Python adapter.
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adapter.py
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25
adapter.py
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import json
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import cv2
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import numpy as np
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from runner import warp_board
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# Run this script as: 'python3 adapter.py < ~/Pictures/board.png' or pipe a binary image to stdin
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# Load binary image from stdin
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with open(0, "rb") as stdin:
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array = np.frombuffer(stdin.read(), dtype='uint8')
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camera_img = cv2.imdecode(array, flags=cv2.IMREAD_COLOR)
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# Warp board, saving the homography points as well
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points = []
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warped = warp_board(camera_img, dst_points=points)
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# Finally, output to stdout for unity to read
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result = {
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"points": 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, 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 +188,9 @@ 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 dst_points is not None:
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dst_points.extend(zip(points1, 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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