#!/usr/bin/env python
######################################################################
# page_dewarp.py - Proof-of-concept of page-dewarping based on a
# "cubic sheet" model. Requires OpenCV (version 3 or greater),
# PIL/Pillow, and scipy.optimize.
######################################################################
# Author: Matt Zucker
# Date: July 2016
# License: MIT License (see LICENSE.txt)
######################################################################
import os
import sys
import datetime
import cv2
from PIL import Image
import numpy as np
import scipy.optimize
# for some reason pylint complains about cv2 members being undefined :(
# pylint: disable=E1101
PAGE_MARGIN_X = 50 # reduced px to ignore near L/R edge
PAGE_MARGIN_Y = 20 # reduced px to ignore near T/B edge
OUTPUT_ZOOM = 1.0 # how much to zoom output relative to *original* image
OUTPUT_DPI = 300 # just affects stated DPI of PNG, not appearance
REMAP_DECIMATE = 16 # downscaling factor for remapping image
ADAPTIVE_WINSZ = 55 # window size for adaptive threshold in reduced px
TEXT_MIN_WIDTH = 15 # min reduced px width of detected text contour
TEXT_MIN_HEIGHT = 2 # min reduced px height of detected text contour
TEXT_MIN_ASPECT = 1.5 # filter out text contours below this w/h ratio
TEXT_MAX_THICKNESS = 10 # max reduced px thickness of detected text contour
EDGE_MAX_OVERLAP = 1.0 # max reduced px horiz. overlap of contours in span
EDGE_MAX_LENGTH = 100.0 # max reduced px length of edge connecting contours
EDGE_ANGLE_COST = 10.0 # cost of angles in edges (tradeoff vs. length)
EDGE_MAX_ANGLE = 7.5 # maximum change in angle allowed between contours
RVEC_IDX = slice(0, 3) # index of rvec in params vector
TVEC_IDX = slice(3, 6) # index of tvec in params vector
CUBIC_IDX = slice(6, 8) # index of cubic slopes in params vector
SPAN_MIN_WIDTH = 30 # minimum reduced px width for span
SPAN_PX_PER_STEP = 20 # reduced px spacing for sampling along spans
FOCAL_LENGTH = 1.2 # normalized focal length of camera
DEBUG_LEVEL = 0 # 0=none, 1=some, 2=lots, 3=all
DEBUG_OUTPUT = 'file' # file, screen, both
WINDOW_NAME = 'Dewarp' # Window name for visualization
# nice color palette for visualizing contours, etc.
CCOLORS = [
(255, 0, 0),
(255, 63, 0),
(255, 127, 0),
(255, 191, 0),
(255, 255, 0),
(191, 255, 0),
(127, 255, 0),
(63, 255, 0),
(0, 255, 0),
(0, 255, 63),
(0, 255, 127),
(0, 255, 191),
(0, 255, 255),
(0, 191, 255),
(0, 127, 255),
(0, 63, 255),
(0, 0, 255),
(63, 0, 255),
(127, 0, 255),
(191, 0, 255),
(255, 0, 255),
(255, 0, 191),
(255, 0, 127),
(255, 0, 63),
]
# default intrinsic parameter matrix
K = np.array([
[FOCAL_LENGTH, 0, 0],
[0, FOCAL_LENGTH, 0],
[0, 0, 1]], dtype=np.float32)
def debug_show(name, step, text, display):
if DEBUG_OUTPUT != 'screen':
filetext = text.replace(' ', '_')
outfile = name + '_debug_' + str(step) + '_' + filetext + '.png'
cv2.imwrite(outfile, display)
if DEBUG_OUTPUT != 'file':
image = display.copy()
height = image.shape[0]
cv2.putText(image, text, (16, height-16),
cv2.FONT_HERSHEY_SIMPLEX, 1.0,
(0, 0, 0), 3, cv2.LINE_AA)
cv2.putText(image, text, (16, height-16),
cv2.FONT_HERSHEY_SIMPLEX, 1.0,
(255, 255, 255), 1, cv2.LINE_AA)
cv2.imshow(WINDOW_NAME, image)
while cv2.waitKey(5) < 0:
pass
def round_nearest_multiple(i, factor):
i = int(i)
rem = i % factor
if not rem:
return i
else:
return i + factor - rem
def pix2norm(shape, pts):
height, width = shape[:2]
scl = 2.0/(max(height, width))
offset = np.array([width, height], dtype=pts.dtype).reshape((-1, 1, 2))*0.5
return (pts - offset) * scl
def norm2pix(shape, pts, as_integer):
height, width = shape[:2]
scl = max(height, width)*0.5
offset = np.array([0.5*width, 0.5*height],
dtype=pts.dtype).reshape((-1, 1, 2))
rval = pts * scl + offset
if as_integer:
return (rval + 0.5).astype(int)
else:
return rval
def fltp(point):
return tuple(point.astype(int).flatten())
def draw_correspondences(img, dstpoints, projpts):
display = img.copy()
dstpoints = norm2pix(img.shape, dstpoints, True)
projpts = norm2pix(img.shape, projpts, True)
for pts, color in [(projpts, (255, 0, 0)),
(dstpoints, (0, 0, 255))]:
for point in pts:
cv2.circle(display, fltp(point), 3, color, -1, cv2.LINE_AA)
for point_a, point_b in zip(projpts, dstpoints):
cv2.line(display, fltp(point_a), fltp(point_b),
(255, 255, 255), 1, cv2.LINE_AA)
return display
def get_default_params(corners, ycoords, xcoords):
# page width and height
page_width = np.linalg.norm(corners[1] - corners[0])
page_height = np.linalg.norm(corners[-1] - corners[0])
rough_dims = (page_width, page_height)
# our initial guess for the cubic has no slope
cubic_slopes = [0.0, 0.0]
# object points of flat page in 3D coordinates
corners_object3d = np.array([
[0, 0, 0],
[page_width, 0, 0],
[page_width, page_height, 0],
[0, page_height, 0]])
# estimate rotation and translation from four 2D-to-3D point
# correspondences
_, rvec, tvec = cv2.solvePnP(corners_object3d,
corners, K, np.zeros(5))
span_counts = [len(xc) for xc in xcoords]
params = np.hstack((np.array(rvec).flatten(),
np.array(tvec).flatten(),
np.array(cubic_slopes).flatten(),
ycoords.flatten()) +
tuple(xcoords))
return rough_dims, span_counts, params
def project_xy(xy_coords, pvec):
# get cubic polynomial coefficients given
#
# f(0) = 0, f'(0) = alpha
# f(1) = 0, f'(1) = beta
alpha, beta = tuple(pvec[CUBIC_IDX])
poly = np.array([
alpha + beta,
-2*alpha - beta,
alpha,
0])
xy_coords = xy_coords.reshape((-1, 2))
z_coords = np.polyval(poly, xy_coords[:, 0])
objpoints = np.hstack((xy_coords, z_coords.reshape((-1, 1))))
image_points, _ = cv2.projectPoints(objpoints,
pvec[RVEC_IDX],
pvec[TVEC_IDX],
K, np.zeros(5))
return image_points
def project_keypoints(pvec, keypoint_index):
xy_coords = pvec[keypoint_index]
xy_coords[0, :] = 0
return project_xy(xy_coords, pvec)
def resize_to_screen(src, maxw=1280, maxh=700, copy=False):
height, width = src.shape[:2]
scl_x = float(width)/maxw
scl_y = float(height)/maxh
scl = int(np.ceil(max(scl_x, scl_y)))
if scl > 1.0:
inv_scl = 1.0/scl
img = cv2.resize(src, (0, 0), None, inv_scl, inv_scl, cv2.INTER_AREA)
elif copy:
img = src.copy()
else:
img = src
return img
def box(width, height):
return np.ones((height, width), dtype=np.uint8)
def get_page_extents(small):
height, width = small.shape[:2]
xmin = PAGE_MARGIN_X
ymin = PAGE_MARGIN_Y
xmax = width-PAGE_MARGIN_X
ymax = height-PAGE_MARGIN_Y
page = np.zeros((height, width), dtype=np.uint8)
cv2.rectangle(page, (xmin, ymin), (xmax, ymax), (255, 255, 255), -1)
outline = np.array([
[xmin, ymin],
[xmin, ymax],
[xmax, ymax],
[xmax, ymin]])
return page, outline
def get_mask(name, small, pagemask, masktype):
sgray = cv2.cvtColor(small, cv2.COLOR_RGB2GRAY)
if masktype == 'text':
mask = cv2.adaptiveThreshold(sgray, 255, cv2.ADAPTIVE_THRESH_MEAN_C,
cv2.THRESH_BINARY_INV,
ADAPTIVE_WINSZ,
25)
if DEBUG_LEVEL >= 3:
debug_show(name, 0.1, 'thresholded', mask)
mask = cv2.dilate(mask, box(9, 1))
if DEBUG_LEVEL >= 3:
debug_show(name, 0.2, 'dilated', mask)
mask = cv2.erode(mask, box(1, 3))
if DEBUG_LEVEL >= 3:
debug_show(name, 0.3, 'eroded', mask)
else:
mask = cv2.adaptiveThreshold(sgray, 255, cv2.ADAPTIVE_THRESH_MEAN_C,
cv2.THRESH_BINARY_INV,
ADAPTIVE_WINSZ,
7)
if DEBUG_LEVEL >= 3:
debug_show(name, 0.4, 'thresholded', mask)
mask = cv2.erode(mask, box(3, 1), iterations=3)
if DEBUG_LEVEL >= 3:
debug_show(name, 0.5, 'eroded', mask)
mask = cv2.dilate(mask, box(8, 2))
if DEBUG_LEVEL >= 3:
debug_show(name, 0.6, 'dilated', mask)
return np.minimum(mask, pagemask)
def interval_measure_overlap(int_a, int_b):
return min(int_a[1], int_b[1]) - max(int_a[0], int_b[0])
def angle_dist(angle_b, angle_a):
diff = angle_b - angle_a
while diff > np.pi:
diff -= 2*np.pi
while diff < -np.pi:
diff += 2*np.pi
return np.abs(diff)
def blob_mean_and_tangent(contour):
moments = cv2.moments(contour)
area = moments['m00']
mean_x = moments['m10'] / area
mean_y = moments['m01'] / area
moments_matrix = np.array([
[moments['mu20'], moments['mu11']],
[moments['mu11'], moments['mu02']]
]) / area
_, svd_u, _ = cv2.SVDecomp(moments_matrix)
center = np.array([mean_x, mean_y])
tangent = svd_u[:, 0].flatten().copy()
return center, tangent
class ContourInfo(object):
def __init__(self, contour, rect, mask):
self.contour = contour
self.rect = rect
self.mask = mask
self.center, self.tangent = blob_mean_and_tangent(contour)
self.angle = np.arctan2(self.tangent[1], self.tangent[0])
clx = [self.proj_x(point) for point in contour]
lxmin = min(clx)
lxmax = max(clx)
self.local_xrng = (lxmin, lxmax)
self.point0 = self.center + self.tangent * lxmin
self.point1 = self.center + self.tangent * lxmax
self.pred = None
self.succ = None
def proj_x(self, point):
return np.dot(self.tangent, point.flatten()-self.center)
def local_overlap(self, other):
xmin = self.proj_x(other.point0)
xmax = self.proj_x(other.point1)
return interval_measure_overlap(self.local_xrng, (xmin, xmax))
def generate_candidate_edge(cinfo_a, cinfo_b):
# we want a left of b (so a's successor will be b and b's
# predecessor will be a) make sure right endpoint of b is to the
# right of left endpoint of a.
if cinfo_a.point0[0] > cinfo_b.point1[0]:
tmp = cinfo_a
cinfo_a = cinfo_b
cinfo_b = tmp
x_overlap_a = cinfo_a.local_overlap(cinfo_b)
x_overlap_b = cinfo_b.local_overlap(cinfo_a)
overall_tangent = cinfo_b.center - cinfo_a.center
overall_angle = np.arctan2(overall_tangent[1], overall_tangent[0])
delta_angle = max(angle_dist(cinfo_a.angle, overall_angle),
angle_dist(cinfo_b.angle, overall_angle)) * 180/np.pi
# we want the largest overlap in x to be small
x_overlap = max(x_overlap_a, x_overlap_b)
dist = np.linalg.norm(cinfo_b.point0 - cinfo_a.point1)
if (dist > EDGE_MAX_LENGTH or
x_overlap > EDGE_MAX_OVERLAP or
delta_angle > EDGE_MAX_ANGLE):
return None
else:
score = dist + delta_angle*EDGE_ANGLE_COST
return (score, cinfo_a, cinfo_b)
def make_tight_mask(contour, xmin, ymin, width, height):
tight_mask = np.zeros((height, width), dtype=np.uint8)
tight_contour = contour - np.array((xmin, ymin)).reshape((-1, 1, 2))
cv2.drawContours(tight_mask, [tight_contour], 0,
(1, 1, 1), -1)
return tight_mask
def get_contours(name, small, pagemask, masktype):
mask = get_mask(name, small, pagemask, masktype)
_, contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_NONE)
contours_out = []
for contour in contours:
rect = cv2.boundingRect(contour)
xmin, ymin, width, height = rect
if (width < TEXT_MIN_WIDTH or
height < TEXT_MIN_HEIGHT or
width < TEXT_MIN_ASPECT*height):
continue
tight_mask = make_tight_mask(contour, xmin, ymin, width, height)
if tight_mask.sum(axis=0).max() > TEXT_MAX_THICKNESS:
continue
contours_out.append(ContourInfo(contour, rect, tight_mask))
if DEBUG_LEVEL >= 2:
visualize_contours(name, small, contours_out)
return contours_out
def assemble_spans(name, small, pagemask, cinfo_list):
# sort list
cinfo_list = sorted(cinfo_list, key=lambda cinfo: cinfo.rect[1])
# generate all candidate edges
candidate_edges = []
for i, cinfo_i in enumerate(cinfo_list):
for j in range(i):
# note e is of the form (score, left_cinfo, right_cinfo)
edge = generate_candidate_edge(cinfo_i, cinfo_list[j])
if edge is not None:
candidate_edges.append(edge)
# sort candidate edges by score (lower is better)
candidate_edges.sort()
# for each candidate edge
for _, cinfo_a, cinfo_b in candidate_edges:
# if left and right are unassigned, join them
if cinfo_a.succ is None and cinfo_b.pred is None:
cinfo_a.succ = cinfo_b
cinfo_b.pred = cinfo_a
# generate list of spans as output
spans = []
# until we have removed everything from the list
while cinfo_list:
# get the first on the list
cinfo = cinfo_list[0]
# keep following predecessors until none exists
while cinfo.pred:
cinfo = cinfo.pred
# start a new span
cur_span = []
width = 0.0
# follow successors til end of span
while cinfo:
# remove from list (sadly making this loop *also* O(n^2)
cinfo_list.remove(cinfo)
# add to span
cur_span.append(cinfo)
width += cinfo.local_xrng[1] - cinfo.local_xrng[0]
# set successor
cinfo = cinfo.succ
# add if long enough
if width > SPAN_MIN_WIDTH:
spans.append(cur_span)
if DEBUG_LEVEL >= 2:
visualize_spans(name, small, pagemask, spans)
return spans
def sample_spans(shape, spans):
span_points = []
for span in spans:
contour_points = []
for cinfo in span:
yvals = np.arange(cinfo.mask.shape[0]).reshape((-1, 1))
totals = (yvals * cinfo.mask).sum(axis=0)
means = totals / cinfo.mask.sum(axis=0)
xmin, ymin = cinfo.rect[:2]
step = SPAN_PX_PER_STEP
start = ((len(means)-1) % step) / 2
contour_points += [(x+xmin, means[x]+ymin)
for x in range(start, len(means), step)]
contour_points = np.array(contour_points,
dtype=np.float32).reshape((-1, 1, 2))
contour_points = pix2norm(shape, contour_points)
span_points.append(contour_points)
return span_points
def keypoints_from_samples(name, small, pagemask, page_outline,
span_points):
all_evecs = np.array([[0.0, 0.0]])
all_weights = 0
for points in span_points:
_, evec = cv2.PCACompute(points.reshape((-1, 2)),
None, maxComponents=1)
weight = np.linalg.norm(points[-1] - points[0])
all_evecs += evec * weight
all_weights += weight
evec = all_evecs / all_weights
x_dir = evec.flatten()
if x_dir[0] < 0:
x_dir = -x_dir
y_dir = np.array([-x_dir[1], x_dir[0]])
pagecoords = cv2.convexHull(page_outline)
pagecoords = pix2norm(pagemask.shape, pagecoords.reshape((-1, 1, 2)))
pagecoords = pagecoords.reshape((-1, 2))
px_coords = np.dot(pagecoords, x_dir)
py_coords = np.dot(pagecoords, y_dir)
px0 = px_coords.min()
px1 = px_coords.max()
py0 = py_coords.min()
py1 = py_coords.max()
p00 = px0 * x_dir + py0 * y_dir
p10 = px1 * x_dir + py0 * y_dir
p11 = px1 * x_dir + py1 * y_dir
p01 = px0 * x_dir + py1 * y_dir
corners = np.vstack((p00, p10, p11, p01)).reshape((-1, 1, 2))
ycoords = []
xcoords = []
for points in span_points:
pts = points.reshape((-1, 2))
px_coords = np.dot(pts, x_dir)
py_coords = np.dot(pts, y_dir)
ycoords.append(py_coords.mean() - py0)
xcoords.append(px_coords - px0)
if DEBUG_LEVEL >= 2:
visualize_span_points(name, small, span_points, corners)
return corners, np.array(ycoords), xcoords
def visualize_contours(name, small, cinfo_list):
regions = np.zeros_like(small)
for j, cinfo in enumerate(cinfo_list):
cv2.drawContours(regions, [cinfo.contour], 0,
CCOLORS[j % len(CCOLORS)], -1)
mask = (regions.max(axis=2) != 0)
display = small.copy()
display[mask] = (display[mask]/2) + (regions[mask]/2)
for j, cinfo in enumerate(cinfo_list):
color = CCOLORS[j % len(CCOLORS)]
color = tuple([c/4 for c in color])
cv2.circle(display, fltp(cinfo.center), 3,
(255, 255, 255), 1, cv2.LINE_AA)
cv2.line(display, fltp(cinfo.point0), fltp(cinfo.point1),
(255, 255, 255), 1, cv2.LINE_AA)
debug_show(name, 1, 'contours', display)
def visualize_spans(name, small, pagemask, spans):
regions = np.zeros_like(small)
for i, span in enumerate(spans):
contours = [cinfo.contour for cinfo in span]
cv2.drawContours(regions, contours, -1,
CCOLORS[i*3 % len(CCOLORS)], -1)
mask = (regions.max(axis=2) != 0)
display = small.copy()
display[mask] = (display[mask]/2) + (regions[mask]/2)
display[pagemask == 0] /= 4
debug_show(name, 2, 'spans', display)
def visualize_span_points(name, small, span_points, corners):
display = small.copy()
for i, points in enumerate(span_points):
points = norm2pix(small.shape, points, False)
mean, small_evec = cv2.PCACompute(points.reshape((-1, 2)),
None,
maxComponents=1)
dps = np.dot(points.reshape((-1, 2)), small_evec.reshape((2, 1)))
dpm = np.dot(mean.flatten(), small_evec.flatten())
point0 = mean + small_evec * (dps.min()-dpm)
point1 = mean + small_evec * (dps.max()-dpm)
for point in points:
cv2.circle(display, fltp(point), 3,
CCOLORS[i % len(CCOLORS)], -1, cv2.LINE_AA)
cv2.line(display, fltp(point0), fltp(point1),
(255, 255, 255), 1, cv2.LINE_AA)
cv2.polylines(display, [norm2pix(small.shape, corners, True)],
True, (255, 255, 255))
debug_show(name, 3, 'span points', display)
def imgsize(img):
height, width = img.shape[:2]
return '{}x{}'.format(width, height)
def make_keypoint_index(span_counts):
nspans = len(span_counts)
npts = sum(span_counts)
keypoint_index = np.zeros((npts+1, 2), dtype=int)
start = 1
for i, count in enumerate(span_counts):
end = start + count
keypoint_index[start:start+end, 1] = 8+i
start = end
keypoint_index[1:, 0] = np.arange(npts) + 8 + nspans
return keypoint_index
def optimize_params(name, small, dstpoints, span_counts, params):
keypoint_index = make_keypoint_index(span_counts)
def objective(pvec):
ppts = project_keypoints(pvec, keypoint_index)
return np.sum((dstpoints - ppts)**2)
print ' initial objective is', objective(params)
if DEBUG_LEVEL >= 1:
projpts = project_keypoints(params, keypoint_index)
display = draw_correspondences(small, dstpoints, projpts)
debug_show(name, 4, 'keypoints before', display)
print ' optimizing', len(params), 'parameters...'
start = datetime.datetime.now()
res = scipy.optimize.minimize(objective, params,
method='Powell')
end = datetime.datetime.now()
print ' optimization took', round((end-start).total_seconds(), 2), 'sec.'
print ' final objective is', res.fun
params = res.x
if DEBUG_LEVEL >= 1:
projpts = project_keypoints(params, keypoint_index)
display = draw_correspondences(small, dstpoints, projpts)
debug_show(name, 5, 'keypoints after', display)
return params
def get_page_dims(corners, rough_dims, params):
dst_br = corners[2].flatten()
dims = np.array(rough_dims)
def objective(dims):
proj_br = project_xy(dims, params)
return np.sum((dst_br - proj_br.flatten())**2)
res = scipy.optimize.minimize(objective, dims, method='Powell')
dims = res.x
print ' got page dims', dims[0], 'x', dims[1]
return dims
def remap_image(name, img, small, page_dims, params):
height = 0.5 * page_dims[1] * OUTPUT_ZOOM * img.shape[0]
height = round_nearest_multiple(height, REMAP_DECIMATE)
width = round_nearest_multiple(height * page_dims[0] / page_dims[1],
REMAP_DECIMATE)
print ' output will be {}x{}'.format(width, height)
height_small = height / REMAP_DECIMATE
width_small = width / REMAP_DECIMATE
page_x_range = np.linspace(0, page_dims[0], width_small)
page_y_range = np.linspace(0, page_dims[1], height_small)
page_x_coords, page_y_coords = np.meshgrid(page_x_range, page_y_range)
page_xy_coords = np.hstack((page_x_coords.flatten().reshape((-1, 1)),
page_y_coords.flatten().reshape((-1, 1))))
page_xy_coords = page_xy_coords.astype(np.float32)
image_points = project_xy(page_xy_coords, params)
image_points = norm2pix(img.shape, image_points, False)
image_x_coords = image_points[:, 0, 0].reshape(page_x_coords.shape)
image_y_coords = image_points[:, 0, 1].reshape(page_y_coords.shape)
image_x_coords = cv2.resize(image_x_coords, (width, height),
interpolation=cv2.INTER_CUBIC)
image_y_coords = cv2.resize(image_y_coords, (width, height),
interpolation=cv2.INTER_CUBIC)
img_gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
remapped = cv2.remap(img_gray, image_x_coords, image_y_coords,
cv2.INTER_CUBIC,
None, cv2.BORDER_REPLICATE)
thresh = cv2.adaptiveThreshold(remapped, 255, cv2.ADAPTIVE_THRESH_MEAN_C,
cv2.THRESH_BINARY, ADAPTIVE_WINSZ, 25)
pil_image = Image.fromarray(thresh)
pil_image = pil_image.convert('1')
threshfile = name + '_thresh.png'
pil_image.save(threshfile, dpi=(OUTPUT_DPI, OUTPUT_DPI))
if DEBUG_LEVEL >= 1:
height = small.shape[0]
width = int(round(height * float(thresh.shape[1])/thresh.shape[0]))
display = cv2.resize(thresh, (width, height),
interpolation=cv2.INTER_AREA)
debug_show(name, 6, 'output', display)
return threshfile
def main():
if len(sys.argv) < 2:
print 'usage:', sys.argv[0], 'IMAGE1 [IMAGE2 ...]'
sys.exit(0)
if DEBUG_LEVEL > 0 and DEBUG_OUTPUT != 'file':
cv2.namedWindow(WINDOW_NAME)
outfiles = []
for imgfile in sys.argv[1:]:
img = cv2.imread(imgfile)
small = resize_to_screen(img)
basename = os.path.basename(imgfile)
name, _ = os.path.splitext(basename)
print 'loaded', basename, 'with size', imgsize(img),
print 'and resized to', imgsize(small)
if DEBUG_LEVEL >= 3:
debug_show(name, 0.0, 'original', small)
pagemask, page_outline = get_page_extents(small)
cinfo_list = get_contours(name, small, pagemask, 'text')
spans = assemble_spans(name, small, pagemask, cinfo_list)
if len(spans) < 3:
print ' detecting lines because only', len(spans), 'text spans'
cinfo_list = get_contours(name, small, pagemask, 'line')
spans2 = assemble_spans(name, small, pagemask, cinfo_list)
if len(spans2) > len(spans):
spans = spans2
if len(spans) < 1:
print 'skipping', name, 'because only', len(spans), 'spans'
continue
span_points = sample_spans(small.shape, spans)
print ' got', len(spans), 'spans',
print 'with', sum([len(pts) for pts in span_points]), 'points.'
corners, ycoords, xcoords = keypoints_from_samples(name, small,
pagemask,
page_outline,
span_points)
rough_dims, span_counts, params = get_default_params(corners,
ycoords, xcoords)
dstpoints = np.vstack((corners[0].reshape((1, 1, 2)),) +
tuple(span_points))
params = optimize_params(name, small,
dstpoints,
span_counts, params)
page_dims = get_page_dims(corners, rough_dims, params)
outfile = remap_image(name, img, small, page_dims, params)
outfiles.append(outfile)
print ' wrote', outfile
print
print 'to convert to PDF (requires ImageMagick):'
print ' convert -compress Group4 ' + ' '.join(outfiles) + ' output.pdf'
if __name__ == '__main__':
main()
