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 pairwise_displacement_worker(index, data_dict, kind):
crops = data_dict['crops']
targets = data_dict['targets']
crop_previous = crops[index - 1]
crop = crops[index]
peak_loc, peak_val = get_phasecorr_peak(crop_previous, crop, 100)
debug_log("Translation of",
os.path.basename(targets[index]), "relative to",
os.path.basename(targets[index - 1]), "is", peak_loc, "value:",
peak_val)
return (kind, index, peak_loc)
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 compute_displacements_direct(crops, reference_index):
displacements = []
crop_ref = crops[reference_index]
for i, crop in enumerate(crops):
if i == reference_index:
displacements.append(np.r_[0, 0])
continue
debug_log("Computing translation of", i, "relative to", reference_index)
peak_loc, peak_val = get_phasecorr_peak(crop_ref, crop, 100)
displacements.append(peak_loc)
debug_log("Translation (row, col) is", peak_loc, "value:", peak_val)
return displacements
def compute_displacements_direct(crops, reference_index, use_old=False):
displacements = []
crop_ref = crops[reference_index]
for i, crop in enumerate(crops):
if i == reference_index:
displacements.append(np.r_[0, 0])
continue
print "Computing translation (row, col) of", i, "relative to", reference_index,
if use_old:
peak_loc, peak_val = get_phasecorr_peak__old(crop_ref, crop)
else:
peak_loc, peak_val = get_phasecorr_peak(crop_ref, crop, 100)
displacements.append(peak_loc)
print ":", peak_loc, "value:", peak_val
return displacements
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)