cameracv/libs/opencv/apps/opencv_stitching_tool/opencv_stitching/stitcher.py
2023-05-18 21:39:43 +03:00

236 lines
10 KiB
Python

from types import SimpleNamespace
from .image_handler import ImageHandler
from .feature_detector import FeatureDetector
from .feature_matcher import FeatureMatcher
from .subsetter import Subsetter
from .camera_estimator import CameraEstimator
from .camera_adjuster import CameraAdjuster
from .camera_wave_corrector import WaveCorrector
from .warper import Warper
from .cropper import Cropper
from .exposure_error_compensator import ExposureErrorCompensator
from .seam_finder import SeamFinder
from .blender import Blender
from .timelapser import Timelapser
from .stitching_error import StitchingError
class Stitcher:
DEFAULT_SETTINGS = {
"medium_megapix": ImageHandler.DEFAULT_MEDIUM_MEGAPIX,
"detector": FeatureDetector.DEFAULT_DETECTOR,
"nfeatures": 500,
"matcher_type": FeatureMatcher.DEFAULT_MATCHER,
"range_width": FeatureMatcher.DEFAULT_RANGE_WIDTH,
"try_use_gpu": False,
"match_conf": None,
"confidence_threshold": Subsetter.DEFAULT_CONFIDENCE_THRESHOLD,
"matches_graph_dot_file": Subsetter.DEFAULT_MATCHES_GRAPH_DOT_FILE,
"estimator": CameraEstimator.DEFAULT_CAMERA_ESTIMATOR,
"adjuster": CameraAdjuster.DEFAULT_CAMERA_ADJUSTER,
"refinement_mask": CameraAdjuster.DEFAULT_REFINEMENT_MASK,
"wave_correct_kind": WaveCorrector.DEFAULT_WAVE_CORRECTION,
"warper_type": Warper.DEFAULT_WARP_TYPE,
"low_megapix": ImageHandler.DEFAULT_LOW_MEGAPIX,
"crop": Cropper.DEFAULT_CROP,
"compensator": ExposureErrorCompensator.DEFAULT_COMPENSATOR,
"nr_feeds": ExposureErrorCompensator.DEFAULT_NR_FEEDS,
"block_size": ExposureErrorCompensator.DEFAULT_BLOCK_SIZE,
"finder": SeamFinder.DEFAULT_SEAM_FINDER,
"final_megapix": ImageHandler.DEFAULT_FINAL_MEGAPIX,
"blender_type": Blender.DEFAULT_BLENDER,
"blend_strength": Blender.DEFAULT_BLEND_STRENGTH,
"timelapse": Timelapser.DEFAULT_TIMELAPSE}
def __init__(self, **kwargs):
self.initialize_stitcher(**kwargs)
def initialize_stitcher(self, **kwargs):
self.settings = Stitcher.DEFAULT_SETTINGS.copy()
self.validate_kwargs(kwargs)
self.settings.update(kwargs)
args = SimpleNamespace(**self.settings)
self.img_handler = ImageHandler(args.medium_megapix,
args.low_megapix,
args.final_megapix)
self.detector = \
FeatureDetector(args.detector, nfeatures=args.nfeatures)
match_conf = \
FeatureMatcher.get_match_conf(args.match_conf, args.detector)
self.matcher = FeatureMatcher(args.matcher_type, args.range_width,
try_use_gpu=args.try_use_gpu,
match_conf=match_conf)
self.subsetter = \
Subsetter(args.confidence_threshold, args.matches_graph_dot_file)
self.camera_estimator = CameraEstimator(args.estimator)
self.camera_adjuster = \
CameraAdjuster(args.adjuster, args.refinement_mask)
self.wave_corrector = WaveCorrector(args.wave_correct_kind)
self.warper = Warper(args.warper_type)
self.cropper = Cropper(args.crop)
self.compensator = \
ExposureErrorCompensator(args.compensator, args.nr_feeds,
args.block_size)
self.seam_finder = SeamFinder(args.finder)
self.blender = Blender(args.blender_type, args.blend_strength)
self.timelapser = Timelapser(args.timelapse)
def stitch(self, img_names):
self.initialize_registration(img_names)
imgs = self.resize_medium_resolution()
features = self.find_features(imgs)
matches = self.match_features(features)
imgs, features, matches = self.subset(imgs, features, matches)
cameras = self.estimate_camera_parameters(features, matches)
cameras = self.refine_camera_parameters(features, matches, cameras)
cameras = self.perform_wave_correction(cameras)
self.estimate_scale(cameras)
imgs = self.resize_low_resolution(imgs)
imgs, masks, corners, sizes = self.warp_low_resolution(imgs, cameras)
self.prepare_cropper(imgs, masks, corners, sizes)
imgs, masks, corners, sizes = \
self.crop_low_resolution(imgs, masks, corners, sizes)
self.estimate_exposure_errors(corners, imgs, masks)
seam_masks = self.find_seam_masks(imgs, corners, masks)
imgs = self.resize_final_resolution()
imgs, masks, corners, sizes = self.warp_final_resolution(imgs, cameras)
imgs, masks, corners, sizes = \
self.crop_final_resolution(imgs, masks, corners, sizes)
self.set_masks(masks)
imgs = self.compensate_exposure_errors(corners, imgs)
seam_masks = self.resize_seam_masks(seam_masks)
self.initialize_composition(corners, sizes)
self.blend_images(imgs, seam_masks, corners)
return self.create_final_panorama()
def initialize_registration(self, img_names):
self.img_handler.set_img_names(img_names)
def resize_medium_resolution(self):
return list(self.img_handler.resize_to_medium_resolution())
def find_features(self, imgs):
return [self.detector.detect_features(img) for img in imgs]
def match_features(self, features):
return self.matcher.match_features(features)
def subset(self, imgs, features, matches):
names, sizes, imgs, features, matches = \
self.subsetter.subset(self.img_handler.img_names,
self.img_handler.img_sizes,
imgs, features, matches)
self.img_handler.img_names, self.img_handler.img_sizes = names, sizes
return imgs, features, matches
def estimate_camera_parameters(self, features, matches):
return self.camera_estimator.estimate(features, matches)
def refine_camera_parameters(self, features, matches, cameras):
return self.camera_adjuster.adjust(features, matches, cameras)
def perform_wave_correction(self, cameras):
return self.wave_corrector.correct(cameras)
def estimate_scale(self, cameras):
self.warper.set_scale(cameras)
def resize_low_resolution(self, imgs=None):
return list(self.img_handler.resize_to_low_resolution(imgs))
def warp_low_resolution(self, imgs, cameras):
sizes = self.img_handler.get_low_img_sizes()
camera_aspect = self.img_handler.get_medium_to_low_ratio()
imgs, masks, corners, sizes = \
self.warp(imgs, cameras, sizes, camera_aspect)
return list(imgs), list(masks), corners, sizes
def warp_final_resolution(self, imgs, cameras):
sizes = self.img_handler.get_final_img_sizes()
camera_aspect = self.img_handler.get_medium_to_final_ratio()
return self.warp(imgs, cameras, sizes, camera_aspect)
def warp(self, imgs, cameras, sizes, aspect=1):
imgs = self.warper.warp_images(imgs, cameras, aspect)
masks = self.warper.create_and_warp_masks(sizes, cameras, aspect)
corners, sizes = self.warper.warp_rois(sizes, cameras, aspect)
return imgs, masks, corners, sizes
def prepare_cropper(self, imgs, masks, corners, sizes):
self.cropper.prepare(imgs, masks, corners, sizes)
def crop_low_resolution(self, imgs, masks, corners, sizes):
imgs, masks, corners, sizes = self.crop(imgs, masks, corners, sizes)
return list(imgs), list(masks), corners, sizes
def crop_final_resolution(self, imgs, masks, corners, sizes):
lir_aspect = self.img_handler.get_low_to_final_ratio()
return self.crop(imgs, masks, corners, sizes, lir_aspect)
def crop(self, imgs, masks, corners, sizes, aspect=1):
masks = self.cropper.crop_images(masks, aspect)
imgs = self.cropper.crop_images(imgs, aspect)
corners, sizes = self.cropper.crop_rois(corners, sizes, aspect)
return imgs, masks, corners, sizes
def estimate_exposure_errors(self, corners, imgs, masks):
self.compensator.feed(corners, imgs, masks)
def find_seam_masks(self, imgs, corners, masks):
return self.seam_finder.find(imgs, corners, masks)
def resize_final_resolution(self):
return self.img_handler.resize_to_final_resolution()
def compensate_exposure_errors(self, corners, imgs):
for idx, (corner, img) in enumerate(zip(corners, imgs)):
yield self.compensator.apply(idx, corner, img, self.get_mask(idx))
def resize_seam_masks(self, seam_masks):
for idx, seam_mask in enumerate(seam_masks):
yield SeamFinder.resize(seam_mask, self.get_mask(idx))
def set_masks(self, mask_generator):
self.masks = mask_generator
self.mask_index = -1
def get_mask(self, idx):
if idx == self.mask_index + 1:
self.mask_index += 1
self.mask = next(self.masks)
return self.mask
elif idx == self.mask_index:
return self.mask
else:
raise StitchingError("Invalid Mask Index!")
def initialize_composition(self, corners, sizes):
if self.timelapser.do_timelapse:
self.timelapser.initialize(corners, sizes)
else:
self.blender.prepare(corners, sizes)
def blend_images(self, imgs, masks, corners):
for idx, (img, mask, corner) in enumerate(zip(imgs, masks, corners)):
if self.timelapser.do_timelapse:
self.timelapser.process_and_save_frame(
self.img_handler.img_names[idx], img, corner
)
else:
self.blender.feed(img, mask, corner)
def create_final_panorama(self):
if not self.timelapser.do_timelapse:
panorama, _ = self.blender.blend()
return panorama
@staticmethod
def validate_kwargs(kwargs):
for arg in kwargs:
if arg not in Stitcher.DEFAULT_SETTINGS:
raise StitchingError("Invalid Argument: " + arg)