potok/opencv
1
0
Fork 0
mirror of https://github.com/opencv/opencv.git synced 2026-07-29 07:13:02 +04:00
Code Packages Releases Wiki Activity

Merge pull request #26221 from asmorkalov:as/refactor_multiview_interface

Browse Source 
Reworked multiview calibration interface #26221

- Use InputArray / OutputArray
- Use enum for camera type
- Sort parameters according guidelines
- Made more outputs optional
- Introduce flags and added tests for intrinsics and extrinsics guess.

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [ ] The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake
This commit is contained in:
Alexander Smorkalov
2024-10-01 16:53:16 +03:00
committed by GitHub
parent 6c743f8e4b
commit 3bcab8db0a
8 changed files with 362 additions and 214 deletions
Download Patch File Download Diff File Expand all files Collapse all files
samples/python/multiview_calibration.py
+29 -25
View File
@@ -275,11 +275,11 @@ def showUndistorted(image_points, Ks, distortions, image_names):
def plotProjection(points_2d, pattern_points, rvec0, tvec0, rvec1, tvec1,
K, dist_coeff, is_fisheye, cam_idx, frame_idx, per_acc,
K, dist_coeff, model, cam_idx, frame_idx, per_acc,
image=None):
rvec2, tvec2 = cv.composeRT(rvec0, tvec0, rvec1, tvec1)[:2]
if is_fisheye:
if model == cv.CALIB_MODEL_FISHEYE:
points_2d_est = cv.fisheye.projectPoints(
pattern_points[:, None], rvec2, tvec2, K, dist_coeff.flatten()
)[0].reshape(-1, 2)
@@ -361,7 +361,7 @@ def calibrateFromPoints(
pattern_points,
image_points,
image_sizes,
is_fisheye,
models,
image_names=None,
find_intrinsics_in_python=False,
Ks=None,
@@ -370,7 +370,7 @@ def calibrateFromPoints(
"""
pattern_points: NUM_POINTS x 3 (numpy array)
image_points: NUM_CAMERAS x NUM_FRAMES x NUM_POINTS x 2
is_fisheye: NUM_CAMERAS (bool)
models: NUM_CAMERAS (cv.CALIB_MODEL_PINHOLE | cv.CALIB_MODEL_FISHEYE)
image_sizes: NUM_CAMERAS x [width, height]
"""
num_cameras = len(image_points)
@@ -380,7 +380,7 @@ def calibrateFromPoints(
with np.printoptions(threshold=np.inf): # type: ignore
print("detection mask Matrix:\n", str(detection_mask).replace('0\n ', '0').replace('1\n ', '1'))
pinhole_flag = cv.CALIB_ZERO_TANGENT_DIST
pinhole_flag = cv.CALIB_RATIONAL_MODEL
fisheye_flag = cv.CALIB_RECOMPUTE_EXTRINSIC+cv.CALIB_FIX_SKEW
if Ks is not None and distortions is not None:
USE_INTRINSICS_GUESS = True
@@ -389,7 +389,7 @@ def calibrateFromPoints(
if find_intrinsics_in_python:
Ks, distortions = [], []
for c in range(num_cameras):
if is_fisheye[c]:
if models[c] == cv.CALIB_MODEL_FISHEYE:
image_points_c = [
image_points[c][f][:, None] for f in range(num_frames) if len(image_points[c][f]) > 0
]
@@ -428,13 +428,13 @@ def calibrateFromPoints(
imagePoints=image_points,
imageSize=image_sizes,
detectionMask=detection_mask,
models=np.array(models, dtype=np.uint8),
Rs=None,
Ts=None,
Ks=Ks,
distortions=distortions,
isFisheye=np.array(is_fisheye, dtype=np.uint8),
useIntrinsicsGuess=USE_INTRINSICS_GUESS,
flagsForIntrinsics=np.array([pinhole_flag if not is_fisheye[x] else fisheye_flag for x in range(num_cameras)], dtype=int),
flagsForIntrinsics=np.array([pinhole_flag if models[x] == cv.CALIB_MODEL_PINHOLE else fisheye_flag for x in range(num_cameras)], dtype=int),
flags = cv.CALIB_USE_INTRINSIC_GUESS if USE_INTRINSICS_GUESS else 0
)
# [multiview_calib]
# except Exception as e:
@@ -461,7 +461,7 @@ def calibrateFromPoints(
'errors_per_frame': errors_per_frame,
'output_pairs': output_pairs,
'image_points': image_points,
'is_fisheye': is_fisheye,
'models': models,
'image_sizes': image_sizes,
'pattern_points': pattern_points,
'detection_mask': detection_mask,
@@ -469,7 +469,7 @@ def calibrateFromPoints(
}
def visualizeResults(detection_mask, Rs, Ts, Ks, distortions, is_fisheye,
def visualizeResults(detection_mask, Rs, Ts, Ks, distortions, models,
image_points, errors_per_frame, rvecs0, tvecs0,
pattern_points, image_sizes, output_pairs, image_names, cam_ids):
rvecs = [cv.Rodrigues(R)[0] for R in Rs]
@@ -509,7 +509,7 @@ def visualizeResults(detection_mask, Rs, Ts, Ks, distortions, is_fisheye,
Ts[cam_idx],
Ks[cam_idx],
distortions[cam_idx],
is_fisheye[cam_idx],
models[cam_idx],
cam_idx,
frame_idx,
(errors_per_frame[cam_idx, frame_idx] < errors).sum() * 100 / len(errors),
@@ -527,7 +527,7 @@ def visualizeFromFile(file):
assert file_read.isOpened(), file
read_keys = [
'Rs', 'distortions', 'Ks', 'Ts', 'rvecs0', 'tvecs0',
'errors_per_frame', 'output_pairs', 'image_points', 'is_fisheye',
'errors_per_frame', 'output_pairs', 'image_points', 'models',
'image_sizes', 'pattern_points', 'detection_mask', 'cam_ids',
]
input = {}
@@ -549,7 +549,7 @@ def saveToFile(path_to_save, **kwargs):
path_to_save = datetime.now().strftime("%d-%b-%Y (%H:%M:%S.%f)")+'.yaml'
save_file = cv.FileStorage(path_to_save, cv.FileStorage_WRITE)
kwargs['is_fisheye'] = np.array(kwargs['is_fisheye'], dtype=int)
kwargs['models'] = np.array(kwargs['models'], dtype=int)
image_points = kwargs['image_points']
for i in range(len(image_points)):
@@ -574,7 +574,7 @@ def saveToFile(path_to_save, **kwargs):
save_file.release()
def compareGT(gt_file, detection_mask, Rs, Ts, Ks, distortions, is_fisheye,
def compareGT(gt_file, detection_mask, Rs, Ts, Ks, distortions, models,
image_points, errors_per_frame, rvecs0, tvecs0,
pattern_points, image_sizes, output_pairs, image_names, cam_ids):
@@ -610,7 +610,7 @@ def compareGT(gt_file, detection_mask, Rs, Ts, Ks, distortions, is_fisheye,
points = np.concatenate([X[:,:,None], Y[:,:,None]], axis=2).reshape([-1, 1, 2])
# Undistort the image points with the estimated distortions
if is_fisheye[cam]:
if models[cam] == cv.CALIB_MODEL_FISHEYE:
points_undist = cv.fisheye.undistortPoints(points, Ks[cam],distortions[cam])
else:
points_undist = cv.undistortPoints(points, Ks[cam], distortions[cam])
@@ -618,7 +618,7 @@ def compareGT(gt_file, detection_mask, Rs, Ts, Ks, distortions, is_fisheye,
pt_norm = np.concatenate([points_undist, np.ones([points_undist.shape[0], 1, 1])], axis=2)
# Distort the image points with the ground truth distortions
if is_fisheye[cam]:
if models[cam] == cv.CALIB_MODEL_FISHEYE:
projected = cv.fisheye.projectPoints(pt_norm, np.zeros([3, 1]), np.zeros([3, 1]), Ks_gt[cam], distortions_gt[cam])[0]
else:
projected = cv.projectPoints(pt_norm, np.zeros([3, 1]), np.zeros([3, 1]), Ks_gt[cam], distortions_gt[cam])[0]
@@ -772,7 +772,7 @@ def detect(cam_idx, frame_idx, img_name, pattern_type,
return cam_idx, frame_idx, img_size, np.array([], dtype=np.float32)
def calibrateFromImages(files_with_images, grid_size, pattern_type, is_fisheye,
def calibrateFromImages(files_with_images, grid_size, pattern_type, models,
dist_m, winsize, points_json_file, debug_corners,
RESIZE_IMAGE, find_intrinsics_in_python,
is_parallel_detection=True, cam_ids=None, intrinsics_dir='', board_dict_path=None):
@@ -780,7 +780,7 @@ def calibrateFromImages(files_with_images, grid_size, pattern_type, is_fisheye,
files_with_images: NUM_CAMERAS - path to file containing image names (NUM_FRAMES)
grid_size: [width, height] -- size of grid pattern
dist_m: length of a grid cell
is_fisheye: NUM_CAMERAS (bool)
models: NUM_CAMERAS (cv.CALIB_MODEL_PINHOLE | cv.CALIB_MODEL_FISHEYE)
"""
# [calib_init]
if pattern_type.lower() == 'checkerboard' or pattern_type.lower() == 'charuco':
@@ -795,10 +795,12 @@ def calibrateFromImages(files_with_images, grid_size, pattern_type, is_fisheye,
if pattern_type.lower() == 'charuco':
assert (board_dict_path is not None) and os.path.exists(board_dict_path)
board_dict = json.load(open(board_dict_path, 'r'))
else:
board_dict = None
# [calib_init]
assert len(files_with_images) == len(is_fisheye) and len(grid_size) == 2
assert len(files_with_images) == len(models) and len(grid_size) == 2
if cam_ids is None:
cam_ids = list(range(len(files_with_images)))
@@ -887,7 +889,7 @@ def calibrateFromImages(files_with_images, grid_size, pattern_type, is_fisheye,
'object_points': pattern.tolist(),
'image_points': image_points_cameras_list,
'image_sizes': image_sizes,
'is_fisheye': is_fisheye,
'model': models,
}, wf)
Ks = None
@@ -909,7 +911,7 @@ def calibrateFromImages(files_with_images, grid_size, pattern_type, is_fisheye,
pattern,
image_points_cameras,
image_sizes,
is_fisheye,
models,
all_images_names,
find_intrinsics_in_python,
Ks=Ks,
@@ -933,7 +935,7 @@ def calibrateFromJSON(json_file, find_intrinsics_in_python):
np.array(data['object_points'], dtype=np.float32).T,
data['image_points'],
data['image_sizes'],
data['is_fisheye'],
data['models'],
images_names,
find_intrinsics_in_python,
Ks,
@@ -992,7 +994,7 @@ if __name__ == '__main__':
files_with_images=[x.strip() for x in params.filenames.split(',')],
grid_size=[int(v) for v in params.pattern_size.split(',')],
pattern_type=params.pattern_type,
is_fisheye=[bool(int(v)) for v in params.is_fisheye.split(',')],
models=[int(v) for v in params.is_fisheye.split(',')],
dist_m=params.pattern_distance,
winsize=tuple([int(v) for v in params.winsize.split(',')]),
points_json_file=params.points_json_file,
@@ -1009,7 +1011,9 @@ if __name__ == '__main__':
if params.gt_file is not None:
assert os.path.exists(params.gt_file), f'Path to gt file does not exist: {params.gt_file}'
compareGT(params.gt_file, **output)
visualizeResults(**output)
if params.visualize:
visualizeResults(**output)
print('Saving:', params.path_to_save)
saveToFile(params.path_to_save, **output)