opencv/apps/python-calibration-generator/utils.py

# This file is part of OpenCV project.
# It is subject to the license terms in the LICENSE file found in the top-level directory
# of this distribution and at http://opencv.org/license.html.

import numpy as np
import math
import cv2 as cv
import json

class RandGen:
    def __init__(self, seed = 0):
        self.rand_gen = np.random.RandomState(seed)

    def randRange(self, min_v, max_v):
        return self.rand_gen.rand(1).item() * (max_v - min_v) + min_v

def project(K, R, t, dist, pts_3d, is_fisheye):
    if is_fisheye:
        pts_2d = cv.fisheye.projectPoints(pts_3d.T[None,:], cv.Rodrigues(R)[0], t, K, dist.flatten())[0].reshape(-1,2).T
    else:
        pts_2d = cv.projectPoints(pts_3d, R, t, K, dist)[0].reshape(-1,2).T
    return pts_2d

def projectCamera(camera, pts_3d):
    return project(camera.K, camera.R, camera.t, camera.distortion, pts_3d, camera.is_fisheye)

def eul2rot(theta): # [x y z]
    # https://learnopencv.com/rotation-matrix-to-euler-angles/
    R_x = np.array([[1,         0,                  0                   ],
                    [0,         math.cos(theta[0]), -math.sin(theta[0]) ],
                    [0,         math.sin(theta[0]), math.cos(theta[0])  ]])
    R_y = np.array([[math.cos(theta[1]),    0,      math.sin(theta[1])  ],
                    [0,                     1,      0                   ],
                    [-math.sin(theta[1]),   0,      math.cos(theta[1])  ]])
    R_z = np.array([[math.cos(theta[2]),    -math.sin(theta[2]),    0],
                    [math.sin(theta[2]),    math.cos(theta[2]),     0],
                    [0,                     0,                      1]])
    return np.dot(R_z, np.dot(R_y, R_x))

def insideImageMask(pts, w, h):
    return np.logical_and(np.logical_and(pts[0] < w, pts[1] < h), np.logical_and(pts[0] > 0, pts[1] > 0))

def insideImage(pts, w, h):
    return insideImageMask(pts, w, h).sum()

def areAllInsideImage(pts, w, h):
    return insideImageMask(pts, w, h).all()

def writeMatrix(file, label, M):
    file.write("%s:\n" % label)
    for i in range(M.shape[0]):
        for j in range(M.shape[1]):
            file.write(str(M[i,j]) + ('\n' if j == M.shape[1]-1 else ' '))

def saveKDRT(cameras, fname):
    file = open(fname, 'w')
    for idx, cam in enumerate(cameras):
        file.write("camera_%d:\n" % idx)
        writeMatrix(file, "K", cam.K)
        writeMatrix(file, "distortion", cam.distortion)
        writeMatrix(file, "R", cam.R)
        writeMatrix(file, "T", cam.t)

def export2JSON(pattern_points, image_points, image_sizes, is_fisheye, json_file):
    image_points = image_points.transpose(1,0,3,2)
    image_points_list = [[] for i in range(len(image_sizes))]
    for c in range(len(image_points)):
        for f in range(len(image_points[c])):
            if insideImage(image_points[c][f].T, image_sizes[c][0], image_sizes[c][1]) >= 4:
                mask = np.logical_not(insideImageMask(image_points[c][f].T, image_sizes[c][0], image_sizes[c][1]))
                image_points[c][f][mask] = -1.
                image_points_list[c].append(image_points[c][f].tolist())
            else:
                image_points_list[c].append([])
    json.dump({'object_points': pattern_points.tolist(), 'image_points': image_points_list, 'image_sizes': image_sizes, 'is_fisheye': is_fisheye}, open(json_file, 'w'))