kopia lustrzana https://github.com/OpenDroneMap/ODM
Merge branch 'smartband' into homographyfix
Piero Toffanin
commit
86b76fe09a
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@ -6,7 +6,8 @@ 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 opendm.io import related_file_path
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from opensfm.io import imread, imwrite
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from skimage import exposure
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from skimage.morphology import disk
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@ -643,4 +644,60 @@ def resize_match(image, dimension):
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fy=fx,
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interpolation=(cv2.INTER_AREA if (fx < 1.0 and fy < 1.0) else cv2.INTER_LANCZOS4))
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return image
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return image
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def dewarp_photos(photos, images_path, scale_factor=1.0):
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# Caution! This will make changes to the photo objects' filename
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for p in photos:
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# This should never happen
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if os.path.splitext(p.filename)[0].endswith("_dewarped"):
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log.ODM_WARNING("Cannot dewarp %s, already dewarped?" % (p.filename))
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continue
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if p.dewarp_data is None:
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log.ODM_WARNING("Cannot dewarp %s, dewarp data is missing" % p.filename)
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continue
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if p.dewarp_data.count(";") != 1:
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log.ODM_WARNING("Cannot dewarp %s, cannot parse dewarp data" % p.filename)
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continue
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datestamp, params = p.dewarp_data.split(";")
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try:
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params = [float(p) for p in params.split(",")]
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except ValueError as e:
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log.ODM_WARNING("Cannot dewarp %s, failed to parse dewarp data" % p.filename)
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continue
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if len(params) != 9:
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log.ODM_WARNING("Cannot dewarp %s, invalid dewarp data parameters (expected 9, got: %s)" % (p.filename, len(params)))
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continue
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dewarped_filename = related_file_path(p.filename, postfix="_dewarped")
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dewarped_path = os.path.join(images_path, dewarped_filename)
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if os.path.isfile(dewarped_path):
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# Already dewarped
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p.filename = dewarped_filename
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else:
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image = imread(os.path.join(images_path, p.filename), unchanged=True, anydepth=True)
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w, h = image.shape[1], image.shape[0]
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fx, fy, cx, cy, k1, k2, p1, p2, k3 = params
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cam_m = np.array([[fx, 0, w / 2 - cx], [0, fy, h / 2 + cy], [0, 0, 1]])
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dist_c = np.array([k1, k2, p1, p2, k3])
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map1, map2 = cv2.initUndistortRectifyMap(cam_m, dist_c, None, cam_m, (w, h), cv2.CV_32FC1)
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dewarped_image = cv2.remap(image, map1, map2, cv2.INTER_LINEAR)
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if scale_factor > 1.0:
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new_w = int(w * (1.0 / scale_factor))
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new_h = int(h * (1.0 / scale_factor))
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center_x, center_y = w // 2, h // 2
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crop_x1 = max(0, center_x - new_w // 2)
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crop_y1 = max(0, center_y - new_h // 2)
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crop_x2 = min(w, center_x + new_w // 2)
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crop_y2 = min(h, center_y + new_h // 2)
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cropped = dewarped_image[crop_y1:crop_y2, crop_x1:crop_x2]
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dewarped_image = cv2.resize(cropped, (w, h), interpolation=cv2.INTER_LANCZOS4)
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imwrite(dewarped_path, dewarped_image)
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log.ODM_INFO("Dewarped %s --> %s" % (p.filename, dewarped_filename))
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p.filename = dewarped_filename
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@ -159,6 +159,9 @@ class ODM_Photo:
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self.gps_xy_stddev = None # Dilution of Precision X/Y
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self.gps_z_stddev = None # Dilution of Precision Z
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# DJI
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self.dewarp_data = None
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# Misc SFM
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self.camera_projection = 'brown'
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self.focal_ratio = 0.85
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@ -413,6 +416,12 @@ class ODM_Photo:
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self.set_attr_from_xmp_tag('speed_z', xtags, [
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'@drone-dji:FlightZSpeed',
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], float)
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# DJI dewarp data
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if '@drone-dji:DewarpData' in xtags:
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self.set_attr_from_xmp_tag('dewarp_data', xtags, [
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'@drone-dji:DewarpData'
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])
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# Account for over-estimation
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if self.gps_xy_stddev is not None:
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@ -22,12 +22,13 @@ from opendm.photo import ODM_Photo
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warnings.filterwarnings("ignore")
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class ODM_Reconstruction(object):
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def __init__(self, photos):
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def __init__(self, photos, images_path):
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self.photos = photos
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self.georef = None
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self.gcp = None
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self.multi_camera = self.detect_multi_camera()
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self.filter_photos()
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self.dewarp_photos(images_path)
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def detect_multi_camera(self):
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"""
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@ -148,13 +149,7 @@ class ODM_Reconstruction(object):
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if 'rgb' in bands or 'redgreenblue' in bands:
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if 'red' in bands and 'green' in bands and 'blue' in bands:
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bands_to_remove.append(bands['rgb'] if 'rgb' in bands else bands['redgreenblue'])
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# Mavic 3M's RGB camera lens are too different than the multispectral ones
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# so we drop the RGB channel instead
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elif self.photos[0].is_make_model("DJI", "M3M") and 'red' in bands and 'green' in bands:
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bands_to_remove.append(bands['rgb'] if 'rgb' in bands else bands['redgreenblue'])
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bands_to_remove.append(bands['rgb'] if 'rgb' in bands else bands['redgreenblue'])
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else:
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for b in ['red', 'green', 'blue']:
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if b in bands:
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@ -285,6 +280,23 @@ class ODM_Reconstruction(object):
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if p.filename == filename:
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return p
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def dewarp_photos(self, images_path):
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if not self.multi_camera:
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return # Nothing to do
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else:
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bands = {}
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for b in self.multi_camera:
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bands[b['name'].lower()] = b['photos']
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# Mavic 3M's RGB camera lens are too different than the multispectral ones
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# so we unwarp them before reconstruction when needed
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if self.photos[0].is_make_model("DJI", "M3M") and 'nir' in bands and ('rgb' in bands or 'redgreenblue' in bands):
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log.ODM_INFO("Dewarping RGB photos before processing")
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rgb = 'rgb' if 'rgb' in bands else 'redgreenblue'
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upscale = max(1.0, bands['nir'][0].focal_ratio / bands[rgb][0].focal_ratio)
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if upscale != 1.0:
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log.ODM_INFO("Adjusting focal distance of RGB images by %s" % upscale)
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multispectral.dewarp_photos(bands['rgb'] if 'rgb' in bands else bands['redgreenblue'], images_path, upscale)
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class ODM_GeoRef(object):
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@staticmethod
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@ -61,7 +61,7 @@ class ODMLoadDatasetStage(types.ODM_Stage):
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def valid_filename(filename, supported_extensions):
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(pathfn, ext) = os.path.splitext(filename)
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return ext.lower() in supported_extensions and pathfn[-5:] != "_mask"
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return ext.lower() in supported_extensions and pathfn[-5:] != "_mask" and pathfn[-9:] != "_dewarped"
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# Get supported images from dir
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def get_images(in_dir):
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@ -162,6 +162,10 @@ class ODMLoadDatasetStage(types.ODM_Stage):
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(p, ext) = os.path.splitext(r)
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if p[-5:] == "_mask" and ext.lower() in context.supported_extensions:
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masks[p] = r
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# Remove dewarped images on re-run
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if p[-9:] == "_dewarped" and self.rerun():
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os.unlink(os.path.join(images_dir, p + ext))
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photos = []
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with open(tree.dataset_list, 'w') as dataset_list:
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@ -298,7 +302,7 @@ class ODMLoadDatasetStage(types.ODM_Stage):
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log.logger.log_json_images(len(photos))
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# Create reconstruction object
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reconstruction = types.ODM_Reconstruction(photos)
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reconstruction = types.ODM_Reconstruction(photos, tree.dataset_raw)
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if tree.odm_georeferencing_gcp and not args.use_exif:
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reconstruction.georeference_with_gcp(tree.odm_georeferencing_gcp,
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