kopia lustrzana https://github.com/OpenDroneMap/ODM
Stability improvements, bug fixes, updated OpenMVS
Piero Toffanin
rodzic
7c42409567
commit
92d05243b1
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@ -20,7 +20,7 @@ ExternalProject_Add(${_proj_name}
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#--Download step--------------
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DOWNLOAD_DIR ${SB_DOWNLOAD_DIR}
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GIT_REPOSITORY https://github.com/OpenDroneMap/openMVS
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GIT_TAG 210
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GIT_TAG 230
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#--Update/Patch step----------
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UPDATE_COMMAND ""
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#--Configure step-------------
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@ -24,7 +24,7 @@ def get_max_memory_mb(minimum = 100, use_at_most = 0.5):
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"""
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return max(minimum, (virtual_memory().available / 1024 / 1024) * use_at_most)
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def parallel_map(func, items, max_workers=1):
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def parallel_map(func, items, max_workers=1, single_thread_fallback=True):
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"""
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Our own implementation for parallel processing
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which handles gracefully CTRL+C and reverts to
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@ -85,7 +85,7 @@ def parallel_map(func, items, max_workers=1):
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stop_workers()
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if error is not None:
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if error is not None and single_thread_fallback:
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# Try to reprocess using a single thread
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# in case this was a memory error
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log.ODM_WARNING("Failed to run process in parallel, retrying with a single thread...")
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@ -5,6 +5,7 @@ import os
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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 opendm.concurrency import parallel_map
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from opensfm.io import imread
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from skimage import exposure
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@ -270,7 +271,7 @@ def compute_band_maps(multi_camera, primary_band):
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return s2p, p2s
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def compute_alignment_matrices(multi_camera, primary_band_name, images_path, s2p, p2s, max_samples=9999):
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def compute_alignment_matrices(multi_camera, primary_band_name, images_path, s2p, p2s, max_concurrency=1, max_samples=30):
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log.ODM_INFO("Computing band alignment")
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alignment_info = {}
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@ -280,46 +281,76 @@ def compute_alignment_matrices(multi_camera, primary_band_name, images_path, s2p
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if band['name'] != primary_band_name:
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matrices = []
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# if band['name'] != "NIR":
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# continue # TODO REMOVE
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def parallel_compute_homography(p):
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try:
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if len(matrices) >= max_samples:
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log.ODM_INFO("Got enough samples for %s (%s)" % (band['name'], max_samples))
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return
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# Find good matrix candidates for alignment
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for p in band['photos']:
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primary_band_photo = s2p.get(p.filename)
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if primary_band_photo is None:
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log.ODM_WARNING("Cannot find primary band photo for %s" % p.filename)
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continue
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warp_matrix, score, dimension = compute_homography(os.path.join(images_path, p.filename),
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primary_band_photo = s2p.get(p['filename'])
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if primary_band_photo is None:
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log.ODM_WARNING("Cannot find primary band photo for %s" % p['filename'])
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return
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warp_matrix, dimension, algo = compute_homography(os.path.join(images_path, p['filename']),
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os.path.join(images_path, primary_band_photo.filename))
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if warp_matrix is not None:
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log.ODM_INFO("%s --> %s good match (score: %s)" % (p.filename, primary_band_photo.filename, score))
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log.ODM_INFO("%s --> %s good match" % (p['filename'], primary_band_photo.filename))
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matrices.append({
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'warp_matrix': warp_matrix,
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'score': score,
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'dimension': dimension
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'eigvals': np.linalg.eigvals(warp_matrix),
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'dimension': dimension,
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'algo': algo
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})
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else:
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log.ODM_INFO("%s --> %s cannot be matched" % (p.filename, primary_band_photo.filename))
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log.ODM_INFO("%s --> %s cannot be matched" % (p['filename'], primary_band_photo.filename))
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except Exception as e:
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log.ODM_WARNING("Failed to compute homography for %s: %s" % (p['filename'], str(e)))
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if len(matrices) >= max_samples:
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log.ODM_INFO("Got enough samples for %s (%s)" % (band['name'], max_samples))
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break
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parallel_map(parallel_compute_homography, [{'filename': p.filename} for p in band['photos']], max_concurrency, single_thread_fallback=False)
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# Choose winning algorithm (doesn't seem to yield improvements)
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# feat_count = 0
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# ecc_count = 0
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# for m in matrices:
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# if m['algo'] == 'feat':
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# feat_count += 1
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# if m['algo'] == 'ecc':
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# ecc_count += 1
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# algo = 'feat' if feat_count >= ecc_count else 'ecc'
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# log.ODM_INFO("Feat: %s | ECC: %s | Winner: %s" % (feat_count, ecc_count, algo))
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# matrices = [m for m in matrices if m['algo'] == algo]
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# Find the matrix that has the most common eigvals
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# among all matrices. That should be the "best" alignment.
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for m1 in matrices:
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acc = np.array([0.0,0.0,0.0])
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e = m1['eigvals']
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for m2 in matrices:
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acc += abs(e - m2['eigvals'])
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m1['score'] = acc.sum()
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# Sort
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matrices.sort(key=lambda x: x['score'], reverse=False)
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if len(matrices) > 0:
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alignment_info[band['name']] = matrices[0]
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print(matrices[0])
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log.ODM_INFO("%s band will be aligned using warp matrix %s (score: %s)" % (band['name'], matrices[0]['warp_matrix'], matrices[0]['score']))
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else:
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log.ODM_WARNING("Cannot find alignment matrix for band %s, The band will likely be misaligned!" % band['name'])
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return alignment_info
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def compute_homography(image_filename, align_image_filename):
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# try:
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try:
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# Convert images to grayscale if needed
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image = imread(image_filename, unchanged=True, anydepth=True)
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if image.shape[2] == 3:
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@ -336,37 +367,43 @@ def compute_homography(image_filename, align_image_filename):
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def compute_using(algorithm):
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h = algorithm(image_gray, align_image_gray)
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if h is None:
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return None, None, (None, None)
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return None, (None, None)
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det = np.linalg.det(h)
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# Check #1 homography's determinant will not be close to zero
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if abs(det) < 0.25:
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return None, None, (None, None)
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return None, (None, None)
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# Check #2 the ratio of the first-to-last singular value is sane (not too high)
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svd = np.linalg.svd(h, compute_uv=False)
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if svd[-1] == 0:
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return None, None, (None, None)
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return None, (None, None)
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ratio = svd[0] / svd[-1]
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if ratio > 100000:
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return None, (None, None)
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return h, (align_image_gray.shape[1], align_image_gray.shape[0])
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algo = 'feat'
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result = compute_using(find_features_homography)
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if result[0] is None:
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algo = 'ecc'
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log.ODM_INFO("Can't use features matching, will use ECC (this might take a bit)")
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result = compute_using(find_ecc_homography)
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if result[0] is None:
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algo = None
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warp_matrix, dimension = result
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return warp_matrix, dimension, algo
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except Exception as e:
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log.ODM_WARNING("Compute homography: %s" % str(e))
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return None, None, (None, None)
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return h, compute_alignment_score(h, image_gray, align_image_gray), (align_image_gray.shape[1], align_image_gray.shape[0])
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result = compute_using(find_features_homography)
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if result[0] is None:
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log.ODM_INFO("Can't use features matching, will use ECC")
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result = compute_using(find_ecc_homography)
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return result
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# except Exception as e:
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# log.ODM_WARNING("Compute homography: %s" % str(e))
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# return None, None, (None, None)
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def find_ecc_homography(image_gray, align_image_gray, number_of_iterations=5000, termination_eps=1e-8):
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def find_ecc_homography(image_gray, align_image_gray, number_of_iterations=2500, termination_eps=1e-9):
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image_gray = to_8bit(gradient(gaussian(image_gray)))
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align_image_gray = to_8bit(gradient(gaussian(align_image_gray)))
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@ -377,10 +414,7 @@ def find_ecc_homography(image_gray, align_image_gray, number_of_iterations=5000,
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criteria = (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT,
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number_of_iterations, termination_eps)
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try:
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(cc, warp_matrix) = cv2.findTransformECC (image_gray,align_image_gray,warp_matrix, cv2.MOTION_HOMOGRAPHY, criteria, inputMask=None, gaussFiltSize=1)
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except:
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(cc, warp_matrix) = cv2.findTransformECC (image_gray,align_image_gray,warp_matrix, cv2.MOTION_HOMOGRAPHY, criteria)
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_, warp_matrix = cv2.findTransformECC (image_gray,align_image_gray,warp_matrix, cv2.MOTION_HOMOGRAPHY, criteria, inputMask=None, gaussFiltSize=9)
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return warp_matrix
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@ -418,37 +452,6 @@ def find_features_homography(image_gray, align_image_gray, feature_retention=0.2
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h, _ = cv2.findHomography(points_image, points_align_image, cv2.RANSAC)
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return h
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def compute_alignment_score(warp_matrix, image_gray, align_image_gray, apply_gradient=True):
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projected = align_image(image_gray, warp_matrix, (align_image_gray.shape[1], align_image_gray.shape[0]))
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borders = projected==0
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if apply_gradient:
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image_gray = to_8bit(gradient(gaussian(image_gray)))
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align_image_gray = to_8bit(gradient(gaussian(align_image_gray)))
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# cv2.imwrite("/datasets/micasense/opensfm/undistorted/align_image_gray.jpg", align_image_gray)
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# cv2.imwrite("/datasets/micasense/opensfm/undistorted/projected.jpg", projected)
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# Threshold
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align_image_gray[align_image_gray > 128] = 255
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projected[projected > 128] = 255
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align_image_gray[align_image_gray <= 128] = 0
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projected[projected <= 128] = 0
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# Mark borders
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align_image_gray[borders] = 0
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projected[borders] = 255
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# cv2.imwrite("/datasets/micasense/opensfm/undistorted/threshold_align_image_gray.jpg", align_image_gray)
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# cv2.imwrite("/datasets/micasense/opensfm/undistorted/threshold_projected.jpg", projected)
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# cv2.imwrite("/datasets/micasense/opensfm/undistorted/delta.jpg", projected - align_image_gray)
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# Compute delta --> the more the images overlap perfectly, the lower the score
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return (projected - align_image_gray).sum()
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def gradient(im, ksize=5):
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im = local_normalize(im)
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grad_x = cv2.Sobel(im,cv2.CV_32F,1,0,ksize=ksize)
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@ -315,11 +315,11 @@ class OSFMContext:
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else:
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log.ODM_INFO("Already extracted cameras")
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def convert_and_undistort(self, rerun=False, imageFilter=None, image_list=None):
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def convert_and_undistort(self, rerun=False, imageFilter=None, image_list=None, runId="nominal"):
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log.ODM_INFO("Undistorting %s ..." % self.opensfm_project_path)
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undistorted_images_path = self.path("undistorted", "images")
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done_flag_file = self.path("undistorted", "%s_done.txt" % runId)
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if not io.dir_exists(undistorted_images_path) or rerun:
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if not io.file_exists(done_flag_file) or rerun:
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ds = DataSet(self.opensfm_project_path)
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if image_list is not None:
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@ -327,32 +327,35 @@ class OSFMContext:
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undistort.run_dataset(ds, "reconstruction.json",
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0, None, "undistorted", imageFilter)
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self.touch(done_flag_file)
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else:
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log.ODM_WARNING("Found an undistorted directory in %s" % undistorted_images_path)
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log.ODM_WARNING("Already undistorted (%s)" % runId)
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def restore_reconstruction_backup(self):
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if os.path.exists(self.recon_backup_file()):
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# This time export the actual reconstruction.json
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# (containing only the primary band)
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if os.path.exists(self.recon_file()):
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os.remove(self.recon_file())
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os.rename(self.recon_backup_file(), self.recon_file())
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log.ODM_INFO("Restored reconstruction.json")
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def backup_reconstruction(self):
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if os.path.exists(self.recon_backup_file()):
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os.remove(self.recon_backup_file())
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log.ODM_INFO("Backing up reconstruction")
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shutil.copyfile(self.recon_file(), self.recon_backup_file())
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def recon_backup_file(self):
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return self.path("reconstruction.backup.json")
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def recon_file(self):
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return self.path("reconstruction.json")
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def add_shots_to_reconstruction(self, p2s):
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recon_file = self.path("reconstruction.json")
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recon_backup_file = self.path("reconstruction.backup.json")
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# tracks_file = self.path("tracks.csv")
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# tracks_backup_file = self.path("tracks.backup.csv")
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# image_list_file = self.path("image_list.txt")
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# image_list_backup_file = self.path("image_list.backup.txt")
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log.ODM_INFO("Adding shots to reconstruction")
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if os.path.exists(recon_backup_file):
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os.remove(recon_backup_file)
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# if os.path.exists(tracks_backup_file):
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# os.remove(tracks_backup_file)
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# if os.path.exists(image_list_backup_file):
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# os.remove(image_list_backup_file)
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log.ODM_INFO("Backing up reconstruction, tracks, image list")
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shutil.copyfile(recon_file, recon_backup_file)
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# shutil.copyfile(tracks_file, tracks_backup_file)
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# shutil.copyfile(image_list_file, image_list_backup_file)
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with open(recon_file) as f:
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with open(self.recon_file()) as f:
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reconstruction = json.loads(f.read())
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# Augment reconstruction.json
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@ -369,13 +372,9 @@ class OSFMContext:
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for p in secondary_photos:
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shots[p.filename] = shots[shot_id]
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with open(recon_file, 'w') as f:
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with open(self.recon_file(), 'w') as f:
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f.write(json.dumps(reconstruction))
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return True #(recon_file, recon_backup_file)
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# (tracks_file, tracks_backup_file),
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# (image_list_file, image_list_backup_file)]
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def update_config(self, cfg_dict):
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cfg_file = self.get_config_file_path()
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@ -23,6 +23,7 @@ PyYAML==5.1
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rasterio==1.1.8
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repoze.lru==0.7
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scikit-learn==0.23.2
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scikit-image==0.17.2
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scipy==1.5.4
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xmltodict==0.12.0
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@ -91,7 +91,6 @@ class ODMOpenSfMStage(types.ODM_Stage):
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log.ODM_WARNING("Cannot align %s, no alignment matrix could be computed. Band alignment quality might be affected." % (shot_id))
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return image
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if args.radiometric_calibration != "none":
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undistort_pipeline.append(radiometric_calibrate)
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@ -113,12 +112,12 @@ class ODMOpenSfMStage(types.ODM_Stage):
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if not io.file_exists(added_shots_file) or self.rerun():
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primary_band_name = multispectral.get_primary_band_name(reconstruction.multi_camera, args.primary_band)
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s2p, p2s = multispectral.compute_band_maps(reconstruction.multi_camera, primary_band_name)
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alignment_info = multispectral.compute_alignment_matrices(reconstruction.multi_camera, primary_band_name, tree.dataset_raw, s2p, p2s)
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alignment_info = multispectral.compute_alignment_matrices(reconstruction.multi_camera, primary_band_name, tree.dataset_raw, s2p, p2s, max_concurrency=args.max_concurrency)
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log.ODM_INFO("Adding shots to reconstruction")
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octx.backup_reconstruction()
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octx.add_shots_to_reconstruction(p2s)
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# TODO: what happens to reconstruction.backup.json
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# if the process fails here?
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octx.touch(added_shots_file)
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undistort_pipeline.append(align_to_primary_band)
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@ -148,16 +147,8 @@ class ODMOpenSfMStage(types.ODM_Stage):
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else:
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log.ODM_WARNING("Found a valid NVM file in %s for %s band" % (nvm_file, band['name']))
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recon_file = octx.path("reconstruction.json")
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recon_backup_file = octx.path("reconstruction.backup.json")
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if os.path.exists(recon_backup_file):
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# This time export the actual reconstruction.json
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# (containing only the primary band)
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if os.path.exists(recon_backup_file):
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os.remove(recon_file)
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os.rename(recon_backup_file, recon_file)
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octx.convert_and_undistort(self.rerun(), undistort_callback)
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octx.restore_reconstruction_backup()
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octx.convert_and_undistort(self.rerun(), undistort_callback, runId='primary')
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if not io.file_exists(tree.opensfm_reconstruction_nvm) or self.rerun():
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octx.run('export_visualsfm --points')
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@ -194,6 +185,9 @@ class ODMOpenSfMStage(types.ODM_Stage):
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if args.optimize_disk_space:
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os.remove(octx.path("tracks.csv"))
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if io.file_exists(octx.recon_backup_file()):
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os.remove(octx.recon_backup_file())
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if io.dir_exists(octx.path("undistorted", "depthmaps")):
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files = glob.glob(octx.path("undistorted", "depthmaps", "*.npz"))
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for f in files:
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|
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