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# Copyright (C) 2021-2024, Mindee.
# This program is licensed under the Apache License 2.0.
# See LICENSE or go to <https://opensource.org/licenses/Apache-2.0> for full license details.
import math
import random
from copy import deepcopy
from typing import Iterable, Optional, Tuple, Union
import numpy as np
import tensorflow as tf
from doctr.utils.geometry import compute_expanded_shape, rotate_abs_geoms
from .base import create_shadow_mask, crop_boxes
__all__ = ["invert_colors", "rotate_sample", "crop_detection", "random_shadow", "rotated_img_tensor"]
def invert_colors(img: tf.Tensor, min_val: float = 0.6) -> tf.Tensor:
"""Invert the colors of an image
Args:
----
img : tf.Tensor, the image to invert
min_val : minimum value of the random shift
Returns:
-------
the inverted image
"""
out = tf.image.rgb_to_grayscale(img) # Convert to gray
# Random RGB shift
shift_shape = [img.shape[0], 1, 1, 3] if img.ndim == 4 else [1, 1, 3]
rgb_shift = tf.random.uniform(shape=shift_shape, minval=min_val, maxval=1)
# Inverse the color
if out.dtype == tf.uint8:
out = tf.cast(tf.cast(out, dtype=rgb_shift.dtype) * rgb_shift, dtype=tf.uint8)
else:
out *= tf.cast(rgb_shift, dtype=out.dtype)
# Inverse the color
out = 255 - out if out.dtype == tf.uint8 else 1 - out
return out
def rotated_img_tensor(img: tf.Tensor, angle: float, expand: bool = False) -> tf.Tensor:
"""Rotate image around the center, interpolation=NEAREST, pad with 0 (black)
Args:
----
img: image to rotate
angle: angle in degrees. +: counter-clockwise, -: clockwise
expand: whether the image should be padded before the rotation
Returns:
-------
the rotated image (tensor)
"""
# Compute the expanded padding
h_crop, w_crop = 0, 0
if expand:
exp_h, exp_w = compute_expanded_shape(img.shape[:-1], angle)
h_diff, w_diff = int(math.ceil(exp_h - img.shape[0])), int(math.ceil(exp_w - img.shape[1]))
h_pad, w_pad = max(h_diff, 0), max(w_diff, 0)
exp_img = tf.pad(img, tf.constant([[h_pad // 2, h_pad - h_pad // 2], [w_pad // 2, w_pad - w_pad // 2], [0, 0]]))
h_crop, w_crop = int(round(max(exp_img.shape[0] - exp_h, 0))), int(round(min(exp_img.shape[1] - exp_w, 0)))
else:
exp_img = img
# Compute the rotation matrix
height, width = tf.cast(tf.shape(exp_img)[0], tf.float32), tf.cast(tf.shape(exp_img)[1], tf.float32)
cos_angle, sin_angle = tf.math.cos(angle * math.pi / 180.0), tf.math.sin(angle * math.pi / 180.0)
x_offset = ((width - 1) - (cos_angle * (width - 1) - sin_angle * (height - 1))) / 2.0
y_offset = ((height - 1) - (sin_angle * (width - 1) + cos_angle * (height - 1))) / 2.0
rotation_matrix = tf.convert_to_tensor(
[cos_angle, -sin_angle, x_offset, sin_angle, cos_angle, y_offset, 0.0, 0.0],
dtype=tf.float32,
)
# Rotate the image
rotated_img = tf.squeeze(
tf.raw_ops.ImageProjectiveTransformV3(
images=exp_img[None], # Add a batch dimension for compatibility with ImageProjectiveTransformV3
transforms=rotation_matrix[None], # Add a batch dimension for compatibility with ImageProjectiveTransformV3
output_shape=tf.shape(exp_img)[:2],
interpolation="NEAREST",
fill_mode="CONSTANT",
fill_value=tf.constant(0.0, dtype=tf.float32),
)
)
# Crop the rest
if h_crop > 0 or w_crop > 0:
h_slice = slice(h_crop // 2, -h_crop // 2) if h_crop > 0 else slice(rotated_img.shape[0])
w_slice = slice(-w_crop // 2, -w_crop // 2) if w_crop > 0 else slice(rotated_img.shape[1])
rotated_img = rotated_img[h_slice, w_slice]
return rotated_img
def rotate_sample(
img: tf.Tensor,
geoms: np.ndarray,
angle: float,
expand: bool = False,
) -> Tuple[tf.Tensor, np.ndarray]:
"""Rotate image around the center, interpolation=NEAREST, pad with 0 (black)
Args:
----
img: image to rotate
geoms: array of geometries of shape (N, 4) or (N, 4, 2)
angle: angle in degrees. +: counter-clockwise, -: clockwise
expand: whether the image should be padded before the rotation
Returns:
-------
A tuple of rotated img (tensor), rotated boxes (np array)
"""
# Rotated the image
rotated_img = rotated_img_tensor(img, angle, expand)
# Get absolute coords
_geoms = deepcopy(geoms)
if _geoms.shape[1:] == (4,):
if np.max(_geoms) <= 1:
_geoms[:, [0, 2]] *= img.shape[1]
_geoms[:, [1, 3]] *= img.shape[0]
elif _geoms.shape[1:] == (4, 2):
if np.max(_geoms) <= 1:
_geoms[..., 0] *= img.shape[1]
_geoms[..., 1] *= img.shape[0]
else:
raise AssertionError
# Rotate the boxes: xmin, ymin, xmax, ymax or polygons --> (4, 2) polygon
rotated_geoms: np.ndarray = rotate_abs_geoms(_geoms, angle, img.shape[:-1], expand).astype(np.float32)
# Always return relative boxes to avoid label confusions when resizing is performed aferwards
rotated_geoms[..., 0] = rotated_geoms[..., 0] / rotated_img.shape[1]
rotated_geoms[..., 1] = rotated_geoms[..., 1] / rotated_img.shape[0]
return rotated_img, np.clip(rotated_geoms, 0, 1)
def crop_detection(
img: tf.Tensor, boxes: np.ndarray, crop_box: Tuple[float, float, float, float]
) -> Tuple[tf.Tensor, np.ndarray]:
"""Crop and image and associated bboxes
Args:
----
img: image to crop
boxes: array of boxes to clip, absolute (int) or relative (float)
crop_box: box (xmin, ymin, xmax, ymax) to crop the image. Relative coords.
Returns:
-------
A tuple of cropped image, cropped boxes, where the image is not resized.
"""
if any(val < 0 or val > 1 for val in crop_box):
raise AssertionError("coordinates of arg `crop_box` should be relative")
h, w = img.shape[:2]
xmin, ymin = int(round(crop_box[0] * (w - 1))), int(round(crop_box[1] * (h - 1)))
xmax, ymax = int(round(crop_box[2] * (w - 1))), int(round(crop_box[3] * (h - 1)))
cropped_img = tf.image.crop_to_bounding_box(img, ymin, xmin, ymax - ymin, xmax - xmin)
# Crop the box
boxes = crop_boxes(boxes, crop_box if boxes.max() <= 1 else (xmin, ymin, xmax, ymax))
return cropped_img, boxes
def _gaussian_filter(
img: tf.Tensor,
kernel_size: Union[int, Iterable[int]],
sigma: float,
mode: Optional[str] = None,
pad_value: Optional[int] = 0,
):
"""Apply Gaussian filter to image.
Adapted from: https://github.com/tensorflow/addons/blob/master/tensorflow_addons/image/filters.py
Args:
----
img: image to filter of shape (N, H, W, C)
kernel_size: kernel size of the filter
sigma: standard deviation of the Gaussian filter
mode: padding mode, one of "CONSTANT", "REFLECT", "SYMMETRIC"
pad_value: value to pad the image with
Returns:
-------
A tensor of shape (N, H, W, C)
"""
ksize = tf.convert_to_tensor(tf.broadcast_to(kernel_size, [2]), dtype=tf.int32)
sigma = tf.convert_to_tensor(tf.broadcast_to(sigma, [2]), dtype=img.dtype)
assert mode in ("CONSTANT", "REFLECT", "SYMMETRIC"), "mode should be one of 'CONSTANT', 'REFLECT', 'SYMMETRIC'"
mode = "CONSTANT" if mode is None else str.upper(mode)
constant_values = (
tf.zeros([], dtype=img.dtype) if pad_value is None else tf.convert_to_tensor(pad_value, dtype=img.dtype)
)
def kernel1d(ksize: tf.Tensor, sigma: tf.Tensor, dtype: tf.DType):
x = tf.range(ksize, dtype=dtype)
x = x - tf.cast(tf.math.floordiv(ksize, 2), dtype=dtype)
x = x + tf.where(tf.math.equal(tf.math.mod(ksize, 2), 0), tf.cast(0.5, dtype), 0)
g = tf.math.exp(-(tf.math.pow(x, 2) / (2 * tf.math.pow(sigma, 2))))
g = g / tf.reduce_sum(g)
return g
def kernel2d(ksize: tf.Tensor, sigma: tf.Tensor, dtype: tf.DType):
kernel_x = kernel1d(ksize[0], sigma[0], dtype)
kernel_y = kernel1d(ksize[1], sigma[1], dtype)
return tf.matmul(
tf.expand_dims(kernel_x, axis=-1),
tf.transpose(tf.expand_dims(kernel_y, axis=-1)),
)
g = kernel2d(ksize, sigma, img.dtype)
# Pad the image
height, width = ksize[0], ksize[1]
paddings = [
[0, 0],
[(height - 1) // 2, height - 1 - (height - 1) // 2],
[(width - 1) // 2, width - 1 - (width - 1) // 2],
[0, 0],
]
img = tf.pad(img, paddings, mode=mode, constant_values=constant_values)
channel = tf.shape(img)[-1]
shape = tf.concat([ksize, tf.constant([1, 1], ksize.dtype)], axis=0)
g = tf.reshape(g, shape)
shape = tf.concat([ksize, [channel], tf.constant([1], ksize.dtype)], axis=0)
g = tf.broadcast_to(g, shape)
return tf.nn.depthwise_conv2d(img, g, [1, 1, 1, 1], padding="VALID", data_format="NHWC")
def random_shadow(img: tf.Tensor, opacity_range: Tuple[float, float], **kwargs) -> tf.Tensor:
"""Apply a random shadow to a given image
Args:
----
img: image to modify
opacity_range: the minimum and maximum desired opacity of the shadow
**kwargs: additional arguments to pass to `create_shadow_mask`
Returns:
-------
shadowed image
"""
shadow_mask = create_shadow_mask(img.shape[:2], **kwargs)
opacity = np.random.uniform(*opacity_range)
shadow_tensor = 1 - tf.convert_to_tensor(shadow_mask[..., None], dtype=tf.float32)
# Add some blur to make it believable
k = 7 + int(2 * 4 * random.random())
sigma = random.uniform(0.5, 5.0)
shadow_tensor = _gaussian_filter(
shadow_tensor[tf.newaxis, ...],
kernel_size=k,
sigma=sigma,
mode="REFLECT",
)
return tf.squeeze(opacity * shadow_tensor * img + (1 - opacity) * img, axis=0)
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