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support for fisheye lenses

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This commit is contained in:
Ruben van de Ven 2025-05-09 21:44:58 +02:00
parent 5f836efcad
commit a7e4380151
7 changed files with 885 additions and 207 deletions
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158
01-calibrate.py
View file

@ -12,13 +12,17 @@ Usage:
''' '''
from pathlib import Path
import time
import numpy as np import numpy as np
import cv2 import cv2
import json import json
import tqdm import tqdm
import math import math
dataset = 'hof2' CALIB_FISHEYE = True
dataset = Path('hof3-cam-baumer')
# set needed detections. Use math.inf to scan the whole video # set needed detections. Use math.inf to scan the whole video
needed_detections = math.inf # 20 needed_detections = math.inf # 20
@ -26,69 +30,147 @@ needed_detections = math.inf # 20
# Defining the dimensions of checkerboard # Defining the dimensions of checkerboard
CHECKERBOARD = (6,9) CHECKERBOARD = (6,9)
# termination criteria CROPPED_HEIGHT = 1520
criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001)
# prepare object points, like (0,0,0), (1,0,0), (2,0,0) ....,(6,5,0) # prepare object points, like (0,0,0), (1,0,0), (2,0,0) ....,(6,5,0)
if not CALIB_FISHEYE:
objp = np.zeros((CHECKERBOARD[0] * CHECKERBOARD[1],3), np.float32) objp = np.zeros((CHECKERBOARD[0] * CHECKERBOARD[1],3), np.float32)
objp[:,:2] = np.mgrid[0:CHECKERBOARD[0],0:CHECKERBOARD[1]].T.reshape(-1,2) objp[:,:2] = np.mgrid[0:CHECKERBOARD[0],0:CHECKERBOARD[1]].T.reshape(-1,2)
else:
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)
# Arrays to store object points and image points from all the images. # Arrays to store object points and image points from all the images.
objpoints = [] # 3d point in real world space objpoints = [] # 3d point in real world space
imgpoints = [] # 2d points in image plane. imgpoints = [] # 2d points in image plane.
cap = cv2.VideoCapture(dataset / "chessboard.mp4") videofile = dataset / "chessboard7.mp4"
cap = cv2.VideoCapture(videofile)
_, img = cap.read()
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
_img_shape = img.shape[:2]
frame_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) frame_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
frame_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) frame_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
dim = { dim = {
'width': frame_width, 'width': frame_width,
'height': frame_height, 'height': frame_height,
} }
found = 0
p = tqdm.tqdm()
p2 = tqdm.tqdm(total=needed_detections)
while ((found < needed_detections) if math.isfinite(needed_detections) else True): loaded = np.load(dataset / "chessboard-points.npz")
ret, img = cap.read() # Capture frame-by-frame objpoints, imgpoints = loaded['objpoints'], loaded['imgpoints']
if not ret:
break
p.update() print(f"Calculating matrixes with {len(objpoints)} detections")
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# Find the chess board corners
ret, corners = cv2.findChessboardCorners(gray, CHECKERBOARD,None)
# If found, add object points, image points (after refining them)
if ret == True:
objpoints.append(objp) # Certainly, every loop objp is the same, in 3D.
corners2 = cv2.cornerSubPix(gray,corners,(11,11),(-1,-1),criteria)
imgpoints.append(corners2)
p2.update()
p2.n
# Draw and display the corners
img = cv2.drawChessboardCorners(img, CHECKERBOARD, corners2, ret)
found += 1
cv2.imshow('img', img)
cv2.waitKey(1)
# When everything done, release the capture
cap.release()
cv2.destroyAllWindows()
print(f"Calculating matrixes with {found} detections")
ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, gray.shape[::-1], None, None)
if not CALIB_FISHEYE:
ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, (frame_height, frame_width), None, None)
# It's very important to transform the matrix to list. # It's very important to transform the matrix to list.
data = {'dim': dim, 'camera_matrix': np.asarray(mtx).tolist(), 'dist_coeff': np.asarray(dist).tolist()} data = {'dim': dim, 'camera_matrix': np.asarray(mtx).tolist(), 'dist_coeff': np.asarray(dist).tolist()}
fn = dataset + "/calibration.json" fn = dataset / "calibration.json"
print(f"write to {fn}") print(f"write to {fn}")
with open(fn, "w") as f: with open(fn, "w") as f:
json.dump(data, f) json.dump(data, f)
else:
#see also: https://medium.com/@kennethjiang/calibrate-fisheye-lens-using-opencv-333b05afa0b0
calibration_flags = cv2.fisheye.CALIB_RECOMPUTE_EXTRINSIC+cv2.fisheye.CALIB_CHECK_COND+cv2.fisheye.CALIB_FIX_SKEW
cv2.CV_32FC3
# objpoints = objpoints.astype(np.int32)
# print(objpoints, imgpoints.dtype)
objpoints = [objp for i in range(len(imgpoints))]
N_OK = len(objpoints)
K = np.zeros((3, 3))
D = np.zeros((4, 1))
rvecs = [np.zeros((1, 1, 3), dtype=np.float64) for i in range(N_OK)]
tvecs = [np.zeros((1, 1, 3), dtype=np.float64) for i in range(N_OK)]
rms, _, _, _, _ = \
cv2.fisheye.calibrate(
objpoints,
imgpoints,
gray.shape[::-1],
K,
D,
rvecs,
tvecs,
calibration_flags,
(cv2.TERM_CRITERIA_EPS+cv2.TERM_CRITERIA_MAX_ITER, 30, 1e-6)
)
DIM=_img_shape[::-1]
print("Found " + str(N_OK) + " valid images for calibration")
print("DIM=" + str(DIM))
print("K=np.array(" + str(K.tolist()) + ")")
print("D=np.array(" + str(D.tolist()) + ")")
balance=0
dim2=None
dim3=None
# img = cv2.imread(dataset / "snapshot.png") # just use video frame
dim1 = img.shape[:2][::-1] #dim1 is the dimension of input image to un-distort
assert dim1[0]/dim1[1] == DIM[0]/DIM[1], "Image to undistort needs to have same aspect ratio as the ones used in calibration"
if not dim2:
dim2 = dim1
if not dim3:
dim3 = dim1
dim3 = (dim1[0], CROPPED_HEIGHT)
scaled_K = K * dim1[0] / DIM[0] # The values of K is to scale with image dimension.
scaled_K[2][2] = 1.0 # Except that K[2][2] is always 1.0
# This is how scaled_K, dim2 and balance are used to determine the final K used to un-distort image. OpenCV document failed to make this clear!
new_K = cv2.fisheye.estimateNewCameraMatrixForUndistortRectify(scaled_K, D, dim2, np.eye(3), balance=balance)
map1, map2 = cv2.fisheye.initUndistortRectifyMap(scaled_K, D, np.eye(3), new_K, dim3, cv2.CV_16SC2)
t = time.perf_counter()
# print(map1, map2)
undistorted_img = cv2.remap(img, map1, map2, interpolation=cv2.INTER_LINEAR, borderMode=cv2.BORDER_CONSTANT)
print(f"Took {time.perf_counter()-t}s")
data = {
'type': 'fisheye',
'dim1': dim1,
'dim2':dim2,
'dim3': dim3,
'K': np.asarray(K).tolist(),
'D':np.asarray(D).tolist(),
'new_K':np.asarray(new_K).tolist(),
'scaled_K':np.asarray(scaled_K).tolist(),
'balance':balance}
print(data)
with open(dataset/"calibration.json", "w") as f:
json.dump(data, f)
# map some points to test:
distorted_img_points = [
[1000,700],
[500,700],
[250,700],
[125,700],
]
new_points = cv2.fisheye.undistortPoints (np.array([distorted_img_points]).astype(np.float32), K=scaled_K, D=D, R=np.eye(3), P=new_K)
print ('new', new_points)
print(undistorted_img.shape)
for i, point in enumerate(distorted_img_points):
cv2.circle(img, point, 5, (0,255,0), 2)
cv2.circle(undistorted_img, new_points[0][i].astype(int), 5, (0,255,0), 2)
# cv2.circle(undistorted_img, new_points[0][0].astype(int), 5, (0,255,0), 2)
cv2.imshow("original", img)
cv2.imshow("undistorted", undistorted_img)
# img2 = cv2.imread("2.png")
# cv2.imshow("none undistorted", img2)
cv2.waitKey(0)
cv2.destroyAllWindows()

43
02-testcalibration-and-draw-points.py
View file

@ -14,26 +14,18 @@ import json
import os import os
import numpy as np import numpy as np
dataset = 'hof2' from load_calibration import load_calibration
snapshot_img = 'snapshot3.png'
with open(dataset + '/calibration.json', 'r') as fp: dataset = 'hof3-cam-baumer'
calibdata = json.load(fp) snapshot_img = 'snapshot.png'
mtx = np.array(calibdata['camera_matrix']) points_fname = dataset + '/points.json'
dist = np.array(calibdata['dist_coeff'])
w, h = calibdata['dim']['width'], calibdata['dim']['height']
w, h, undistorted_img, undistort_points = load_calibration(dataset + "/fisheye_calibration_data.json")
# with open(dataset + '/calibration-chessboard4.json', 'r') as fp:
# with open(dataset + '/calibration-chessboard4.json', 'r') as fp:
if os.path.exists(points_fname):
with open(points_fname, 'r') as fp:
# # Refining the camera matrix using parameters obtained by calibration
# if we don't set this, the new image will be cropped to the minimum size
# this way, no cropping occurs
# w, h = 2560, 1440
newcameramtx, roi = cv2.getOptimalNewCameraMatrix(mtx, dist, (w,h), 1, (w,h))
if os.path.exists(dataset + '/points.json'):
with open(dataset + '/points.json', 'r') as fp:
points = json.load(fp) points = json.load(fp)
else: else:
points = [[500,500],[1000,1000]] points = [[500,500],[1000,1000]]
@ -64,6 +56,8 @@ cap = cv2.VideoCapture(dataset + "/" + snapshot_img)
img_w = cap.get(cv2.CAP_PROP_FRAME_WIDTH) img_w = cap.get(cv2.CAP_PROP_FRAME_WIDTH)
img_h = cap.get(cv2.CAP_PROP_FRAME_HEIGHT) img_h = cap.get(cv2.CAP_PROP_FRAME_HEIGHT)
print(w,h)
print(img_w,img_h)
# scale saved points to snapshot # scale saved points to snapshot
points = [ points = [
[p[0]*(img_w/w), p[1]*(img_h/h)] for p in points [p[0]*(img_w/w), p[1]*(img_h/h)] for p in points
@ -79,8 +73,11 @@ while True: # (found < needed): # Here, 10 can be changed to whatever number yo
imgOld = img imgOld = img
# Method 1 to undistort the image # Method 1 to undistort the image
dst = cv2.undistort(img, mtx, dist, None, newcameramtx) # dst = cv2.undistort(img, mtx, dist, None, newcameramtx)
dstPoints = cv2.undistortPoints(np.array(points).astype('float32'), mtx, dist, None, P=newcameramtx) # dstPoints = cv2.undistortPoints(np.array(points).astype('float32'), mtx, dist, None, P=newcameramtx)
dst = undistorted_img(img)
dstPoints = undistort_points(np.array(points).astype('float32'))[0]
# dst = cv2.undistort(img, mtx, dist, None) # dst = cv2.undistort(img, mtx, dist, None)
@ -92,9 +89,9 @@ while True: # (found < needed): # Here, 10 can be changed to whatever number yo
# Displaying the undistorted image # Displaying the undistorted image
drawImg = img.copy() drawImg = img.copy()
drawDst = dst.copy() drawDst = dst.copy()
for p in dstPoints: for i, p in enumerate(dstPoints):
x = int(p[0][0]) x = int(p[0])
y= int(p[0][1]) y = int(p[1])
cv2.circle(drawDst, (x,y), radius=2, color=(0, 0, 255), thickness=-1) cv2.circle(drawDst, (x,y), radius=2, color=(0, 0, 255), thickness=-1)
for i, p in enumerate(points): for i, p in enumerate(points):
x = int(p[0]) x = int(p[0])
@ -109,7 +106,7 @@ while True: # (found < needed): # Here, 10 can be changed to whatever number yo
print("write points.json") print("write points.json")
with open(dataset + '/points.json', 'w') as fp: with open(points_fname, 'w') as fp:
points = [ points = [
[p[0]*(w/img_w), p[1]*(h/img_h)] for p in points [p[0]*(w/img_w), p[1]*(h/img_h)] for p in points
] ]

33
03-homography.py
View file

@ -16,8 +16,10 @@ import cv2
import json import json
import numpy as np import numpy as np
dataset = 'hof2' from load_calibration import load_calibration
dataset_sample_img = "snapshot3.png"
dataset = 'hof3-cam-baumer'
dataset_sample_img = "snapshot.png"
with open(dataset + '/img_points.json', 'r') as fp: with open(dataset + '/img_points.json', 'r') as fp:
img_points = np.array(json.load(fp)) img_points = np.array(json.load(fp))
@ -30,7 +32,10 @@ with open(dataset + '/irl_points.json', 'r') as fp:
irl_points = np.array(irl_points) irl_points = np.array(irl_points)
# I measured IRL points in cm. Scale to meters # I measured IRL points in cm. Scale to meters
irl_points /= 100 # irl_points /= 100
# with open(dataset + '/irl_points-corrected.json', 'w') as fp:
# json.dump(irl_points.tolist(), fp, indent=4)
def points_on_img(in_img, points) -> cv2.Mat: def points_on_img(in_img, points) -> cv2.Mat:
img = in_img.copy() img = in_img.copy()
@ -44,30 +49,25 @@ def points_on_img(in_img, points) -> cv2.Mat:
return img return img
w, h, undistort_img, undistort_points = load_calibration(dataset + "/fisheye_calibration_data.json")
with open(dataset + '/calibration.json', 'r') as fp:
calibdata = json.load(fp)
mtx = np.array(calibdata['camera_matrix'])
dist = np.array(calibdata['dist_coeff'])
w, h = calibdata['dim']['width'], calibdata['dim']['height']
img = cv2.resize(cv2.imread(dataset + "/" + dataset_sample_img), (w, h)) img = cv2.resize(cv2.imread(dataset + "/" + dataset_sample_img), (w, h))
newcameramtx, roi = cv2.getOptimalNewCameraMatrix(mtx, dist, (w,h), 1, (w,h))
# first undistort the points so that lines are actually straight # first undistort the points so that lines are actually straight
undistorted_img_points = cv2.undistortPoints(np.array([img_points]).astype('float32'), mtx, dist, None, newcameramtx) undistorted_img_points = undistort_points(img_points)
undistorted_img = undistort_img(img)
undistorted_img = cv2.undistort(img, mtx, dist, None, newcameramtx) # undistorted_img_points = cv2.undistortPoints(np.array([img_points]).astype('float32'), mtx, dist, None, newcameramtx)
# undistorted_img = cv2.undistort(img, mtx, dist, None, newcameramtx)
cv2.imshow('original', points_on_img(img, img_points)) cv2.imshow('original', points_on_img(img, img_points))
cv2.imshow('undistorted', points_on_img(undistorted_img, undistorted_img_points)) cv2.imshow('undistorted', points_on_img(undistorted_img, undistorted_img_points[0]))
H, status = cv2.findHomography(undistorted_img_points,irl_points) H, status = cv2.findHomography(undistorted_img_points,irl_points)
# Homography converts to meters, this make the picture miniscule. # Homography converts to meters, this make the picture miniscule.
# Scale up for preview # Scale up for preview
view_H = H view_H = H.copy()
view_H[:2] = H[:2]*100 view_H[:2] = H[:2]*100
dst_img = cv2.warpPerspective(undistorted_img,view_H,(w,h)) dst_img = cv2.warpPerspective(undistorted_img,view_H,(w,h))
dst_img_points = cv2.perspectiveTransform(np.array(undistorted_img_points), view_H) dst_img_points = cv2.perspectiveTransform(np.array(undistorted_img_points), view_H)
@ -75,10 +75,9 @@ dst_img_points = cv2.perspectiveTransform(np.array(undistorted_img_points), view
# inv_H = np.linalg.pinv(H) # inv_H = np.linalg.pinv(H)
print(dst_img_points) print(dst_img_points)
dst = points_on_img(dst_img, dst_img_points) dst = points_on_img(dst_img, dst_img_points[0])
# print(dst.shape) # print(dst.shape)
cv2.imshow('sampl', dst) cv2.imshow('sampl', dst)
for a,b, c, d in zip(img_points, undistorted_img_points, irl_points, dst_img_points): for a,b, c, d in zip(img_points, undistorted_img_points, irl_points, dst_img_points):
print(f"{a} -> {b} -> {c} -> {d}") print(f"{a} -> {b} -> {c} -> {d}")

49
load_calibration.py Normal file
View file

@ -0,0 +1,49 @@
import json
import cv2
import numpy as np
from pathlib import Path
def load_calibration(calibration_json_filename: Path):
# with open(dataset + '/calibration-chessboard4.json', 'r') as fp:
# with open(dataset + '/calibration-chessboard4.json', 'r') as fp:
with open(calibration_json_filename, 'r') as fp:
calibdata = json.load(fp)
print(calibdata)
if 'type' in calibdata and calibdata['type'] == 'fisheye':
dim1 = calibdata['dim1']
dim2 = calibdata['dim2']
dim3 = calibdata['dim3']
K = np.array(calibdata['K'])
D = np.array(calibdata['D'])
new_K = np.array(calibdata['new_K'])
scaled_K = np.array(calibdata['scaled_K'])
balance = calibdata['balance']
# print(f"{K=}")
map1, map2 = cv2.fisheye.initUndistortRectifyMap(scaled_K, D, np.eye(3), new_K, dim3, cv2.CV_16SC2)
undistorted_img = lambda img: cv2.remap(img, map1, map2, interpolation=cv2.INTER_LINEAR, borderMode=cv2.BORDER_CONSTANT)
undistort_points = lambda distorted_img_points: cv2.fisheye.undistortPoints (np.array([distorted_img_points]).astype(np.float32), K=scaled_K, D=D, R=np.eye(3), P=new_K)
# undistorted_img = cv2.remap(img, map1, map2, interpolation=cv2.INTER_LINEAR, borderMode=cv2.BORDER_CONSTANT)
# new_points = cv2.fisheye.undistortPoints (np.array([distorted_img_points]).astype(np.float32), K=scaled_K, D=D, R=np.eye(3), P=new_K)
w, h = dim1[0], dim1[1]
else:
mtx = np.array(calibdata['camera_matrix'])
dist = np.array(calibdata['dist_coeff'])
w, h = calibdata['dim']['width'], calibdata['dim']['height']
# # Refining the camera matrix using parameters obtained by calibration
# if we don't set this, the new image will be cropped to the minimum size
# this way, no cropping occurs
# w, h = 2560, 1440
newcameramtx, roi = cv2.getOptimalNewCameraMatrix(mtx, dist, (w,h), 1, (w,h))
undistorted_img = lambda img: cv2.undistort(img, mtx, dist, None, newcameramtx)
undistort_points = lambda distorted_img_points: cv2.undistortPoints(np.array(distorted_img_points).astype('float32'), mtx, dist, None, P=newcameramtx)
return w, h, undistorted_img, undistort_points

118
poetry.lock generated
View file

@ -1,118 +0,0 @@
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name = "colorama"
version = "0.4.6"
description = "Cross-platform colored terminal text."
optional = false
python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*,>=2.7"
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]
[[package]]
name = "numpy"
version = "2.1.2"
description = "Fundamental package for array computing in Python"
optional = false
python-versions = ">=3.10"
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