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board-mate/rpi/models/detection/board_manager.py
2025-12-23 14:31:54 +01:00

75 lines
3.0 KiB
Python

import cv2
import numpy as np
from typing import Tuple, Any
from numpy import ndarray
class BoardManager:
def process_frame(self, prediction: object, image : np.ndarray, scale_size: tuple[int, int]) -> tuple[ndarray, ndarray] | None:
try :
mask = self.__get_mask(prediction)
contour = self.__get_largest_contour(mask)
corners = self.__approx_corners(contour)
scaled_corners = self.__scale_corners(corners, mask.shape, image.shape)
ordered_corners = self.__order_corners(scaled_corners)
transformation_matrix = self.__calculte_transformation_matrix(ordered_corners, scale_size)
warped_corners = cv2.perspectiveTransform(
np.array(ordered_corners, np.float32).reshape(-1, 1, 2),
transformation_matrix
).reshape(-1, 2)
return warped_corners, transformation_matrix
except Exception as e:
print(e)
return None
def __calculte_transformation_matrix(self, corners: np.ndarray, output_size : tuple[int, int]) -> np.ndarray:
width = output_size[0]
height = output_size[1]
dst = np.array([
[0, 0], # top-left
[width - 1, 0], # top-right
[width - 1, height - 1], # bottom-right
[0, height - 1] # bottom-left
], dtype=np.float32)
return cv2.getPerspectiveTransform(corners, dst)
def __get_mask(self, pred: object) -> Any:
if pred.masks is None:
raise ValueError("Board contour is not 4 corners")
mask = pred.masks.data[0].cpu().numpy()
mask = (mask * 255).astype(np.uint8)
return mask
def __get_largest_contour(self, mask: np.ndarray) -> np.ndarray:
contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
if not contours:
raise ValueError("No contours found")
return max(contours, key=cv2.contourArea)
def __approx_corners(self, contour: np.ndarray) -> np.ndarray:
epsilon = 0.02 * cv2.arcLength(contour, True)
approx = cv2.approxPolyDP(contour, epsilon, True)
if len(approx) != 4:
raise ValueError("Board contour is not 4 corners")
return approx.reshape(4, 2)
def __scale_corners(self, pts: np.ndarray, mask_shape: Tuple[int, int], image_shape: Tuple[int, int, int]) -> np.ndarray:
mask_h, mask_w = mask_shape
img_h, img_w = image_shape[:2]
scale_x = img_w / mask_w
scale_y = img_h / mask_h
scaled_pts = [(int(p[0] * scale_x), int(p[1] * scale_y)) for p in pts]
return np.array(scaled_pts, dtype=np.float32)
def __order_corners(self, pts: np.ndarray) -> np.ndarray:
rect = np.zeros((4, 2), dtype="float32")
s = pts.sum(axis=1)
rect[0] = pts[np.argmin(s)] # top-left
rect[2] = pts[np.argmax(s)] # bottom-right
diff = np.diff(pts, axis=1)
rect[1] = pts[np.argmin(diff)] # top-right
rect[3] = pts[np.argmax(diff)] # bottom-left
return rect