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Last active May 24, 2020 08:44
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mpv user shader to fix radial distortion commonly found in wide angle action cameras
Raw
.gitignore
*.mp4
*.mov
*.png
*.txt
Raw
calibrate.py
#!/usr/bin/env python
import cv2
import numpy as np
import os
import sys
import math
# Defining the dimensions of checkerboard
CHECKERBOARD = (6,9)
criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001)
# Creating vector to store vectors of 3D points for each checkerboard image
objpoints = []
# Creating vector to store vectors of 2D points for each checkerboard image
imgpoints = []
# Defining the world coordinates for 3D points
objp = np.zeros((1, CHECKERBOARD[0] * CHECKERBOARD[1], 3), np.float32)
objp[0,:,:2] = np.mgrid[0:CHECKERBOARD[0], 0:CHECKERBOARD[1]].T.reshape(-1, 2)
prev_img_shape = None
# Extracting path of individual image stored in a given directory
images = sys.argv[1:]
width = None
height = None
for fname in images:
img = cv2.imread(fname)
if img is None:
print("file %s not found" % fname)
sys.exit(-1)
if width is not None and width != img.shape[1]:
print("width not match")
sys.exit(-1)
width = img.shape[1]
if height is not None and height != img.shape[0]:
print("height not match")
sys.exit(-1)
height = img.shape[0]
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
# Find the chess board corners
# If desired number of corners are found in the image then ret = true
ret, corners = cv2.findChessboardCorners(gray, CHECKERBOARD, cv2.CALIB_CB_ADAPTIVE_THRESH + cv2.CALIB_CB_FAST_CHECK + cv2.CALIB_CB_NORMALIZE_IMAGE)
"""
If desired number of corner are detected,
we refine the pixel coordinates and display
them on the images of checker board
"""
if ret == True:
objpoints.append(objp)
# refining pixel coordinates for given 2d points.
corners2 = cv2.cornerSubPix(gray, corners, (11,11),(-1,-1), criteria)
imgpoints.append(corners2)
# Draw and display the corners
img = cv2.drawChessboardCorners(img, CHECKERBOARD, corners2, ret)
cv2.imshow('img',img)
cv2.waitKey(0)
cv2.destroyAllWindows()
h,w = img.shape[:2]
"""
Performing camera calibration by
passing the value of known 3D points (objpoints)
and corresponding pixel coordinates of the
detected corners (imgpoints)
"""
ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, gray.shape[::-1], None, None)
inf = 1e9
eps = 1e-7
def solve(k1, k2, k3, y):
# find minimal sqrt(x) s.t. y = (k3 * x^3 + k2 * x^2 + k1 * x + 1) * sqrt(x)
def f(x):
return (((k3 * x + k2) * x + k1) * x + 1) * math.sqrt(x) - y
def diff_f(x):
return (((7 * k3 * x + 5 * k2) * x + 3 * k1) * x + 1) / (2 * math.sqrt(x))
min_sqrtx = inf
for i in range(200):
x = (i + 0.5) / 100.0
for j in range(30):
if (x < eps):
break
x -= f(x) / diff_f(x)
if (x < eps):
continue
if (abs(f(x)) < eps):
min_sqrtx = min(min_sqrtx, math.sqrt(x))
return min_sqrtx
k1, k2, p1, p3, k3 = dist[0]
diag = math.sqrt(width ** 2 + height ** 2)
w = width / diag
h = height / diag
ws = solve(k1, k2, k3, w) / w
hs = solve(k1, k2, k3, h) / h
ds = solve(k1, k2, k3, 1.0)
zoom = min(ws, hs, ds)
print("#define K1 %s" % k1)
print("#define K2 %s" % k2)
print("#define K3 %s" % k3)
print("#define ZOOM %s" % zoom)
if ws < zoom * 3 and hs < zoom * 3:
new_width = math.floor(ws / zoom * width + eps)
new_height = math.floor(hs / zoom * height + eps)
print("")
print("For stretched lense fixing, use --video-aspect-override=%d:%d (and --vf=gpu=w=%d:h=%d for encoding):" % (new_width, new_height, new_width, new_height))
print("")
print("//!DESC lensfix-stretch")
print("//!HOOK MAIN")
print("//!BIND HOOKED")
print("//!WIDTH %d" % new_width)
print("//!HEIGHT %d" % new_height)
print("#define K1 %s" % k1)
print("#define K2 %s" % k2)
print("#define K3 %s" % k3)
print("#define ZOOM vec2(%d, %d) / vec2(%d, %d) * %s" % (new_width, new_height, width, height, zoom))
Raw
lensfix-ewa.hook
//!DESC lensfix (with ewa_lanczossharp resampler)
//!HOOK MAIN
//!BIND HOOKED
//!BIND FILTER_LUT
//!WIDTH OUTPUT.width
//!HEIGHT OUTPUT.height
// The following parameters are calibrated by opencv.
// See More:
// https://docs.opencv.org/2.4/modules/calib3d/doc/camera_calibration_and_3d_reconstruction.html
// https://www.learnopencv.com/camera-calibration-using-opencv/
// https://docs.opencv.org/2.4/doc/tutorials/calib3d/camera_calibration/camera_calibration.html#cameracalibrationopencv
//
// 1. Print https://github.com/opencv/opencv/blob/master/doc/pattern.png
// 2. Take photos of the pattern with camera from different angles
// 3. $ ./calibrate.py photo1.png photo2.png ...
// Press any key for each shown photos.
#define K1 -0.36273784475910814
#define K2 0.14395222613195036
#define K3 -0.02752716367238447
#define ZOOM 1.1045972020461248
vec4 hook() {
vec2 unit_rect = HOOKED_size / length(HOOKED_size);
vec2 pos = (HOOKED_pos - 0.5) * 2.0 * unit_rect;
pos *= ZOOM;
float r2 = pos.x * pos.x + pos.y * pos.y;
pos *= 1.0 + (((K3) * r2 + (K2)) * r2 + (K1)) * r2;
pos = pos / unit_rect / 2.0 + 0.5;
vec2 size = HOOKED_size;
vec2 pt = HOOKED_pt;
vec4 color = vec4(0.0);
vec2 fcoord = fract(pos * size - vec2(0.5));
vec2 base = pos - fcoord * pt;
float w, d, wsum = 0.0;
vec4 in0;
vec4 in1;
vec4 in2;
int idx;
// scaler samples
d = length(vec2(-2.0, -2.0) - fcoord);
if (d < 3.051550) {
w = tex1D(FILTER_LUT, LUT_POS(d * 1.0/3.238315, 1024.0)).r;
wsum += w;
in0 = HOOKED_tex(base + pt * vec2(-2.0, -2.0));
color += vec4(w) * in0;
}
d = length(vec2(0.0, -3.0) - fcoord);
if (d < 3.051550) {
w = tex1D(FILTER_LUT, LUT_POS(d * 1.0/3.238315, 1024.0)).r;
wsum += w;
in0 = HOOKED_tex(base + pt * vec2(0.0, -3.0));
color += vec4(w) * in0;
}
d = length(vec2(-1.0, -2.0) - fcoord);
if (d < 3.051550) {
w = tex1D(FILTER_LUT, LUT_POS(d * 1.0/3.238315, 1024.0)).r;
wsum += w;
in0 = HOOKED_tex(base + pt * vec2(-1.0, -2.0));
color += vec4(w) * in0;
}
d = length(vec2(0.0, -2.0) - fcoord);
if (d < 3.051550) {
w = tex1D(FILTER_LUT, LUT_POS(d * 1.0/3.238315, 1024.0)).r;
wsum += w;
in0 = HOOKED_tex(base + pt * vec2(0.0, -2.0));
color += vec4(w) * in0;
}
d = length(vec2(1.0, -3.0) - fcoord);
if (d < 3.051550) {
w = tex1D(FILTER_LUT, LUT_POS(d * 1.0/3.238315, 1024.0)).r;
wsum += w;
in0 = HOOKED_tex(base + pt * vec2(1.0, -3.0));
color += vec4(w) * in0;
}
d = length(vec2(1.0, -2.0) - fcoord);
if (d < 3.051550) {
w = tex1D(FILTER_LUT, LUT_POS(d * 1.0/3.238315, 1024.0)).r;
wsum += w;
in0 = HOOKED_tex(base + pt * vec2(1.0, -2.0));
color += vec4(w) * in0;
}
d = length(vec2(2.0, -2.0) - fcoord);
if (d < 3.051550) {
w = tex1D(FILTER_LUT, LUT_POS(d * 1.0/3.238315, 1024.0)).r;
wsum += w;
in0 = HOOKED_tex(base + pt * vec2(2.0, -2.0));
color += vec4(w) * in0;
}
d = length(vec2(3.0, -2.0) - fcoord);
if (d < 3.051550) {
w = tex1D(FILTER_LUT, LUT_POS(d * 1.0/3.238315, 1024.0)).r;
wsum += w;
in0 = HOOKED_tex(base + pt * vec2(3.0, -2.0));
color += vec4(w) * in0;
}
d = length(vec2(-2.0, -1.0) - fcoord);
if (d < 3.051550) {
w = tex1D(FILTER_LUT, LUT_POS(d * 1.0/3.238315, 1024.0)).r;
wsum += w;
in0 = HOOKED_tex(base + pt * vec2(-2.0, -1.0));
color += vec4(w) * in0;
}
d = length(vec2(-3.0, 0.0) - fcoord);
if (d < 3.051550) {
w = tex1D(FILTER_LUT, LUT_POS(d * 1.0/3.238315, 1024.0)).r;
wsum += w;
in0 = HOOKED_tex(base + pt * vec2(-3.0, 0.0));
color += vec4(w) * in0;
}
d = length(vec2(-2.0, 0.0) - fcoord);
if (d < 3.051550) {
w = tex1D(FILTER_LUT, LUT_POS(d * 1.0/3.238315, 1024.0)).r;
wsum += w;
in0 = HOOKED_tex(base + pt * vec2(-2.0, 0.0));
color += vec4(w) * in0;
}
d = length(vec2(-1.0, -1.0) - fcoord);
w = tex1D(FILTER_LUT, LUT_POS(d * 1.0/3.238315, 1024.0)).r;
wsum += w;
in0 = HOOKED_tex(base + pt * vec2(-1.0, -1.0));
color += vec4(w) * in0;
d = length(vec2(0.0, -1.0) - fcoord);
w = tex1D(FILTER_LUT, LUT_POS(d * 1.0/3.238315, 1024.0)).r;
wsum += w;
in0 = HOOKED_tex(base + pt * vec2(0.0, -1.0));
color += vec4(w) * in0;
d = length(vec2(-1.0, 0.0) - fcoord);
w = tex1D(FILTER_LUT, LUT_POS(d * 1.0/3.238315, 1024.0)).r;
wsum += w;
in0 = HOOKED_tex(base + pt * vec2(-1.0, 0.0));
color += vec4(w) * in0;
d = length(vec2(0.0, 0.0) - fcoord);
w = tex1D(FILTER_LUT, LUT_POS(d * 1.0/3.238315, 1024.0)).r;
wsum += w;
in0 = HOOKED_tex(base + pt * vec2(0.0, 0.0));
color += vec4(w) * in0;
d = length(vec2(1.0, -1.0) - fcoord);
w = tex1D(FILTER_LUT, LUT_POS(d * 1.0/3.238315, 1024.0)).r;
wsum += w;
in0 = HOOKED_tex(base + pt * vec2(1.0, -1.0));
color += vec4(w) * in0;
d = length(vec2(2.0, -1.0) - fcoord);
w = tex1D(FILTER_LUT, LUT_POS(d * 1.0/3.238315, 1024.0)).r;
wsum += w;
in0 = HOOKED_tex(base + pt * vec2(2.0, -1.0));
color += vec4(w) * in0;
d = length(vec2(1.0, 0.0) - fcoord);
w = tex1D(FILTER_LUT, LUT_POS(d * 1.0/3.238315, 1024.0)).r;
wsum += w;
in0 = HOOKED_tex(base + pt * vec2(1.0, 0.0));
color += vec4(w) * in0;
d = length(vec2(2.0, 0.0) - fcoord);
w = tex1D(FILTER_LUT, LUT_POS(d * 1.0/3.238315, 1024.0)).r;
wsum += w;
in0 = HOOKED_tex(base + pt * vec2(2.0, 0.0));
color += vec4(w) * in0;
d = length(vec2(3.0, -1.0) - fcoord);
if (d < 3.051550) {
w = tex1D(FILTER_LUT, LUT_POS(d * 1.0/3.238315, 1024.0)).r;
wsum += w;
in0 = HOOKED_tex(base + pt * vec2(3.0, -1.0));
color += vec4(w) * in0;
}
d = length(vec2(3.0, 0.0) - fcoord);
if (d < 3.051550) {
w = tex1D(FILTER_LUT, LUT_POS(d * 1.0/3.238315, 1024.0)).r;
wsum += w;
in0 = HOOKED_tex(base + pt * vec2(3.0, 0.0));
color += vec4(w) * in0;
}
d = length(vec2(4.0, 0.0) - fcoord);
if (d < 3.051550) {
w = tex1D(FILTER_LUT, LUT_POS(d * 1.0/3.238315, 1024.0)).r;
wsum += w;
in0 = HOOKED_tex(base + pt * vec2(4.0, 0.0));
color += vec4(w) * in0;
}
d = length(vec2(-3.0, 1.0) - fcoord);
if (d < 3.051550) {
w = tex1D(FILTER_LUT, LUT_POS(d * 1.0/3.238315, 1024.0)).r;
wsum += w;
in0 = HOOKED_tex(base + pt * vec2(-3.0, 1.0));
color += vec4(w) * in0;
}
d = length(vec2(-2.0, 1.0) - fcoord);
if (d < 3.051550) {
w = tex1D(FILTER_LUT, LUT_POS(d * 1.0/3.238315, 1024.0)).r;
wsum += w;
in0 = HOOKED_tex(base + pt * vec2(-2.0, 1.0));
color += vec4(w) * in0;
}
d = length(vec2(-2.0, 2.0) - fcoord);
if (d < 3.051550) {
w = tex1D(FILTER_LUT, LUT_POS(d * 1.0/3.238315, 1024.0)).r;
wsum += w;
in0 = HOOKED_tex(base + pt * vec2(-2.0, 2.0));
color += vec4(w) * in0;
}
d = length(vec2(-1.0, 1.0) - fcoord);
w = tex1D(FILTER_LUT, LUT_POS(d * 1.0/3.238315, 1024.0)).r;
wsum += w;
in0 = HOOKED_tex(base + pt * vec2(-1.0, 1.0));
color += vec4(w) * in0;
d = length(vec2(0.0, 1.0) - fcoord);
w = tex1D(FILTER_LUT, LUT_POS(d * 1.0/3.238315, 1024.0)).r;
wsum += w;
in0 = HOOKED_tex(base + pt * vec2(0.0, 1.0));
color += vec4(w) * in0;
d = length(vec2(-1.0, 2.0) - fcoord);
w = tex1D(FILTER_LUT, LUT_POS(d * 1.0/3.238315, 1024.0)).r;
wsum += w;
in0 = HOOKED_tex(base + pt * vec2(-1.0, 2.0));
color += vec4(w) * in0;
d = length(vec2(0.0, 2.0) - fcoord);
w = tex1D(FILTER_LUT, LUT_POS(d * 1.0/3.238315, 1024.0)).r;
wsum += w;
in0 = HOOKED_tex(base + pt * vec2(0.0, 2.0));
color += vec4(w) * in0;
d = length(vec2(1.0, 1.0) - fcoord);
w = tex1D(FILTER_LUT, LUT_POS(d * 1.0/3.238315, 1024.0)).r;
wsum += w;
in0 = HOOKED_tex(base + pt * vec2(1.0, 1.0));
color += vec4(w) * in0;
d = length(vec2(2.0, 1.0) - fcoord);
w = tex1D(FILTER_LUT, LUT_POS(d * 1.0/3.238315, 1024.0)).r;
wsum += w;
in0 = HOOKED_tex(base + pt * vec2(2.0, 1.0));
color += vec4(w) * in0;
d = length(vec2(1.0, 2.0) - fcoord);
w = tex1D(FILTER_LUT, LUT_POS(d * 1.0/3.238315, 1024.0)).r;
wsum += w;
in0 = HOOKED_tex(base + pt * vec2(1.0, 2.0));
color += vec4(w) * in0;
d = length(vec2(2.0, 2.0) - fcoord);
w = tex1D(FILTER_LUT, LUT_POS(d * 1.0/3.238315, 1024.0)).r;
wsum += w;
in0 = HOOKED_tex(base + pt * vec2(2.0, 2.0));
color += vec4(w) * in0;
d = length(vec2(3.0, 1.0) - fcoord);
if (d < 3.051550) {
w = tex1D(FILTER_LUT, LUT_POS(d * 1.0/3.238315, 1024.0)).r;
wsum += w;
in0 = HOOKED_tex(base + pt * vec2(3.0, 1.0));
color += vec4(w) * in0;
}
d = length(vec2(4.0, 1.0) - fcoord);
if (d < 3.051550) {
w = tex1D(FILTER_LUT, LUT_POS(d * 1.0/3.238315, 1024.0)).r;
wsum += w;
in0 = HOOKED_tex(base + pt * vec2(4.0, 1.0));
color += vec4(w) * in0;
}
d = length(vec2(3.0, 2.0) - fcoord);
if (d < 3.051550) {
w = tex1D(FILTER_LUT, LUT_POS(d * 1.0/3.238315, 1024.0)).r;
wsum += w;
in0 = HOOKED_tex(base + pt * vec2(3.0, 2.0));
color += vec4(w) * in0;
}
d = length(vec2(-2.0, 3.0) - fcoord);
if (d < 3.051550) {
w = tex1D(FILTER_LUT, LUT_POS(d * 1.0/3.238315, 1024.0)).r;
wsum += w;
in0 = HOOKED_tex(base + pt * vec2(-2.0, 3.0));
color += vec4(w) * in0;
}
d = length(vec2(-1.0, 3.0) - fcoord);
if (d < 3.051550) {
w = tex1D(FILTER_LUT, LUT_POS(d * 1.0/3.238315, 1024.0)).r;
wsum += w;
in0 = HOOKED_tex(base + pt * vec2(-1.0, 3.0));
color += vec4(w) * in0;
}
d = length(vec2(0.0, 3.0) - fcoord);
if (d < 3.051550) {
w = tex1D(FILTER_LUT, LUT_POS(d * 1.0/3.238315, 1024.0)).r;
wsum += w;
in0 = HOOKED_tex(base + pt * vec2(0.0, 3.0));
color += vec4(w) * in0;
}
d = length(vec2(0.0, 4.0) - fcoord);
if (d < 3.051550) {
w = tex1D(FILTER_LUT, LUT_POS(d * 1.0/3.238315, 1024.0)).r;
wsum += w;
in0 = HOOKED_tex(base + pt * vec2(0.0, 4.0));
color += vec4(w) * in0;
}
d = length(vec2(1.0, 3.0) - fcoord);
if (d < 3.051550) {
w = tex1D(FILTER_LUT, LUT_POS(d * 1.0/3.238315, 1024.0)).r;
wsum += w;
in0 = HOOKED_tex(base + pt * vec2(1.0, 3.0));
color += vec4(w) * in0;
}
d = length(vec2(2.0, 3.0) - fcoord);
if (d < 3.051550) {
w = tex1D(FILTER_LUT, LUT_POS(d * 1.0/3.238315, 1024.0)).r;
wsum += w;
in0 = HOOKED_tex(base + pt * vec2(2.0, 3.0));
color += vec4(w) * in0;
}
d = length(vec2(1.0, 4.0) - fcoord);
if (d < 3.051550) {
w = tex1D(FILTER_LUT, LUT_POS(d * 1.0/3.238315, 1024.0)).r;
wsum += w;
in0 = HOOKED_tex(base + pt * vec2(1.0, 4.0));
color += vec4(w) * in0;
}
d = length(vec2(3.0, 3.0) - fcoord);
if (d < 3.051550) {
w = tex1D(FILTER_LUT, LUT_POS(d * 1.0/3.238315, 1024.0)).r;
wsum += w;
in0 = HOOKED_tex(base + pt * vec2(3.0, 3.0));
color += vec4(w) * in0;
}
color = color / vec4(wsum);
return color;
}
//!TEXTURE FILTER_LUT
//!SIZE 1024
//!FORMAT r32f
//!FILTER LINEAR
//!BORDER CLAMP
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
Raw
lensfix.hook
//!DESC lensfix
//!HOOK MAIN
//!BIND HOOKED
// The following parameters are calibrated by opencv.
// See More:
// https://docs.opencv.org/2.4/modules/calib3d/doc/camera_calibration_and_3d_reconstruction.html
// https://www.learnopencv.com/camera-calibration-using-opencv/
// https://docs.opencv.org/2.4/doc/tutorials/calib3d/camera_calibration/camera_calibration.html#cameracalibrationopencv
//
// 1. Print https://github.com/opencv/opencv/blob/master/doc/pattern.png
// 2. Take photos of the pattern with camera from different angles
// 3. $ ./calibrate.py photo1.png photo2.png ...
// Press any key for each shown photos.
#define K1 -0.36273784475910814
#define K2 0.14395222613195036
#define K3 -0.02752716367238447
#define ZOOM 1.1045972020461248
vec4 hook() {
vec2 unit_rect = HOOKED_size / length(HOOKED_size);
vec2 pos = (HOOKED_pos - 0.5) * 2.0 * unit_rect;
pos *= ZOOM;
float r2 = pos.x * pos.x + pos.y * pos.y;
pos *= 1.0 + (((K3) * r2 + (K2)) * r2 + (K1)) * r2;
pos = pos / unit_rect / 2.0 + 0.5;
return HOOKED_tex(pos);
}
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