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from numba import jit, njit
import numpy as np
import time
import cv2
import math
from pathlib import Path
import os.path as osp
import torch
from .cupy_utils import launch_kernel, preprocess_kernel
import cupy
def bokeh_filter_cupy(img, depth, dx, dy, im_h, im_w, num_samples=32):
blurred = img.clone()
n = im_h * im_w
str_kernel = '''
extern "C" __global__ void kernel_bokeh(
const int n,
const int h,
const int w,
const int nsamples,
const float dx,
const float dy,
const float* img,
const float* depth,
float* blurred
) {
const int im_size = min(h, w);
const int sample_offset = nsamples / 2;
for (int intIndex = (blockIdx.x * blockDim.x) + threadIdx.x; intIndex < n * 3; intIndex += blockDim.x * gridDim.x) {
const int intSample = intIndex / 3;
const int c = intIndex % 3;
const int y = ( intSample / w) % h;
const int x = intSample % w;
const int flatten_xy = y * w + x;
const int fid = flatten_xy * 3 + c;
const float d = depth[flatten_xy];
const float _dx = dx * d;
const float _dy = dy * d;
float weight = 0;
float color = 0;
for (int s = 0; s < nsamples; s += 1) {
const int sp = (s - sample_offset) * im_size;
const int x_ = x + int(round(_dx * sp));
const int y_ = y + int(round(_dy * sp));
if ((x_ >= w) | (y_ >= h) | (x_ < 0) | (y_ < 0))
continue;
const int flatten_xy_ = y_ * w + x_;
const float w_ = depth[flatten_xy_];
weight += w_;
const int fid_ = flatten_xy_ * 3 + c;
color += img[fid_] * w_;
}
if (weight != 0) {
color /= weight;
}
else {
color = img[fid];
}
blurred[fid] = color;
}
}
'''
launch_kernel('kernel_bokeh', str_kernel)(
grid=tuple([ int((n + 512 - 1) / 512), 1, 1 ]),
block=tuple([ 512, 1, 1 ]),
args=[ cupy.int32(n), cupy.int32(im_h), cupy.int32(im_w), \
cupy.int32(num_samples), cupy.float32(dx), cupy.float32(dy),
img.data_ptr(), depth.data_ptr(), blurred.data_ptr() ]
)
return blurred
def np2flatten_tensor(arr: np.ndarray, to_cuda: bool = True) -> torch.Tensor:
c = 1
if len(arr.shape) == 3:
c = arr.shape[2]
else:
arr = arr[..., None]
arr = arr.transpose((2, 0, 1))[None, ...]
t = torch.from_numpy(arr).view(1, c, -1)
if to_cuda:
t = t.cuda()
return t
def ftensor2img(t: torch.Tensor, im_h, im_w):
t = t.detach().cpu().numpy().squeeze()
c = t.shape[0]
t = t.transpose((1, 0)).reshape((im_h, im_w, c))
return t
@njit
def bokeh_filter(img, depth, dx, dy, num_samples=32):
sample_offset = num_samples // 2
# _scale = 0.0005
# depth = depth * _scale
im_h, im_w = img.shape[0], img.shape[1]
im_size = min(im_h, im_w)
blured = np.zeros_like(img)
for x in range(im_w):
for y in range(im_h):
d = depth[y, x]
_color = np.array([0, 0, 0], dtype=np.float32)
_dx = dx * d
_dy = dy * d
weight = 0
for s in range(num_samples):
s = (s - sample_offset) * im_size
x_ = x + int(round(_dx * s))
y_ = y + int(round(_dy * s))
if x_ >= im_w or y_ >= im_h or x_ < 0 or y_ < 0:
continue
_w = depth[y_, x_]
weight += _w
_color += img[y_, x_] * _w
if weight == 0:
blured[y, x] = img[y, x]
else:
blured[y, x] = _color / np.array([weight, weight, weight], dtype=np.float32)
return blured
def bokeh_blur(img, depth, num_samples=32, lightness_factor=10, depth_factor=2, use_cuda=False, focal_plane=None):
img = np.ascontiguousarray(img)
if depth is not None:
depth = depth.astype(np.float32)
if focal_plane is not None:
depth = depth.max() - np.abs(depth - focal_plane)
if depth_factor != 1:
depth = np.power(depth, depth_factor)
depth = depth - depth.min()
depth = depth.astype(np.float32) / depth.max()
depth = 1 - depth
img = img.astype(np.float32) / 255
img_hightlighted = np.power(img, lightness_factor)
# img =
im_h, im_w = img.shape[:2]
PI = math.pi
_scale = 0.0005
depth = depth * _scale
if use_cuda:
img_hightlighted = np2flatten_tensor(img_hightlighted, True)
depth = np2flatten_tensor(depth, True)
vertical_blured = bokeh_filter_cupy(img_hightlighted, depth, 0, 1, im_h, im_w, num_samples)
diag_blured = bokeh_filter_cupy(vertical_blured, depth, math.cos(-PI/6), math.sin(-PI/6), im_h, im_w, num_samples)
rhom_blur = bokeh_filter_cupy(diag_blured, depth, math.cos(-PI * 5 /6), math.sin(-PI * 5 /6), im_h, im_w, num_samples)
blured = (diag_blured + rhom_blur) / 2
blured = ftensor2img(blured, im_h, im_w)
else:
vertical_blured = bokeh_filter(img_hightlighted, depth, 0, 1, num_samples)
diag_blured = bokeh_filter(vertical_blured, depth, math.cos(-PI/6), math.sin(-PI/6), num_samples)
rhom_blur = bokeh_filter(diag_blured, depth, math.cos(-PI * 5 /6), math.sin(-PI * 5 /6), num_samples)
blured = (diag_blured + rhom_blur) / 2
blured = np.power(blured, 1 / lightness_factor)
blured = (blured * 255).astype(np.uint8)
return blured |