Test the method of bypassing 128-Tag limit.

GCPMarkerAdditions/ImportMarkerFeatures.py

@@ -1,11 +1,12 @@
-__version__ = "1.0"
+__version__ = ""
 
 from meshroom.core import desc
 
 import os
 import csv
 import json
 import struct
+import itertools
 
 class ImportMarkerFeatures(desc.Node):
     category = 'Utils'
@@ -50,11 +51,18 @@ class ImportMarkerFeatures(desc.Node):
             value = "",
             uid = [0]
         ),
+        desc.BoolParam(
+            name = "hack",
+            label = "Enable to Bypass 128-Tag Limit",
+            description = "This option skips the FeatureMatching node and directly generates a matches.txt file for cctag match.",
+            value = False,
+            uid = [0]
+        ),
         desc.ChoiceParam(
             name = "delimiter",
             label = "Delimiter",
             description = "Delimiter character used in the input CSV file.",
-            value = "space",
+            value = "comma",
             values = ["space", "tab", "comma", "colon", "semicolon"],
             exclusive = True,
             uid = [0]
@@ -86,6 +94,13 @@ class ImportMarkerFeatures(desc.Node):
             description = "Output path for the features and descriptors files (*.feat, *.desc).",
             value = desc.Node.internalFolder,
             uid = []
+        ),
+        desc.File(
+            name = "matches_out",
+            label = "Matches Folder",
+            description = "Link to the SFM node's Matches Folder input, witch supports multiple elements.",
+            value = desc.Node.internalFolder,
+            uid = []
         )
     ]
 
@@ -95,7 +110,7 @@ def load_images(self, chunk, filepath, delimiter):
 
         with open(filepath) as file:
             gcp_file = csv.reader(file, delimiter=delimiter)
-            csv_data = [(row[2], float(row[0]), float(row[1]), float(row[4]), int(row[3])) for row in gcp_file]
+            csv_data = [(row[2], row[0], row[1], row[4], int(row[3])) for row in gcp_file]
 
         images.update({item[0]: [] for item in csv_data})
 
@@ -127,24 +142,32 @@ def write_describers(self, chunk, images, lookup):
         chunk.logManager.makeProgressBar(len(lookup))
 
         found_markers = {i: 0 for i in list(set([marker[3] for img in images for marker in images[img]]))}
+        feature_lookup = {viewid: {} for viewid in lookup.values()} # feature_lookup[viewid][tagid]
 
         for i, img in enumerate(lookup):
             viewid = lookup[img]
+
             feat = open(os.path.join(chunk.node.output.value, viewid + (".%s.feat" % chunk.node.type.value)), "w")
             desc = open(os.path.join(chunk.node.output.value, viewid + (".%s.desc" % chunk.node.type.value)), "wb")
 
             if img in images:
+                feat_idx = 0
                 markers = images[img]
-
                 desc.write(struct.pack('<Q', len(markers)))
 
                 for marker in markers:
-                    found_markers[marker[3]] += 1
-                    feat.write("%.2f %.2f %.4f 0\n" % (marker[0], marker[1], marker[2]))
+                    feat_x, feat_y, feat_size, feat_orientation = marker[0], marker[1], marker[2], "0"
+                    tagid = marker[3]
+                    found_markers[tagid] += 1
+                    feat.write(" ".join((feat_x, feat_y, feat_size, feat_orientation + "\n")))
+                    feature_lookup[viewid][tagid] = str(feat_idx)
+                    feat_idx += 1
 
+                if chunk.node.hack.value is False:
                     data = bytearray(128)
                     data[marker[3]] = 255
                     desc.write(data)
+
             else:
                 desc.write(struct.pack('<Q', 0))
 
@@ -157,6 +180,26 @@ def write_describers(self, chunk, images, lookup):
         for marker in found_markers:
             chunk.logger.info("\tFound marker %d in %d view(s)" % (marker, found_markers[marker]))
 
+        return feature_lookup
+
+    def make_matches_txt(self, chunk, image_pairs, feature_lookup):
+        out_temp = []
+        for pair in image_pairs:
+            viewid_A, viewid_B = pair
+            tags_detected_A = feature_lookup[viewid_A].keys()
+            tags_detected_B = feature_lookup[viewid_B].keys()
+            match = tags_detected_A & tags_detected_B
+            if match:
+                out_temp.append(" ".join((viewid_A, viewid_B)))
+                out_temp.append("1")
+                out_temp.append(f"{chunk.node.type.value} {len(match)}")
+                for tagid in match:
+                    feature_index_A = feature_lookup[viewid_A][tagid]
+                    feature_index_B = feature_lookup[viewid_B][tagid]
+                    out_temp.append(" ".join((feature_index_A, feature_index_B)))
+
+        with open(os.path.join(chunk.node.matches_out.value, "0.matches.txt"), "w") as matches_txt:
+            matches_txt.write("\n".join(out_temp))
 
     def processChunk(self, chunk):
         delimiters_options = {
@@ -176,8 +219,11 @@ def processChunk(self, chunk):
                 raise OSError("Marker features list file not found")
 
             lookup = self.load_viewids(chunk)
+            image_pairs = list(itertools.combinations(lookup.values(), 2)) # This method can produce the same results as the ImageMatching node that uses the "Exhaustive" method.
             images = self.load_images(chunk, chunk.node.matches.value, delimiters_options[chunk.node.delimiter.value])
-            self.write_describers(chunk, images, lookup)
+            feature_lookup = self.write_describers(chunk, images, lookup)
+            if chunk.node.hack.value:
+                self.make_matches_txt(chunk, image_pairs, feature_lookup)
 
             chunk.logger.info("Task done")
 
@@ -186,4 +232,4 @@ def processChunk(self, chunk):
             chunk.logger.error(e)
             raise
         finally:
-            chunk.logManager.end()
+            chunk.logManager.end()

README.md

@@ -78,6 +78,8 @@ The built-in marker detection support in Meshroom is rather obviously an afterth
 
     The marker feature describer files generated from the supplied data will be placed in this folder. This folder can then be added to the Features Folders input of the FeatureMatching node. (The desired marker family feature type - cctag3 or cctag4 - has to be manually enabled in the FeatureMatching node!!!)
 
+    NOTE: A known issue(on Windows) is that when the SFM node uses the CCTAG describer, if you manually enter the SFM node's input paths (such as SfMData and Features Folders), please verify the drive letter's case. Otherwise, a fatal error: "Error while loading markers regions" will occur at the end of the SFM process. This issue does not occur when using other describer types.
+
 ## SfMTransformFromMarkers node
 
 ![](images/SfMTransformFromMarkers_node.png)
@@ -167,4 +169,4 @@ The SfMTransform node included in Meshroom can be used to georeference the spars
 
 2. Poses
 
-    File containing view descriptions, adjusted intrinsic parameters, and georeferenced extrinsic parameters.
+    File containing view descriptions, adjusted intrinsic parameters, and georeferenced extrinsic parameters.