[multiview] New pipeline Photogrammetry and Camera Tracking

meshroom/multiview.py

@@ -472,7 +472,7 @@ def sfmAugmentation(graph, sourceSfm, withMVS=False):
     return sfmNodes, mvsNodes
 
 
-def cameraTrackingPipeline(graph):
+def cameraTrackingPipeline(graph, sourceSfm=None):
     """
     Instantiate a camera tracking pipeline inside 'graph'.
 
@@ -484,30 +484,33 @@ def cameraTrackingPipeline(graph):
     """
 
     with GraphModification(graph):
+        if sourceSfm is None:
+            cameraInitT, featureExtractionT, imageMatchingT, featureMatchingT, structureFromMotionT = sfmPipeline(graph)
+        else:
+            sfmNodes, _ = sfmAugmentation(graph, sourceSfm)
+            cameraInitT, featureExtractionT, imageMatchingT, featureMatchingT, structureFromMotionT = sfmNodes
 
-        cameraInit, featureExtraction, imageMatching, featureMatching, structureFromMotion = sfmPipeline(graph)
+        imageMatchingT.attribute("nbMatches").value = 5  # voctree nb matches
+        imageMatchingT.attribute("nbNeighbors").value = 10
 
-        imageMatching.attribute("nbMatches").value = 5  # voctree nb matches
+        structureFromMotionT.attribute("minNumberOfMatches").value = 0
-        imageMatching.attribute("nbNeighbors").value = 10
+        structureFromMotionT.attribute("minInputTrackLength").value = 5
+        structureFromMotionT.attribute("minNumberOfObservationsForTriangulation").value = 3
+        structureFromMotionT.attribute("minAngleForTriangulation").value = 1.0
+        structureFromMotionT.attribute("minAngleForLandmark").value = 0.5
 
-        structureFromMotion.attribute("minNumberOfMatches").value = 0
+        exportAnimatedCameraT = graph.addNewNode('ExportAnimatedCamera', input=structureFromMotionT.output)
-        structureFromMotion.attribute("minInputTrackLength").value = 5
-        structureFromMotion.attribute("minNumberOfObservationsForTriangulation").value = 3
-        structureFromMotion.attribute("minAngleForTriangulation").value = 1.0
-        structureFromMotion.attribute("minAngleForLandmark").value = 0.5
-
-        exportAnimatedCamera = graph.addNewNode('ExportAnimatedCamera', input=structureFromMotion.output)
 
     # store current pipeline version in graph header
     graph.header.update({'pipelineVersion': __version__})
 
     return [
-        cameraInit,
+        cameraInitT,
-        featureExtraction,
+        featureExtractionT,
-        imageMatching,
+        imageMatchingT,
-        featureMatching,
+        featureMatchingT,
-        structureFromMotion,
+        structureFromMotionT,
-        exportAnimatedCamera,
+        exportAnimatedCameraT,
         ]
 
 
@@ -527,3 +530,21 @@ def cameraTracking(inputImages=list(), inputViewpoints=list(), inputIntrinsics=l
 
     return graph
 
+
+def photogrammetryAndCameraTracking(inputImages=list(), inputViewpoints=list(), inputIntrinsics=list(), output='', graph=None):
+    if not graph:
+        graph = Graph('Photogrammetry And Camera Tracking')
+    with GraphModification(graph):
+        cameraInit, featureExtraction, imageMatching, featureMatching, structureFromMotion = sfmPipeline(graph)
+
+        cameraInitT, featureExtractionT, imageMatchingMultiT, featureMatchingT, structureFromMotionT, exportAnimatedCameraT = cameraTrackingPipeline(graph, structureFromMotion)
+
+        cameraInit.viewpoints.extend([{'path': image} for image in inputImages])
+        cameraInit.viewpoints.extend(inputViewpoints)
+        cameraInit.intrinsics.extend(inputIntrinsics)
+
+        if output:
+            graph.addNewNode('Publish', output=output, inputFiles=[exportAnimatedCameraT.output])
+
+    return graph
+

meshroom/nodes/aliceVision/ImageMatchingMultiSfM.py

@@ -48,9 +48,16 @@ Thanks to this node, the FeatureMatching node will only compute the matches betw
         desc.ChoiceParam(
             name='method',
             label='Method',
-            description='Method used to select the image pairs to match.',
+            description='Method used to select the image pairs to match:\n'
-            value='VocabularyTree',
+            ' * VocabularyTree:  It uses image retrieval techniques to find images that share some content without the cost of resolving all \n'
-            values=['VocabularyTree', 'Sequential', 'SequentialAndVocabularyTree','Exhaustive','Frustum'],
+            'feature matches in details. Each image is represented in a compact image descriptor which allows to compute the distance between all \n'
+            'images descriptors very efficiently. If your scene contains less than "Voc Tree: Minimal Number of Images", all image pairs will be selected.\n'
+            ' * SequentialAndVocabularyTree:  Combines sequential approach with VocTree to enable connections between keyframes at different times.\n'
+            ' * Exhaustive: Export all image pairs.\n'
+            ' * Frustum: If images have known poses, computes the intersection between cameras frustums to create the list of image pairs.\n'
+            ' * FrustumOrVocabularyTree: If images have known poses, use frustum intersection else use VocabularyTree.\n',
+            value='SequentialAndVocabularyTree',
+            values=['VocabularyTree', 'SequentialAndVocabularyTree', 'Exhaustive', 'Frustum'],
             exclusive=True,
             uid=[0],
         ),
@@ -60,6 +67,7 @@ Thanks to this node, the FeatureMatching node will only compute the matches betw
             description='Input name for the vocabulary tree file.',
             value=os.environ.get('ALICEVISION_VOCTREE', ''),
             uid=[],
+            enabled=lambda node: 'VocabularyTree' in node.method.value,
         ),
         desc.File(
             name='weights',
@@ -68,6 +76,7 @@ Thanks to this node, the FeatureMatching node will only compute the matches betw
             value='',
             uid=[0],
             advanced=True,
+            enabled=lambda node: 'VocabularyTree' in node.method.value,
         ),
         desc.ChoiceParam(
             name='matchingMode',
@@ -86,6 +95,7 @@ Thanks to this node, the FeatureMatching node will only compute the matches betw
             range=(0, 500, 1),
             uid=[0],
             advanced=True,
+            enabled=lambda node: 'VocabularyTree' in node.method.value,
         ),
         desc.IntParam(
             name='maxDescriptors',
@@ -95,24 +105,27 @@ Thanks to this node, the FeatureMatching node will only compute the matches betw
             range=(0, 100000, 1),
             uid=[0],
             advanced=True,
+            enabled=lambda node: 'VocabularyTree' in node.method.value,
         ),
         desc.IntParam(
             name='nbMatches',
             label='Voc Tree: Nb Matches',
             description='The number of matches to retrieve for each image (If 0 it will retrieve all the matches).',
-            value=50,
+            value=40,
             range=(0, 1000, 1),
             uid=[0],
             advanced=True,
+            enabled=lambda node: 'VocabularyTree' in node.method.value,
         ),
         desc.IntParam(
             name='nbNeighbors',
             label='Sequential: Nb Neighbors',
             description='The number of neighbors to retrieve for each image (If 0 it will retrieve all the neighbors).',
-            value=50,
+            value=5,
             range=(0, 1000, 1),
             uid=[0],
             advanced=True,
+            enabled=lambda node: 'Sequential' in node.method.value,
         ),
         desc.ChoiceParam(
             name='verboseLevel',

meshroom/ui/qml/main.qml

@@ -408,6 +408,13 @@ ApplicationWindow {
             }
             Menu {
                 title: "New Pipeline"
+                TextMetrics {
+                    id: textMetrics
+                    font: action_PG_CT.font
+                    elide: Text.ElideNone
+                    text: action_PG_CT.text
+                }
+                implicitWidth: textMetrics.width + 10  // largest text width + margin
                 Action {
                     text: "Photogrammetry"
                     onTriggered: ensureSaved(function() { _reconstruction.new("photogrammetry") })
@@ -420,6 +427,11 @@ ApplicationWindow {
                     text: "Panorama Fisheye HDR"
                     onTriggered: ensureSaved(function() { _reconstruction.new("panoramafisheyehdr") })
                 }
+                Action {
+                    id: action_PG_CT
+                    text: "Photogrammetry and Camera Tracking (experimental)"
+                    onTriggered: ensureSaved(function() { _reconstruction.new("photogrammetryandcameratracking") })
+                }
                 Action {
                     text: "Camera Tracking (experimental)"
                     onTriggered: ensureSaved(function() { _reconstruction.new("cameratracking") })

meshroom/ui/reconstruction.py

@@ -490,6 +490,9 @@ class Reconstruction(UIGraph):
         elif p.lower() == "panoramafisheyehdr":
             # default panorama fisheye hdr pipeline
             self.setGraph(multiview.panoramaFisheyeHdr())
+        elif p.lower() == "photogrammetryandcameratracking":
+            # default camera tracking pipeline
+            self.setGraph(multiview.photogrammetryAndCameraTracking())
         elif p.lower() == "cameratracking":
             # default camera tracking pipeline
             self.setGraph(multiview.cameraTracking())