Increase amount of sense being made.

ChessAR/.gitignore

@@ -0,0 +1,409 @@
+
+# Created by https://www.gitignore.io/api/unity,visualstudio,visualstudiocode
+# Edit at https://www.gitignore.io/?templates=unity,visualstudio,visualstudiocode
+
+### Unity ###
+[Ll]ibrary/
+[Tt]emp/
+[Oo]bj/
+[Bb]uild/
+[Bb]uilds/
+[Ll]ogs/
+
+# Never ignore Asset meta data
+![Aa]ssets/**/*.meta
+
+# Uncomment this line if you wish to ignore the asset store tools plugin
+# [Aa]ssets/AssetStoreTools*
+
+# TextMesh Pro files
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+
+# Visual Studio cache directory
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+
+# Gradle cache directory
+.gradle/
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+# Autogenerated VS/MD/Consulo solution and project files
+ExportedObj/
+.consulo/
+*.csproj
+*.unityproj
+*.sln
+*.suo
+*.tmp
+*.user
+*.userprefs
+*.pidb
+*.booproj
+*.svd
+*.pdb
+*.mdb
+*.opendb
+*.VC.db
+
+# Unity3D generated meta files
+*.pidb.meta
+*.pdb.meta
+*.mdb.meta
+
+# Unity3D generated file on crash reports
+sysinfo.txt
+
+# Builds
+*.apk
+*.unitypackage
+
+# Crashlytics generated file
+crashlytics-build.properties
+
+
+### VisualStudioCode ###
+.vscode/*
+!.vscode/settings.json
+!.vscode/tasks.json
+!.vscode/launch.json
+!.vscode/extensions.json
+
+### VisualStudioCode Patch ###
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+.history
+
+### VisualStudio ###
+## Ignore Visual Studio temporary files, build results, and
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+##
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+
+# User-specific files
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+# Visual Studio 2015/2017 cache/options directory
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+# Visual Studio 2017 auto generated files
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+# MSTest test Results
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+# NUNIT
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+TestResult.xml
+
+# Build Results of an ATL Project
+[Dd]ebugPS/
+[Rr]eleasePS/
+dlldata.c
+
+# Benchmark Results
+BenchmarkDotNet.Artifacts/
+
+# .NET Core
+project.lock.json
+project.fragment.lock.json
+artifacts/
+
+# StyleCop
+StyleCopReport.xml
+
+# Files built by Visual Studio
+*_i.c
+*_p.c
+*_h.h
+*.ilk
+*.meta
+*.obj
+*.iobj
+*.pch
+*.ipdb
+*.pgc
+*.pgd
+*.rsp
+*.sbr
+*.tlb
+*.tli
+*.tlh
+*.tmp_proj
+*_wpftmp.csproj
+*.log
+*.vspscc
+*.vssscc
+.builds
+*.svclog
+*.scc
+
+# Chutzpah Test files
+_Chutzpah*
+
+# Visual C++ cache files
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+*.aps
+*.ncb
+*.opensdf
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+*.cachefile
+*.VC.VC.opendb
+
+# Visual Studio profiler
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+*.vspx
+*.sap
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+# Visual Studio Trace Files
+*.e2e
+
+# TFS 2012 Local Workspace
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+# Guidance Automation Toolkit
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+
+# ReSharper is a .NET coding add-in
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+*.[Rr]e[Ss]harper
+*.DotSettings.user
+
+# JustCode is a .NET coding add-in
+.JustCode
+
+# TeamCity is a build add-in
+_TeamCity*
+
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+*.dotCover
+
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+.axoCover/*
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+
+# Visual Studio code coverage results
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+
+# NCrunch
+_NCrunch_*
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+nCrunchTemp_*
+
+# MightyMoose
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+AutoTest.Net/
+
+# Web workbench (sass)
+.sass-cache/
+
+# Installshield output folder
+[Ee]xpress/
+
+# DocProject is a documentation generator add-in
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+DocProject/Help/*.HxT
+DocProject/Help/*.HxC
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+DocProject/Help/*.hhk
+DocProject/Help/*.hhp
+DocProject/Help/Html2
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+# Click-Once directory
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+
+# Publish Web Output
+*.[Pp]ublish.xml
+*.azurePubxml
+# Note: Comment the next line if you want to checkin your web deploy settings,
+# but database connection strings (with potential passwords) will be unencrypted
+*.pubxml
+*.publishproj
+
+# Microsoft Azure Web App publish settings. Comment the next line if you want to
+# checkin your Azure Web App publish settings, but sensitive information contained
+# in these scripts will be unencrypted
+PublishScripts/
+
+# NuGet Packages
+*.nupkg
+# The packages folder can be ignored because of Package Restore
+**/[Pp]ackages/*
+# except build/, which is used as an MSBuild target.
+!**/[Pp]ackages/build/
+# Uncomment if necessary however generally it will be regenerated when needed
+#!**/[Pp]ackages/repositories.config
+# NuGet v3's project.json files produces more ignorable files
+*.nuget.props
+*.nuget.targets
+
+# Microsoft Azure Build Output
+csx/
+*.build.csdef
+
+# Microsoft Azure Emulator
+ecf/
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+
+# Windows Store app package directories and files
+AppPackages/
+BundleArtifacts/
+Package.StoreAssociation.xml
+_pkginfo.txt
+*.appx
+*.appxbundle
+*.appxupload
+
+# Visual Studio cache files
+# files ending in .cache can be ignored
+*.[Cc]ache
+# but keep track of directories ending in .cache
+!?*.[Cc]ache/
+
+# Others
+ClientBin/
+~$*
+*~
+*.dbmdl
+*.dbproj.schemaview
+*.jfm
+*.pfx
+*.publishsettings
+orleans.codegen.cs
+
+# Including strong name files can present a security risk
+# (https://github.com/github/gitignore/pull/2483#issue-259490424)
+#*.snk
+
+# Since there are multiple workflows, uncomment next line to ignore bower_components
+# (https://github.com/github/gitignore/pull/1529#issuecomment-104372622)
+#bower_components/
+
+# RIA/Silverlight projects
+Generated_Code/
+
+# Backup & report files from converting an old project file
+# to a newer Visual Studio version. Backup files are not needed,
+# because we have git ;-)
+_UpgradeReport_Files/
+Backup*/
+UpgradeLog*.XML
+UpgradeLog*.htm
+ServiceFabricBackup/
+*.rptproj.bak
+
+# SQL Server files
+*.mdf
+*.ldf
+*.ndf
+
+# Business Intelligence projects
+*.rdl.data
+*.bim.layout
+*.bim_*.settings
+*.rptproj.rsuser
+*- Backup*.rdl
+
+# Microsoft Fakes
+FakesAssemblies/
+
+# GhostDoc plugin setting file
+*.GhostDoc.xml
+
+# Node.js Tools for Visual Studio
+.ntvs_analysis.dat
+node_modules/
+
+# Visual Studio 6 build log
+*.plg
+
+# Visual Studio 6 workspace options file
+*.opt
+
+# Visual Studio 6 auto-generated workspace file (contains which files were open etc.)
+*.vbw
+
+# Visual Studio LightSwitch build output
+**/*.HTMLClient/GeneratedArtifacts
+**/*.DesktopClient/GeneratedArtifacts
+**/*.DesktopClient/ModelManifest.xml
+**/*.Server/GeneratedArtifacts
+**/*.Server/ModelManifest.xml
+_Pvt_Extensions
+
+# Paket dependency manager
+.paket/paket.exe
+paket-files/
+
+# FAKE - F# Make
+.fake/
+
+# CodeRush personal settings
+.cr/personal
+
+# Python Tools for Visual Studio (PTVS)
+__pycache__/
+*.pyc
+
+# Cake - Uncomment if you are using it
+# tools/**
+# !tools/packages.config
+
+# Tabs Studio
+*.tss
+
+# Telerik's JustMock configuration file
+*.jmconfig
+
+# BizTalk build output
+*.btp.cs
+*.btm.cs
+*.odx.cs
+*.xsd.cs
+
+# OpenCover UI analysis results
+OpenCover/
+
+# Azure Stream Analytics local run output
+ASALocalRun/
+
+# MSBuild Binary and Structured Log
+*.binlog
+
+# NVidia Nsight GPU debugger configuration file
+*.nvuser
+
+# MFractors (Xamarin productivity tool) working folder
+.mfractor/
+
+# Local History for Visual Studio
+.localhistory/
+
+# BeatPulse healthcheck temp database
+healthchecksdb
+
+# Backup folder for Package Reference Convert tool in Visual Studio 2017
+MigrationBackup/
+
+# End of https://www.gitignore.io/api/unity,visualstudio,visualstudiocode

adapter.py

@@ -1,12 +1,11 @@
 import base64
 import json
+import sys
 
 import cv2
-import sys
 import numpy as np
 
-from runner import warp_board
+from runner import find_keypoints
-
 
 # Load base64 encoded image from stdin
 stdin = sys.stdin.readline()
@@ -15,17 +14,13 @@ img_array = np.frombuffer(stdin_decoded, dtype=np.uint8)
 camera_img = cv2.imdecode(img_array, flags=cv2.COLOR_BGR2RGB)
 camera_img = cv2.cvtColor(camera_img, cv2.COLOR_BGR2RGB)
 
-# Warp board, saving the homography points as well
+# Find keypoints in image and pass them back to unity
-src_points = dst_points = []
+src_points, dst_points = find_keypoints(camera_img)
-#cv2.imshow("ppslpsl", camera_img)
-#cv2.waitKey(0)
-
-points1, points2 = warp_board(camera_img, src_points=src_points, dst_points=dst_points, short_circuit=True)
 
 # Finally, output to stdout for unity to read
 result = {
-    "src_points": [p.tolist() for p in points1],
+    "src_points": [p.tolist() for p in src_points],
-    "dst_points": [p.tolist() for p in points2],
+    "dst_points": [p.tolist() for p in dst_points],
 }
 
 print(json.dumps(result))

runner.py

@@ -1,18 +1,15 @@
-from os import path
-from pathlib import Path
-
-import cv2
 import glob
 import os
 from datetime import datetime
+from pathlib import Path
 from typing import Tuple
 
+import cv2
 import numpy as np
-from sklearn import cluster, metrics, svm
+from sklearn import cluster, metrics, svm, neural_network
 from sklearn.externals import joblib
 from sklearn.pipeline import make_pipeline
 from sklearn.preprocessing import StandardScaler
-from sklearn import neural_network
 
 from util import RANK, POSITION, imwrite, PIECE, COLOR, Squares, OUR_PIECES
 
@@ -136,10 +133,14 @@ def train_pieces_svm_canny() -> None:
         joblib.dump(classifier, f"classifiers/classifier_empty/white_piece_on_{square_color}_square.pkl")
 
 
-def warp_board(camera_image, debug_image=None, src_points: list = None, dst_points: list = None, short_circuit=False) -> np.ndarray:
+def find_keypoints(camera_image: np.ndarray,
-
+                   baseline: np.ndarray = cv2.imread(str(here.joinpath("new_baseline_board.png"))),
-    baseline = cv2.imread(str(here.joinpath("new_baseline_board.png")))
+                   debug=False) -> Tuple[np.ndarray, np.ndarray]:
+    """
+    Find keypoints in raw camera image of board.
 
+    :return: (src points, dest points)
+    """
     camera_image_gray = cv2.cvtColor(camera_image, cv2.COLOR_BGR2GRAY)
     baseline_gray = cv2.cvtColor(baseline, cv2.COLOR_BGR2GRAY)
 
@@ -150,16 +151,12 @@ def warp_board(camera_image, debug_image=None, src_points: list = None, dst_poin
     camera_image_keypoints, des = sift.compute(camera_image_gray, camera_image_keypoints)
     baseline_keypoints, des2 = sift.compute(baseline_gray, baseline_keypoints)
 
-    if debug_image is not None:
-        cv2.drawKeypoints(camera_image, keypoints=camera_image_keypoints, outImage=debug_image)
-        cv2.imwrite("keypoints_img.jpg", camera_image)
-
     # FLANN parameters
     FLANN_INDEX_KDTREE = 0
     index_params = dict(algorithm=FLANN_INDEX_KDTREE, trees=8)
-    search_params = dict(checks=100)   # or pass empty dictionary
+    search_params = dict(checks=100)  # or pass empty dictionary
 
-    flann = cv2.FlannBasedMatcher(index_params,search_params)
+    flann = cv2.FlannBasedMatcher(index_params, search_params)
     matches = flann.knnMatch(des, des2, k=2)
 
     # Need to draw only good matches, so create a mask
@@ -168,43 +165,48 @@ def warp_board(camera_image, debug_image=None, src_points: list = None, dst_poin
     # Ratio test as per Lowe's paper
     good_matches = []
     for i, (m, n) in enumerate(matches):
-        if m.distance < 0.55*n.distance:
+        if m.distance < 0.55 * n.distance:
             matchesMask[i] = [1, 0]
             good_matches.append([m, n])
 
-    img3 = cv2.drawMatchesKnn(
+    if debug:
-        camera_image,
+        # Save keypoints
-        camera_image_keypoints,
+        keypoints_image = camera_image.copy()
-        baseline,
+        cv2.drawKeypoints(camera_image, keypoints=camera_image_keypoints, outImage=keypoints_image)
-        baseline_keypoints,
+        cv2.imwrite("keypoints.png", keypoints_image)
-        matches,
+        # Save matches
-        None,
+        matches_image = cv2.drawMatchesKnn(
-        matchColor=(0, 255, 0),
+            camera_image,
-        singlePointColor=(255, 0, 0),
+            camera_image_keypoints,
-        matchesMask=matchesMask,
+            baseline,
-        flags=0
+            baseline_keypoints,
-    )
+            matches,
-    cv2.imwrite("matches.jpg", img3)
+            None,
+            matchColor=(0, 255, 0),
+            singlePointColor=(255, 0, 0),
+            matchesMask=matchesMask,
+            flags=0
+        )
+        cv2.imwrite("matches.png", matches_image)
 
     # Extract location of good matches
-    points1 = np.zeros((len(good_matches), 2), dtype=np.float32)
+    src_points = np.zeros((len(good_matches), 2), dtype=np.float32)
-    points2 = np.zeros((len(good_matches), 2), dtype=np.float32)
+    dst_points = np.zeros((len(good_matches), 2), dtype=np.float32)
 
     for i, (m, n) in enumerate(good_matches):
-        points1[i, :] = camera_image_keypoints[m.queryIdx].pt
+        src_points[i, :] = camera_image_keypoints[m.queryIdx].pt
-        points2[i, :] = baseline_keypoints[m.trainIdx].pt
+        dst_points[i, :] = baseline_keypoints[m.trainIdx].pt
 
-    if src_points is not None:
+    return src_points, dst_points
-        src_points.extend(points1)
-    if dst_points is not None:
-        dst_points.extend(points2)
 
-    if short_circuit:
-        return points1, points2
 
-    h, mask = cv2.findHomography(points1, points2, cv2.RANSAC)
+def warp_board(camera_image: np.ndarray, debug=False) -> np.ndarray:
+    baseline = cv2.imread(str(here.joinpath("new_baseline_board.png")))
+    src_points, dst_points = find_keypoints(camera_image, baseline, debug=debug)
 
+    h, mask = cv2.findHomography(src_points, dst_points, cv2.RANSAC)
     height, width, channels = baseline.shape
+
     return cv2.warpPerspective(camera_image, h, (width, height))
 
 

util.py

@@ -1,11 +1,11 @@
 from __future__ import annotations
 
-import cv2
 from enum import Enum
 from functools import lru_cache
 from pathlib import Path
-from typing import NewType, NamedTuple, Dict, Tuple
+from typing import NamedTuple, Dict, Tuple
 
+import cv2
 import numpy as np
 from sklearn.externals import joblib
 
@@ -76,10 +76,10 @@ class _Position(NamedTuple):
 
 
 # POSITION.{A8, A7, ..., H1}
-POSITION = Enum("POSITION", {str(_Position(f, r)): _Position(f, r) for f in FILE for r in RANK}, type=_Position)
+POSITION = Enum("POSITION", {str(_Position(f, r)): _Position(f, r) for f in FILE for r in RANK}, type=_Position)  # NOQA
 
 # Squares is a dict mapping positions to square images, i.e. a board container during image processing
-Squares = NewType("Squares", Dict[POSITION, np.ndarray])
+Squares = Dict[POSITION, np.ndarray]
 
 
 class Board(Dict[POSITION, PIECE]):