def fine_dewarp(im, lines):
im_h, im_w = im.shape[:2]
debug = cv2.cvtColor(im, cv2.COLOR_GRAY2BGR)
points = []
y_offsets = []
for line in lines:
if len(line) < 10 or abs(line.fit_line().angle()) > 0.001: continue
line.fit_line().draw(debug, thickness=1)
base_points = np.array([letter.base_point() for letter in line.inliers()])
median_y = np.median(base_points[:, 1])
y_offsets.append(median_y - base_points[:, 1])
points.append(base_points)
for underline in line.underlines:
mid_contour = (underline.top_contour() + underline.bottom_contour()) / 2
all_mid_points = np.stack([
underline.x + np.arange(underline.w), mid_contour,
])
mid_points = all_mid_points[:, ::4]
points.append(mid_points)
for p in base_points:
pt = tuple(np.round(p).astype(int))
cv2.circle(debug, (pt[0], int(median_y)), 2, lib.RED, -1)
cv2.circle(debug, pt, 2, lib.GREEN, -1)
cv2.imwrite('points.png', debug)
points = np.concatenate(points)
y_offsets = np.concatenate(y_offsets)
mesh = np.mgrid[:im_w, :im_h].astype(np.float32)
xmesh, ymesh = mesh
# y_offset_interp = interpolate.griddata(points, y_offsets, xmesh, ymesh, method='nearest')
# y_offset_interp = y_offset_interp.clip(-5, 5)
# mesh[1] += y_offset_interp # (mesh[0], mesh[1], grid=False)
y_offset_interp = interpolate.SmoothBivariateSpline(
points[:, 0], points[:, 1], y_offsets.clip(-3, 3),
s=4 * points.shape[0]
)
ymesh -= y_offset_interp(xmesh, ymesh, grid=False).clip(-3, 3)
conv_xmesh, conv_ymesh = cv2.convertMaps(xmesh, ymesh, cv2.CV_16SC2)
out = cv2.remap(im, conv_xmesh, conv_ymesh,
interpolation=cv2.INTER_LINEAR,
borderValue=np.median(im)).T
cv2.imwrite('corrected.png', out)
debug = cv2.cvtColor(out, cv2.COLOR_GRAY2BGR)
for line in lines:
base_points = np.array([letter.base_point() for letter in line.inliers()[1:-1]])
base_points[:, 1] -= y_offset_interp(base_points[:, 0], base_points[:, 1], grid=False)
Line.fit(base_points).draw(debug, thickness=1)
cv2.imwrite('corrected_line.png', debug)
return out
def fit_line(self):
if self.model_line is None:
if len(self) <= 3:
self.model_line = Line.fit(self.base_points())
else:
model, inliers = ransac(self.base_points(), TextLine.LineModel,
3, 4)
self.model_line = model.params
self._line_inliers = list(
itertools.compress(self.letters, inliers))
return self.model_line
def vanishing_point(lines, v0, O):
C0 = lines[-1] if v0[1] < 0 else lines[0]
others = lines[:-1] if v0[1] < 0 else lines[1:]
domain = np.linspace(C0.left(), C0.right(), N_LONGS + 2)[1:-1]
C0_points = np.array([domain, C0.model(domain)]).T
longitudes = [Line.from_points(v0, p) for p in C0_points]
lefts = [longitudes[0].text_line_intersect(line)[0] for line in others]
rights = [longitudes[-1].text_line_intersect(line)[0] for line in others]
valid_mask = [line.left() <= L and R < line.right() \
for line, L, R in zip(others, lefts, rights)]
valid_lines = [C0] + compress(others, valid_mask)
derivs = [line.model.deriv() for line in valid_lines]
print('valid lines:', len(others))
convergences = []
for longitude in longitudes:
intersects = [
longitude.text_line_intersect(line) for line in valid_lines
]
tangents = [Line.from_point_slope(p, d(p[0])) \
for p, d in zip(intersects, derivs)]
convergences.append(Line.best_intersection(tangents))
# x vx + y vy + f^2 = 0
# m = -vx / vy
# b = -f^2 / vy
L = Line.fit(convergences)
# shift into O-origin coords
L_O = L.offset(-O)
vy = -(f**2) / L_O.b
vx = -vy * L_O.m
v = np.array((vx, vy)) + O
debug = cv2.cvtColor(bw, cv2.COLOR_GRAY2BGR)
for t in tangents:
t.draw(debug, color=RED)
for longitude in longitudes:
longitude.draw(debug)
L.draw(debug, color=GREEN)
lib.debug_imwrite('vanish.png', debug)
return v, f, L
def estimate(self, data):
self.params = Line.fit(data)
return True
