kopia lustrzana https://github.com/OpenDroneMap/ODM
Merge pull request #1493 from pierotofy/auto
Faster band alignment, minor fixes, thermal improvementspull/1496/head
Piero Toffanin
GitHub
commit
cc7fb2efa5
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@ -25,13 +25,11 @@ def dn_to_radiance(photo, image):
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image = image.astype("float32")
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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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# Handle thermal bands (experimental)
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if photo.band_name == 'LWIR':
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image -= (273.15 * 100.0) # Convert Kelvin to Celsius
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image *= 0.01
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return image
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# Thermal (this should never happen, but just in case..)
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if photo.is_thermal():
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return image
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# All others
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a1, a2, a3 = photo.get_radiometric_calibration()
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dark_level = photo.get_dark_level()
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@ -381,12 +379,24 @@ def compute_homography(image_filename, align_image_filename):
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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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warp_matrix = None
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dimension = None
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algo = None
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if result[0] is None:
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if max_dim > 320:
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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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else: # ECC only for low resolution images
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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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log.ODM_INFO("Using 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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@ -396,7 +406,7 @@ def compute_homography(image_filename, align_image_filename):
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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 None, (None, None), None
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def find_ecc_homography(image_gray, align_image_gray, number_of_iterations=1000, termination_eps=1e-8, start_eps=1e-4):
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pyramid_levels = 0
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@ -413,10 +423,14 @@ def find_ecc_homography(image_gray, align_image_gray, number_of_iterations=1000,
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if align_image_gray.shape[0] != image_gray.shape[0]:
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align_image_gray = to_8bit(align_image_gray)
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image_gray = to_8bit(image_gray)
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fx = align_image_gray.shape[1]/image_gray.shape[1]
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fy = align_image_gray.shape[0]/image_gray.shape[0]
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image_gray = cv2.resize(image_gray, None,
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fx=align_image_gray.shape[1]/image_gray.shape[1],
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fy=align_image_gray.shape[0]/image_gray.shape[0],
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interpolation=cv2.INTER_AREA)
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fx=fx,
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fy=fy,
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interpolation=(cv2.INTER_AREA if (fx < 1.0 and fy < 1.0) else cv2.INTER_LANCZOS4))
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# Build pyramids
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image_gray_pyr = [image_gray]
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@ -430,8 +444,9 @@ def find_ecc_homography(image_gray, align_image_gray, number_of_iterations=1000,
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align_image_pyr.insert(0, cv2.resize(align_image_pyr[0], None, fx=1/2, fy=1/2,
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interpolation=cv2.INTER_AREA))
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# Define the motion model
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# Define the motion model, scale the initial warp matrix to smallest level
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warp_matrix = np.eye(3, 3, dtype=np.float32)
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warp_matrix = warp_matrix * np.array([[1,1,2],[1,1,2],[0.5,0.5,1]], dtype=np.float32)**(1-(pyramid_levels+1))
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for level in range(pyramid_levels+1):
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ig = gradient(gaussian(image_gray_pyr[level]))
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@ -453,6 +468,7 @@ def find_ecc_homography(image_gray, align_image_gray, number_of_iterations=1000,
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if level != pyramid_levels:
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log.ODM_INFO("Could not compute ECC warp_matrix at pyramid level %s, resetting matrix" % level)
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warp_matrix = np.eye(3, 3, dtype=np.float32)
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warp_matrix = warp_matrix * np.array([[1,1,2],[1,1,2],[0.5,0.5,1]], dtype=np.float32)**(1-(pyramid_levels+1))
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else:
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raise e
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@ -462,29 +478,33 @@ def find_ecc_homography(image_gray, align_image_gray, number_of_iterations=1000,
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return warp_matrix
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def find_features_homography(image_gray, align_image_gray, feature_retention=0.25):
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def find_features_homography(image_gray, align_image_gray, feature_retention=0.7, min_match_count=10):
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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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FLANN_INDEX_KDTREE = 1
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index_params = dict(algorithm=FLANN_INDEX_KDTREE, trees=5)
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search_params = dict(checks=50)
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flann = cv2.FlannBasedMatcher(index_params, search_params)
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try:
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matches = bf.match(desc_image, desc_align_image)
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matches = flann.knnMatch(desc_image, desc_align_image, k=2)
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except Exception as e:
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log.ODM_INFO("Cannot match features")
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return None
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# Sort by score
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matches.sort(key=lambda x: x.distance, reverse=False)
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# Filter good matches following Lowe's ratio test
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good_matches = []
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for m, n in matches:
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if m.distance < feature_retention * n.distance:
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good_matches.append(m)
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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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matches = good_matches
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if len(matches) < 4:
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log.ODM_INFO("Insufficient features: %s" % len(matches))
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if len(matches) < min_match_count:
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return None
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# Debug
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@ -44,9 +44,22 @@ def generate_png(orthophoto_file, output_file=None, outsize=None):
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# See if we need to select top three bands
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bandparam = ""
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gtif = gdal.Open(orthophoto_file)
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if gtif.RasterCount > 4:
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bandparam = "-b 1 -b 2 -b 3 -a_nodata 0"
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bands = []
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for idx in range(1, gtif.RasterCount+1):
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bands.append(gtif.GetRasterBand(idx).GetColorInterpretation())
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bands = dict(zip(bands, range(1, len(bands)+1)))
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try:
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red = bands.get(gdal.GCI_RedBand)
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green = bands.get(gdal.GCI_GreenBand)
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blue = bands.get(gdal.GCI_BlueBand)
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bandparam = "-b %s -b %s -b %s -a_nodata 0" % (red, green, blue)
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except:
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bandparam = "-b 1 -b 2 -b 3 -a_nodata 0"
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gtif = None
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osparam = ""
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if outsize is not None:
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@ -339,6 +339,7 @@ class ODM_Photo:
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self.set_attr_from_xmp_tag('capture_uuid', xtags, [
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'@drone-dji:CaptureUUID', # DJI
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'MicaSense:CaptureId', # MicaSense Altum
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'@Camera:ImageUniqueID', # sentera 6x
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])
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