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 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 displacement_compute_worker(index, data_dict):
crops = data_dict['crops']
targets = data_dict['targets']
crop_ref = cvt.color_to_gray(crops[index-1])
crop = cvt.color_to_gray(crops[index])
peak_loc, peak_val = get_phasecorr_peak(crop_ref, crop, 100)
print ("Translation of", pth.basename(targets[index]),
"relative to", pth.basename(targets[index-1]), "is",
peak_loc, "value:", peak_val)
return (index, peak_loc)
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 patch_body_corner_inmem(body, body_mat, corner_rot, corner_mat):
print "Body dim:", body.shape
print "Corner dim:", corner_rot.shape
corner_shape = corner_rot.shape[:2]
c_h, c_w = corner_shape
body_mat[2, :2] = 0
body_mat[:2, 2] = 0
corner_mat[:2, 2] = 0
corner_mat[2, :2] = 0
corner_body_rotation = np.dot(body_mat, np.linalg.inv(corner_mat))
corner = cv2.warpPerspective(corner_rot, corner_body_rotation,
tuple(corner_rot.shape[:2][::-1]))
g_body_chunk = cvt.color_to_gray(body[:c_h, :c_w, :])
bl_left = find_left_border(g_body_chunk, c_w)
bl_top = find_top_border(g_body_chunk, c_h)
blacks = np.r_[bl_top, bl_left]
chunk_dim = np.minimum(2 * blacks, corner_shape)
g_body_chunk = g_body_chunk[:chunk_dim[0], :chunk_dim[1]]
g_corner_chunk = cvt.color_to_gray(corner[:chunk_dim[0], :chunk_dim[1], :])
corr_peak, peak_val = dsl.get_phasecorr_peak(g_body_chunk, g_corner_chunk)
mat = dsl.get_translation_matrix(corr_peak)[:2]
corner_trans = dsl.warp_image(corner[:chunk_dim[0], :chunk_dim[1], :], mat,
tuple(g_body_chunk.shape[::-1]))
blend_area = np.minimum((1.5 * blacks).astype(int), corner_shape)
body_blend_area = body[:blend_area[0], :blend_area[1], :]
corner_blend_area = corner_trans[:blend_area[0], :blend_area[1], :]
weight_gen = lp2d.CornerDirichlet(blend_area[1], blend_area[0], bl_left,
bl_top)
weight_gen.set_boundaries([[1, 1, 0], [1, 1, 0]])
solu, residuals = weight_gen.solve()
corner_weight = weight_gen.get_solution()
body_weight = 1 - corner_weight
blend = np.uint8(corner_blend_area * corner_weight[:, :, np.newaxis] +
body_blend_area * body_weight[:, :, np.newaxis])
body[:blend_area[0], :blend_area[1], :] = blend
return body
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 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 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)
# 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)
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 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)
basenames = [os.path.splitext(os.path.basename(fi))[0] for fi in opt.files]
# queryImage
img1 = cvt.color_to_gray(cvt.image_load_resize(opt.files[0],
opt.reduction))
#img1_wim = cvt.simple_grayscale_stretch(scharr(img1))
#img1_wim = cv2.Canny(img1, 100, 200)
img1_wim = img1
# trainImage
img2 = cvt.color_to_gray(cvt.image_load_resize(opt.files[1],
opt.reduction))
#img2_wim = cvt.simple_grayscale_stretch(scharr(img2))
#img2_wim = cv2.Canny(img2, 100, 200)
img2_wim = img2
# Initiate SIFT detector
sift = cv2.xfeatures2d.SIFT_create(contrastThreshold=opt.sift_contrast_thd,
sigma=opt.sift_sigma)
# find the keypoints and descriptors with SIFT
with Timer(True, True, "Get SIFT for images"):
kp1, des1 = sift.detectAndCompute(img1_wim, None)
kp2, des2 = sift.detectAndCompute(img2_wim, None)
file_keyp = os.path.join(opt.work_dir,
"{}-siftkeys.jpg".format(basenames[0]))
draw_keypoints(img1_wim, kp1, 3, (0, 255, 0), file_keyp)
print "Image 1 has {} keypoints".format(len(kp1))
file_keyp = os.path.join(opt.work_dir,
"{}-siftkeys.jpg".format(basenames[1]))
draw_keypoints(img2_wim, kp2, 3, (0, 255, 0), file_keyp)
print "Image 2 has {} keypoints".format(len(kp2))
src_pts, dst_pts, good = match_points_bf((kp1, des1), (kp2, des2))
#src_pts, dst_pts, good = match_points_flann((kp1, des1), (kp2, des2))
M, mask = get_transf_homography(src_pts, dst_pts)
print "Homography matrix:\n", M
trf, mask = get_transf_similarity(src_pts, dst_pts)
print "Similarity matrix:\n", trf.params
print "Similarity params: R({}), S({}), T({})".format(
trf.rotation, trf.scale, trf.translation)
h, w = img1.shape
pts = np.float32([[0, 0], [0, h - 1], [w - 1, h - 1],
[w - 1, 0]]).reshape(-1, 1, 2)
dst = cv2.perspectiveTransform(pts, trf.params)
img2 = cv2.polylines(img2, [np.int32(dst)], True, 255, 3, cv2.LINE_AA)
matchesMask = mask.ravel().astype(bool).tolist()
draw_params = dict(
matchColor=(0, 255, 0), # draw matches in green color
singlePointColor=None,
matchesMask=matchesMask, # draw only inliers
flags=2)
img3 = cv2.drawMatches(img1, kp1, img2, kp2, good, None, **draw_params)
out_file = os.path.join(opt.work_dir, "match_{}-{}.jpg".format(*basenames))
cv2.imwrite(out_file, img3)
return 0
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 compute_registration(img_reference,
img_target,
detector,
strip_width,
clearance,
min_matches=10):
imgref_subg = cvt.color_to_gray(img_reference)
target_size = imgref_subg.shape[::-1]
imgref_clearance = -1
kp_ref, des_ref = [], None
ref_max_clearance = min(target_size) - 2 * strip_width
img2_subg = cvt.color_to_gray(img_target)
keypoints = []
descriptors = []
s_clearance = 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 (XPL)"
"; using current data")
else:
imgref_bands = get_image_bands(imgref_subg, strip_width,
imgref_clearance)
debug_log("Gather features of reference image", "with w =",
strip_width, "and c =", imgref_clearance)
kp_, des_ = get_features_on_bands(imgref_subg, imgref_bands,
detector)
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; Aborting")
success = False
break
debug_log("Gather features of target image 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,
detector)
keypoints.extend(kp_tgt)
descriptors.append(des_tgt)
target_package = (keypoints, np.vstack(descriptors))
warp_matrix = match_get_transf(ref_package, target_package,
min_matches)
success = (warp_matrix is not None
and is_good_matrix(warp_matrix, 1e-7))
converged = success
if not success:
debug_log("Not good enough matching achieved.",
"Increasing bands width")
s_clearance += strip_width
continue
return success, warp_matrix