import gamma
import math
import numpy
+import os
import perspective
import scipy
import scipy.ndimage
XS = 64
YS = 64
-CORNER_CANDIDATES = 8
+CORNER_CANDIDATES = 16
CUTOFF0 = 64
CUTOFF1 = 4
# ] = 0.
# gamma.write_image(f'diag_{xc:d}_{yc:d}_0.jpg', diag0)
- # diag1 = block1 + .5
+ # diag1 = block1 + .5
# diag1[
# max(y - 21, 0):max(y + 22, 0),
# max(x - 1, 0):max(x + 2, 0),
# return offset and feature relative to block centre
return xo - XS, yo - YS, xf - XM // 2, yf - YM // 2
-
-
diag = False
if len(sys.argv) >= 2 and sys.argv[1] == '--diag':
diag = True
del sys.argv[1]
+if len(sys.argv) < 3:
+ print(f'usage: {sys.argv[0]} in_prefix out_prefix')
+ sys.exit(1)
+in_prefix = sys.argv[1]
+out_prefix = sys.argv[2]
+
+# compile list of input and output files by directory search
+i = in_prefix.rfind('/') + 1
+in_prefix0 = in_prefix[:i] # directory part
+in_prefix1 = in_prefix[i:] # filename part
+files = [
+ (file, out_prefix + file[len(in_prefix):])
+ for file in sorted(
+ [
+ in_prefix0 + file
+ for file in os.listdir(in_prefix0 if len(in_prefix0) else '.')
+ if file[:len(in_prefix1)] == in_prefix1
+ ]
+ )
+]
-in_jpg0 = 'tank_battle/down_2364.jpg'
-in_jpg1 = 'tank_battle/down_2365.jpg'
-out_jpg0 = 'out0_2364.jpg'
-out_jpg1 = 'out0_2365.jpg'
+# first file is special as no transformation needs to be done
+in_jpg, out_jpg = files[0]
-print(f'read {in_jpg0:s}')
-image0 = gamma.read_image(in_jpg0)
+print(f'read {in_jpg:s}')
+image0 = gamma.read_image(in_jpg)
shape = image0.shape
-print('bandpass')
-bandpass0 = calc_bandpass(image0)
-if diag:
- gamma.write_image('bandpass0.jpg', bandpass0 + .5)
-
-print(f'read {in_jpg1:s}')
-image1 = gamma.read_image(in_jpg1)
-assert image1.shape == shape
-
-print('bandpass')
-bandpass1 = calc_bandpass(image1)
-if diag:
- gamma.write_image('bandpass1.jpg', bandpass1 + .5)
+sys.stderr.write(f'write {out_jpg:s}\n')
+gamma.write_image(out_jpg, image0)
ys, xs, cs = shape
xb = (xs // 2 - XM - 2 * XS) // XP
yb = (ys // 2 - YM - 2 * YS) // YP
print('xb', xb, 'yb', yb)
-print('find corner candidates')
-p_all = []
-q_all = []
-corner_candidates = []
-for i in range(2):
- for j in range(2):
- print('i', i, 'j', j)
-
- # correlate blocks in (i, j)-corner
- offsets = []
- blocks = []
- for k in range(yb):
- yc = YS + YM // 2 + k * YP
- if i:
- yc = ys - yc
- for l in range(xb):
- xc = XS + XM // 2 + l * XP
- if j:
- xc = xs - xc
- match = calc_match(bandpass0, bandpass1, xc, yc)
- if match is not None:
- offsets.append(match [:2])
- xo, yo, xf, yf = match
- xf0 = xc + xf
- yf0 = yc + yf
- xf1 = xf0 + xo
- yf1 = yf0 + yo
- p_all.append(numpy.array([xf0, yf0], numpy.double))
- q_all.append(numpy.array([xf1, yf1], numpy.double))
- blocks.append((xf0, yf0, xf1, yf1))
-
- # find the offset trend (median offset per axis) in (i, j)-corner
- k = len(blocks) // 2
- xo_median = sorted([xo for xo, _ in offsets])[k]
- yo_median = sorted([yo for _, yo in offsets])[k]
- #print('i', i, 'j', j, 'xo_median', xo_median, 'yo_median', yo_median)
-
- # choose CORNER_CANDIDATES blocks closest to trend in (i, j)-corner
- u = numpy.array(offsets, numpy.double)
- v = numpy.array([xo_median, yo_median], numpy.double)
- dist = numpy.sum(numpy.square(u - v[numpy.newaxis, :]), 1)
- corner_candidates.append(
- sorted(
- [(dist[i],) + blocks[i] for i in range(len(blocks))]
- )[:CORNER_CANDIDATES]
- )
-p_all = numpy.stack(p_all, 1)
-q_all = numpy.stack(q_all, 1)
-
-# try all combinations of the corner candidates
-print('try corner candidates')
-p = numpy.zeros((2, 4), numpy.double)
-q = numpy.zeros((2, 4), numpy.double)
-best_dist = None
-best_A = None
-best_p = None # for diag
-for _, xf00, yf00, xf10, yf10 in corner_candidates[0]:
- p[0, 0] = xf00
- p[1, 0] = yf00
- q[0, 0] = xf10
- q[1, 0] = yf10
- for _, xf01, yf01, xf11, yf11 in corner_candidates[1]:
- p[0, 1] = xf01
- p[1, 1] = yf01
- q[0, 1] = xf11
- q[1, 1] = yf11
- for _, xf02, yf02, xf12, yf12 in corner_candidates[2]:
- p[0, 2] = xf02
- p[1, 2] = yf02
- q[0, 2] = xf12
- q[1, 2] = yf12
- for _, xf03, yf03, xf13, yf13 in corner_candidates[3]:
- p[0, 3] = xf03
- p[1, 3] = yf03
- q[0, 3] = xf13
- q[1, 3] = yf13
-
- A = perspective.calc_transform(p, q)
- dist = numpy.sum(
- numpy.square(
- q_all - perspective.apply_transform_multi(A, p_all)
- )
- )
- if best_dist is None or dist < best_dist:
- best_dist = dist
- best_A = A
- best_p = numpy.copy(p) # for diag
-
-print('remap')
-out_image1 = perspective.remap_image(best_A, image1)
+print('bandpass')
+bandpass0 = calc_bandpass(image0)
if diag:
- for i in range(4):
- xf0, yf0 = numpy.floor(best_p[:, i]).astype(numpy.int32)
-
- image0[
- max(yf0 - 21, 0):max(yf0 + 22, 0),
- max(xf0 - 1, 0):max(xf0 + 2, 0),
- :
- ] = 0.
- image0[
- max(yf0 - 1, 0):max(yf0 + 2, 0),
- max(xf0 - 21, 0):max(xf0 + 22, 0),
- :
- ] = 0.
-
- out_image1[
- max(yf0 - 21, 0):max(yf0 + 22, 0),
- max(xf0 - 1, 0):max(xf0 + 2, 0),
- :
- ] = 0.
- out_image1[
- max(yf0 - 1, 0):max(yf0 + 2, 0),
- max(xf0 - 21, 0):max(xf0 + 22, 0),
- :
- ] = 0.
-
-sys.stderr.write(f'write {out_jpg0:s}\n')
-gamma.write_image(out_jpg0, image0)
-
-sys.stderr.write(f'write {out_jpg1:s}\n')
-gamma.write_image(out_jpg1, out_image1)
+ gamma.write_image('bandpass0.jpg', bandpass0 + .5)
+
+# loop through remaining files comparing each to the previous
+cumulative_A = numpy.identity(3, numpy.double)
+for file in range(1, len(files)):
+ in_jpg, out_jpg = files[file]
+
+ print(f'read {in_jpg:s}')
+ image1 = gamma.read_image(in_jpg)
+ assert image1.shape == shape
+
+ print('bandpass')
+ bandpass1 = calc_bandpass(image1)
+ if diag:
+ gamma.write_image(f'bandpass{file:d}.jpg', bandpass1 + .5)
+
+ print('find corner candidates')
+ p_all = []
+ q_all = []
+ corner_candidates = []
+ for i in range(2):
+ for j in range(2):
+ print('i', i, 'j', j)
+
+ # correlate blocks in (i, j)-corner
+ offsets = []
+ blocks = []
+ for k in range(yb):
+ yc = YS + YM // 2 + k * YP
+ if i:
+ yc = ys - yc
+ for l in range(xb):
+ xc = XS + XM // 2 + l * XP
+ if j:
+ xc = xs - xc
+ match = calc_match(bandpass0, bandpass1, xc, yc)
+ if match is not None:
+ offsets.append(match [:2])
+ xo, yo, xf, yf = match
+ xf0 = xc + xf
+ yf0 = yc + yf
+ xf1 = xf0 + xo
+ yf1 = yf0 + yo
+ p_all.append(numpy.array([xf0, yf0], numpy.double))
+ q_all.append(numpy.array([xf1, yf1], numpy.double))
+ blocks.append((xf0, yf0, xf1, yf1))
+
+ # find the offset trend (median offset per axis) in (i, j)-corner
+ k = len(blocks) // 2
+ xo_median = sorted([xo for xo, _ in offsets])[k]
+ yo_median = sorted([yo for _, yo in offsets])[k]
+ #print('i', i, 'j', j, 'xo_median', xo_median, 'yo_median', yo_median)
+
+ # choose CORNER_CANDIDATES blocks closest to trend in (i, j)-corner
+ u = numpy.array(offsets, numpy.double)
+ v = numpy.array([xo_median, yo_median], numpy.double)
+ dist = numpy.sum(numpy.square(u - v[numpy.newaxis, :]), 1)
+ corner_candidates.append(
+ sorted(
+ [(dist[i],) + blocks[i] for i in range(len(blocks))]
+ )[:CORNER_CANDIDATES]
+ )
+ p_all = numpy.stack(p_all, 1)
+ q_all = numpy.stack(q_all, 1)
+
+ # try all combinations of the corner candidates
+ print('try corner candidates')
+ p = numpy.zeros((2, 4), numpy.double)
+ q = numpy.zeros((2, 4), numpy.double)
+ best_dist = None
+ best_A = None
+ best_p = None # for diag
+ for _, xf00, yf00, xf10, yf10 in corner_candidates[0]:
+ p[0, 0] = xf00
+ p[1, 0] = yf00
+ q[0, 0] = xf10
+ q[1, 0] = yf10
+ for _, xf01, yf01, xf11, yf11 in corner_candidates[1]:
+ p[0, 1] = xf01
+ p[1, 1] = yf01
+ q[0, 1] = xf11
+ q[1, 1] = yf11
+ for _, xf02, yf02, xf12, yf12 in corner_candidates[2]:
+ p[0, 2] = xf02
+ p[1, 2] = yf02
+ q[0, 2] = xf12
+ q[1, 2] = yf12
+ for _, xf03, yf03, xf13, yf13 in corner_candidates[3]:
+ p[0, 3] = xf03
+ p[1, 3] = yf03
+ q[0, 3] = xf13
+ q[1, 3] = yf13
+
+ A = perspective.calc_transform(p, q)
+ dist = list(
+ numpy.sum(
+ numpy.square(
+ q_all - perspective.apply_transform_multi(A, p_all)
+ ),
+ 0
+ )
+ )
+ dist = sorted(dist)[len(dist) // 2] # median position error
+
+ if best_dist is None or dist < best_dist:
+ best_dist = dist
+ best_A = A
+ best_p = numpy.copy(p) # for diag
+ cumulative_A = best_A @ cumulative_A
+
+ print('remap')
+ out_image1 = perspective.remap_image(cumulative_A, image1)
+ if diag:
+ for i in range(4):
+ xf0, yf0 = numpy.floor(best_p[:, i]).astype(numpy.int32)
+
+ out_image1[
+ max(yf0 - 21, 0):max(yf0 + 22, 0),
+ max(xf0 - 1, 0):max(xf0 + 2, 0),
+ :
+ ] = 0.
+ out_image1[
+ max(yf0 - 1, 0):max(yf0 + 2, 0),
+ max(xf0 - 21, 0):max(xf0 + 22, 0),
+ :
+ ] = 0.
+
+ sys.stderr.write(f'write {out_jpg:s}\n')
+ gamma.write_image(out_jpg, out_image1)
+
+ image0 = image1
+ bandpass0 = bandpass1
public / stop_motion.git / commitdiff