, warp_matrix, target_size)
aligned_file = os.path.join(opt.work_dir, "pre-" + basename)
success = cvt.cv2_to_file(img_aligned, aligned_file)
if success:
pre_aligned_files.append(aligned_file)
result = "done" if success else "failed"
debug_log("Alignment of", img_file, "into", aligned_file, result)
common_box = intersect_bounding_boxes(target_size, all_bboxes)
for fi_aligned in pre_aligned_files:
debug_log("Cropping", fi_aligned, newline=False)
aligned = cvt.file_to_cv2(fi_aligned)
cropped = crop_to_bounding_box(aligned, common_box)
cf_name = (("reg-" + basename_ref) if fi_aligned == reference else
os.path.basename(fi_aligned).replace("pre-", "reg-"))
cropped_file = os.path.join(opt.work_dir, cf_name)
success = cvt.cv2_to_file(cropped, cropped_file)
if success:
center_crop = crop_center_chunk(cropped, 1024)
center_crop_name = "crop-" + cf_name.replace(".png", ".jpg")
center_crop_file = os.path.join(opt.work_dir, center_crop_name)
cvt.cv2_to_file(center_crop, center_crop_file)
if not opt.keep_uncropped and fi_aligned != reference:
os.remove(fi_aligned)
result = "done" if success else "failed"
print(result)
def blur_blankfield(bfield_file, bfield_blurred_file):
bfield = file_to_cv2(bfield_file)
cv2_to_file(simple_blur(bfield), bfield_blurred_file, 0.01)
def main():
opt = process_command_line()
print opt
ensure_dir(opt.work_dir)
# Feature detector
sift_options = dict(sigma=opt.sigma,
contrastThreshold=opt.contrast_threshold)
base_csize = opt.crop_size
# Reference image
basename_ref = os.path.basename(opt.reference)
image_ref = cvt.file_to_cv2(opt.reference)
imgref_subg = cvt.color_to_gray(image_ref)
target_size = imgref_subg.shape[::-1]
imgref_csize = -1
kp_ref, des_ref = [], None
ref_max_size = min(target_size)
# Target images
targets = sorted(set(opt.files) - set([
opt.reference,
]))
for img_file in targets:
basename = os.path.basename(img_file)
img2 = cvt.file_to_cv2(img_file)
img2_subg = cvt.color_to_gray(img2)
keypoints = []
descriptors = []
csize = base_csize
tgt_max_size = min(img2_subg.shape)
converged = False
while not converged:
if csize > imgref_csize:
imgref_csize = csize
if ref_max_size < imgref_csize:
debug_log("Cannot expand feature extraction for reference",
basename_ref, "; using current data")
else:
debug_log("Gather features of reference image",
basename_ref, "with c =", imgref_csize)
imgref_bands = get_center_crop_bounding_box(
imgref_subg, imgref_csize)
print("Crop box:", imgref_bands)
sift = cv2.xfeatures2d.SIFT_create(**sift_options)
roi = crop_to_bounding_box(imgref_subg, imgref_bands)
#borders = cvt.simple_grayscale_stretch(scharr(roi))
kp_ref, des_ref = sift.detectAndCompute(roi, None)
ref_package = (kp_ref, des_ref)
if tgt_max_size < csize:
debug_log("Cannot expand feature extraction for target",
basename_ref, "; Aborting")
success = False
break
debug_log("Gather features of image", basename, "with c =", csize)
sift = cv2.xfeatures2d.SIFT_create(**sift_options)
roi = crop_to_bounding_box(img2_subg, imgref_bands)
#borders = cvt.simple_grayscale_stretch(scharr(roi))
keypoints, descriptors = sift.detectAndCompute(roi, None)
target_package = (keypoints, descriptors)
src_pts_flann, dst_pts_flann = match_points_flann(
ref_package, target_package, opt.min_matches)
similarity = get_transf_similarity(src_pts_flann, dst_pts_flann)
scale_change_pct = 100 * abs(similarity.scale - 1)
debug_log("FLANN Similarity transform: scale change pct.:",
scale_change_pct, "Trl:", similarity.translation, "Rot:",
similarity.rotation)
warp_matrix = get_transf_homography(src_pts_flann, dst_pts_flann)
print(
"Compare FLANN matrices:\n Homography:\n{}\n Similarity:\n{}".
format(warp_matrix, similarity.params))
""" BFmatcher test
src_pts_bf, dst_pts_bf = match_points_bf(ref_package, target_package,
opt.min_matches)
similarity = get_transf_similarity(src_pts_bf, dst_pts_bf, min_samples=2)
scale_change_pct = 100*abs(similarity.scale - 1)
debug_log("BF Similarity transform: scale change pct.:",
scale_change_pct, "Trl:", similarity.translation,
"Rot:", similarity.rotation)
transform = SimilarityTransform(scale=1, rotation=similarity.rotation,
translation=similarity.translation)
warp_matrix_bf = get_transf_homography(src_pts_bf, dst_pts_bf)
print("Compare BF matrices:\n Homography:\n{}\n Similarity:\n{}".format(warp_matrix_bf, transform.params))
"""
success = (scale_change_pct < 1)
converged = success
if not success:
debug_log("Not good enough matching achieved.",
"Increasing bands width")
csize += base_csize
continue
transform = SimilarityTransform(scale=1,
rotation=similarity.rotation,
translation=similarity.translation)
img2_aligned = align_image(img2, transform.params, target_size)
aligned_file = os.path.join(opt.work_dir, "reg-" + basename)
success = cvt.cv2_to_file(img2_aligned, aligned_file)
if success:
center_crop = crop_center_chunk(img2_aligned, 1024)
center_crop_name = "crop-" + basename.replace(".png", ".jpg")
center_crop_file = os.path.join(opt.work_dir, center_crop_name)
cvt.cv2_to_file(center_crop, center_crop_file)
if opt.small_images:
small = cvt.image_resize(img2_aligned, 30)
small_name = "small-" + basename.replace(".png", ".jpg")
small_file = os.path.join(opt.work_dir, small_name)
cvt.cv2_to_file(small, small_file)
result = "done" if success else "failed"
debug_log("Alignment of", img_file, result)
