Revert M3M RGB dewarp

opendm/multispectral.py

@@ -6,8 +6,6 @@
 import numpy as np
 from opendm import log
 from opendm.concurrency import parallel_map
-from opendm.io import related_file_path
-from opensfm.io import imread, imwrite
 
 from skimage import exposure
 from skimage.morphology import disk
@@ -647,69 +645,3 @@ def resize_match(image, dimension):
                 interpolation=(cv2.INTER_AREA if (fx < 1.0 and fy < 1.0) else cv2.INTER_LANCZOS4))
 
     return image
-
-def dewarp_photos(photos, images_path, scale_factor=1.0, size=None, max_concurrency=1):
-    # Caution! This will make changes to the photo objects' filename
-
-    def dewarp_photo(p):
-        # This should never happen
-        if os.path.splitext(p.filename)[0].endswith("_dewarped"):
-            log.ODM_WARNING("Cannot dewarp %s, already dewarped?" % (p.filename))
-            return
-
-        if p.dewarp_data is None:
-            log.ODM_WARNING("Cannot dewarp %s, dewarp data is missing" % p.filename)
-            return
-
-        if p.dewarp_data.count(";") != 1:
-            log.ODM_WARNING("Cannot dewarp %s, cannot parse dewarp data" % p.filename)
-            return
-
-        datestamp, params = p.dewarp_data.split(";")
-        try:
-            params = [float(p) for p in params.split(",")]
-        except ValueError as e:
-            log.ODM_WARNING("Cannot dewarp %s, failed to parse dewarp data" % p.filename)
-            return
-
-        if len(params) != 9:
-            log.ODM_WARNING("Cannot dewarp %s, invalid dewarp data parameters (expected 9, got: %s)" % (p.filename, len(params)))
-            return
-
-        dewarped_filename = related_file_path(p.filename, postfix="_dewarped")
-        dewarped_path = os.path.join(images_path, dewarped_filename)
-        if os.path.isfile(dewarped_path):
-            # Already dewarped
-            p.filename = dewarped_filename
-        else:
-            image = imread(os.path.join(images_path, p.filename), unchanged=True, anydepth=True)
-            w, h = image.shape[1], image.shape[0]
-            fx, fy, cx, cy, k1, k2, p1, p2, k3 = params
-            cam_m = np.array([[fx, 0, w / 2 - cx], [0, fy, h / 2 + cy], [0, 0, 1]])
-            dist_c = np.array([k1, k2, p1, p2, k3])
-            map1, map2 = cv2.initUndistortRectifyMap(cam_m, dist_c, None, cam_m, (w, h), cv2.CV_32FC1)
-            dewarped_image = cv2.remap(image, map1, map2, cv2.INTER_LINEAR)
-
-            if scale_factor > 1.0:
-                new_w = int(w * (1.0 / scale_factor))
-                new_h = int(h * (1.0 / scale_factor))
-                center_x, center_y = w // 2, h // 2
-                crop_x1 = max(0, center_x - new_w // 2)
-                crop_y1 = max(0, center_y - new_h // 2)
-                crop_x2 = min(w, center_x + new_w // 2)
-                crop_y2 = min(h, center_y + new_h // 2)
-                cropped = dewarped_image[crop_y1:crop_y2, crop_x1:crop_x2]
-                dewarp_size = (w, h)
-                if size is not None:
-                    dewarp_size = size
-                dewarped_image = cv2.resize(cropped, dewarp_size, interpolation=cv2.INTER_LANCZOS4)
-            imwrite(dewarped_path, dewarped_image)
-            log.ODM_INFO("Dewarped %s --> %s" % (p.filename, dewarped_filename))
-            p.filename = dewarped_filename
-
-        # Disable vignetting correction
-        p.vignetting_polynomial = None
-        p.vignetting_center = None
-
-
-    parallel_map(dewarp_photo, photos, max_concurrency)

opendm/photo.py

@@ -159,9 +159,6 @@ def __init__(self, path_file):
         self.gps_xy_stddev = None # Dilution of Precision X/Y
         self.gps_z_stddev = None # Dilution of Precision Z
 
-        # DJI
-        self.dewarp_data = None
-
         # Misc SFM
         self.camera_projection = 'brown'
         self.focal_ratio = 0.85
@@ -417,12 +414,6 @@ def parse_exif_values(self, _path_file):
                             '@drone-dji:FlightZSpeed',
                         ], float)
 
-                    # DJI dewarp data
-                    if '@drone-dji:DewarpData' in xtags:
-                        self.set_attr_from_xmp_tag('dewarp_data', xtags, [
-                            '@drone-dji:DewarpData'
-                        ])
-
                     # Account for over-estimation
                     if self.gps_xy_stddev is not None:
                         self.gps_xy_stddev *= 2.0

opendm/types.py

@@ -22,13 +22,12 @@
 warnings.filterwarnings("ignore")
 
 class ODM_Reconstruction(object):
-    def __init__(self, photos, images_path, max_concurrency=1):
+    def __init__(self, photos):
         self.photos = photos
         self.georef = None
         self.gcp = None
         self.multi_camera = self.detect_multi_camera()
         self.filter_photos()
-        self.dewarp_photos(images_path, max_concurrency)
 
     def detect_multi_camera(self):
         """
@@ -149,7 +148,13 @@ def filter_photos(self):
 
             if 'rgb' in bands or 'redgreenblue' in bands:
                 if 'red' in bands and 'green' in bands and 'blue' in bands:
-                    bands_to_remove.append(bands['rgb'] if 'rgb' in bands else bands['redgreenblue'])                
+                    bands_to_remove.append(bands['rgb'] if 'rgb' in bands else bands['redgreenblue'])
+
+                # Mavic 3M's RGB camera lens are too different than the multispectral ones
+                # so we drop the RGB channel instead
+                elif self.photos[0].is_make_model("DJI", "M3M") and 'red' in bands and 'green' in bands:
+                    bands_to_remove.append(bands['rgb'] if 'rgb' in bands else bands['redgreenblue'])
+
                 else:
                     for b in ['red', 'green', 'blue']:
                         if b in bands:
@@ -280,25 +285,6 @@ def get_photo(self, filename):
             if p.filename == filename:
                 return p
 
-    def dewarp_photos(self, images_path, max_concurrency):
-        if not self.multi_camera:
-            return # Nothing to do
-        else:
-            bands = {}
-            for b in self.multi_camera:
-                bands[b['name'].lower()] = b['photos']
-
-            # Mavic 3M's RGB camera lens are too different than the multispectral ones
-            # so we unwarp them before reconstruction when needed
-            if self.photos[0].is_make_model("DJI", "M3M") and 'nir' in bands and ('rgb' in bands or 'redgreenblue' in bands):
-                log.ODM_INFO("Dewarping RGB photos before processing")
-                rgb = 'rgb' if 'rgb' in bands else 'redgreenblue'
-                upscale = max(1.0, bands['nir'][0].focal_ratio / bands[rgb][0].focal_ratio)
-                if upscale != 1.0:
-                    log.ODM_INFO("Adjusting focal distance of RGB images by %s" % upscale)
-                size = (bands['nir'][0].width, bands['nir'][0].height)
-                multispectral.dewarp_photos(bands['rgb'] if 'rgb' in bands else bands['redgreenblue'], images_path, upscale, size, max_concurrency)
-
 class ODM_GeoRef(object):
     @staticmethod
     def FromCoordsFile(coords_file):

stages/dataset.py

@@ -61,7 +61,7 @@ def process(self, args, outputs):
 
         def valid_filename(filename, supported_extensions):
             (pathfn, ext) = os.path.splitext(filename)
-            return ext.lower() in supported_extensions and pathfn[-5:] != "_mask" and pathfn[-9:] != "_dewarped"
+            return ext.lower() in supported_extensions and pathfn[-5:] != "_mask"
 
         # Get supported images from dir
         def get_images(in_dir):
@@ -163,10 +163,6 @@ def find_mask(photo_path, masks):
                     if p[-5:] == "_mask" and ext.lower() in context.supported_extensions:
                         masks[p] = r
 
-                    # Remove dewarped images on re-run
-                    if p[-9:] == "_dewarped" and self.rerun():
-                        os.unlink(os.path.join(images_dir, p + ext))
-
                 photos = []
                 with open(tree.dataset_list, 'w') as dataset_list:
                     log.ODM_INFO("Loading %s images" % len(path_files))
@@ -302,7 +298,7 @@ def parallel_bg_filter(item):
         log.logger.log_json_images(len(photos))
 
         # Create reconstruction object
-        reconstruction = types.ODM_Reconstruction(photos, tree.dataset_raw, args.max_concurrency)
+        reconstruction = types.ODM_Reconstruction(photos)
 
         if tree.odm_georeferencing_gcp and not args.use_exif:
             reconstruction.georeference_with_gcp(tree.odm_georeferencing_gcp,