diff --git a/resources/python/dewarp b/resources/python/dewarp
deleted file mode 160000
index c40293a..0000000
--- a/resources/python/dewarp
+++ /dev/null
@@ -1 +0,0 @@
-Subproject commit c40293a606194a322fa116ba5a7bec38148e07ff
diff --git a/resources/python/dewarp_2/LICENSE.txt b/resources/python/dewarp_2/LICENSE.txt
new file mode 100644
index 0000000..e35410a
--- /dev/null
+++ b/resources/python/dewarp_2/LICENSE.txt
@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2016, Matt Zucker
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
diff --git a/resources/python/dewarp_2/README.md b/resources/python/dewarp_2/README.md
new file mode 100644
index 0000000..7ea5f8c
--- /dev/null
+++ b/resources/python/dewarp_2/README.md
@@ -0,0 +1,14 @@
+page_dewarp
+===========
+
+Page dewarping and thresholding using a "cubic sheet" model - see full writeup at <https://mzucker.github.io/2016/08/15/page-dewarping.html>
+
+Requirements:
+
+ - scipy
+ - OpenCV 3.0 or greater
+ - Image module from PIL or Pillow
+ 
+Usage:
+
+    page_dewarp.py IMAGE1 [IMAGE2 ...]
diff --git a/resources/python/dewarp_2/derive_cubic.py b/resources/python/dewarp_2/derive_cubic.py
new file mode 100644
index 0000000..7b4c993
--- /dev/null
+++ b/resources/python/dewarp_2/derive_cubic.py
@@ -0,0 +1,46 @@
+from __future__ import print_function
+import matplotlib.pyplot as plt
+import numpy as np
+import sympy
+
+# create a bunch of symbols
+a, b, c, d, x, alpha, beta = sympy.symbols('a b c d x alpha beta')
+
+# create a polynomial function f(x)
+f = a*x**3 + b*x**2 + c*x + d
+
+# get its derivative f'(x)
+fp = f.diff(x)
+
+# evaluate both at x=0 and x=1
+f0 = f.subs(x, 0)
+f1 = f.subs(x, 1)
+fp0 = fp.subs(x, 0)
+fp1 = fp.subs(x, 1)
+
+# we want a, b, c, d such that the following conditions hold:
+#
+#  f(0) = 0
+#  f(1) = 0
+#  f'(0) = alpha
+#  f'(1) = beta
+
+S = sympy.solve([f0, f1, fp0-alpha, fp1-beta], [a, b, c, d])
+
+# print the analytic solution and plot a graphical example
+coeffs = []
+
+num_alpha = 0.3
+num_beta = 0.03
+
+for key in [a, b, c, d]:
+    print(key, '=', S[key])
+    coeffs.append(S[key].subs(dict(alpha=num_alpha,
+                                   beta=num_beta)))
+
+xvals = np.linspace(0, 1, 101)
+yvals = np.polyval(coeffs, xvals)
+
+plt.plot(xvals, yvals)
+plt.show()
+
diff --git a/resources/python/dewarp_2/page_dewarp.py b/resources/python/dewarp_2/page_dewarp.py
new file mode 100755
index 0000000..222df32
--- /dev/null
+++ b/resources/python/dewarp_2/page_dewarp.py
@@ -0,0 +1,922 @@
+#!/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)
+######################################################################
+
+from __future__ import division
+from __future__ import print_function
+from builtins import zip
+from builtins import str
+from builtins import range
+from builtins import object
+from past.utils import old_div
+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 = old_div(moments['m10'], area)
+    mean_y = old_div(moments['m01'], area)
+
+    moments_matrix = old_div(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 = old_div(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 = old_div(totals, cinfo.mask.sum(axis=0))
+
+            xmin, ymin = cinfo.rect[:2]
+
+            step = SPAN_PX_PER_STEP
+            start = old_div(((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 = old_div(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] = (old_div(display[mask],2)) + (old_div(regions[mask],2))
+
+    for j, cinfo in enumerate(cinfo_list):
+        color = CCOLORS[j % len(CCOLORS)]
+        color = tuple([old_div(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] = (old_div(display[mask],2)) + (old_div(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(old_div(height * page_dims[0], page_dims[1]),
+                                   REMAP_DECIMATE)
+
+    print('  output will be {}x{}'.format(width, height))
+
+    height_small = old_div(height, REMAP_DECIMATE)
+    width_small = old_div(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), end=' ')
+        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', end=' ')
+        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()
diff --git a/resources/python/dewarp_2/requirements.txt b/resources/python/dewarp_2/requirements.txt
new file mode 100644
index 0000000..c716cb0
--- /dev/null
+++ b/resources/python/dewarp_2/requirements.txt
@@ -0,0 +1,5 @@
+numpy
+scipy
+Pillow
+opencv-python
+future
diff --git a/resources/python/unproject b/resources/python/unproject
deleted file mode 160000
index 734d21a..0000000
--- a/resources/python/unproject
+++ /dev/null
@@ -1 +0,0 @@
-Subproject commit 734d21ad596000cff276ccaf118954362b5eb37e
diff --git a/resources/python/unproject_2/LICENSE.txt b/resources/python/unproject_2/LICENSE.txt
new file mode 100644
index 0000000..df5f7c8
--- /dev/null
+++ b/resources/python/unproject_2/LICENSE.txt
@@ -0,0 +1,24 @@
+The following license agreement applies to ellipse.py and rectify_ellipse.py;
+also see the licensing information at the top of moments_from_contour.py.
+
+MIT License
+
+Copyright (c) 2016, Matt Zucker
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
diff --git a/resources/python/unproject_2/README.md b/resources/python/unproject_2/README.md
new file mode 100644
index 0000000..eae67d8
--- /dev/null
+++ b/resources/python/unproject_2/README.md
@@ -0,0 +1,16 @@
+# unproject_text
+Perspective recovery of text using transformed ellipses. See full writeup at <https://mzucker.github.io/2016/10/11/unprojecting-text-with-ellipses.html>
+
+# Requirements
+
+  - Python 2 or 3
+  - NumPy
+  - SciPy
+  - cv2 (from OpenCV 3.0 or later)
+  - matplotlib
+
+# Usage
+
+Try running
+
+    python unproject_text.py deskew0.jpg
diff --git a/resources/python/unproject_2/ellipse.py b/resources/python/unproject_2/ellipse.py
new file mode 100644
index 0000000..7563ac7
--- /dev/null
+++ b/resources/python/unproject_2/ellipse.py
@@ -0,0 +1,630 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+'''Functions for representing ellipses using various
+parameterizations, and converting between them. There are three
+parameterizations implemented by this module:
+
+Geometric parameters:
+---------------------
+
+The geometric parameters are
+
+  (x₀, y₀, a, b, θ)
+
+The most simple parameterization of an ellipse is by its center point
+(x0, y0), its semimajor and semiminor axes a and b, and its rotation
+angle θ.
+
+Conic:
+------
+
+This parameterization consists of six parameters A-F which establish
+the implicit equation for a general conic:
+
+  AX² + BXY + CY² + DX + EY + F = 0
+
+Note that this equation may not represent only ellipses (it also
+includes hyperbolas and parabolas).
+
+Since multiplying the entire equation by any non-zero constant results
+in the same ellipse, the six parameters are only described up to
+scale, yielding five degrees of freedom. We can determine a canonical
+scale factor k to scale this equation by such that
+
+  A = a²(sin θ)² + b²(cos θ)²
+  B = 2(b² - a²) sin θ cos θ
+  C = a²(cos θ)² + b²(sin θ)²
+  D = -2Ax₀ - By₀
+  E = -Bx₀ - 2Cy₀
+  F = Ax₀² + Bx₀y₀ + Cy₀² - a²b²
+
+...in terms of the geometric parameters (x₀, y₀, a, b, θ).
+
+Shape moments:
+--------------
+
+The shape moment parameters are
+
+ (m₀₀, m₁₀, m₀₁, mu₂₀, mu₁₁, mu₀₂)
+
+An ellipse may be completely specified by its shape moments up to
+order 2. These include the area m₀₀, area-weighted center (m₁₀, m₀₁),
+and the three second order central moments (mu₂₀, mu₁₁, mu₀₂).
+
+'''
+
+# pylint: disable=C0103
+# pylint: disable=R0914
+# pylint: disable=E1101
+
+from __future__ import print_function
+
+import numpy
+
+def _params_str(names, params):
+
+    '''Helper function for printing out the various parameters.'''
+
+    return '({})'.format(', '.join('{}: {:g}'.format(n, p)
+                                   for (n, p) in zip(names, params)))
+
+######################################################################
+
+GPARAMS_NAMES = ('x0', 'y0', 'a', 'b', 'theta')
+GPARAMS_DISPLAY_NAMES = ('x₀', 'y₀', 'a', 'b', 'θ')
+
+def gparams_str(gparams):
+    '''Convert geometric parameters to nice printable string.'''
+    return _params_str(GPARAMS_DISPLAY_NAMES, gparams)
+
+def gparams_evaluate(gparams, phi):
+
+    '''Evaluate the parametric formula for an ellipse at each angle
+specified in the phi array. Returns two arrays x and y of the same
+size as phi.
+
+    '''
+
+    x0, y0, a, b, theta = tuple(gparams)
+
+    c = numpy.cos(theta)
+    s = numpy.sin(theta)
+
+    cp = numpy.cos(phi)
+    sp = numpy.sin(phi)
+
+    x = a*cp*c - b*sp*s + x0
+    y = a*cp*s + b*sp*c + y0
+
+    return x, y
+
+def gparams_from_conic(conic):
+
+    '''Convert the given conic parameters to geometric ellipse parameters.'''
+
+    k, ab = conic_scale(conic)
+
+    if numpy.isinf(ab):
+        return None
+
+    A, B, C, D, E, F = tuple(conic)
+
+    T = B**2 - 4*A*C
+    
+    x0 = (2*C*D - B*E)/T
+    y0 = (2*A*E - B*D)/T
+
+    S = A*E**2 + C*D**2 - B*D*E + (B**2 - 4*A*C)*F
+    U = numpy.sqrt((A - C)**2 + B**2)
+
+    a = -numpy.sqrt(2*S*(A+C+U))/T
+    b = -numpy.sqrt(2*S*(A+C-U))/T
+
+    theta = numpy.arctan2(C-A-U, B)
+    
+    return numpy.array((x0, y0, a, b, theta))
+
+def _gparams_sincos_from_moments(m):
+
+    '''Convert from moments to canonical parameters, except postpone the
+    final arctan until later. Formulas determined largely by trial and
+    error.
+
+    '''
+
+    m00, m10, m01, mu20, mu11, mu02 = tuple(m)
+
+    x0 = m10 / m00
+    y0 = m01 / m00
+
+    A = 4*mu02/m00
+    B = -8*mu11/m00
+    C = 4*mu20/m00
+
+    U = numpy.sqrt((A - C)**2 + B**2)
+    T = B**2 - 4*A*C
+    S = 1.0
+
+    a = -numpy.sqrt(2*S*(A+C+U))/T
+    b = -numpy.sqrt(2*S*(A+C-U))/T
+
+    # we want a * b * pi = m00
+    #
+    # so if we are off by some factor, we should scale a and b by this factor
+    #
+    # we need to fix things up somehow because moments have 6 DOF and
+    # ellipse has only 5.
+    area = numpy.pi * a * b
+    scl = numpy.sqrt(m00 / area)
+    a *= scl
+    b *= scl
+
+    sincos = numpy.array([C-A-U, B])
+    sincos /= numpy.linalg.norm(sincos)
+
+    s, c = sincos
+
+    return numpy.array((x0, y0, a, b, s, c))
+
+def gparams_from_moments(m):
+
+    '''Convert the given moment parameters to geometric ellipse parameters.
+    Formula derived through trial and error.'''
+
+    x0, y0, a, b, s, c = _gparams_sincos_from_moments(m)
+    
+    theta = numpy.arctan2(s, c)
+
+    return numpy.array((x0, y0, a, b, theta))
+
+######################################################################
+
+CONIC_NAMES = ('A', 'B', 'C', 'D', 'E', 'F')
+CONIC_DISPLAY_NAMES = ('A', 'B', 'C', 'D', 'E', 'F')
+
+def conic_str(conic):
+
+    '''Convert conic parameters to nice printable string.'''
+    return _params_str(CONIC_DISPLAY_NAMES, conic)
+
+def conic_scale(conic):
+
+    '''Returns a pair (k, ab) for the given conic parameters, where k is
+the scale factor to divide all parameters by in order to normalize
+them, and ab is the product of the semimajor and semiminor axis
+(i.e. the ellipse's area, divided by pi). If the conic does not
+describe an ellipse, then this returns infinity, infinity.
+
+    '''
+
+    A, B, C, D, E, F = tuple(conic)
+
+    T = 4*A*C - B*B
+
+    if T < 0.0:
+        return numpy.inf, numpy.inf
+
+    S = A*E**2 + B**2*F + C*D**2 - B*D*E - 4*A*C*F
+
+    if not S:
+        return numpy.inf, numpy.inf
+
+
+    k = 0.25*T**2/S
+    ab = 2.0*S/(T*numpy.sqrt(T))
+
+    return k, ab
+
+def conic_from_points(x, y):
+
+    '''Fits conic pararameters using homogeneous least squares. The
+resulting fit is unlikely to be numerically robust when the x/y
+coordinates given are far from the [-1,1] interval.'''
+
+    x = x.reshape((-1, 1))
+    y = y.reshape((-1, 1))
+
+    M = numpy.hstack((x**2, x*y, y**2, x, y, numpy.ones_like(x)))
+
+    _, _, v = numpy.linalg.svd(M)
+
+    return v[5, :].copy()
+
+def conic_transform(conic, H):
+
+    '''Returns the parameters of a conic after being transformed through a
+3x3 homography H. This is straightforward since a conic can be
+represented as a symmetric matrix (see
+https://en.wikipedia.org/wiki/Matrix_representation_of_conic_sections).
+
+    '''
+
+    A, B, C, D, E, F = tuple(conic)
+
+    M = numpy.array([[A, 0.5*B, 0.5*D],
+                     [0.5*B, C, 0.5*E],
+                     [0.5*D, 0.5*E, F]])
+
+    Hinv = numpy.linalg.inv(H)
+
+    M = numpy.dot(Hinv.T, numpy.dot(M, Hinv))
+
+    A = M[0, 0]
+    B = M[0, 1]*2
+    C = M[1, 1]
+    D = M[0, 2]*2
+    E = M[1, 2]*2
+    F = M[2, 2]
+
+    return numpy.array((A, B, C, D, E, F))
+
+def _conic_from_gparams_sincos(gparams_sincos):
+
+    x0, y0, a, b, s, c = gparams_sincos
+    
+    A = a**2 * s**2 + b**2 * c**2
+    B = 2*(b**2 - a**2) * s * c
+    C = a**2 * c**2 + b**2 * s**2
+    D = -2*A*x0 - B*y0
+    E = -B*x0 - 2*C*y0
+    F = A*x0**2 + B*x0*y0 + C*y0**2 - a**2*b**2
+
+    return numpy.array((A, B, C, D, E, F))
+
+def conic_from_gparams(gparams):
+
+    '''Convert geometric parameters to conic parameters. Formulas from
+https://en.wikipedia.org/wiki/Ellipse#General_ellipse.
+
+    '''
+
+    x0, y0, a, b, theta = tuple(gparams)
+    c = numpy.cos(theta)
+    s = numpy.sin(theta)
+
+    return _conic_from_gparams_sincos((x0, y0, a, b, s, c))
+
+def conic_from_moments(moments):
+
+    g = _gparams_sincos_from_moments(moments)
+    
+    return _conic_from_gparams_sincos(g)
+
+######################################################################
+
+MOMENTS_NAMES = ('m00', 'm10', 'm01', 'mu20', 'mu11', 'mu02')
+MOMENTS_DISPLAY_NAMES = ('m₀₀', 'm₁₀', 'm₀₁', 'mu₂₀', 'mu₁₁', 'mu₀₂')
+
+def moments_from_dict(m):
+
+    '''Create shape moments tuple from a dictionary (i.e. returned by cv2.moments).'''
+    return numpy.array([m[n] for n in MOMENTS_NAMES])
+
+def moments_str(m):
+    '''Convert shape moments to nice printable string.'''
+    return _params_str(MOMENTS_DISPLAY_NAMES, m)
+
+
+def moments_from_gparams(gparams):
+
+    '''Create shape moments from geometric parameters.'''
+    x0, y0, a, b, theta = tuple(gparams)
+    c = numpy.cos(theta)
+    s = numpy.sin(theta)
+
+    m00 = a*b*numpy.pi
+    m10 = x0 * m00
+    m01 = y0 * m00
+
+    mu20 = (a**2 * c**2 + b**2 * s**2) * m00 * 0.25
+    mu11 = -(b**2-a**2) * s * c * m00 * 0.25
+    mu02 = (a**2 * s**2 + b**2 * c**2) * m00 * 0.25
+
+    return numpy.array((m00, m10, m01, mu20, mu11, mu02))
+
+def moments_from_conic(scaled_conic):
+
+    '''Create shape moments from conic parameters.'''
+
+    k, ab = conic_scale(scaled_conic)
+
+    if numpy.isinf(ab):
+        return None
+
+    conic = numpy.array(scaled_conic)/k
+
+    A, B, C, D, E, _ = tuple(conic)
+
+    x0 = (B*E - 2*C*D)/(4*A*C - B**2)
+    y0 = (-2*A*E + B*D)/(4*A*C - B**2)
+
+    m00 = numpy.pi*ab
+    m10 = x0*m00
+    m01 = y0*m00
+
+    mu20 = 0.25*C*m00
+    mu11 = -0.125*B*m00
+    mu02 = 0.25*A*m00
+
+    return numpy.array((m00, m10, m01, mu20, mu11, mu02))
+
+
+######################################################################
+
+def _perspective_transform(pts, H):
+
+    '''Used for testing only.'''
+
+    assert len(pts.shape) == 3
+    assert pts.shape[1:] == (1, 2)
+
+    pts = numpy.hstack((pts.reshape((-1, 2)),
+                        numpy.ones((len(pts), 1), dtype=pts.dtype)))
+
+    pts = numpy.dot(pts, H.T)
+
+    pts = pts[:, :2] / pts[:, 2].reshape((-1, 1))
+
+    return pts.reshape((-1, 1, 2))
+
+def _test_moments():
+
+    # so I just realized that moments have actually 6 DOF but all
+    # ellipse parameterizations have 5, therefore information is lost
+    # when going back and forth.
+    
+    m = numpy.array([59495.5, 5.9232e+07, 1.84847e+07, 3.34079e+08, -1.94055e+08, 3.74633e+08])
+    gp = gparams_from_moments(m)
+    
+    m2 = moments_from_gparams(gp)
+    gp2 = gparams_from_moments(m2)
+
+    c = conic_from_moments(m)
+    m3 = moments_from_conic(c)
+
+    assert numpy.allclose(gp, gp2)
+    assert numpy.allclose(m2, m3)
+
+    print('here is the first thing:')
+    print('  {}'.format(moments_str(m)))
+    print()
+    print('the rest should all be equal pairs:')
+    print('  {}'.format(moments_str(m2)))
+    print('  {}'.format(moments_str(m3)))
+    print()
+    print('  {}'.format(gparams_str(gp)))
+    print('  {}'.format(gparams_str(gp2)))
+    print()
+
+
+def _test_ellipse():
+
+    print('testing moments badness')
+    _test_moments()
+    print('pass')
+
+    # test that we can go from conic to geometric and back
+    x0 = 450
+    y0 = 320
+    a = 300
+    b = 200
+    theta = -0.25
+
+    gparams = numpy.array((x0, y0, a, b, theta))
+
+    conic = conic_from_gparams(gparams)
+    k, ab = conic_scale(conic)
+
+    # ensure conic created from geometric params has trivial scale
+    assert numpy.allclose((k, ab), (1.0, a*b))
+
+    # evaluate parametric curve at different angles phi
+    phi = numpy.linspace(0, 2*numpy.pi, 1001).reshape((-1, 1))
+    x, y = gparams_evaluate(gparams, phi)
+
+    # evaluate implicit conic formula at x,y pairs
+    M = numpy.hstack((x**2, x*y, y**2, x, y, numpy.ones_like(x)))
+    implicit_output = numpy.dot(M, conic)
+    implicit_max = numpy.abs(implicit_output).max()
+
+    # ensure implicit evaluates near 0 everywhere
+    print('max item from implicit: {} (should be close to 0)'.format(implicit_max))
+    print()
+    
+    assert implicit_max < 1e-5
+
+    # ensure that scaled_conic has the scale we expect
+    k = 1e-3
+    scaled_conic = conic*k
+
+    k2, ab2 = conic_scale(scaled_conic)
+
+    print('these should all be equal:')
+    print()
+    print('  k  =', k)
+    print('  k2 =', k2)
+    assert numpy.allclose((k2, ab2), (k, a*b))
+    print()
+
+    # convert the scaled conic back to geometric parameters
+    gparams2 = gparams_from_conic(scaled_conic)
+
+    print('  gparams  =', gparams_str(gparams))
+
+    # ensure that converting back from scaled conic to geometric params is correct
+    print('  gparams2 =', gparams_str(gparams2))
+    assert numpy.allclose(gparams, gparams2)
+
+    # convert original geometric parameters to moments
+    m = moments_from_gparams(gparams)
+    # ...and back
+    gparams3 = gparams_from_moments(m)
+
+    # ensure that converting back from moments to geometric params is correct
+    print('  gparams3 =', gparams_str(gparams3))
+    print()
+    assert numpy.allclose(gparams, gparams3)
+
+    # convert moments parameterization to conic
+    conic2 = conic_from_moments(m)
+
+    # ensure that converting from moments to conics is correct
+    print('  conic  =', conic_str(conic))
+    print('  conic2 =', conic_str(conic2))
+    assert numpy.allclose(conic, conic2)
+
+    # create conic from homogeneous least squares fit of points
+    skip = len(x) / 10
+    conic3 = conic_from_points(x[::skip], y[::skip])
+
+    # ensure that it has non-infinite area
+    k3, ab3 = conic_scale(conic3)
+    assert not numpy.isinf(ab3)
+
+    # normalize
+    conic3 /= k3
+
+    # ensure that conic from HLS fit is same as other 2
+    print('  conic3 =', conic_str(conic3))
+    print()
+    assert numpy.allclose(conic, conic3)
+
+    # convert from conic to moments
+    m2 = moments_from_conic(scaled_conic)
+
+    print('  m  =', moments_str(m))
+
+    # ensure that conics->moments yields the same result as geometric
+    # params -> moments.
+    print('  m2 =', moments_str(m2))
+    assert numpy.allclose(m, m2)
+
+    from moments_from_contour import moments_from_contour
+
+    # create moments from contour
+    pts = numpy.hstack((x, y)).reshape((-1, 1, 2))
+    m3 = moments_from_contour(pts)
+
+    # ensure that moments from contour is reasonably close to moments
+    # from geometric params.
+    print('  m3 =', moments_str(m3))
+    print()
+    assert numpy.allclose(m3, m, 1e-4, 1e-4)
+
+    # create a homography H to map the ellipse through
+    hx = 0.001
+    hy = 0.0015
+
+    H = numpy.array([
+        [1, -0.2, 0],
+        [0, 0.7, 0],
+        [hx, hy, 1]])
+
+    T = numpy.array([
+        [1, 0, 400],
+        [0, 1, 300],
+        [0, 0, 1]])
+
+    H = numpy.dot(T, numpy.dot(H, numpy.linalg.inv(T)))
+
+    # transform the original points thru H
+    Hpts = _perspective_transform(pts, H)
+
+    # transform the conic parameters directly thru H
+    Hconic = conic_transform(conic, H)
+
+    # get the HLS fit of the conic corresponding to the transformed points
+    Hconic2 = conic_from_points(Hpts[::skip, :, 0], Hpts[::skip, :, 1])
+
+    # normalize the two conics
+    Hk, Hab = conic_scale(Hconic)
+    Hk2, Hab2 = conic_scale(Hconic2)
+    assert not numpy.isinf(Hab) and not numpy.isinf(Hab2)
+
+    Hconic /= Hk
+    Hconic2 /= Hk2
+
+    # ensure that the two conics are equal
+    print('  Hconic  =', conic_str(Hconic))
+    print('  Hconic2 =', conic_str(Hconic2))
+    print()
+    assert numpy.allclose(Hconic, Hconic2)
+
+    # get the moments from Hconic
+    Hm = moments_from_conic(Hconic)
+
+    # get the moments from the transformed points
+    Hm2 = moments_from_contour(Hpts)
+
+    # ensure that the two moments are close enough
+    print('  Hm  =', moments_str(Hm))
+    print('  Hm2 =', moments_str(Hm2))
+    print()
+    assert numpy.allclose(Hm, Hm2, 1e-4, 1e-4)
+
+    # tests complete, now visualize
+    print('all tests passed!')
+
+    try:
+        import cv2
+        print('visualizing results...')
+    except ImportError:
+        import sys
+        print('not visualizing results since module cv2 not found')
+        sys.exit(0)
+
+    shift = 3
+    pow2 = 2**shift
+
+    p0 = numpy.array([x0, y0], dtype=numpy.float32)
+    p1 = _perspective_transform(p0.reshape((-1, 1, 2)), H).flatten()
+
+    Hgparams = gparams_from_conic(Hconic)
+    Hp0 = Hgparams[:2]
+
+    skip = len(pts)/100
+
+    display = numpy.zeros((600, 800, 3), numpy.uint8)
+
+    def _asint(x, as_tuple=True):
+        x = x*pow2 + 0.5
+        x = x.astype(int)
+        if as_tuple:
+            return tuple(x)
+        else:
+            return x
+
+    for (a, b) in zip(pts.reshape((-1, 2))[::skip],
+                      Hpts.reshape((-1, 2))[::skip]):
+
+        cv2.line(display, _asint(a), _asint(b),
+                 (255, 0, 255), 1, cv2.LINE_AA, shift)
+
+    cv2.polylines(display, [_asint(pts, False)], True,
+                  (0, 255, 0), 1, cv2.LINE_AA, shift)
+
+    cv2.polylines(display, [_asint(Hpts, False)], True,
+                  (0, 0, 255), 1, cv2.LINE_AA, shift)
+
+    r = 3.0
+
+    cv2.circle(display, _asint(p0), int(r*pow2+0.5),
+               (0, 255, 0), 1, cv2.LINE_AA, shift)
+
+    cv2.circle(display, _asint(p1), int(r*pow2+0.5),
+               (255, 0, 255), 1, cv2.LINE_AA, shift)
+
+    cv2.circle(display, _asint(Hp0), int(r*pow2+0.5),
+               (0, 0, 255), 1, cv2.LINE_AA, shift)
+
+    cv2.imshow('win', display)
+
+    print('click in the display window & hit any key to quit.')
+
+    while cv2.waitKey(5) < 0:
+        pass
+
+if __name__ == '__main__':
+
+    _test_ellipse()
diff --git a/resources/python/unproject_2/moments_from_contour.py b/resources/python/unproject_2/moments_from_contour.py
new file mode 100644
index 0000000..6dbee44
--- /dev/null
+++ b/resources/python/unproject_2/moments_from_contour.py
@@ -0,0 +1,123 @@
+'''The function below is ported from the OpenCV project's
+contourMoments function in opencv/modules/imgproc/src/moments.cpp,
+licensed as follows:
+
+----------------------------------------------------------------------
+
+By downloading, copying, installing or using the software you agree to
+this license.  If you do not agree to this license, do not download,
+install, copy or use the software.
+
+
+                          License Agreement
+               For Open Source Computer Vision Library
+                       (3-clause BSD License)
+
+Copyright (C) 2000-2016, Intel Corporation, all rights reserved.
+Copyright (C) 2009-2011, Willow Garage Inc., all rights reserved.
+Copyright (C) 2009-2016, NVIDIA Corporation, all rights reserved.
+Copyright (C) 2010-2013, Advanced Micro Devices, Inc., all rights reserved.
+Copyright (C) 2015-2016, OpenCV Foundation, all rights reserved.
+Copyright (C) 2015-2016, Itseez Inc., all rights reserved.
+Third party copyrights are property of their respective owners.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are
+met:
+
+  * Redistributions of source code must retain the above copyright
+    notice, this list of conditions and the following disclaimer.
+
+  * Redistributions in binary form must reproduce the above copyright
+    notice, this list of conditions and the following disclaimer in
+    the documentation and/or other materials provided with the
+    distribution.
+
+  * Neither the names of the copyright holders nor the names of the
+    contributors may be used to endorse or promote products derived
+    from this software without specific prior written permission.
+
+This software is provided by the copyright holders and contributors
+"as is" and any express or implied warranties, including, but not
+limited to, the implied warranties of merchantability and fitness for
+a particular purpose are disclaimed.  In no event shall copyright
+holders or contributors be liable for any direct, indirect,
+incidental, special, exemplary, or consequential damages (including,
+but not limited to, procurement of substitute goods or services; loss
+of use, data, or profits; or business interruption) however caused and
+on any theory of liability, whether in contract, strict liability, or
+tort (including negligence or otherwise) arising in any way out of the
+use of this software, even if advised of the possibility of such
+damage.
+
+'''
+
+
+def moments_from_contour(xypoints):
+
+    '''Create shape moments from points sampled from the outline of an
+ellipse (note this is numerically inaccurate even for arrays of 1000s
+of points). Included in this project primarily for testing purposes.
+
+    '''
+
+    assert len(xypoints.shape) == 3
+    assert xypoints.shape[1:] == (1, 2)
+
+    xypoints = xypoints.reshape((-1, 2))
+
+    a00 = 0
+    a10 = 0
+    a01 = 0
+    a20 = 0
+    a11 = 0
+    a02 = 0
+
+    xi_1, yi_1 = xypoints[-1]
+
+    for xy in xypoints:
+
+        xi, yi = xy
+        xi2 = xi * xi
+        yi2 = yi * yi
+        dxy = xi_1 * yi - xi * yi_1
+        xii_1 = xi_1 + xi
+        yii_1 = yi_1 + yi
+
+        a00 += dxy
+        a10 += dxy * xii_1
+        a01 += dxy * yii_1
+        a20 += dxy * (xi_1 * xii_1 + xi2)
+        a11 += dxy * (xi_1 * (yii_1 + yi_1) + xi * (yii_1 + yi))
+        a02 += dxy * (yi_1 * yii_1 + yi2)
+
+        xi_1 = xi
+        yi_1 = yi
+
+    if a00 > 0:
+        db1_2 = 0.5
+        db1_6 = 0.16666666666666666666666666666667
+        db1_12 = 0.083333333333333333333333333333333
+        db1_24 = 0.041666666666666666666666666666667
+    else:
+        db1_2 = -0.5
+        db1_6 = -0.16666666666666666666666666666667
+        db1_12 = -0.083333333333333333333333333333333
+        db1_24 = -0.041666666666666666666666666666667
+
+    m00 = a00 * db1_2
+    m10 = a10 * db1_6
+    m01 = a01 * db1_6
+    m20 = a20 * db1_12
+    m11 = a11 * db1_24
+    m02 = a02 * db1_12
+
+    inv_m00 = 1. / m00
+    cx = m10 * inv_m00
+    cy = m01 * inv_m00
+
+    mu20 = m20 - m10 * cx
+    mu11 = m11 - m10 * cy
+    mu02 = m02 - m01 * cy
+
+    return m00, m10, m01, mu20, mu11, mu02
diff --git a/resources/python/unproject_2/requirements.txt b/resources/python/unproject_2/requirements.txt
new file mode 100644
index 0000000..a739100
--- /dev/null
+++ b/resources/python/unproject_2/requirements.txt
@@ -0,0 +1,5 @@
+unproject_text
+cv2
+numpy
+scipy
+matplotlib
diff --git a/resources/python/unproject_2/unproject_text.py b/resources/python/unproject_2/unproject_text.py
new file mode 100644
index 0000000..65cd98e
--- /dev/null
+++ b/resources/python/unproject_2/unproject_text.py
@@ -0,0 +1,504 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+from __future__ import unicode_literals, print_function
+
+import sys
+import numpy as np
+import scipy.optimize
+import matplotlib.pyplot as plt
+import cv2
+import ellipse
+
+DEBUG_IMAGES = []
+
+def debug_show(name, src):
+
+    global DEBUG_IMAGES
+
+    filename = 'debug{:02d}_{}.png'.format(len(DEBUG_IMAGES), name)
+    cv2.imwrite(filename, src)
+
+    h, w = src.shape[:2]
+
+    fx = w/1280.0
+    fy = h/700.0
+
+    f = 1.0/np.ceil(max(fx, fy))
+
+    if f < 1.0:
+        img = cv2.resize(src, (0, 0), None, f, f, cv2.INTER_AREA)
+    else:
+        img = src.copy()
+
+    DEBUG_IMAGES.append(img)
+
+def translation(x, y):
+    return np.array([[1, 0, x], [0, 1, y], [0, 0, 1]], dtype=float)
+
+def rotation(theta):
+    c = np.cos(theta)
+    s = np.sin(theta)
+    return np.array([[c, -s, 0], [s, c, 0], [0, 0, 1]], dtype=float)
+
+def perspective_warp(a, b):
+    return np.array([[1, 0, 0], [0, 1, 0], [a, b, 1]], dtype=float)
+
+def slant(sx):
+    return np.array([[1, sx, 0], [0, 1, 0], [0, 0, 1]], dtype=float)
+
+def softmax(x, k=1.0):
+    b = x.max()
+    return np.log( np.exp(k*(x-b)).sum() ) / k + b
+
+def skewed_widths(contours, H):
+    xvals = []
+    for c in contours:
+        pts = cv2.perspectiveTransform(c, H)
+        x = pts[:,:,0]
+        xvals.append( x.max() - x.min() )
+    xvals = np.array(xvals)
+    return softmax(xvals, 0.1)
+
+def centered_warp(u0, v0, a, b):
+    return np.dot(translation(u0, v0),
+                  np.dot(perspective_warp(a, b),
+                         translation(-u0, -v0)))
+
+def warp_containing_points(img, pts, H, border=4, shape_only=False):
+
+    '''
+    display = img.copy()
+    for pt in pts.reshape((-1,2)).astype(int):
+        cv2.circle(display, tuple(pt), 4, (255, 0, 0),
+                   -1, cv2.LINE_AA)
+    debug_show('warp', display)
+    '''
+
+    pts2 = cv2.perspectiveTransform(pts, H)
+    x0, y0, w, h = cv2.boundingRect(pts2)
+    print('got bounding rect', x0, y0, w, h)
+    T = translation(-x0+border, -y0+border)
+    TH = np.dot(T, H)
+
+    if shape_only:
+        return (h+2*border, w+2*border), TH
+    else:
+        dst = cv2.warpPerspective(img, TH, (w+2*border, h+2*border),
+                                  borderMode=cv2.BORDER_REPLICATE)
+        return dst, TH
+
+def conic_area_discrepancy(conics, x, H, opt_results=None):
+
+    areas = []
+
+    for conic in conics:
+        cx = ellipse.conic_transform(conic, H)
+        k, ab = ellipse.conic_scale(cx)
+        if np.isinf(ab):
+            areas.append(1e20)
+        else:
+            areas.append(ab)
+
+    areas = np.array(areas)
+
+    areas /= areas.mean() # rescale so mean is 1.0
+    areas -= 1 # subtract off mean
+
+    rval = 0.5*np.dot(areas, areas)
+
+    if opt_results is not None:
+        if not opt_results or rval < opt_results[-1][-1]:
+            opt_results.append( (x, H, rval) )
+
+    return rval
+
+def threshold(img):
+
+    if len(img.shape) > 2:
+        img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
+
+    mean = img.mean()
+    if mean < 100:
+        img = 255-img
+
+    return cv2.adaptiveThreshold(img, 255, cv2.ADAPTIVE_THRESH_MEAN_C,
+                                 cv2.THRESH_BINARY_INV, 101, 21)
+
+def get_contours(img):
+
+    work = threshold(img)
+
+    debug_show('threshold', work)
+
+    contours, hierarchy = cv2.findContours(work, cv2.RETR_CCOMP,
+                                              cv2.CHAIN_APPROX_NONE)
+
+    return contours, hierarchy
+
+def get_conics(img, contours, hierarchy,
+               abs_area_cutoff=0.0001, mean_area_cutoff=0.15):
+
+    hierarchy = hierarchy.reshape((-1, 4))
+
+    conics = []
+    used_contours = []
+    areas = []
+    okcontours = []
+    allchildren = []
+    pts = np.empty((0,1,2), dtype='float32')
+    centroid_accum = np.zeros(2)
+    total_area = 0.0
+
+    centroids = []
+
+    abs_area_cutoff *= img.shape[0] * img.shape[1]
+    print('abs_area_cutoff = ',abs_area_cutoff)
+
+    for i, (c, h) in enumerate(zip(contours, hierarchy.reshape((-1, 4)))):
+
+        next_idx, prev_idx, child_idx, parent_idx = h
+
+        if parent_idx >= 0:
+            continue
+
+        m = ellipse.moments_from_dict(cv2.moments(c))
+
+        if m[0] <= abs_area_cutoff:
+            continue
+
+        children = []
+
+        while child_idx >= 0:
+            child_contour = contours[child_idx]
+            cm = cv2.moments(child_contour)
+            if cm['m00'] > abs_area_cutoff:
+                children.append(child_contour)
+                allchildren.append(child_contour)
+            child_idx = hierarchy[child_idx][0]
+
+        if children:
+            work = np.zeros(img.shape[:2], dtype=np.uint8)
+            cv2.drawContours(work, contours, i, (1,1,1), -1)
+            cv2.drawContours(work, children, -1, (0,0,0), -1)
+            m = ellipse.moments_from_dict(cv2.moments(work, True))
+
+        centroids.append(m[1:3]/m[0])
+        centroid_accum += m[1:3]
+        total_area += m[0]
+        pts = np.vstack((pts, c.astype('float32')))
+        conic = ellipse.conic_from_moments(m)
+        okcontours.append(c)
+        conics.append(conic)
+        areas.append(m[0])
+
+    display = img.copy()
+    cv2.drawContours(display, okcontours+allchildren,
+                     -1, (0, 255, 0),
+                     6, cv2.LINE_AA)
+
+    debug_show('contours_only', display)
+
+    for c, a in zip(okcontours, areas):
+
+        x, y, w, h = cv2.boundingRect(c)
+
+
+        s = str('{:,d}'.format(int(a)))
+        #ctr = (x + w/2 - 15*len(s), y+h/2+10)
+        ctr = (x, y+h+20)
+
+        cv2.putText(display, s, ctr,
+                    cv2.FONT_HERSHEY_SIMPLEX, 2.0,
+                    (0, 0, 0), 12, cv2.LINE_AA)
+
+        cv2.putText(display, s, ctr,
+                    cv2.FONT_HERSHEY_SIMPLEX, 2.0,
+                    (0, 255, 0), 6, cv2.LINE_AA)
+
+    debug_show('contours', display)
+
+    areas = np.array(areas)
+    amean = areas.mean()
+
+    print('got {} contours with {} small.'.format(
+        len(areas), (areas < mean_area_cutoff*amean).sum()))
+
+    idx = np.where(areas > mean_area_cutoff*amean)[0]
+
+    conics = np.array(conics)
+    conics = conics[idx]
+    centroid_accum /= total_area
+
+    display = img.copy()
+    for conic in conics:
+        x0, y0, a, b, theta = ellipse.gparams_from_conic(conic)
+        cv2.ellipse(display, (int(x0), int(y0)), (int(a), int(b)),
+                    theta*180/np.pi, 0, 360, (0,0,255), 6, cv2.LINE_AA)
+
+    debug_show('conics', display)
+
+    contours = [okcontours[i].astype('float32') for i in idx]
+
+    if 0:
+
+        centroids = np.array([centroids[i] for i in idx])
+        areas = areas[idx]
+
+        def polyfit(x, y):
+            coeffs = np.polyfit(x, y, deg=1)
+            ypred = np.polyval(coeffs, x)
+            ymean = np.mean(y)
+            sstot = np.sum((y - ymean)**2)
+            ssres = np.sum((y.flatten() - ypred.flatten())**2)
+            r2 = 1 - ssres/sstot
+            return coeffs, r2
+
+        xfit, xr2 = polyfit(centroids[:,0], areas)
+        yfit, yr2 = polyfit(centroids[:,1], areas)
+
+        xlabel = 'X coordinate (r²={:.2f})'.format(xr2)
+        ylabel = 'Y coordinate (r²={:.2f})'.format(yr2)
+
+        plt.plot(centroids[:,0], areas, 'b.', zorder=1)
+        plt.plot(centroids[:,1], areas, 'r.', zorder=1)
+        plt.gca().autoscale(False)
+        plt.plot([0, 3000], np.polyval(xfit, [0,3000]), 'b--',
+                 zorder=0, label=xlabel)
+        plt.plot([0, 3000], np.polyval(yfit, [0,3000]), 'r--',
+                 zorder=0, label=ylabel)
+        plt.legend(loc='upper right')
+        plt.xlabel('X/Y coordinate (px)')
+        plt.ylabel('Contour area (px²)')
+        plt.savefig('position-vs-area.pdf')
+
+
+
+    return conics, contours, centroid_accum
+
+def optimize_conics(conics, p0):
+
+    x0 = np.array([0.0, 0.0])
+
+    hfunc = lambda x: centered_warp(p0[0], p0[1], x[0], x[1])
+
+    opt_results = []
+
+    f = lambda x: conic_area_discrepancy(conics, x, hfunc(x), opt_results)
+
+    res = scipy.optimize.minimize(f, x0, method='Powell')
+
+    H = hfunc(res.x)
+
+    rects = []
+
+    if 0:
+
+        phi = np.linspace(0, 2*np.pi, 16, endpoint=False)
+        width, height = 0, 0
+        for x, H, fval in opt_results:
+            allxy = []
+            for conic in conics:
+                Hconic = ellipse.conic_transform(conic, H)
+                gparams = ellipse.gparams_from_conic(Hconic)
+                x, y = ellipse.gparams_evaluate(gparams, phi)
+                xy = np.dstack((x.reshape((-1, 1, 1)), y.reshape((-1, 1, 1))))
+                allxy.append(xy)
+            allxy = np.vstack(tuple(allxy)).astype(np.float32)
+            rect = cv2.boundingRect(allxy)
+            rects.append(rect)
+            x, y, w, h = rect
+            width = max(width, w)
+            height = max(height, h)
+        border = int(0.05 * min(width, height))
+        width += border
+        height += border
+        aspect = float(width)/height
+        if aspect < 2.0:
+            width = 2*height
+        else:
+            height = width/2
+
+        for i, (rect, (x, H, fval)) in enumerate(zip(rects, opt_results)):
+            display = np.zeros((height, width), dtype=np.uint8)
+            x, y, w, h = rect
+            xoffs = width/2 - (x+w/2)
+            yoffs = height/2 - (y+h/2)
+            for conic in conics:
+                Hconic = ellipse.conic_transform(conic, H)
+                x0, y0, a, b, theta = ellipse.gparams_from_conic(Hconic)
+                cv2.ellipse(display, (int(x0+xoffs), int(y0+yoffs)), (int(a), int(b)),
+                            theta*180/np.pi, 0, 360, (255,255,255), 6, cv2.LINE_AA)
+            cv2.putText(display, 'Area discrepancy: {:.3f}'.format(fval),
+                        (16, height-24), cv2.FONT_HERSHEY_SIMPLEX, 2.0,
+                        (255,255,255), 6, cv2.LINE_AA)
+            cv2.imwrite('frame{:04d}.png'.format(i), display)
+
+    return H
+
+def orientation_detect(img, contours, H, rho=8.0, ntheta=512):
+
+    # ignore this, just deal with edge-detected text
+
+    pts = np.vstack(tuple(contours))
+
+    shape, TH = warp_containing_points(img, pts, H, shape_only=True)
+
+    text_edges = np.zeros(shape, dtype=np.uint8)
+
+    for contour in contours:
+        contour = cv2.perspectiveTransform(contour.astype(np.float32), TH)
+        cv2.drawContours(text_edges, [contour.astype(int)], 0, (255,255,255))
+
+    debug_show('edges', text_edges)
+
+    # generate a linspace of thetas
+    thetas = np.linspace(-0.5*np.pi, 0.5*np.pi, ntheta, endpoint=False)
+
+    # rho is pixels per r bin in polar (theta, r) histogram
+    # irho is bins per pixel
+    irho = 1.0/rho
+
+    # get height and width
+    h, w = text_edges.shape
+
+    # maximum bin index is given by hypotenuse of (w, h) divided by pixels per bin
+    bin_max = int(np.ceil(np.hypot(w, h)*irho))
+
+    # initialize zeroed histogram height bin_max and width num theta
+    hist = np.zeros((bin_max, ntheta))
+
+    # let u and v be x and y coordinates (respectively) of non-zero
+    # pixels in edge map
+    v, u = np.mgrid[0:h, 0:w]
+    v = v[text_edges.view(bool)]
+    u = u[text_edges.view(bool)]
+
+    # get center coordinates
+    u0 = w*0.5
+    v0 = h*0.5
+
+    # for each i and theta = thetas[i]
+    for i, theta in enumerate(thetas):
+
+        # for each nonzero edge pixel, compute bin in r direction from
+        # pixel location and cos/sin of theta
+        bin_idx =  ( (-(u-u0)*np.sin(theta) # x term
+                      + (v-v0)*np.cos(theta))*irho # y term, both
+                                                   # divided by pixels
+                                                   # per bin
+                     + 0.5*bin_max ) # offset for center pixel
+
+        assert( bin_idx.min() >= 0 and bin_idx.max() < bin_max )
+
+        # 0.5 is for correct rounding here
+        #
+        # e.g. np.bincount([1, 1, 0, 3]) = [1, 2, 0, 1]
+        # returns count of each integer in the array
+
+        bc = np.bincount((bin_idx + 0.5).astype(int))
+
+        # push this into the histogram
+        hist[:len(bc),i] = bc
+
+    # number of zero pixels in each column
+    num_zero = (hist == 0).sum(axis=0)
+
+    # find the maximum number of zero pixels
+    best_theta_idx = num_zero.argmax()
+
+    # actual detected theta - could just return this now
+    theta = thetas[best_theta_idx]
+
+    # compose with previous homography
+    RH = np.dot(rotation(-theta), H)
+
+    if 1: # just debug visualization
+
+        debug_hist = (255*hist/hist.max()).astype('uint8')
+        debug_hist = cv2.cvtColor(debug_hist, cv2.COLOR_GRAY2RGB)
+
+        cv2.line(debug_hist,
+                 (best_theta_idx, 0),
+                 (best_theta_idx, bin_max), (255,0,0),
+                 1, cv2.LINE_AA)
+
+        debug_show('histogram', debug_hist)
+
+        p0 = np.array((u0, v0))
+        t = np.array((np.cos(theta), np.sin(theta)))
+
+        warped = cv2.warpPerspective(img, TH, (shape[1], shape[0]),
+                                     borderMode=cv2.BORDER_REPLICATE)
+
+
+        debug_show('prerotate_noline', warped)
+
+        cv2.line(warped,
+                 tuple(map(int, p0 - rho*bin_max*t)),
+                 tuple(map(int, p0 + rho*bin_max*t)),
+                 (255, 0, 0),
+                 6, cv2.LINE_AA)
+
+        debug_show('prerotate', warped)
+
+        warped, _ = warp_containing_points(img, pts, RH)
+        debug_show('preskew', warped)
+
+    return RH
+
+
+def skew_detect(img, contours, RH):
+
+    hulls = [cv2.convexHull(c) for c in contours]
+    pts = np.vstack(tuple(hulls))
+
+
+
+    display, TRH = warp_containing_points(img, pts, RH)
+
+    for h in hulls:
+        h = cv2.perspectiveTransform(h, TRH).astype(int)
+        cv2.drawContours(display, [h], 0, (255, 0, 255), 6, cv2.LINE_AA)
+
+    debug_show('convex_hulls_before', display)
+
+    f = lambda x: skewed_widths(contours, np.dot(slant(x), RH))
+
+    res = scipy.optimize.minimize_scalar(f, (-2.0, 0.0, 2.0))
+
+    SRH = np.dot(slant(res.x), RH)
+    warped, Hfinal = warp_containing_points(img, pts, SRH)
+
+    display = warped.copy()
+
+    for h in hulls:
+        h = cv2.perspectiveTransform(h, Hfinal).astype(int)
+        cv2.drawContours(display, [h], 0, (255, 0, 255), 6, cv2.LINE_AA)
+
+    debug_show('convex_hulls_after', display)
+
+    debug_show('final', warped)
+
+    return SRH
+
+def main():
+
+    img = cv2.imread(sys.argv[1])
+    debug_show('input', img)
+
+    contours, hierarchy = get_contours(img)
+
+    conics, contours, centroid = get_conics(img, contours, hierarchy)
+    H = optimize_conics(conics, centroid)
+    RH = orientation_detect(img, contours, H)
+    SRH = skew_detect(img, contours, RH)
+
+    for img in DEBUG_IMAGES:
+        cv2.imshow('Debug', img)
+        while cv2.waitKey(5) < 0:
+            pass
+
+if __name__ == '__main__':
+    main()
+