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from functools import wraps
from typing import Callable, Union
import cv2
import numpy as np
from typing_extensions import Concatenate, ParamSpec
from custom_albumentations.core.keypoints_utils import angle_to_2pi_range
from custom_albumentations.core.transforms_interface import KeypointInternalType
__all__ = [
"read_bgr_image",
"read_rgb_image",
"MAX_VALUES_BY_DTYPE",
"NPDTYPE_TO_OPENCV_DTYPE",
"clipped",
"get_opencv_dtype_from_numpy",
"angle_2pi_range",
"clip",
"preserve_shape",
"preserve_channel_dim",
"ensure_contiguous",
"is_rgb_image",
"is_grayscale_image",
"is_multispectral_image",
"get_num_channels",
"non_rgb_warning",
"_maybe_process_in_chunks",
]
P = ParamSpec("P")
MAX_VALUES_BY_DTYPE = {
np.dtype("uint8"): 255,
np.dtype("uint16"): 65535,
np.dtype("uint32"): 4294967295,
np.dtype("float32"): 1.0,
}
NPDTYPE_TO_OPENCV_DTYPE = {
np.uint8: cv2.CV_8U, # type: ignore[attr-defined]
np.uint16: cv2.CV_16U, # type: ignore[attr-defined]
np.int32: cv2.CV_32S, # type: ignore[attr-defined]
np.float32: cv2.CV_32F, # type: ignore[attr-defined]
np.float64: cv2.CV_64F, # type: ignore[attr-defined]
np.dtype("uint8"): cv2.CV_8U, # type: ignore[attr-defined]
np.dtype("uint16"): cv2.CV_16U, # type: ignore[attr-defined]
np.dtype("int32"): cv2.CV_32S, # type: ignore[attr-defined]
np.dtype("float32"): cv2.CV_32F, # type: ignore[attr-defined]
np.dtype("float64"): cv2.CV_64F, # type: ignore[attr-defined]
}
def read_bgr_image(path):
return cv2.imread(path, cv2.IMREAD_COLOR)
def read_rgb_image(path):
image = cv2.imread(path, cv2.IMREAD_COLOR)
return cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
def clipped(func: Callable[Concatenate[np.ndarray, P], np.ndarray]) -> Callable[Concatenate[np.ndarray, P], np.ndarray]:
@wraps(func)
def wrapped_function(img: np.ndarray, *args: P.args, **kwargs: P.kwargs) -> np.ndarray:
dtype = img.dtype
maxval = MAX_VALUES_BY_DTYPE.get(dtype, 1.0)
return clip(func(img, *args, **kwargs), dtype, maxval)
return wrapped_function
def clip(img: np.ndarray, dtype: np.dtype, maxval: float) -> np.ndarray:
return np.clip(img, 0, maxval).astype(dtype)
def get_opencv_dtype_from_numpy(value: Union[np.ndarray, int, np.dtype, object]) -> int:
"""
Return a corresponding OpenCV dtype for a numpy's dtype
:param value: Input dtype of numpy array
:return: Corresponding dtype for OpenCV
"""
if isinstance(value, np.ndarray):
value = value.dtype
return NPDTYPE_TO_OPENCV_DTYPE[value]
def angle_2pi_range(
func: Callable[Concatenate[KeypointInternalType, P], KeypointInternalType]
) -> Callable[Concatenate[KeypointInternalType, P], KeypointInternalType]:
@wraps(func)
def wrapped_function(keypoint: KeypointInternalType, *args: P.args, **kwargs: P.kwargs) -> KeypointInternalType:
(x, y, a, s) = func(keypoint, *args, **kwargs)[:4]
return (x, y, angle_to_2pi_range(a), s)
return wrapped_function
def preserve_shape(
func: Callable[Concatenate[np.ndarray, P], np.ndarray]
) -> Callable[Concatenate[np.ndarray, P], np.ndarray]:
"""Preserve shape of the image"""
@wraps(func)
def wrapped_function(img: np.ndarray, *args: P.args, **kwargs: P.kwargs) -> np.ndarray:
shape = img.shape
result = func(img, *args, **kwargs)
result = result.reshape(shape)
return result
return wrapped_function
def preserve_channel_dim(
func: Callable[Concatenate[np.ndarray, P], np.ndarray]
) -> Callable[Concatenate[np.ndarray, P], np.ndarray]:
"""Preserve dummy channel dim."""
@wraps(func)
def wrapped_function(img: np.ndarray, *args: P.args, **kwargs: P.kwargs) -> np.ndarray:
shape = img.shape
result = func(img, *args, **kwargs)
if len(shape) == 3 and shape[-1] == 1 and len(result.shape) == 2:
result = np.expand_dims(result, axis=-1)
return result
return wrapped_function
def ensure_contiguous(
func: Callable[Concatenate[np.ndarray, P], np.ndarray]
) -> Callable[Concatenate[np.ndarray, P], np.ndarray]:
"""Ensure that input img is contiguous."""
@wraps(func)
def wrapped_function(img: np.ndarray, *args: P.args, **kwargs: P.kwargs) -> np.ndarray:
img = np.require(img, requirements=["C_CONTIGUOUS"])
result = func(img, *args, **kwargs)
return result
return wrapped_function
def is_rgb_image(image: np.ndarray) -> bool:
return len(image.shape) == 3 and image.shape[-1] == 3
def is_grayscale_image(image: np.ndarray) -> bool:
return (len(image.shape) == 2) or (len(image.shape) == 3 and image.shape[-1] == 1)
def is_multispectral_image(image: np.ndarray) -> bool:
return len(image.shape) == 3 and image.shape[-1] not in [1, 3]
def get_num_channels(image: np.ndarray) -> int:
return image.shape[2] if len(image.shape) == 3 else 1
def non_rgb_warning(image: np.ndarray) -> None:
if not is_rgb_image(image):
message = "This transformation expects 3-channel images"
if is_grayscale_image(image):
message += "\nYou can convert your grayscale image to RGB using cv2.cvtColor(image, cv2.COLOR_GRAY2RGB))"
if is_multispectral_image(image): # Any image with a number of channels other than 1 and 3
message += "\nThis transformation cannot be applied to multi-spectral images"
raise ValueError(message)
def _maybe_process_in_chunks(
process_fn: Callable[Concatenate[np.ndarray, P], np.ndarray], **kwargs
) -> Callable[[np.ndarray], np.ndarray]:
"""
Wrap OpenCV function to enable processing images with more than 4 channels.
Limitations:
This wrapper requires image to be the first argument and rest must be sent via named arguments.
Args:
process_fn: Transform function (e.g cv2.resize).
kwargs: Additional parameters.
Returns:
numpy.ndarray: Transformed image.
"""
@wraps(process_fn)
def __process_fn(img: np.ndarray) -> np.ndarray:
num_channels = get_num_channels(img)
if num_channels > 4:
chunks = []
for index in range(0, num_channels, 4):
if num_channels - index == 2:
# Many OpenCV functions cannot work with 2-channel images
for i in range(2):
chunk = img[:, :, index + i : index + i + 1]
chunk = process_fn(chunk, **kwargs)
chunk = np.expand_dims(chunk, -1)
chunks.append(chunk)
else:
chunk = img[:, :, index : index + 4]
chunk = process_fn(chunk, **kwargs)
chunks.append(chunk)
img = np.dstack(chunks)
else:
img = process_fn(img, **kwargs)
return img
return __process_fn
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