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cgtinker avatar cgtinker commented on June 14, 2024 1

I guess the issue is that bpy.ops.wm.cgt_feature_detection_operator is bound to blenders window manager (mainly to show realtime updates) and while running blender headless this results in an error.

BlendArMocap/src/cgt_mediapipe/cgt_mp_detection_operator.py
if you check the operator, you'll see it uses the window manager. You can just use the operator as baseline to create your own operator, remove the modal, window manger stuff and just use a while loop in the execute method.

If you have an issue with the opencv image display (idk if that's an issue in the docker as there is no window manager), you'll find a get stream method in the operator. BlendArMocap/src/cgt_mediapipe/cgt_mp_core/cv_stream.py
-> You'll probably just overwrite the streams 'draw' method so it just passes on draw and should be good in this case.

So either overwrite in the add-ons operator directly or create an own operator :)

Quick outline:

class DOCKER_CGT_MP_modal_detection_operator(bpy.types.Operator):
    # register the new operator with a custom id
    ...

    def get_chain(self, stream) -> cgt_nodes.NodeChain:
        ...
       return chain

    def get_stream(self):
        # overwrite the stream if necessary
        return stream

    def execute(self, context):
        # here are some changes
        self.user = context.scene.cgtinker_mediapipe  # noqa

        # init stream and chain
        stream = self.get_stream()
        self.node_chain = self.get_chain(stream)
        if self.node_chain is None:
            self.user.modal_active = False
            return {'FINISHED'}

        # memo skipped frames
        self.memo = []
        while self. overwritten_modal(context) == {'PASS_THROUGH'}:
              pass
        return {'OPERATOR_FINISHED'}

    @staticmethod
    def simple_smoothing(memo, cur):
         ...
        return memo

    def overwritten_modal(self, context):
        """ Run detection as modal operation, finish with 'Q', 'ESC' or 'RIGHT MOUSE'. """
       if self.user.detection_input_type == 'movie':
           # get data
           data, _frame = self.node_chain.nodes[0].update([], self.frame)
           if data is None:
               return self.cancel(context)
           self.simple_smoothing(self.memo, data)
           if self.frame % self.key_step == 0:
               for node in self.node_chain.nodes[1:]:
                   node.update(self.memo, self.frame)
               self.memo.clear()

           self.frame += 1
        return {'PASS_THROUGH'}

    def cancel(self, context):
        """ Upon finishing detection clear the handlers. """
        del self.node_chain
        return {'FINISHED'}

from blendarmocap.

NikKaem avatar NikKaem commented on June 14, 2024

I just found the time to adapt the operator and it works like a charm. Haven't build a Docker container out of it yet but I can fully run the end-2-end process from the terminal now. Thank you so much! Is that something you would be interested in having as well?

from blendarmocap.

cgtinker avatar cgtinker commented on June 14, 2024

You are welcome, glad it works!
Feel free to create a separate folder like src/docker with a short readme so others which might need to run detection in a docker can use your implementation swell :)

from blendarmocap.

Niko-shvets avatar Niko-shvets commented on June 14, 2024

@NikKaem It will be great if you would sare your solution. Thank you!

from blendarmocap.

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