jens/ocv_cam_pose
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refactored

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jens
2025-11-24 12:30:41 +01:00
parent 8cfb6a44b7
commit 4ea570e2ab
2 changed files with 31 additions and 23 deletions
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cam_calib.py
+11 -7
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@@ -2,13 +2,17 @@ import numpy as np
import cv2 as cv import cv2 as cv
import glob import glob
PAT_NX = 7
PAT_NY = 6
def main(): def main():
# termination criteria # termination criteria
criteria = (cv.TERM_CRITERIA_EPS + cv.TERM_CRITERIA_MAX_ITER, 30, 0.001) criteria = (cv.TERM_CRITERIA_EPS + cv.TERM_CRITERIA_MAX_ITER, 30, 0.01)
# 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)
objp = np.zeros((6 * 7, 3), np.float32) objp = np.zeros((PAT_NX * PAT_NY, 3), np.float32)
objp[:, :2] = np.mgrid[0:7, 0:6].T.reshape(-1, 2) objp[:, :2] = np.mgrid[0:PAT_NX, 0:PAT_NY].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
@@ -21,7 +25,7 @@ def main():
gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY) gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
# Find the chess board corners # Find the chess board corners
ret, corners = cv.findChessboardCorners(gray, (7, 6), None) ret, corners = cv.findChessboardCorners(gray, (PAT_NX, PAT_NY), None)
# If found, add object points, image points (after refining them) # If found, add object points, image points (after refining them)
if ret: if ret:
@@ -31,9 +35,9 @@ def main():
imgpoints.append(corners2) imgpoints.append(corners2)
# Draw and display the corners # Draw and display the corners
cv.drawChessboardCorners(img, (7, 6), corners2, ret) cv.drawChessboardCorners(img, (PAT_NX, PAT_NY), corners2, ret)
cv.imshow('img', img) cv.imshow('img', img)
cv.waitKey(500) cv.waitKey(-1)
# Calibration # Calibration
ret, mtx, dist, rvecs, tvecs = cv.calibrateCamera(objpoints, imgpoints, gray.shape[::-1], None, None) ret, mtx, dist, rvecs, tvecs = cv.calibrateCamera(objpoints, imgpoints, gray.shape[::-1], None, None)
@@ -46,7 +50,7 @@ def main():
h, w = img.shape[:2] h, w = img.shape[:2]
newcameramtx, roi = cv.getOptimalNewCameraMatrix(mtx, dist, (w, h), 1, (w, h)) newcameramtx, roi = cv.getOptimalNewCameraMatrix(mtx, dist, (w, h), 1, (w, h))
# 1. Using cv.undistort() # 1. Undistort using cv.undistort()
dst = cv.undistort(img, mtx, dist, None, newcameramtx) dst = cv.undistort(img, mtx, dist, None, newcameramtx)
# crop the image # crop the image
cam_pose.py
+20 -16
View File
@@ -2,6 +2,9 @@ import numpy as np
import cv2 as cv import cv2 as cv
import glob import glob
PAT_NX = 7
PAT_NY = 6
def draw_gizmo(img, corners, imgpts): def draw_gizmo(img, corners, imgpts):
corner = tuple(corners[0].ravel().astype("int32")) corner = tuple(corners[0].ravel().astype("int32"))
imgpts = imgpts.astype("int32") imgpts = imgpts.astype("int32")
@@ -26,6 +29,14 @@ def draw_cube(img, corners, imgpts):
return img return img
def init_data():
criteria = (cv.TERM_CRITERIA_EPS + cv.TERM_CRITERIA_MAX_ITER, 30, 0.001)
objp = np.zeros((PAT_NX * PAT_NY, 3), np.float32)
objp[:, :2] = np.mgrid[0:PAT_NX, 0:PAT_NY].T.reshape(-1, 2)
axis = np.float32([[3, 0, 0], [0, 3, 0], [0, 0, -3]]).reshape(-1, 3)
return criteria, objp, axis
def main(): def main():
process_video() process_video()
@@ -34,11 +45,8 @@ def process_video():
with np.load('results/cam.npz') as X: with np.load('results/cam.npz') as X:
mtx, dist, _, _ = [X[i] for i in ('mtx', 'dist', 'rvecs', 'tvecs')] mtx, dist, _, _ = [X[i] for i in ('mtx', 'dist', 'rvecs', 'tvecs')]
criteria = (cv.TERM_CRITERIA_EPS + cv.TERM_CRITERIA_MAX_ITER, 30, 0.001) # Init
objp = np.zeros((6 * 7, 3), np.float32) criteria, objp, axis = init_data()
objp[:, :2] = np.mgrid[0:7, 0:6].T.reshape(-1, 2)
axis = np.float32([[3, 0, 0], [0, 3, 0], [0, 0, -3]]).reshape(-1, 3)
# Open camera # Open camera
video = cv.VideoCapture(0) video = cv.VideoCapture(0)
@@ -51,7 +59,7 @@ def process_video():
ok, img = video.read() ok, img = video.read()
if not ok: if not ok:
return return
process_img(img, criteria, objp, axis, mtx, dist) process(img, mtx, dist, criteria, objp, axis)
k = cv.waitKey(1) & 0xFF k = cv.waitKey(1) & 0xFF
if k == ord('q'): if k == ord('q'):
break break
@@ -60,25 +68,21 @@ def process_still():
with np.load('results/cam.npz') as X: with np.load('results/cam.npz') as X:
mtx, dist, _, _ = [X[i] for i in ('mtx', 'dist', 'rvecs', 'tvecs')] mtx, dist, _, _ = [X[i] for i in ('mtx', 'dist', 'rvecs', 'tvecs')]
criteria = (cv.TERM_CRITERIA_EPS + cv.TERM_CRITERIA_MAX_ITER, 30, 0.001) # Init
objp = np.zeros((6 * 7, 3), np.float32) criteria, objp, axis = init_data()
objp[:, :2] = np.mgrid[0:7, 0:6].T.reshape(-1, 2)
axis = np.float32([[3, 0, 0], [0, 3, 0], [0, 0, -3]]).reshape(-1, 3)
for fname in glob.glob('check*.jpg'): for fname in glob.glob('check*.jpg'):
img = cv.imread(fname) img = cv.imread(fname)
process_img(img, criteria, objp, axis, mtx, dist) process(img, mtx, dist, criteria, objp, axis)
k = cv.waitKey(1) & 0xFF k = cv.waitKey(1) & 0xFF
if k == ord('q'): if k == ord('q'):
break break
def process_img(img, criteria, objp, axis, mtx, dist): # Load previously saved data def process(img, mtx, dist, criteria, objp, axis): # Load previously saved data
pattern_size = (7, 6)
gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY) gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
ret, corners = cv.findChessboardCorners(gray, pattern_size, corners=None, flags=cv.CALIB_CB_ADAPTIVE_THRESH+cv.CALIB_CB_NORMALIZE_IMAGE+cv.CALIB_CB_FAST_CHECK) ret, corners = cv.findChessboardCorners(gray, (PAT_NX, PAT_NY), corners=None, flags=cv.CALIB_CB_ADAPTIVE_THRESH+cv.CALIB_CB_NORMALIZE_IMAGE+cv.CALIB_CB_FAST_CHECK)
cv.drawChessboardCorners(img, corners=corners, patternSize=pattern_size, patternWasFound=ret) cv.drawChessboardCorners(img, corners=corners, patternSize=(PAT_NX, PAT_NY), patternWasFound=ret)
if ret: if ret:
corners2 = cv.cornerSubPix(gray, corners, (11, 11), (-1, -1), criteria) corners2 = cv.cornerSubPix(gray, corners, (11, 11), (-1, -1), criteria)