99 lines
3.0 KiB
Python
99 lines
3.0 KiB
Python
import cv2
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import numpy as np
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from ultralytics.engine.results import Results
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from models.detection.detector import Detector
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from models.detection.board_manager import BoardManager
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from models.detection.pieces_manager import PiecesManager
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class DetectionService:
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edges_detector : Detector
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pieces_detector : Detector
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board_manager : BoardManager
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pieces_manager : PiecesManager
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scale_size : tuple[int, int]
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def __init__(self):
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self.edges_detector = Detector("../assets/models/edges.pt")
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self.pieces_detector = Detector("../assets/models/unified-nano-refined.pt")
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self.pieces_manager = PiecesManager()
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self.board_manager = BoardManager()
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self.scale_size = (800, 800)
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def run_complete_detection(self, frame : np.ndarray, display=False) -> dict[str, list[Results]] :
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pieces_prediction = self.run_pieces_detection(frame)
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edges_prediction = self.run_edges_detection(frame)
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if display:
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edges_annotated_frame = edges_prediction[0].plot()
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pieces_annotated_frame = pieces_prediction[0].plot(img=edges_annotated_frame)
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self.__display_frame(pieces_annotated_frame)
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return { "edges" : edges_prediction, "pieces" : pieces_prediction}
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def run_pieces_detection(self, frame : np.ndarray, display=False) -> list[Results]:
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prediction = self.pieces_detector.make_prediction(frame)
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if display:
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self.__display_frame(prediction[0].plot())
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return prediction
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def run_edges_detection(self, frame : np.ndarray, display=False) -> list[Results]:
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prediction = self.edges_detector.make_prediction(frame)
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if display:
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self.__display_frame(prediction[0].plot())
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return prediction
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def get_fen(self, frame : np.ndarray) -> str | None:
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result = self.run_complete_detection(frame)
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edges_prediction = result["edges"]
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pieces_prediction = result["pieces"]
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warped_corners, matrix = self.board_manager.process_frame(edges_prediction[0], frame, self.scale_size)
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if matrix is None:
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return None
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detections = self.pieces_manager.extract_pieces(pieces_prediction)
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board = self.pieces_manager.pieces_to_board(detections, warped_corners, matrix, self.scale_size)
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return self.pieces_manager.board_to_fen(board)
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def __display_frame(self, frame : np.ndarray):
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cv2.namedWindow("Frame", cv2.WINDOW_NORMAL)
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cv2.resizeWindow("Frame", self.scale_size[0], self.scale_size[1])
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cv2.imshow("Frame", frame)
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cv2.waitKey(0)
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cv2.destroyAllWindows()
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return
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if __name__ == "__main__" :
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import os
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import random
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service = DetectionService()
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img_folder = "../training/datasets/pieces/unified/test/images/"
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test_images = os.listdir(img_folder)
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rnd = random.randint(0, len(test_images) - 1)
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img_path = os.path.join(img_folder, test_images[rnd])
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image = cv2.imread(img_path)
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fen = service.get_fen(image)
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print(fen)
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service.run_complete_detection(image, display=True) |