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from typing import *
from pathlib import Path
import cv2
import numpy as np
from scipy.stats import norm
def read_image(
path: Union[str, Path],
image_size: Union[int, Tuple[int, int], None]=None
) -> np.ndarray:
"""Read image from file path in RGB format.
Args:
path (str | Path): Path to image file.
image_size (int | tuple[int, int] | None, optional): Resize image.
Returns:
image (np.ndarray): The image array.
Example:
>>> read_image("/path/to/image.jpg", 256)
"""
if isinstance(path, Path):
path = str(path)
image = cv2.imread(path)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
if image_size:
if isinstance(image_size, int):
image_size = (image_size, image_size)
image = cv2.resize(image, image_size, interpolation=cv2.INTER_AREA)
return image
def standardize(
targets: np.ndarray,
mean: float,
std: float,
center_at: float | None = None
) -> np.ndarray:
"""Standardize the targets to the z-domain."""
targets = np.log(targets)
standardized = (targets - mean) / std
if center_at:
standardized -= (center_at - mean) / std
return standardized
def normalize_cdf(targets: np.ndarray, threshold: float) -> np.ndarray:
return norm.cdf(targets - threshold)
def normalize_min_max(
targets: np.ndarray | np.float32,
threshold: float | np.ndarray,
min_val: float | np.ndarray,
max_val: float | np.ndarray
) -> np.ndarray:
normalized = ((targets - threshold) / (max_val - min_val)) + 0.5
normalized = np.minimum(normalized, 1)
normalized = np.maximum(normalized, 0)
return normalized
def get_boxes(mask: np.ndarray) -> np.ndarray:
"""Get bounding boxes from masks.
Args:
masks (np.ndarray): Input mask of shape (H, W).
Returns:
boxes (np.ndarray): Array of shape (N, 4) containing bounding boxes in xyxy format.
"""
_, comps = cv2.connectedComponents(mask)
labels = comps = np.unique(comps)
boxes = []
for label in labels[labels != 0]:
y_loc, x_loc = np.where(comps == label)
boxes.append((np.min(x_loc), np.min(y_loc), np.max(x_loc), np.ma(y_loc)))
boxes = np.stack(boxes) if boxes else np.empty((0, 4))
return boxes |