Merge branch 'master' of gitfub.space:Pownie/AdvancedSkrald
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commit
1e833313f5
28
adapter.py
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28
adapter.py
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import base64
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import json
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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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# Load base64 encoded image from stdin
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stdin = sys.stdin.readline()
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stdin_decoded = base64.b64decode(stdin)
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img_array = np.frombuffer(stdin_decoded, dtype=np.uint8)
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camera_img = cv2.imdecode(img_array, flags=cv2.IMREAD_COLOR)
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# Warp board, saving the homography points as well
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src_points = dst_points = []
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warped = warp_board(camera_img, src_points=src_points, dst_points=dst_points)
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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 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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@ -131,7 +131,7 @@ def train_pieces_svm_canny() -> None:
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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) -> np.ndarray:
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def warp_board(camera_image, debug_image=None, src_points: list = None, dst_points: list = None) -> np.ndarray:
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baseline = cv2.imread("new_baseline_board.png")
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camera_image_gray = cv2.cvtColor(camera_image, cv2.COLOR_BGR2GRAY)
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@ -188,6 +188,11 @@ def warp_board(camera_image, debug_image=None) -> np.ndarray:
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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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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)
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h, mask = cv2.findHomography(points1, points2, cv2.RANSAC)
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height, width, channels = baseline.shape
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