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smooth_filter.py
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smooth_filter.py
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"""
Copyright (C) 2018 NVIDIA Corporation. All rights reserved.
Licensed under the CC BY-NC-SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).
"""
src = '''
#include <math_constants.h>
#include <math_functions.h>
#define TB 256
#define EPS 1e-7
__device__ bool InverseMat4x4(double m_in[4][4], double inv_out[4][4]) {
double m[16], inv[16];
for (int i = 0; i < 4; i++) {
for (int j = 0; j < 4; j++) {
m[i * 4 + j] = m_in[i][j];
}
}
inv[0] = m[5] * m[10] * m[15] -
m[5] * m[11] * m[14] -
m[9] * m[6] * m[15] +
m[9] * m[7] * m[14] +
m[13] * m[6] * m[11] -
m[13] * m[7] * m[10];
inv[4] = -m[4] * m[10] * m[15] +
m[4] * m[11] * m[14] +
m[8] * m[6] * m[15] -
m[8] * m[7] * m[14] -
m[12] * m[6] * m[11] +
m[12] * m[7] * m[10];
inv[8] = m[4] * m[9] * m[15] -
m[4] * m[11] * m[13] -
m[8] * m[5] * m[15] +
m[8] * m[7] * m[13] +
m[12] * m[5] * m[11] -
m[12] * m[7] * m[9];
inv[12] = -m[4] * m[9] * m[14] +
m[4] * m[10] * m[13] +
m[8] * m[5] * m[14] -
m[8] * m[6] * m[13] -
m[12] * m[5] * m[10] +
m[12] * m[6] * m[9];
inv[1] = -m[1] * m[10] * m[15] +
m[1] * m[11] * m[14] +
m[9] * m[2] * m[15] -
m[9] * m[3] * m[14] -
m[13] * m[2] * m[11] +
m[13] * m[3] * m[10];
inv[5] = m[0] * m[10] * m[15] -
m[0] * m[11] * m[14] -
m[8] * m[2] * m[15] +
m[8] * m[3] * m[14] +
m[12] * m[2] * m[11] -
m[12] * m[3] * m[10];
inv[9] = -m[0] * m[9] * m[15] +
m[0] * m[11] * m[13] +
m[8] * m[1] * m[15] -
m[8] * m[3] * m[13] -
m[12] * m[1] * m[11] +
m[12] * m[3] * m[9];
inv[13] = m[0] * m[9] * m[14] -
m[0] * m[10] * m[13] -
m[8] * m[1] * m[14] +
m[8] * m[2] * m[13] +
m[12] * m[1] * m[10] -
m[12] * m[2] * m[9];
inv[2] = m[1] * m[6] * m[15] -
m[1] * m[7] * m[14] -
m[5] * m[2] * m[15] +
m[5] * m[3] * m[14] +
m[13] * m[2] * m[7] -
m[13] * m[3] * m[6];
inv[6] = -m[0] * m[6] * m[15] +
m[0] * m[7] * m[14] +
m[4] * m[2] * m[15] -
m[4] * m[3] * m[14] -
m[12] * m[2] * m[7] +
m[12] * m[3] * m[6];
inv[10] = m[0] * m[5] * m[15] -
m[0] * m[7] * m[13] -
m[4] * m[1] * m[15] +
m[4] * m[3] * m[13] +
m[12] * m[1] * m[7] -
m[12] * m[3] * m[5];
inv[14] = -m[0] * m[5] * m[14] +
m[0] * m[6] * m[13] +
m[4] * m[1] * m[14] -
m[4] * m[2] * m[13] -
m[12] * m[1] * m[6] +
m[12] * m[2] * m[5];
inv[3] = -m[1] * m[6] * m[11] +
m[1] * m[7] * m[10] +
m[5] * m[2] * m[11] -
m[5] * m[3] * m[10] -
m[9] * m[2] * m[7] +
m[9] * m[3] * m[6];
inv[7] = m[0] * m[6] * m[11] -
m[0] * m[7] * m[10] -
m[4] * m[2] * m[11] +
m[4] * m[3] * m[10] +
m[8] * m[2] * m[7] -
m[8] * m[3] * m[6];
inv[11] = -m[0] * m[5] * m[11] +
m[0] * m[7] * m[9] +
m[4] * m[1] * m[11] -
m[4] * m[3] * m[9] -
m[8] * m[1] * m[7] +
m[8] * m[3] * m[5];
inv[15] = m[0] * m[5] * m[10] -
m[0] * m[6] * m[9] -
m[4] * m[1] * m[10] +
m[4] * m[2] * m[9] +
m[8] * m[1] * m[6] -
m[8] * m[2] * m[5];
double det = m[0] * inv[0] + m[1] * inv[4] + m[2] * inv[8] + m[3] * inv[12];
if (abs(det) < 1e-9) {
return false;
}
det = 1.0 / det;
for (int i = 0; i < 4; i++) {
for (int j = 0; j < 4; j++) {
inv_out[i][j] = inv[i * 4 + j] * det;
}
}
return true;
}
extern "C"
__global__ void best_local_affine_kernel(
float *output, float *input, float *affine_model,
int h, int w, float epsilon, int kernel_radius
)
{
int size = h * w;
int id = blockIdx.x * blockDim.x + threadIdx.x;
if (id < size) {
int x = id % w, y = id / w;
double Mt_M[4][4] = {}; // 4x4
double invMt_M[4][4] = {};
double Mt_S[3][4] = {}; // RGB -> 1x4
double A[3][4] = {};
for (int i = 0; i < 4; i++)
for (int j = 0; j < 4; j++) {
Mt_M[i][j] = 0, invMt_M[i][j] = 0;
if (i != 3) {
Mt_S[i][j] = 0, A[i][j] = 0;
if (i == j)
Mt_M[i][j] = 1e-3;
}
}
for (int dy = -kernel_radius; dy <= kernel_radius; dy++) {
for (int dx = -kernel_radius; dx <= kernel_radius; dx++) {
int xx = x + dx, yy = y + dy;
int id2 = yy * w + xx;
if (0 <= xx && xx < w && 0 <= yy && yy < h) {
Mt_M[0][0] += input[id2 + 2*size] * input[id2 + 2*size];
Mt_M[0][1] += input[id2 + 2*size] * input[id2 + size];
Mt_M[0][2] += input[id2 + 2*size] * input[id2];
Mt_M[0][3] += input[id2 + 2*size];
Mt_M[1][0] += input[id2 + size] * input[id2 + 2*size];
Mt_M[1][1] += input[id2 + size] * input[id2 + size];
Mt_M[1][2] += input[id2 + size] * input[id2];
Mt_M[1][3] += input[id2 + size];
Mt_M[2][0] += input[id2] * input[id2 + 2*size];
Mt_M[2][1] += input[id2] * input[id2 + size];
Mt_M[2][2] += input[id2] * input[id2];
Mt_M[2][3] += input[id2];
Mt_M[3][0] += input[id2 + 2*size];
Mt_M[3][1] += input[id2 + size];
Mt_M[3][2] += input[id2];
Mt_M[3][3] += 1;
Mt_S[0][0] += input[id2 + 2*size] * output[id2 + 2*size];
Mt_S[0][1] += input[id2 + size] * output[id2 + 2*size];
Mt_S[0][2] += input[id2] * output[id2 + 2*size];
Mt_S[0][3] += output[id2 + 2*size];
Mt_S[1][0] += input[id2 + 2*size] * output[id2 + size];
Mt_S[1][1] += input[id2 + size] * output[id2 + size];
Mt_S[1][2] += input[id2] * output[id2 + size];
Mt_S[1][3] += output[id2 + size];
Mt_S[2][0] += input[id2 + 2*size] * output[id2];
Mt_S[2][1] += input[id2 + size] * output[id2];
Mt_S[2][2] += input[id2] * output[id2];
Mt_S[2][3] += output[id2];
}
}
}
bool success = InverseMat4x4(Mt_M, invMt_M);
for (int i = 0; i < 3; i++) {
for (int j = 0; j < 4; j++) {
for (int k = 0; k < 4; k++) {
A[i][j] += invMt_M[j][k] * Mt_S[i][k];
}
}
}
for (int i = 0; i < 3; i++) {
for (int j = 0; j < 4; j++) {
int affine_id = i * 4 + j;
affine_model[12 * id + affine_id] = A[i][j];
}
}
}
return ;
}
extern "C"
__global__ void bilateral_smooth_kernel(
float *affine_model, float *filtered_affine_model, float *guide,
int h, int w, int kernel_radius, float sigma1, float sigma2
)
{
int id = blockIdx.x * blockDim.x + threadIdx.x;
int size = h * w;
if (id < size) {
int x = id % w;
int y = id / w;
double sum_affine[12] = {};
double sum_weight = 0;
for (int dx = -kernel_radius; dx <= kernel_radius; dx++) {
for (int dy = -kernel_radius; dy <= kernel_radius; dy++) {
int yy = y + dy, xx = x + dx;
int id2 = yy * w + xx;
if (0 <= xx && xx < w && 0 <= yy && yy < h) {
float color_diff1 = guide[yy*w + xx] - guide[y*w + x];
float color_diff2 = guide[yy*w + xx + size] - guide[y*w + x + size];
float color_diff3 = guide[yy*w + xx + 2*size] - guide[y*w + x + 2*size];
float color_diff_sqr =
(color_diff1*color_diff1 + color_diff2*color_diff2 + color_diff3*color_diff3) / 3;
float v1 = exp(-(dx * dx + dy * dy) / (2 * sigma1 * sigma1));
float v2 = exp(-(color_diff_sqr) / (2 * sigma2 * sigma2));
float weight = v1 * v2;
for (int i = 0; i < 3; i++) {
for (int j = 0; j < 4; j++) {
int affine_id = i * 4 + j;
sum_affine[affine_id] += weight * affine_model[id2*12 + affine_id];
}
}
sum_weight += weight;
}
}
}
for (int i = 0; i < 3; i++) {
for (int j = 0; j < 4; j++) {
int affine_id = i * 4 + j;
filtered_affine_model[id*12 + affine_id] = sum_affine[affine_id] / sum_weight;
}
}
}
return ;
}
extern "C"
__global__ void reconstruction_best_kernel(
float *input, float *filtered_affine_model, float *filtered_best_output,
int h, int w
)
{
int id = blockIdx.x * blockDim.x + threadIdx.x;
int size = h * w;
if (id < size) {
double out1 =
input[id + 2*size] * filtered_affine_model[id*12 + 0] + // A[0][0] +
input[id + size] * filtered_affine_model[id*12 + 1] + // A[0][1] +
input[id] * filtered_affine_model[id*12 + 2] + // A[0][2] +
filtered_affine_model[id*12 + 3]; //A[0][3];
double out2 =
input[id + 2*size] * filtered_affine_model[id*12 + 4] + //A[1][0] +
input[id + size] * filtered_affine_model[id*12 + 5] + //A[1][1] +
input[id] * filtered_affine_model[id*12 + 6] + //A[1][2] +
filtered_affine_model[id*12 + 7]; //A[1][3];
double out3 =
input[id + 2*size] * filtered_affine_model[id*12 + 8] + //A[2][0] +
input[id + size] * filtered_affine_model[id*12 + 9] + //A[2][1] +
input[id] * filtered_affine_model[id*12 + 10] + //A[2][2] +
filtered_affine_model[id*12 + 11]; // A[2][3];
filtered_best_output[id] = out1;
filtered_best_output[id + size] = out2;
filtered_best_output[id + 2*size] = out3;
}
return ;
}
'''
import torch
import numpy as np
from PIL import Image
from cupy.cuda import function
from pynvrtc.compiler import Program
from collections import namedtuple
def smooth_local_affine(output_cpu, input_cpu, epsilon, patch, h, w, f_r, f_e):
program = Program(src, 'best_local_affine_kernel.cu')
ptx = program.compile(['-I/usr/local/cuda-9.1/include'])
m = function.Module()
m.load(bytes(ptx.encode()))
_reconstruction_best_kernel = m.get_function('reconstruction_best_kernel')
_bilateral_smooth_kernel = m.get_function('bilateral_smooth_kernel')
_best_local_affine_kernel = m.get_function('best_local_affine_kernel')
Stream = namedtuple('Stream', ['ptr'])
s = Stream(ptr=torch.cuda.current_stream().cuda_stream)
filter_radius = f_r
sigma1 = filter_radius / 3
sigma2 = f_e
radius = (patch - 1) / 2
filtered_best_output = torch.zeros(np.shape(input_cpu)).cuda()
affine_model = torch.zeros((h * w, 12)).cuda()
filtered_affine_model =torch.zeros((h * w, 12)).cuda()
input_ = torch.from_numpy(input_cpu).cuda()
output_ = torch.from_numpy(output_cpu).cuda()
_best_local_affine_kernel(
grid=(int((h * w) / 256 + 1), 1),
block=(256, 1, 1),
args=[output_.data_ptr(), input_.data_ptr(), affine_model.data_ptr(),
np.int32(h), np.int32(w), np.float32(epsilon), np.int32(radius)], stream=s
)
_bilateral_smooth_kernel(
grid=(int((h * w) / 256 + 1), 1),
block=(256, 1, 1),
args=[affine_model.data_ptr(), filtered_affine_model.data_ptr(), input_.data_ptr(), np.int32(h), np.int32(w), np.int32(f_r), np.float32(sigma1), np.float32(sigma2)], stream=s
)
_reconstruction_best_kernel(
grid=(int((h * w) / 256 + 1), 1),
block=(256, 1, 1),
args=[input_.data_ptr(), filtered_affine_model.data_ptr(), filtered_best_output.data_ptr(),
np.int32(h), np.int32(w)], stream=s
)
numpy_filtered_best_output = filtered_best_output.cpu().numpy()
return numpy_filtered_best_output
def smooth_filter(init_img_name, content_img_name, f_radius=15,f_edge=1e-1):
best_image_bgr = np.array(Image.open(init_img_name).convert("RGB"), dtype=np.float32)
bW, bH, bC = best_image_bgr.shape
best_image_bgr = best_image_bgr[:, :, ::-1]
best_image_bgr = best_image_bgr.transpose((2, 0, 1))
content_input = Image.open(content_img_name).convert("RGB")
content_input = content_input.resize((bH,bW))
content_input = np.array(content_input, dtype=np.float32)
content_input = content_input[:, :, ::-1]
content_input = content_input.transpose((2, 0, 1))
input_ = np.ascontiguousarray(content_input, dtype=np.float32) / 255.
_, H, W = np.shape(input_)
output_ = np.ascontiguousarray(best_image_bgr, dtype=np.float32) / 255.
best_ = smooth_local_affine(output_, input_, 1e-7, 3, H, W, f_radius, f_edge)
best_ = best_.transpose(1, 2, 0)
result = Image.fromarray(np.uint8(np.clip(best_ * 255., 0, 255.)))
return result