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import cv2
import imageio
import numpy as np
from typing import Union, Tuple, List
from pathlib import Path
def flex_resize_img(
img : np.ndarray,
tgt_wh : Union[Tuple[int, int], None] = None,
ratio : Union[float, None] = None,
kp_mod : int = 1,
):
'''
Resize the image to the target width and height. Set one of width and height to -1 to keep the aspect ratio.
Only one of `tgt_wh` and `ratio` can be set, if both are set, `tgt_wh` will be used.
### Args
- img: np.ndarray, (H, W, 3)
- tgt_wh: Tuple[int, int], default=None
- The target width and height, set one of them to -1 to keep the aspect ratio.
- ratio: float, default=None
- The ratio to resize the frames. It will be used if `tgt_wh` is not set.
- kp_mod: int, default 1
- Keep the width and height as multiples of `kp_mod`.
- For example, if `kp_mod=16`, the width and height will be rounded to the nearest multiple of 16.
### Returns
- np.ndarray, (H', W', 3)
- The resized iamges.
'''
assert len(img.shape) == 3, 'img must have 3 dimensions.'
return flex_resize_video(img[None], tgt_wh, ratio, kp_mod)[0]
def flex_resize_video(
frames : np.ndarray,
tgt_wh : Union[Tuple[int, int], None] = None,
ratio : Union[float, None] = None,
kp_mod : int = 1,
):
'''
Resize the frames to the target width and height. Set one of width and height to -1 to keep the aspect ratio.
Only one of `tgt_wh` and `ratio` can be set, if both are set, `tgt_wh` will be used.
### Args
- frames: np.ndarray, (L, H, W, 3)
- tgt_wh: Tuple[int, int], default=None
- The target width and height, set one of them to -1 to keep the aspect ratio.
- ratio: float, default=None
- The ratio to resize the frames. It will be used if `tgt_wh` is not set.
- kp_mod: int, default 1
- Keep the width and height as multiples of `kp_mod`.
- For example, if `kp_mod=16`, the width and height will be rounded to the nearest multiple of 16.
### Returns
- np.ndarray, (L, H', W', 3)
- The resized frames.
'''
assert tgt_wh is not None or ratio is not None, 'At least one of tgt_wh and ratio must be set.'
if tgt_wh is not None:
assert len(tgt_wh) == 2, 'tgt_wh must be a tuple of 2 elements.'
assert tgt_wh[0] > 0 or tgt_wh[1] > 0, 'At least one of width and height must be positive.'
if ratio is not None:
assert ratio > 0, 'ratio must be positive.'
assert len(frames.shape) == 4, 'frames must have 3 or 4 dimensions.'
def align_size(val:float):
''' It will round the value to the nearest multiple of `kp_mod`. '''
return int(round(val / kp_mod) * kp_mod)
# Calculate the target width and height.
orig_h, orig_w = frames.shape[1], frames.shape[2]
tgt_wh = (int(orig_w * ratio), int(orig_h * ratio)) if tgt_wh is None else tgt_wh # Get wh from ratio if not given. # type: ignore
tgt_w, tgt_h = tgt_wh
tgt_w = align_size(orig_w * tgt_h / orig_h) if tgt_w == -1 else align_size(tgt_w)
tgt_h = align_size(orig_h * tgt_w / orig_w) if tgt_h == -1 else align_size(tgt_h)
# Resize the frames.
resized_frames = np.stack([cv2.resize(frame, (tgt_w, tgt_h)) for frame in frames])
return resized_frames
def splice_img(
img_grids : Union[List[np.ndarray], np.ndarray],
grid_ids : Union[List[int], np.ndarray],
):
'''
Splice the images with the same size, according to the grid index.
For example, you have 3 images [i1, i2, i3], and a `grid_ids` matrix:
[[ 0, 1], |i1|i2|
[ 2, -1], , then the results will be |i3|ib| , where ib means a black place holder.
[-1, -1]] |ib|ib|
### Args
- img_grids: List[np.ndarray] or np.ndarray, (K, H, W, 3)
- The source images to splice. It indicates that all the images have the same size.
- grid_ids: List[int] or np.ndarray, (Y, X)
- The grid index of each image. It should be a 2D matrix with integers as the type of elements.
- The value in this matrix indexed the image in the `video_grids`, so it ranges from 0 to K-1.
- Specially, set the grid index to -1 to use a black place holder.
### Returns
- np.ndarray, (H*Y, W*X, 3)
- The spliced images.
'''
if isinstance(img_grids, List):
img_grids = np.stack(img_grids)
if isinstance(grid_ids, List):
grid_ids = np.array(grid_ids)
assert len(img_grids.shape) == 4, 'img_grids must be in shape (K, H, W, 3).'
return splice_video(img_grids[:, None], grid_ids)[0]
def splice_video(
video_grids : Union[List[np.ndarray], np.ndarray],
grid_ids : Union[List[int], np.ndarray],
):
'''
Splice the videos with the same size, according to the grid index.
For example, you have 3 videos [v1, v2, v3], and a `grid_ids` matrix:
[[ 0, 1], |v1|v2|
[ 2, -1], , then the results will be |v3|vb| , wher vb means a black place holder.
[-1, -1]] |vb|vb|
### Args
- video_grids: List[np.ndarray] or np.ndarray, (K, L, H, W, C)
- The source videos to splice. It indicates that all the videos have the same size.
- grid_ids: List[int] or np.ndarray, (Y, X)
- The grid index of each video. It should be a 2D matrix with integers as the type of elements.
- The value in this matrix indexed the video in the `video_grids`, so it ranges from 0 to K-1.
- Specially, set the grid index to -1 to use a black place holder.
### Returns
- np.ndarray, (L, H*Y, W*X, C)
- The spliced video.
'''
if isinstance(video_grids, List):
video_grids = np.stack(video_grids)
if isinstance(grid_ids, List):
grid_ids = np.array(grid_ids)
assert len(video_grids.shape) == 5, 'video_grids must be in shape (K, L, H, W, 3).'
assert len(grid_ids.shape) == 2, 'grid_ids must be a 2D matrix.'
assert isinstance(grid_ids[0, 0].item(), int), f'grid_ids must be an integer matrix, but got {grid_ids.dtype}.'
# Splice the videos.
K, L, H, W, C = video_grids.shape
Y, X = grid_ids.shape
# Initialize the spliced video.
spliced_video = np.zeros((L, H*Y, W*X, C), dtype=np.uint8)
for x in range(X):
for y in range(Y):
grid_id = grid_ids[y, x]
if grid_id == -1:
continue
spliced_video[:, y*H:(y+1)*H, x*W:(x+1)*W, :] = video_grids[grid_id]
return spliced_video
def crop_img(
img : np.ndarray,
lurb : Union[np.ndarray, List],
):
'''
Crop the image with the given bounding box.
The data should be represented in uint8.
If the bounding box is out of the image, pad the image with zeros.
### Args
- img: np.ndarray, (H, W, C)
- lurb: np.ndarray or list, (4,)
- The bounding box in the format of left, up, right, bottom.
### Returns
- np.ndarray, (H', W', C)
- The cropped image.
'''
return crop_video(img[None], lurb)[0]
def crop_video(
frames : np.ndarray,
lurb : Union[np.ndarray, List],
):
'''
Crop the video with the given bounding box.
The data should be represented in uint8.
If the bounding box is out of the video, pad the frames with zeros.
### Args
- frames: np.ndarray, (L, H, W, C)
- lurb: np.ndarray or list, (4,)
- The bounding box in the format of left, up, right, bottom.
### Returns
- np.ndarray, (L, H', W', C)
- The cropped video.
'''
assert len(frames.shape) == 4, 'framess must have 4 dimensions.'
if isinstance(lurb, List):
lurb = np.array(lurb)
l, u, r, b = lurb.astype(int)
L, H, W = frames.shape[:3]
l_, u_, r_, b_ = max(0, l), max(0, u), min(W, r), min(H, b)
cropped_frames = np.zeros((L, b-u, r-l, 3), dtype=np.uint8)
cropped_frames[:, u_-u:b_-u, l_-l:r_-l] = frames[:, u_:b, l_:r]
return cropped_frames
def pad_img(
img : np.ndarray,
tgt_wh : Tuple[int, int],
pad_val : int = 0,
align : str = 'c-c',
):
'''
Pad the image to the target width and height.
### Args
- img: np.ndarray, (H, W, 3)
- tgt_wh: Tuple[int, int]
- The target width and height. Use -1 to indicate the original scale.
- pad_value: int, default 0
- The value to pad the image.
- align: str, default 'c-c'
- The alignment of the image. It should be in the format of 'h-v',
where 'h' and 'v' can be 'l', 'c', 'r' and 't', 'c', 'b' respectively.
### Returns
- np.ndarray, (H', W', 3)
- The padded image.
'''
assert len(img.shape) == 3, 'img must have 3 dimensions.'
return pad_video(img[None], tgt_wh, pad_val, align)[0]
def pad_video(
frames : np.ndarray,
tgt_wh : Tuple[int, int],
pad_val : int = 0,
align : str = 'c-c',
):
'''
Pad the video to the target width and height.
### Args
- frames: np.ndarray, (L, H, W, 3)
- tgt_wh: Tuple[int, int]
- The target width and height. Use -1 to indicate the original scale.
- pad_value: int, default 0
- The value to pad the frames.
### Returns
- np.ndarray, (L, H', W', 3)
- The padded frames.
'''
# Check data validity.
assert len(frames.shape) == 4, 'frames must have 4 dimensions.'
assert len(tgt_wh) == 2, 'tgt_wh must be a tuple of 2 elements.'
H, W = frames.shape[1], frames.shape[2]
if tgt_wh[0] == -1: tgt_wh = (W, tgt_wh[1])
if tgt_wh[1] == -1: tgt_wh = (tgt_wh[0], H)
assert tgt_wh[0] >= frames.shape[2] and tgt_wh[1] >= frames.shape[1], 'The target size must be larger than the original size.'
assert pad_val >= 0 and pad_val <= 255, 'The pad value must be in the range of [0, 255].'
# Check align pattern.
align = align.split('-')
assert len(align) == 2, 'align must be in the format of "h-v".'
assert align[0] in ['l', 'c', 'r'] and align[1] in ['l', 'c', 'r'], 'align must be in ["l", "c", "r"].'
tgt_w, tgt_h = tgt_wh
pad_pix = [tgt_w - W, tgt_h - H] # indicate how many pixels to be padded
pad_lu = [0, 0] # how many pixels to pad on the left and the up side
for direction in [0, 1]:
if align[direction] == 'c':
pad_lu[direction] = pad_pix[direction] // 2
elif align[direction] == 'r':
pad_lu[direction] = pad_pix[direction]
pad_l, pad_r, pad_u, pad_b = pad_lu[0], pad_pix[0] - pad_lu[0], pad_lu[1], pad_pix[1] - pad_lu[1]
padded_frames = np.pad(frames, ((0, 0), (pad_u, pad_b), (pad_l, pad_r), (0, 0)), 'constant', constant_values=pad_val)
return padded_frames |