35 lines
1.2 KiB
Python
35 lines
1.2 KiB
Python
from PIL import Image
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import cv2
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import numpy as np
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import constants
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def cv2_to_pil(cv_image: np.ndarray) -> Image.Image:
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"""
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cv2.MatLike (BGR/RGB/Grayscale) → PIL.Image
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"""
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if len(cv_image.shape) == 3 and cv_image.shape[2] == 3:
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cv_image = cv2.cvtColor(cv_image, cv2.COLOR_BGR2RGB)
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return Image.fromarray(cv_image)
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def image_to_numpy(self, image: Image.Image) -> np.ndarray:
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"""Конвертирует PIL Image в numpy array для OpenCV"""
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return np.array(image)
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def get_normals(H: np.ndarray, R: np.ndarray, T: np.ndarray) -> np.ndarray:
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n = cv2.decomposeHomographyMat(H, constants.K, R, T)
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return n
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def estimate_transformation_matrix(src_pts: np.ndarray, dst_pts: np.ndarray) -> tuple[np.ndarray, float | None]:
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"""Оценивает матрицу трансформации на основе сопоставленных точек"""
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# Используем RANSAC для оценки матрицы гомографии
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H, _ = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC, 3.0, maxIters=1000)
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return H
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def calc_camera_matrix(w: float, h: float):
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f = constants._K_FOCUS_DISTANCE
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return np.array([
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[f, 0, w / 2],
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[0, f, h / 2],
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[0, 0, 1]
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])
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