kopia lustrzana https://github.com/OpenDroneMap/ODM
Band alignment PoC working
Piero Toffanin
rodzic
509035d5e9
commit
e819dab16b
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@ -157,7 +157,7 @@ def config(argv=None, parser=None):
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'Can be one of: %(choices)s. Default: '
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'%(default)s'))
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parser.add_argument('--feature-matcher',
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parser.add_argument('--matcher-type',
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metavar='<string>',
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action=StoreValue,
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default='flann',
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@ -50,7 +50,7 @@ class GeoFile:
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horizontal_accuracy, vertical_accuracy,
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extras)
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else:
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logger.warning("Malformed geo line: %s" % line)
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log.ODM_WARNING("Malformed geo line: %s" % line)
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def get_entry(self, filename):
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return self.entries.get(filename)
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@ -1,7 +1,10 @@
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from opendm import dls
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import math
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import re
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import cv2
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from opendm import dls
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import numpy as np
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from opendm import log
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from opensfm.io import imread
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# Loosely based on https://github.com/micasense/imageprocessing/blob/master/micasense/utils.py
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@ -152,17 +155,174 @@ def compute_irradiance(photo, use_sun_sensor=True):
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return 1.0
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def get_photos_by_band(multi_camera, band_name):
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def get_photos_by_band(multi_camera, user_band_name):
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band_name = get_primary_band_name(multi_camera, user_band_name)
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for band in multi_camera:
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if band['name'] == band_name:
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return band['photos']
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def get_primary_band_name(multi_camera, user_band_name):
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if len(multi_camera) < 1:
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raise Exception("Invalid multi_camera list")
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# multi_camera is already sorted by band_index
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if band_name == "auto":
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return multi_camera[0]['photos']
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if user_band_name == "auto":
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return multi_camera[0]['name']
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for band in multi_camera:
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if band['name'].lower() == band_name.lower():
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return band['photos']
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if band['name'].lower() == user_band_name.lower():
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return band['name']
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band_name_fallback = multi_camera[0]['name']
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logger.ODM_WARNING("Cannot find band name \"%s\", will use \"auto\" instead" % band_name)
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return multi_camera[0]['photos']
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log.ODM_WARNING("Cannot find band name \"%s\", will use \"%s\" instead" % (user_band_name, band_name_fallback))
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return band_name_fallback
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def compute_primary_band_map(multi_camera, primary_band):
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"""
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Computes a map of { photo filename --> associated primary band photo }
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by looking at capture time or filenames as a fallback
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"""
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band_name = get_primary_band_name(multi_camera, primary_band)
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primary_band_photos = None
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for band in multi_camera:
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if band['name'] == band_name:
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primary_band_photos = band['photos']
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break
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# Try using capture time as the grouping factor
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try:
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capture_time_map = {}
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result = {}
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for p in primary_band_photos:
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t = p.get_utc_time()
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if t is None:
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raise Exception("Cannot use capture time (no information in %s)" % p.filename)
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# Should be unique across primary band
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if capture_time_map.get(t) is not None:
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raise Exception("Unreliable capture time detected (duplicate)")
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capture_time_map[t] = p
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for band in multi_camera:
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photos = band['photos']
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for p in photos:
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t = p.get_utc_time()
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if t is None:
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raise Exception("Cannot use capture time (no information in %s)" % p.filename)
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# Should match the primary band
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if capture_time_map.get(t) is None:
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raise Exception("Unreliable capture time detected (no primary band match)")
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result[p.filename] = capture_time_map[t]
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return result
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except Exception as e:
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# Fallback on filename conventions
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log.ODM_WARNING("%s, will use filenames instead" % str(e))
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filename_map = {}
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result = {}
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file_regex = re.compile(r"^(.+)[-_]\w+(\.[A-Za-z]{3,4})$")
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for p in primary_band_photos:
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filename_without_band = re.sub(file_regex, "\\1\\2", p.filename)
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# Quick check
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if filename_without_band == p.filename:
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raise Exception("Cannot match bands by filename on %s, make sure to name your files [filename]_band[.ext] uniformly." % p.filename)
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filename_map[filename_without_band] = p
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for band in multi_camera:
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photos = band['photos']
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for p in photos:
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filename_without_band = re.sub(file_regex, "\\1\\2", p.filename)
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# Quick check
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if filename_without_band == p.filename:
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raise Exception("Cannot match bands by filename on %s, make sure to name your files [filename]_band[.ext] uniformly." % p.filename)
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result[p.filename] = filename_map[filename_without_band]
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return result
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def compute_aligned_image(image, align_image_filename, feature_retention=0.15):
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if len(image.shape) != 3:
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raise ValueError("Image should have shape length of 3 (got: %s)" % len(image.shape))
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# Convert images to grayscale if needed
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if image.shape[2] == 3:
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image_gray = to_8bit(cv2.cvtColor(image, cv2.COLOR_BGR2GRAY))
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else:
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image_gray = to_8bit(image[:,:,0])
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align_image = imread(align_image_filename, unchanged=True, anydepth=True)
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if align_image.shape[2] == 3:
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align_image_gray = to_8bit(cv2.cvtColor(align_image, cv2.COLOR_BGR2GRAY))
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else:
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align_image_gray = to_8bit(align_image[:,:,0])
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# Detect SIFT features and compute descriptors.
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detector = cv2.SIFT_create(edgeThreshold=10, contrastThreshold=0.1)
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kp_image, desc_image = detector.detectAndCompute(image_gray, None)
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kp_align_image, desc_align_image = detector.detectAndCompute(align_image_gray, None)
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# Match
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bf = cv2.BFMatcher(cv2.NORM_L1,crossCheck=True)
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matches = bf.match(desc_image, desc_align_image)
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# Sort by score
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matches.sort(key=lambda x: x.distance, reverse=False)
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# Remove bad matches
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num_good_matches = int(len(matches) * feature_retention)
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matches = matches[:num_good_matches]
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# Debug
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# imMatches = cv2.drawMatches(im1, kp_image, im2, kp_align_image, matches, None)
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# cv2.imwrite("matches.jpg", imMatches)
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# Extract location of good matches
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points_image = np.zeros((len(matches), 2), dtype=np.float32)
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points_align_image = np.zeros((len(matches), 2), dtype=np.float32)
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for i, match in enumerate(matches):
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points_image[i, :] = kp_image[match.queryIdx].pt
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points_align_image[i, :] = kp_align_image[match.trainIdx].pt
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# Find homography
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h, _ = cv2.findHomography(points_image, points_align_image, cv2.RANSAC)
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# Use homography
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height, width = align_image_gray.shape
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aligned_image = cv2.warpPerspective(image, h, (width, height))
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return aligned_image
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def to_8bit(image):
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if image.dtype == np.uint8:
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return image
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# Convert to 8bit
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try:
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data_range = np.iinfo(image.dtype)
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value_range = float(data_range.max) - float(data_range.min)
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except ValueError:
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# For floats use the actual range of the image values
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value_range = float(image.max()) - float(image.min())
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image = image.astype(np.float32)
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image *= 255.0 / value_range
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np.around(image, out=image)
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image = image.astype(np.uint8)
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return image
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@ -56,7 +56,7 @@ class OSFMContext:
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exit(1)
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def setup(self, args, images_path, photos, reconstruction, append_config = [], rerun=False):
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def setup(self, args, images_path, reconstruction, append_config = [], rerun=False):
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"""
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Setup a OpenSfM project
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"""
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@ -68,12 +68,12 @@ class OSFMContext:
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list_path = os.path.join(self.opensfm_project_path, 'image_list.txt')
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if not io.file_exists(list_path) or rerun:
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if reconstruction.multi_camera:
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photos = get_photos_by_band(reconstruction.multi_camera, args.primary_band)
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if len(photos) < 1:
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raise Exception("Not enough images in selected band %s" % args.primary_band.lower())
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logger.ODM_INFO("Reconstruction will use %s images from %s band" % (len(photos), args.primary_band.lower()))
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log.ODM_INFO("Reconstruction will use %s images from %s band" % (len(photos), args.primary_band.lower()))
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else:
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photos = reconstruction.photos
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@ -105,7 +105,7 @@ class OSFMContext:
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except Exception as e:
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log.ODM_WARNING("Cannot set camera_models_overrides.json: %s" % str(e))
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use_bow = args.feature_matcher == "bow"
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use_bow = args.matcher_type == "bow"
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feature_type = "SIFT"
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# GPSDOP override if we have GPS accuracy information (such as RTK)
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@ -181,7 +181,7 @@ class OSFMContext:
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"feature_process_size: %s" % feature_process_size,
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"feature_min_frames: %s" % args.min_num_features,
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"processes: %s" % args.max_concurrency,
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"matching_gps_neighbors: %s" % matcher_neighbors,
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"matching_gps_neighbors: %s" % args.matcher_neighbors,
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"matching_gps_distance: %s" % args.matcher_distance,
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"depthmap_method: %s" % args.opensfm_depthmap_method,
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"depthmap_resolution: %s" % depthmap_resolution,
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@ -62,6 +62,8 @@ class ODMOpenSfMStage(types.ODM_Stage):
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octx.touch(updated_config_flag_file)
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# Undistorted images will be used for texturing / MVS
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primary_band_map = None
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undistort_pipeline = []
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def undistort_callback(shot_id, image):
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@ -73,14 +75,37 @@ class ODMOpenSfMStage(types.ODM_Stage):
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photo = reconstruction.get_photo(shot_id)
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return multispectral.dn_to_reflectance(photo, image, use_sun_sensor=args.radiometric_calibration=="camera+sun")
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def align_to_primary_band(shot_id, image):
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# TODO
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primary_band_photo = primary_band_map.get(shot_id)
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try:
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if primary_band_photo is None:
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raise Exception("Cannot find primary band file for %s" % shot_id)
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if primary_band_photo.filename == shot_id:
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# This is a photo from the primary band, skip
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return image
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align_image_filename = os.path.join(tree.dataset_raw, primary_band_map[shot_id].filename)
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image = multispectral.compute_aligned_image(image, align_image_filename)
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except Exception as e:
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log.ODM_WARNING("Cannot align %s: %s. Output quality might be affected." % (shot_id, str(e)))
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return image
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if args.radiometric_calibration != "none"
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if args.radiometric_calibration != "none":
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undistort_pipeline.append(radiometric_calibrate)
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if reconstruction.multi_camera:
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primary_band_map = multispectral.compute_primary_band_map(reconstruction.multi_camera, args.primary_band)
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# TODO:
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# if (not flag_file exists or rerun)
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# Change reconstruction.json and tracks.csv by adding copies of the primary
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# band camera to the reconstruction
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undistort_pipeline.append(align_to_primary_band)
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octx.convert_and_undistort(self.rerun(), undistort_callback)
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@ -102,6 +127,8 @@ class ODMOpenSfMStage(types.ODM_Stage):
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log.ODM_WARNING("Found a valid image list in %s for %s band" % (image_list_file, band['name']))
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# TODO!! CHANGE
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print("TODO!")
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exit(1)
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nvm_file = octx.path("undistorted", "reconstruction_%s.nvm" % band['name'].lower())
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if not io.file_exists(nvm_file) or self.rerun():
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octx.run('export_visualsfm --points --image_list "%s"' % image_list_file)
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