add vidoe nodes

Improve node category organization

README.md

@@ -216,6 +216,37 @@ Integrate the [wan2.1-vace] video generation model to inpaint empty or newly rev
 
 ---
 
+## Trajectory Concept
+
+A **trajectory** in camera-comfyUI is a sequence of camera poses, each represented as a 4×4 transformation matrix. This set of matrices defines the path and orientation of the camera through 3D space, enabling smooth and complex camera movements for view synthesis, point cloud rendering, and video generation.
+
+### Creating Trajectories
+
+There are two main ways to create a trajectory:
+
+- **Camera Matrices Interpolation:**  
+  Define two or more camera poses (as matrices), and interpolate between them to generate a smooth path. The `CameraInterpolationNode` automates this process, producing a trajectory tensor for use in camera motion nodes.
+
+- **Walking in Open3D Environment:**  
+  Use the interactive Open3D GUI (`CameraTrajectoryNode`) to "walk" through the point cloud. As you move the camera, waypoints (poses) are recorded, forming a trajectory that can be exported and reused.
+
+### Using Trajectories
+
+The `CameraMotionNode` takes a trajectory (set of matrices) and interpolates camera positions and orientations along it, producing smooth camera movements for rendering sequences or videos.
+
+---
+
+## Point Cloud Formats
+
+Point clouds can be saved and loaded in two formats:
+
+- **.npy**: Numpy array format (fast, preserves all tensor data, recommended for internal pipelines).
+- **.ply**: Polygon File Format (widely supported, viewable in external 3D tools).
+
+Use the `SavePointCloud` and `LoadPointCloud` nodes to handle I/O operations in either format.
+
+---
+
 ## Contributing
 
 Contributions welcome! Please open issues or PRs to add features, improve docs, or refine workflows.
@@ -229,5 +260,5 @@ Contributions welcome! Please open issues or PRs to add features, improve docs,
 * [x] Implement easier and more flexible camera control - more complex camera movements with more than 2 points.
 * [x] Add more examples and documentation for each node.
 * [x] Add pointcloud union
-* [ ] Fix imports for renamed folders (e.g., inpainting_flux)
+* [x] Fix imports for renamed folders (e.g., inpainting_flux)
 * [x] Integrate camera movement pipeline with video models (e.g., wan2.1) for smooth, high-quality inpainting along camera trajectories.

__init__.py

@@ -3,6 +3,7 @@ from .reprojection_nodes import NODE_CLASS_MAPPINGS as NCM2
 from .metric_depth_nodes import NODE_CLASS_MAPPINGS as NCM3
 from .flux_fisheye_filling_nodes import NODE_CLASS_MAPPINGS as NCM4
 from .complex_nodes import NODE_CLASS_MAPPINGS as NCM5
-NODE_CLASS_MAPPINGS = {**NCM1, **NCM2, **NCM3, **NCM4, **NCM5}
+from .video_nodes import NODE_CLASS_MAPPINGS as NCM6
+NODE_CLASS_MAPPINGS = {**NCM1, **NCM2, **NCM3, **NCM4, **NCM5, **NCM6}
 
 __all__ = ["NODE_CLASS_MAPPINGS"]

pointcloud_nodes.py

@@ -393,6 +393,7 @@ class ProjectPointCloud:
         img4 = flat.view(output_height, output_width, 4)
         rgb   = img4[..., :3].clamp(0, 255)
         alpha = (img4[..., 3] > 0).float()
+        mask_init= (img4[..., 3] > 0)  # initial mask
         rgb  *= alpha.unsqueeze(-1)
         depth_img = z_front.view(output_height, output_width)
         rgb_HR = rgb
@@ -406,7 +407,6 @@ class ProjectPointCloud:
             idxbuf.fill_(-1)
             idxbuf.scatter_reduce_(0, pix, order_m, reduce='amax', include_self=True)
             win_back = idxbuf[pix] >= 0
-
             flat.fill_(0)
             flat[pix[win_back]] = colors[win_back]
             back4 = flat.view(output_height, output_width, 4)
@@ -430,8 +430,29 @@ class ProjectPointCloud:
                 # merge only at hole locations
                 rgb[hole] = rgb_med[hole]
                 # alpha already set to 1.0 for holes
+            # 8 apply median blur to mask if point_size > 1 and to initial image  
+            mask_t = alpha.unsqueeze(0).unsqueeze(0)  # [1,1,H,W]
+            pad    = point_size // 2
+            ksize  = (point_size, point_size)
 
-        # 8) Pack and return with original script shapes
+            # b) grow (dilate) mask by max‑pool
+            mask_grow = F.max_pool2d(mask_t, kernel_size=ksize, stride=1, padding=pad)
+
+            # c) shrink (erode) by inverting, max‑pool, then inverting back
+            mask_shrink = 1.0 - F.max_pool2d(1.0 - mask_grow, kernel_size=ksize, stride=1, padding=pad)
+
+            # d) back to [H,W] and use as our new alpha
+            alpha = mask_shrink.squeeze(0).squeeze(0)
+            print(1)
+            # e) median‑filter the *whole* RGB image
+            #    prep for kornia: [B,C,H,W]
+            rgb_t_full   = rgb.permute(2,0,1).unsqueeze(0)       # [1,3,H,W]
+            rgb_med_full = median_blur(rgb_t_full, ksize)        # [1,3,H,W]
+            rgb_med_full = rgb_med_full.squeeze(0).permute(1,2,0)  # [H,W,3]
+
+            # g) refill *only* the original holes with the median result
+            rgb[~mask_init] = rgb_med_full[~mask_init]
+        # 9) Pack and return with original script shapes
         img       = rgb.unsqueeze(0)  # [1,H,W,3]
         mask_out  = alpha             # [H,W]
         depth4    = depth_img.unsqueeze(0).unsqueeze(-1)  # [1,H,W,1]

video_nodes.py

@@ -0,0 +1,265 @@
+import torch
+import torch.nn.functional as F
+import numpy as np
+import os
+import sys
+from typing import Dict, Any, Tuple
+from tqdm import tqdm  # Added tqdm import
+
+# Import existing pointcloud nodes and projection definitions
+from .pointcloud_nodes import DepthToPointCloud, TransformPointCloud, ProjectPointCloud, Projection, PointCloudCleaner
+import folder_paths
+
+# Ensure video_depth_anything is on path
+video_depth_path = os.path.join("/root/Video-Depth-Anything", "metric_depth")
+if video_depth_path not in sys.path:
+    sys.path.append(video_depth_path)
+try:
+    from video_depth_anything.video_depth import VideoDepthAnything
+    print("video_depth_anything module loaded successfully.")
+except ImportError:
+    VideoDepthAnything = None
+    print("Warning: video_depth_anything module not found. Ensure it is installed correctly.")
+    print("error: ", sys.exc_info()[1])
+
+
+class VideoCameraMotionSequence:
+    """
+    Takes a sequence of RGB frames and corresponding depth maps,
+    converts each frame+depth to a pointcloud, interpolates a camera
+    trajectory to match video length, cleans the pointcloud if needed,
+    and outputs reprojected images, masks, and depth maps per frame.
+    """
+    @classmethod
+    def INPUT_TYPES(cls) -> Dict[str, Any]:
+        return {
+            "required": {
+                # Sequence of frames: Tensor [T, H, W, 3]
+                "frames": ("IMAGE", {"shape_hint": [None, None, None, 3]}),
+                # Sequence of depth maps: Tensor [T, H, W] or [T, H, W, 1]
+                "depth_seq": ("TENSOR", {"shape_hint": [None, None, None]}),
+                # Camera trajectory waypoints: Tensor [K, 4, 4]
+                "trajectory": ("TENSOR", {"shape_hint": [None, 4, 4]}),
+                # Input projection parameters
+                "input_projection": (Projection.PROJECTIONS, {}),
+                "input_horizontal_fov": ("FLOAT", {"default": 90.0}),
+                "depth_scale": ("FLOAT", {"default": 1.0}),
+                "invert_depth": ("BOOLEAN", {"default": False}),
+                # Output projection parameters
+                "output_projection": (Projection.PROJECTIONS, {}),
+                "output_horizontal_fov": ("FLOAT", {"default": 90.0}),
+                "output_width": ("INT", {"default": 512, "min": 1}),
+                "output_height": ("INT", {"default": 512, "min": 1}),
+                "point_size": ("INT", {"default": 1, "min": 1}),
+                # Cleaning parameters
+                "voxel_size": ("FLOAT", {"default": 1.0, "min": 1e-3}),
+                "min_points_per_voxel": ("INT", {"default": 3, "min": 1}),
+            }
+        }
+
+    RETURN_TYPES = ("IMAGE", "MASK", "TENSOR")
+    RETURN_NAMES = ("video_frames", "mask_frames", "depths")
+    FUNCTION = "process_sequence"
+    CATEGORY = "Camera/Video"
+
+    def process_sequence(
+        self,
+        frames: torch.Tensor,
+        depth_seq: torch.Tensor,
+        trajectory: torch.Tensor,
+        input_projection: str,
+        input_horizontal_fov: float,
+        depth_scale: float,
+        invert_depth: bool,
+        output_projection: str,
+        output_horizontal_fov: float,
+        output_width: int,
+        output_height: int,
+        point_size: int,
+        voxel_size: float,
+        min_points_per_voxel: int,
+    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+        # frames: [T, H, W, 3]
+        # depth_seq: [T, H, W] or [T, H, W, 1]
+        T, H, W, _ = frames.shape
+
+        # Interpolate trajectory to match T
+        K = trajectory.shape[0]
+        if K < 2:
+            interp_traj = trajectory.expand(T, 4, 4).clone()
+        else:
+            idxs = torch.linspace(0, K - 1, T, device=trajectory.device)
+            lower = idxs.floor().long().clamp(max=K - 2)
+            upper = lower + 1
+            alpha = (idxs - lower.float()).unsqueeze(-1).unsqueeze(-1)
+            traj_lower = trajectory[lower]
+            traj_upper = trajectory[upper]
+            interp_traj = traj_lower * (1 - alpha) + traj_upper * alpha
+
+        out_frames = []
+        out_masks = []
+        out_depths = []
+
+        # Add tqdm progress bar for the sequence
+        for frame, depth, pose in tqdm(zip(frames, depth_seq, interp_traj), total=T, desc="Processing video frames"):
+            if depth.dim() == 3 and depth.shape[-1] == 1:
+                depth = depth.squeeze(-1)
+            # to pointcloud
+            pc, = DepthToPointCloud().depth_to_pointcloud(
+                image=frame.permute(2, 0, 1),
+                input_projection=input_projection,
+                input_horizontal_fov=input_horizontal_fov,
+                depth_scale=depth_scale,
+                invert_depth=invert_depth,
+                depthmap=depth,
+                mask=None,
+            )
+            # optional cleaning
+            if min_points_per_voxel > 1:
+                pc, = PointCloudCleaner().clean_pointcloud(
+                    pointcloud=pc,
+                    width=output_width,
+                    height=output_height,
+                    voxel_size=voxel_size,
+                    min_points_per_voxel=min_points_per_voxel,
+                )
+            # transform and project
+            pc_t, = TransformPointCloud().transform_pointcloud(pc, pose)
+            img_t, mask_t, depth_t = ProjectPointCloud().project_pointcloud(
+                pointcloud=pc_t,
+                output_projection=output_projection,
+                output_horizontal_fov=output_horizontal_fov,
+                output_width=output_width,
+                output_height=output_height,
+                point_size=point_size,
+            )
+
+            out_frames.append(img_t[0])
+            out_masks.append(mask_t)
+            out_depths.append(depth_t)
+
+        return (
+            torch.stack(out_frames, dim=0),  # [T, 3, H, W]
+            torch.stack(out_masks, dim=0),   # [T, H, W]
+            torch.stack(out_depths, dim=0),  # [T, H, W]
+        )
+
+
+class DepthFramesToVideo:
+    """
+    Converts a sequence of depth maps into video frame tensors for saving.
+    """
+    @classmethod
+    def INPUT_TYPES(cls) -> Dict[str, Any]:
+        return {
+            "required": {
+                "depth_seq": ("TENSOR", {"shape_hint": [None, None, None]}),
+                "mask_seq": ("MASK", {"shape_hint": [None, None, None]}),
+                "normalize": ("BOOLEAN", {"default": True}),
+                "invert_depth": ("BOOLEAN", {"default": False}),
+            }
+        }
+
+    RETURN_TYPES = ("TENSOR", "IMAGE")
+    RETURN_NAMES = ("video_frames", "depth_video")
+    FUNCTION = "depth_to_video_frames"
+    CATEGORY = "Camera/Video"
+
+    def depth_to_video_frames(
+        self,
+        depth_seq: torch.Tensor,
+        normalize: bool,
+        invert_depth: bool,
+        mask_seq: torch.Tensor,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        ds = depth_seq.clone().squeeze()
+        if ds.dim() == 2:
+            ds = ds.unsqueeze(0)  # [H, W] -> [1, H, W]
+        if ds.dim() != 3:
+            raise ValueError(f"Expected ds to be 3D [T, H, W], got shape {ds.shape}")
+        if invert_depth:
+            ds= 1.0 / (ds + 1e-8)  # Avoid division by zero
+        if normalize:
+            # Mask: only normalize where depth > 0
+            mask = mask_seq>0.5
+            if mask.any():
+                #percentile first 10 percent min
+                # sample 
+                minv = ds[mask]
+                # sample 10000 and find 10% quantile
+                if minv.numel() > 10000:
+                    minv = minv[torch.randperm(minv.numel())[:10000]]
+                
+                minv = minv.quantile(0.2)
+                minv = minv if minv > 0.1 else 0.1  # Avoid division by zero
+                #percentile last 10 percent max
+                maxv = ds[mask]
+                if maxv.numel() > 10000:
+                    maxv = maxv[torch.randperm(maxv.numel())[:10000]]
+                maxv = maxv.quantile(0.98)
+                maxv = maxv if maxv < 100 else 100
+                print(f"Normalizing depth: min={minv}, max={maxv}")
+                ds_norm = (ds - minv) / (maxv - minv + 1e-8)
+                ds = ds_norm.clamp(0, 1)  # torch.where(mask, ds_norm, ds)  # Only normalize valid values
+            else:
+                print("Warning: No valid depth values for normalization.")
+        # expand to 3 channels: [T, H, W] -> [T, 3, H, W]
+        raw = depth_seq.clone().squeeze()
+        ds_u8 = (ds * 255.0).round().to(torch.uint8)
+        raw_u8 = (raw.clamp(0, 255)).to(torch.uint8)  # if raw is already in a displayable range
+
+        # expand to 3 channels and permute to HWC
+        ds_color = ds_u8.unsqueeze(1).repeat(1, 3, 1, 1).permute(0, 2, 3, 1)
+        raw_color = raw_u8.unsqueeze(1).repeat(1, 3, 1, 1).permute(0, 2, 3, 1)
+        return raw_color, ds_color   # [T, 3, H, W] -> [T, H, W, 3]
+
+class VideoMetricDepthEstimate:
+    """
+    Estimates metric depth for a sequence of frames using VideoDepthAnything.
+    """
+    @classmethod
+    def INPUT_TYPES(cls) -> Dict[str, Any]:
+        # model files (.pth) in input directory
+        model_dir = os.path.join(os.getcwd(), "models", "checkpoints")
+        os.makedirs(model_dir, exist_ok=True)
+        files = [f for f in os.listdir(model_dir) if f.lower().endswith(('.pth', '.ckpt', '.safetensors'))]
+        return {
+            "required": {
+                "frames":     ("IMAGE", {"shape_hint": [None, None, None, 3]}),
+                "model_checkpoint": (files,   {"file_chooser": True}),
+                "input_size": ("INT",  {"default": 518, "min": 64, "max": 2048}),
+                "max_fps":    ("INT",  {"default": 60,  "min": 1}),
+            }
+        }
+    RETURN_TYPES = ("TENSOR", "FLOAT")
+    RETURN_NAMES = ("metric_depths", "fps")
+    FUNCTION = "estimate_metric_depth"
+    CATEGORY = "Camera/Video"
+
+    def estimate_metric_depth(
+        self,
+        frames: torch.Tensor,
+        model_checkpoint: str,
+        input_size: int,
+        max_fps: int,
+    ) -> Tuple[torch.Tensor, float]:
+        if VideoDepthAnything is None:
+            raise ImportError("VideoDepthAnything library not found")
+        device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        # if max input<1.5 normalize to 0-255
+        if frames.max() < 1.5:
+            frames = (frames * 255)
+        model = VideoDepthAnything(**{"encoder": "vitl", "features": 256, "out_channels": [256,512,1024,1024]})
+        state = torch.load("/root/ComfyUI/models/checkpoints/{}".format(model_checkpoint), map_location='cpu')
+        model.load_state_dict(state, strict=True)
+        model = model.to(device).eval()
+        np_frames = frames.cpu().numpy().astype(np.uint8)
+        metric_depths, fps = model.infer_video_depth(np_frames, max_fps, input_size=input_size, device=device.type, fp32=False)
+        return (torch.from_numpy(metric_depths), float(fps))
+
+# Register nodes
+NODE_CLASS_MAPPINGS = {
+    "VideoCameraMotionSequence": VideoCameraMotionSequence,
+    "VideoMetricDepthEstimate": VideoMetricDepthEstimate,
+    "DepthFramesToVideo": DepthFramesToVideo,
+}

workflows/Test pointcloud_loading.json

@@ -1 +1,289 @@
-{"id":"dd56c0bf-7405-406e-924f-42b2feacb73f","revision":0,"last_node_id":6,"last_link_id":4,"nodes":[{"id":1,"type":"TransformToMatrix","pos":[-337.9580993652344,1500.5211181640625],"size":[315,154],"flags":{},"order":0,"mode":0,"inputs":[],"outputs":[{"localized_name":"MAT_4X4","name":"MAT_4X4","type":"MAT_4X4","links":[1]}],"properties":{"Node name for S&R":"TransformToMatrix"},"widgets_values":[0,0,0,0,0]},{"id":2,"type":"CameraMotion","pos":[130.0218505859375,1516.7567138671875],"size":[367.79998779296875,218],"flags":{},"order":3,"mode":0,"inputs":[{"localized_name":"pointcloud","name":"pointcloud","type":"TENSOR","link":4},{"localized_name":"initial_matrix","name":"initial_matrix","type":"MAT_4X4","link":1},{"localized_name":"final_matrix","name":"final_matrix","type":"MAT_4X4","link":2}],"outputs":[{"localized_name":"IMAGE","name":"IMAGE","type":"IMAGE","links":[3]}],"properties":{"Node name for S&R":"CameraMotion"},"widgets_values":[24,"PINHOLE",90,1024,1024,2]},{"id":3,"type":"SaveWEBM","pos":[606.5880737304688,1515.5966796875],"size":[315,437],"flags":{},"order":4,"mode":0,"inputs":[{"localized_name":"images","name":"images","type":"IMAGE","link":3}],"outputs":[],"properties":{},"widgets_values":["ComfyUI","vp9",10.000000000000002,32]},{"id":5,"type":"TransformToMatrix","pos":[-262.9134521484375,1740.8876953125],"size":[315,154],"flags":{},"order":1,"mode":0,"inputs":[],"outputs":[{"localized_name":"MAT_4X4","name":"MAT_4X4","type":"MAT_4X4","links":[2]}],"properties":{"Node name for S&R":"TransformToMatrix"},"widgets_values":[0.10000000000000002,0,0,0,0]},{"id":6,"type":"LoadPointCloud","pos":[-357.24761962890625,1294.427734375],"size":[315,58],"flags":{},"order":2,"mode":0,"inputs":[],"outputs":[{"localized_name":"TENSOR","name":"TENSOR","type":"TENSOR","links":[4]}],"properties":{"Node name for S&R":"LoadPointCloud"},"widgets_values":["ComfyUIPointCloud_00001.ply"]}],"links":[[1,1,0,2,1,"MAT_4X4"],[2,5,0,2,2,"MAT_4X4"],[3,2,0,3,0,"IMAGE"],[4,6,0,2,0,"TENSOR"]],"groups":[],"config":{},"extra":{"ds":{"scale":1.351305709310409,"offset":[-187.6257577580669,-1567.2143321744395]}},"version":0.4}
+{
+  "id": "dd56c0bf-7405-406e-924f-42b2feacb73f",
+  "revision": 0,
+  "last_node_id": 8,
+  "last_link_id": 9,
+  "nodes": [
+    {
+      "id": 3,
+      "type": "SaveWEBM",
+      "pos": [
+        606.5880737304688,
+        1515.5966796875
+      ],
+      "size": [
+        315,
+        437
+      ],
+      "flags": {},
+      "order": 5,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "images",
+          "type": "IMAGE",
+          "link": 5
+        }
+      ],
+      "outputs": [],
+      "properties": {},
+      "widgets_values": [
+        "ComfyUI",
+        "vp9",
+        10.000000000000002,
+        32
+      ]
+    },
+    {
+      "id": 7,
+      "type": "CameraMotionNode",
+      "pos": [
+        176.07933044433594,
+        1504.22705078125
+      ],
+      "size": [
+        278.75,
+        270
+      ],
+      "flags": {},
+      "order": 4,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "pointcloud",
+          "type": "TENSOR",
+          "link": 6
+        },
+        {
+          "name": "trajectory",
+          "type": "TENSOR",
+          "link": 9
+        }
+      ],
+      "outputs": [
+        {
+          "name": "motion_frames",
+          "type": "IMAGE",
+          "links": [
+            5
+          ]
+        },
+        {
+          "name": "mask_frames",
+          "type": "MASK",
+          "links": null
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "CameraMotionNode"
+      },
+      "widgets_values": [
+        10,
+        "PINHOLE",
+        90,
+        512,
+        512,
+        1,
+        0,
+        false,
+        false
+      ]
+    },
+    {
+      "id": 6,
+      "type": "LoadPointCloud",
+      "pos": [
+        -357.24761962890625,
+        1294.427734375
+      ],
+      "size": [
+        315,
+        58
+      ],
+      "flags": {},
+      "order": 0,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [
+        {
+          "name": "loaded pointcloud",
+          "type": "TENSOR",
+          "links": [
+            6
+          ]
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "LoadPointCloud"
+      },
+      "widgets_values": [
+        "ComfyUIPointCloud_00001.ply"
+      ]
+    },
+    {
+      "id": 1,
+      "type": "TransformToMatrix",
+      "pos": [
+        -537.2319946289062,
+        1491.40380859375
+      ],
+      "size": [
+        315,
+        154
+      ],
+      "flags": {},
+      "order": 1,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [
+        {
+          "name": "transformation matrix",
+          "type": "MAT_4X4",
+          "links": [
+            7
+          ]
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "TransformToMatrix"
+      },
+      "widgets_values": [
+        0,
+        0,
+        0,
+        0,
+        0
+      ]
+    },
+    {
+      "id": 5,
+      "type": "TransformToMatrix",
+      "pos": [
+        -531.216796875,
+        1701.1632080078125
+      ],
+      "size": [
+        315,
+        154
+      ],
+      "flags": {},
+      "order": 2,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [
+        {
+          "name": "transformation matrix",
+          "type": "MAT_4X4",
+          "links": [
+            8
+          ]
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "TransformToMatrix"
+      },
+      "widgets_values": [
+        0.10000000000000002,
+        0,
+        0,
+        0,
+        0
+      ]
+    },
+    {
+      "id": 8,
+      "type": "CameraInterpolationNode",
+      "pos": [
+        -121.91971588134766,
+        1597.3514404296875
+      ],
+      "size": [
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