Increase amount of sense being made.
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ChessAR/.gitignore
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ChessAR/.gitignore
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# Created by https://www.gitignore.io/api/unity,visualstudio,visualstudiocode
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# Edit at https://www.gitignore.io/?templates=unity,visualstudio,visualstudiocode
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# End of https://www.gitignore.io/api/unity,visualstudio,visualstudiocode
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17
adapter.py
17
adapter.py
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import base64
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import json
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import sys
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import cv2
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import sys
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import numpy as np
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from runner import warp_board
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from runner import find_keypoints
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# Load base64 encoded image from stdin
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stdin = sys.stdin.readline()
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@ -15,17 +14,13 @@ img_array = np.frombuffer(stdin_decoded, dtype=np.uint8)
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camera_img = cv2.imdecode(img_array, flags=cv2.COLOR_BGR2RGB)
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camera_img = cv2.cvtColor(camera_img, cv2.COLOR_BGR2RGB)
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# Warp board, saving the homography points as well
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src_points = dst_points = []
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#cv2.imshow("ppslpsl", camera_img)
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#cv2.waitKey(0)
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points1, points2 = warp_board(camera_img, src_points=src_points, dst_points=dst_points, short_circuit=True)
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# Find keypoints in image and pass them back to unity
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src_points, dst_points = find_keypoints(camera_img)
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# Finally, output to stdout for unity to read
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result = {
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"src_points": [p.tolist() for p in points1],
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"dst_points": [p.tolist() for p in points2],
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"src_points": [p.tolist() for p in src_points],
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"dst_points": [p.tolist() for p in dst_points],
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}
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print(json.dumps(result))
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runner.py
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runner.py
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from os import path
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from pathlib import Path
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import cv2
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import glob
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import os
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from datetime import datetime
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from pathlib import Path
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from typing import Tuple
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import cv2
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import numpy as np
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from sklearn import cluster, metrics, svm
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from sklearn import cluster, metrics, svm, neural_network
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from sklearn.externals import joblib
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from sklearn.pipeline import make_pipeline
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from sklearn.preprocessing import StandardScaler
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from sklearn import neural_network
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from util import RANK, POSITION, imwrite, PIECE, COLOR, Squares, OUR_PIECES
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joblib.dump(classifier, f"classifiers/classifier_empty/white_piece_on_{square_color}_square.pkl")
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def warp_board(camera_image, debug_image=None, src_points: list = None, dst_points: list = None, short_circuit=False) -> np.ndarray:
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baseline = cv2.imread(str(here.joinpath("new_baseline_board.png")))
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def find_keypoints(camera_image: np.ndarray,
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baseline: np.ndarray = cv2.imread(str(here.joinpath("new_baseline_board.png"))),
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debug=False) -> Tuple[np.ndarray, np.ndarray]:
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"""
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Find keypoints in raw camera image of board.
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:return: (src points, dest points)
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"""
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camera_image_gray = cv2.cvtColor(camera_image, cv2.COLOR_BGR2GRAY)
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baseline_gray = cv2.cvtColor(baseline, cv2.COLOR_BGR2GRAY)
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camera_image_keypoints, des = sift.compute(camera_image_gray, camera_image_keypoints)
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baseline_keypoints, des2 = sift.compute(baseline_gray, baseline_keypoints)
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if debug_image is not None:
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cv2.drawKeypoints(camera_image, keypoints=camera_image_keypoints, outImage=debug_image)
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cv2.imwrite("keypoints_img.jpg", camera_image)
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# FLANN parameters
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FLANN_INDEX_KDTREE = 0
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index_params = dict(algorithm=FLANN_INDEX_KDTREE, trees=8)
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matchesMask[i] = [1, 0]
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good_matches.append([m, n])
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img3 = cv2.drawMatchesKnn(
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if debug:
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# Save keypoints
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keypoints_image = camera_image.copy()
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cv2.drawKeypoints(camera_image, keypoints=camera_image_keypoints, outImage=keypoints_image)
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cv2.imwrite("keypoints.png", keypoints_image)
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# Save matches
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matches_image = cv2.drawMatchesKnn(
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camera_image,
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camera_image_keypoints,
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baseline,
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@ -184,27 +187,26 @@ def warp_board(camera_image, debug_image=None, src_points: list = None, dst_poin
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matchesMask=matchesMask,
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flags=0
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)
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cv2.imwrite("matches.jpg", img3)
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cv2.imwrite("matches.png", matches_image)
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# Extract location of good matches
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points1 = np.zeros((len(good_matches), 2), dtype=np.float32)
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points2 = np.zeros((len(good_matches), 2), dtype=np.float32)
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src_points = np.zeros((len(good_matches), 2), dtype=np.float32)
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dst_points = np.zeros((len(good_matches), 2), dtype=np.float32)
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for i, (m, n) in enumerate(good_matches):
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points1[i, :] = camera_image_keypoints[m.queryIdx].pt
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points2[i, :] = baseline_keypoints[m.trainIdx].pt
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src_points[i, :] = camera_image_keypoints[m.queryIdx].pt
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dst_points[i, :] = baseline_keypoints[m.trainIdx].pt
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if src_points is not None:
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src_points.extend(points1)
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if dst_points is not None:
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dst_points.extend(points2)
|
||||
return src_points, dst_points
|
||||
|
||||
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))
|
||||
|
||||
|
||||
|
|
8
util.py
8
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]):
|
||||
|
|
Loading…
Reference in New Issue
Block a user