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gmonkmangmonkman
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registration and bug fixes
1 parent 490fafc commit 84a5c6f

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Lines changed: 871 additions & 99 deletions

‎dliblib/dliblib.ini‎

Lines changed: 9 additions & 3 deletions
Original file line numberDiff line numberDiff line change
@@ -1,4 +1,10 @@
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[train_shape_predictor]
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images_folder=C:/Users/Graham Monkman/OneDrive/Documents/PHD/images/bass/fiducial/train/candidate/roi_whole/resized
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training_xml_file=C:/Users/Graham Monkman/OneDrive/Documents/PHD/images/bass/fiducial/train/candidate/roi_whole/resized/vgg_keypoints.xml
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predictor_out=C:/Users/Graham Monkman/OneDrive/Documents/PHD/images/bass/fiducial/train/candidate/roi_whole/resized/predictor.dat
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images_folder=C:/Users/Graham Monkman/OneDrive/Documents/PHD/images/bass/fiducial/train/candidate/roi_grow/resized
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training_xml_file=C:/Users/Graham Monkman/OneDrive/Documents/PHD/images/bass/fiducial/train/candidate/roi_grow/resized/vgg_landmarks.xml
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predictor_out=C:/Users/Graham Monkman/OneDrive/Documents/PHD/images/bass/fiducial/train/candidate/roi_grow/resized/predictor.dat
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[predict]
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images_folder=C:/Users/Graham Monkman/OneDrive/Documents/PHD/images/bass/fiducial/charter/fujifilm/rotated
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vgg_file=C:/Users/Graham Monkman/OneDrive/Documents/PHD/images/bass/fiducial/charter/fujifilm/rotated/vgg_testdlib.json
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predictor_out=C:/Users/Graham Monkman/OneDrive/Documents/PHD/images/bass/fiducial/train/candidate/roi_grow/resized/predictor.dat

‎dliblib/dliblib.pyproj‎

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@@ -15,7 +15,7 @@
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<InterpreterId />
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<InterpreterVersion />
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<IsWindowsApplication>False</IsWindowsApplication>
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<CommandLineArguments>"C:/Users/Graham Monkman/OneDrive/Documents/PHD/images/bass/fiducial/train/candidate/roi_grow/resized/vgg_landmarks_633bad.json"</CommandLineArguments>
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<CommandLineArguments>"C:/Users/Graham Monkman/OneDrive/Documents/PHD/images/bass/fiducial/train/candidate/roi_grow/resized/vgg_landmarks.json"</CommandLineArguments>
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<EnableNativeCodeDebugging>False</EnableNativeCodeDebugging>
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<Environment>vgg_pts_to_xml.py "C:/Users/Graham Monkman/OneDrive/Documents/PHD/images/bass/fiducial/train/candidate/roi_whole/resized/vgg_keypoints.json" "C:/Users/Graham Monkman/OneDrive/Documents/PHD/images/bass/fiducial/train/candidate/roi_whole/resized/vgg_keypoints.xml"
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@@ -29,6 +29,9 @@
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</PropertyGroup>
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<ItemGroup>
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<Compile Include="ini.py" />
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<Compile Include="scripts\predict.py">
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<SubType>Code</SubType>
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</Compile>
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<Compile Include="scripts\train_shape_predictor.py">
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<SubType>Code</SubType>
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</Compile>

‎dliblib/scripts/predict.py‎

Lines changed: 51 additions & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -0,0 +1,51 @@
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# pylint: disable=C0103, too-few-public-methods, locally-disabled, no-self-use, unused-argument
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'''
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Run shape predictor example.
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Reads resources from dliblib.ini
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'''
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import os
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import os.path as path
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import sys
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import glob
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import dlib
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import numpy as np
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import dliblib.ini as ini
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from funclib import iolib
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from opencvlib.info import ImageInfo
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from opencvlib.view import show
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from opencvlib.common import draw_points
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from opencvlib.display_utils import KeyBoardInput as Key
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from opencvlib import getimg
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from opencvlib.imgpipes import vgg
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images_folder = path.normpath(ini.Cfg.tryread('predict', 'images_folder'))
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assert iolib.folder_exists(images_folder), 'Images folder %s not found or not specified in ini file.' % images_folder
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predictor_dat = path.normpath(ini.Cfg.tryread('predict', 'predictor_out'))
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predictor = dlib.shape_predictor(predictor_dat)
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vgg_file = path.normpath(ini.Cfg.tryread('predict', 'vgg_file'))
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print("Press 'q' to quit")
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vgg.load_json(vgg_file)
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for Img in vgg.imagesGenerator():
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assert isinstance(Img, vgg.Image)
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i = getimg(path.normpath(Img.filepath))
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for roi in Img.roi_generator(shape_type='rect'):
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assert isinstance(roi, vgg.Region)
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d = dlib.rectangle(min(roi.all_points_x), max(roi.all_points_x), min(roi.all_points_y), max(roi.all_points_y))
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i = draw_points(roi.all_points, i, join=True)
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shape = predictor(i, d)
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pts = [(pt.x, pt.y) for pt in shape.parts()]
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i = draw_points(pts, i)
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if Key.check_pressed_key('q', show(i)):
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quit()
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‎dliblib/scripts/train_shape_predictor.py‎

Lines changed: 42 additions & 38 deletions
Original file line numberDiff line numberDiff line change
@@ -5,6 +5,8 @@
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Set the image folders and the path to the XML
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file which defines the points in dliblib.ini
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in the root of this module.
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Create the training XML from running dliblib.vgg2xml first.
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'''
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import os
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import sys
@@ -32,42 +34,44 @@
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options.be_verbose = True
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dlib.train_shape_predictor(training_xml_file, predictor_out, options)
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print("\nTraining accuracy: {}".format(
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dlib.test_shape_predictor(training_xml_file, predictor_out)))
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#testing_xml_path = os.path.join(images_folder, "testing_with_face_landmarks.xml")
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#print("Testing accuracy: {}".format(
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# dlib.test_shape_predictor(testing_xml_path, "predictor.dat")))
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# Now let's use it as you would in a normal application. First we will load it
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# from disk. We also need to load a face detector to provide the initial
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# estimate of the facial location.
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predictor = dlib.shape_predictor("predictor.dat")
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detector = dlib.get_frontal_face_detector()
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print("Showing detections and predictions on the images in the faces folder...")
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win = dlib.image_window()
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for f in glob.glob(os.path.join(images_folder, "*.jpg")):
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print("Processing file: {}".format(f))
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img = io.imread(f)
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win.clear_overlay()
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win.set_image(img)
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dets = detector(img, 1)
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print("Number of faces detected: {}".format(len(dets)))
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for k, d in enumerate(dets):
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print("Detection {}: Left: {} Top: {} Right: {} Bottom: {}".format(
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k, d.left(), d.top(), d.right(), d.bottom()))
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# Get the landmarks/parts for the face in box d.
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shape = predictor(img, d)
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print("Part 0: {}, Part 1: {} ...".format(shape.part(0),
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shape.part(1)))
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# Draw the face landmarks on the screen.
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win.add_overlay(shape)
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win.add_overlay(dets)
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dlib.hit_enter_to_continue()
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print("\nTraining accuracy: {}".format(dlib.test_shape_predictor(training_xml_file, predictor_out)))
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if False:
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#testing_xml_path = os.path.join(images_folder, "testing_with_face_landmarks.xml")
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#print("Testing accuracy: {}".format(
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# dlib.test_shape_predictor(testing_xml_path, "predictor.dat")))
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# Now let's use it as you would in a normal application. First we will load it
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# from disk. We also need to load a face detector to provide the initial
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# estimate of the facial location.
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predictor = dlib.shape_predictor("predictor.dat")
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detector = dlib.get_frontal_face_detector() #find the faces
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52+
if False:
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print("Showing detections and predictions on the images in the faces folder...")
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win = dlib.image_window()
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for f in glob.glob(os.path.join(images_folder, "*.jpg")):
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print("Processing file: {}".format(f))
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img = io.imread(f)
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win.clear_overlay()
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win.set_image(img)
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dets = detector(img, 1)
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print("Number of faces detected: {}".format(len(dets)))
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for k, d in enumerate(dets):
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print("Detection {}: Left: {} Top: {} Right: {} Bottom: {}".format(
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k, d.left(), d.top(), d.right(), d.bottom()))
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# Get the landmarks/parts for the face in box d.
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shape = predictor(img, d)
70+
print("Part 0: {}, Part 1: {} ...".format(shape.part(0),
71+
shape.part(1)))
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# Draw the face landmarks on the screen.
73+
win.add_overlay(shape)
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75+
win.add_overlay(dets)
76+
dlib.hit_enter_to_continue()
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‎dliblib/scripts/validate_points2.py‎

Lines changed: 2 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -61,14 +61,14 @@ def main():
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lbl = int(vggReg.region_json_key) + 1 if vggReg.region_attr.get('pts', '') == '' else int(vggReg.region_attr['pts'])
6262
Pts[lbl-1] = [vggReg.x, vggReg.y]
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64-
train_pts[j, ...] = np.array(Pts)
64+
train_pts[j, ...] = np.array(Pts) #this contains all the points
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train_distances[j, :, :] = distances(Pts, Pts).squeeze()
6666
train_angles[j, ...] = angles_between(Pts, Pts).squeeze()
6767
j += 1
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6969
mean_distances = np.nanmean(train_distances, axis=0) #19x19 array of mean distances between points
7070
mean_angles = np.nanmean(train_angles, axis=0)
71-
71+
mean_points = np.nanmean(train_pts, axis=0)
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7373
#region Load all data
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x = sum([1 for x in _vgg.imagesGenerator(skip_imghdr_check=True)])

‎dliblib/vgg2xml.py‎

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@@ -1,5 +1,13 @@
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# pylint: disable=C0103, too-few-public-methods, locally-disabled, unused-import
2-
'''Work with vgg and the dblib/W-300 point format files'''
2+
'''Work with vgg and the dblib/W-300 point format files.
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4+
Currently just works with images, rather than roi with
5+
points defined inside.
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The W300 XML format was designed to accept an image with
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face regions defined by top, left, width and height coords
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and points are defined in a CVXY coord system with respect
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to the image (and not the bounding box)'''
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import xml.etree.ElementTree as _et
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from xml.dom import minidom as _minidom
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from os import path as _path

‎funclib/arraylib.py‎

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@@ -51,6 +51,7 @@ def hstackt(arrays):
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return out
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5353

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def check_array(a, b):
5556
'''(ndarray,ndarray)
5657
perform array checks raising error if problem
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Create 2d array of distance between n-dimensional points
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origs is broadcasted to dests
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363362
'''
364363
nd_o = makenp(origs)
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nd_d = makenp(dests)

‎futerize_userdict.bat‎

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REM c:\python352\Scripts\futurize -w -1 "C:/Python354x64/Lib/site-packages/UserDict.py"
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c:\python352\Scripts\futurize -w -1 "C:/Python354x64/Lib/site-packages/_abcoll.py"
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pause

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