wip fixup automasking, show coord point

nodes.py

@@ -8,6 +8,8 @@ import ast
 
 import sys
 
+import cv2
+
 # Add the directory containing 'sam2_realtime' to sys.path
 current_directory = os.path.dirname(os.path.abspath(__file__))
 sam2_realtime_path = os.path.join(current_directory)  # Adjust the relative path
@@ -31,7 +33,7 @@ class DownloadAndLoadSAM2RealtimeModel:
     def INPUT_TYPES(s):
         return {"required": {
             "model": ([ 
-                    'sam2_hiera_tiny.pt', 'sam2_hiera_small.pt',
+                    'sam2_hiera_tiny.pt',
                     ],),
             "segmentor": (
                     ['realtime'],
@@ -64,14 +66,12 @@ class DownloadAndLoadSAM2RealtimeModel:
 
         download_path = os.path.join(folder_paths.models_dir, "sam2")
         model_path = os.path.join(download_path, model)
+        print("model_path: ", model_path)
 
-        if not os.path.exists(download_path):
-            os.makedirs(download_path)
+        url = "https://dl.fbaipublicfiles.com/segment_anything_2/072824/sam2_hiera_tiny.pt"
 
         if not os.path.exists(model_path):
             print(f"Downloading SAM2 model to: {model_path}")
-            base_url = "https://dl.fbaipublicfiles.com/segment_anything_2/072824/"
-            url = f"{base_url}{model}"
             response = requests.get(url, stream=True)
             response.raise_for_status()
 
@@ -84,9 +84,8 @@ class DownloadAndLoadSAM2RealtimeModel:
 
         config_dir = os.path.join(script_directory, "sam2_configs") 
 
-        model_cfg = model.replace(".pt", ".yaml")
-
         # Code ripped out of sam2.build_sam.build_sam2_camera_predictor to appease Hydra
+        model_cfg = "sam2_hiera_t.yaml" #TODO(pschroedl): remove hardcoded config and path
         with initialize_config_dir(config_dir=config_dir, version_base=None):
             cfg = compose(config_name=model_cfg)
 
@@ -144,18 +143,20 @@ class Sam2RealtimeSegmentation:
                 "sam2_model": ("SAM2MODEL",),
                 # "keep_model_loaded": ("BOOLEAN", {"default": True}),
             },
-            "optional": {
-                "coordinates_positive": ("STRING", ),
-                "coordinates_negative": ("STRING", ),
-                "reset_tracking": ("BOOLEAN", {"default": False}),
+           "optional": {
+                "coordinates_positive": ("STRING", {"forceInput": True}),
+                "point_labels": ("STRING", {"forceInput": True}),
+                # "coordinates_negative": ("STRING", {"forceInput": True}),
                 # "bboxes": ("BBOX", ),
                 # "individual_objects": ("BOOLEAN", {"default": False}),
                 # "mask": ("MASK", ),
+                "threshold": ("FLOAT", {"forceInput": True}),
+                "show_point": ("BOOLEAN", {"default": False}),
             },
         }
 
-    RETURN_NAMES = ("PROCESSED_IMAGES", "MASK",)
-    RETURN_TYPES = ("IMAGE", "MASK",)
+    RETURN_NAMES = ("PROCESSED_IMAGES","MASK",)
+    RETURN_TYPES = ("IMAGE", "IMAGE",)
     FUNCTION = "segment_images"
     CATEGORY = "SAM2-Realtime"
 
@@ -163,31 +164,39 @@ class Sam2RealtimeSegmentation:
         self.predictor = None
         self.if_init = False
 
-    def _process_coordinate_input(self, coordinates, label):
-        """Helper function to process coordinate inputs safely"""
-        if not coordinates:
-            return [], []
-        try:
-            coord_list = ast.literal_eval(coordinates)
-            points = [tuple(map(int, point)) for point in coord_list]
-            labels = [label] * len(points)
-            return points, labels
-        except (ValueError, SyntaxError) as e:
-            print(f"Error processing coordinates: {e}")
-            return [], []
 
-    def _process_mask_logits(self, out_mask_logits, frame_shape, device):
-        """Helper function to process mask logits"""
-        if out_mask_logits.shape[0] > 0:
-            mask = (out_mask_logits[0, 0] > 0.5).byte()
-            mask = torch.nn.functional.interpolate(
-                mask.unsqueeze(0).unsqueeze(0).float(), 
-                size=frame_shape[:2],
-                mode='nearest'
-            ).squeeze().byte().to(device)
-        else:
-            mask = torch.ones(frame_shape[:2], device=device, dtype=torch.uint8)
-        return mask
+    def _process_mask(self, mask: np.ndarray, frame_shape: tuple) -> np.ndarray:
+        if mask.shape[0] == 0:
+            logging.warning("Empty mask received")
+            return np.zeros((frame_shape[0], frame_shape[1]), dtype="uint8")
+        
+        colors = [
+            [255, 0, 255],  # Purple
+            [0, 255, 255],  # Yellow
+            [255, 255, 0],  # Cyan
+            [0, 255, 0],    # Green
+            [255, 0, 0],    # Blue
+        ]
+        
+        combined_colored_mask = np.zeros((frame_shape[0], frame_shape[1], 4), dtype="uint8")
+        
+        for i in range(mask.shape[0]):
+            current_mask = (mask[i, 0] > 0).cpu().numpy().astype("uint8") * 255
+            if current_mask.shape[:2] != frame_shape[:2]:
+                current_mask = cv2.resize(current_mask, (frame_shape[1], frame_shape[0]))
+            
+            # Create BGRA mask with transparency
+            colored_mask = np.zeros((frame_shape[0], frame_shape[1], 4), dtype="uint8")
+            color = colors[i % len(colors)]
+            colored_mask[current_mask > 0] = color + [128]  # Add alpha value of 128
+            
+            # Alpha blend with existing masks
+            alpha = colored_mask[:, :, 3:4] / 255.0
+            combined_colored_mask = (1 - alpha) * combined_colored_mask + alpha * colored_mask
+
+        # Convert back to BGR for display
+        combined_colored_mask = combined_colored_mask[:, :, :3].astype("uint8")
+        return combined_colored_mask
 
     def segment_images(
         self,
@@ -195,80 +204,85 @@ class Sam2RealtimeSegmentation:
         sam2_model,
         # keep_model_loaded,
         coordinates_positive=None,
-        coordinates_negative=None,
-        reset_tracking=False,
-        #point_labels=None,
+        # coordinates_negative=None,
+        point_labels=None,
         # bboxes=None,
         # individual_objects=False,
         # mask=None,
+        threshold=0.5,
+        show_point=False,
     ):
         model = sam2_model["model"]
+        device = sam2_model["device"]
+
         device = torch.device("cuda")
         model.to(device)
 
         processed_frames = []
         mask_list = []
-        # The `model` variable is now ready and equivalent to `predictor` returned by sam2.build_sam.build_sam2_camera_predictor
-        
-        if reset_tracking:
-            self.if_init = False
-            self.predictor = None
-
+        # The `model` is equivalent to `predictor` returned by sam2.build_sam.build_sam2_camera_predictor
         if self.predictor is None:
-            self.predictor = model
+            self.predictor = model   
 
-        # Process coordinates once, outside the frame loop
-        pos_points, pos_labels = self._process_coordinate_input(coordinates_positive, 1)
-        neg_points, neg_labels = self._process_coordinate_input(coordinates_negative, 0)
-        all_points = pos_points + neg_points
-        all_labels = pos_labels + neg_labels
-
-        if all_points:
-            points_tensor = torch.tensor([all_points], device=device)
-            labels_tensor = torch.tensor([all_labels], device=device)
-
-        with torch.inference_mode(), torch.autocast("cuda", dtype=torch.float16):
-            for frame_idx, frame in enumerate(images):
+        def process_frame(frame, frame_idx):
+            with torch.inference_mode(), torch.autocast("cuda", dtype=torch.float16):
                 frame = frame.to(device).float()
 
                 if not self.if_init:
                     self.predictor.load_first_frame(frame)
                     self.if_init = True
 
-                    if all_points:
+                    coordinates_positive_list = ast.literal_eval(coordinates_positive)
+                    point_labels_list = ast.literal_eval(point_labels)
+                    point_labels_list = list(map(int, point_labels_list))
+
+                    for idx, point in enumerate(coordinates_positive_list):
+                        point_tuple = tuple(map(int, point))
                         _, _, out_mask_logits = self.predictor.add_new_prompt(
-                            frame_idx=0,
-                            obj_id=1,
-                            points=points_tensor,
-                            labels=labels_tensor,
+                            frame_idx=0, 
+                            obj_id=idx + 1, 
+                            points=[point_tuple], 
+                            labels=[point_labels_list[idx]]
                         )
-                    else:
-                        out_mask_logits = torch.zeros((0,), device=device)
                 else:
                     out_obj_ids, out_mask_logits = self.predictor.track(frame)
 
-                # Process mask logits
-                mask = self._process_mask_logits(out_mask_logits, frame.shape, device)
+                if out_mask_logits.shape[0] > 0:
+                    mask = (out_mask_logits[0, 0] > threshold).byte()
+                    mask = torch.nn.functional.interpolate(
+                        mask.unsqueeze(0).unsqueeze(0).float(), 
+                        size=(frame.shape[0], frame.shape[1]), 
+                        mode='nearest'
+                    ).squeeze(0).squeeze(0).byte()  # Move the interpolated mask to the correct device
+                else:
+                    mask = torch.ones((frame.shape[0], frame.shape[1]), device=device, dtype=torch.uint8)
 
-                # Create colored overlay for processed frames
-                mask_colored = torch.stack([mask] * 3, dim=2)
+                automask_colored = self._process_mask(mask,frame.shape)
 
-                overlayed_frame = torch.add(frame * 0.7, mask_colored * 0.3)
-                
-                processed_frames.append(overlayed_frame)
-                mask_list.append(mask)
+                # Draw points on the mask
+                if show_point:
+                    for point in coordinates_positive:
+                        cv2.circle(automask_colored, tuple(point), radius=5, color=(0, 0, 255), thickness=-1)
+
+                automasked_frame = torch.add(frame * 0.7, automask_colored * 0.3)
+                processed_frames.append(automasked_frame)
+
+                # TODO: This "mask" should be 1 channel to be returned as MASK type
+                constructed_mask = torch.add(frame * 0.1, mask * 0.9)
+                mask_list.append(constructed_mask)
+
+
+        for frame_idx, img in enumerate(images):
+            process_frame(img, frame_idx)
 
-        # Stack masks and frames
         stacked_masks = torch.stack(mask_list, dim=0)
-        stacked_frames = torch.stack(processed_frames, dim=0)
-        
+        stacked_frames = torch.stack(processed_frames, dim=0) 
         return (stacked_frames, stacked_masks)
 
 NODE_CLASS_MAPPINGS = {
     "DownloadAndLoadSAM2RealtimeModel": DownloadAndLoadSAM2RealtimeModel,
     "Sam2RealtimeSegmentation": Sam2RealtimeSegmentation
 }
-
 NODE_DISPLAY_NAME_MAPPINGS = {
     "DownloadAndLoadSAM2RealtimeModel": "(Down)Load sam2_realtime Model",
     "Sam2RealtimeSegmentation": "Sam2RealtimeSegmentation"

pyproject.toml

@@ -1,29 +1,6 @@
-[project]
-name = "sam2_realtime_forktest"
-description = "This extension provides object segmentation capabilities for ComfyUI workflows"
-version = "0.0.4"
-license = { file = "LICENSE" }
-dependencies = [
-    "sam2_realtime @ git+https://github.com/pschroedl/ComfyUI-SAM2-Realtime.git@main",
-    "pyyaml>6.0.2",
-    "numpy>=1.24.4",
-    "tqdm>=4.66.1",
-    "hydra-core>=1.3.2",
-    "iopath>=0.1.10",
-    "pillow>=9.4.0"
-]
-
-[project.urls]
-Repository = "https://github.com/eliteprox/ComfyUI-SAM2-Realtime"
-
 [build-system]
 requires = [
     "setuptools>=61.0",  
     "torch>=2.3.1",
     ]
 build-backend = "setuptools.build_meta"
-
-[tool.comfy]
-PublisherId = "eliteprox"
-DisplayName = "ComfyUI-SAM2-Realtime-TEST"
-Icon = ""

requirements.txt

@@ -3,5 +3,4 @@ numpy>=1.24.4
 tqdm>=4.66.1
 hydra-core>=1.3.2
 iopath>=0.1.10
-pillow>=9.4.0
-git+https://github.com/pschroedl/ComfyUI-SAM2-Realtime.git@main#egg=sam2_realtime
+pillow>=9.4.0

sam2_configs/sam2_hiera_small.yaml

@@ -1,116 +0,0 @@
-# @package _global_
-
-# Model
-model:
-  _target_: sam2_realtime.modeling.sam2_base.SAM2Base
-  image_encoder:
-    _target_: sam2_realtime.modeling.backbones.image_encoder.ImageEncoder
-    scalp: 1
-    trunk:
-      _target_: sam2_realtime.modeling.backbones.hieradet.Hiera
-      embed_dim: 96
-      num_heads: 1
-      stages: [1, 2, 11, 2]
-      global_att_blocks: [7, 10, 13]
-      window_pos_embed_bkg_spatial_size: [7, 7]
-    neck:
-      _target_: sam2_realtime.modeling.backbones.image_encoder.FpnNeck
-      position_encoding:
-        _target_: sam2_realtime.modeling.position_encoding.PositionEmbeddingSine
-        num_pos_feats: 256
-        normalize: true
-        scale: null
-        temperature: 10000
-      d_model: 256
-      backbone_channel_list: [768, 384, 192, 96]
-      fpn_top_down_levels: [2, 3]  # output level 0 and 1 directly use the backbone features
-      fpn_interp_model: nearest
-
-  memory_attention:
-    _target_: sam2_realtime.modeling.memory_attention.MemoryAttention
-    d_model: 256
-    pos_enc_at_input: true
-    layer:
-      _target_: sam2_realtime.modeling.memory_attention.MemoryAttentionLayer
-      activation: relu
-      dim_feedforward: 2048
-      dropout: 0.1
-      pos_enc_at_attn: false
-      self_attention:
-        _target_: sam2_realtime.modeling.sam.transformer.RoPEAttention
-        rope_theta: 10000.0
-        feat_sizes: [32, 32]
-        embedding_dim: 256
-        num_heads: 1
-        downsample_rate: 1
-        dropout: 0.1
-      d_model: 256
-      pos_enc_at_cross_attn_keys: true
-      pos_enc_at_cross_attn_queries: false
-      cross_attention:
-        _target_: sam2_realtime.modeling.sam.transformer.RoPEAttention
-        rope_theta: 10000.0
-        feat_sizes: [32, 32]
-        rope_k_repeat: True
-        embedding_dim: 256
-        num_heads: 1
-        downsample_rate: 1
-        dropout: 0.1
-        kv_in_dim: 64
-    num_layers: 4
-
-  memory_encoder:
-      _target_: sam2_realtime.modeling.memory_encoder.MemoryEncoder
-      out_dim: 64
-      position_encoding:
-        _target_: sam2_realtime.modeling.position_encoding.PositionEmbeddingSine
-        num_pos_feats: 64
-        normalize: true
-        scale: null
-        temperature: 10000
-      mask_downsampler:
-        _target_: sam2_realtime.modeling.memory_encoder.MaskDownSampler
-        kernel_size: 3
-        stride: 2
-        padding: 1
-      fuser:
-        _target_: sam2_realtime.modeling.memory_encoder.Fuser
-        layer:
-          _target_: sam2_realtime.modeling.memory_encoder.CXBlock
-          dim: 256
-          kernel_size: 7
-          padding: 3
-          layer_scale_init_value: 1e-6
-          use_dwconv: True  # depth-wise convs
-        num_layers: 2
-
-  num_maskmem: 7
-  image_size: 512
-  # apply scaled sigmoid on mask logits for memory encoder, and directly feed input mask as output mask
-  sigmoid_scale_for_mem_enc: 20.0
-  sigmoid_bias_for_mem_enc: -10.0
-  use_mask_input_as_output_without_sam: true
-  # Memory
-  directly_add_no_mem_embed: true
-  # use high-resolution feature map in the SAM mask decoder
-  use_high_res_features_in_sam: true
-  # output 3 masks on the first click on initial conditioning frames
-  multimask_output_in_sam: true
-  # SAM heads
-  iou_prediction_use_sigmoid: True
-  # cross-attend to object pointers from other frames (based on SAM output tokens) in the encoder
-  use_obj_ptrs_in_encoder: true
-  add_tpos_enc_to_obj_ptrs: false
-  only_obj_ptrs_in_the_past_for_eval: true
-  # object occlusion prediction
-  pred_obj_scores: true
-  pred_obj_scores_mlp: true
-  fixed_no_obj_ptr: true
-  # multimask tracking settings
-  multimask_output_for_tracking: true
-  use_multimask_token_for_obj_ptr: true
-  multimask_min_pt_num: 0
-  multimask_max_pt_num: 1
-  use_mlp_for_obj_ptr_proj: true
-  # Compilation flag
-  compile_image_encoder: False

sam2_configs/sam2_hiera_t.yaml

@@ -85,7 +85,7 @@ model:
         num_layers: 2
 
   num_maskmem: 7
-  image_size: 512
+  image_size: 1024
   # apply scaled sigmoid on mask logits for memory encoder, and directly feed input mask as output mask
   # SAM decoder
   sigmoid_scale_for_mem_enc: 20.0

sam2_realtime/build_sam.py

@@ -85,7 +85,7 @@ def build_sam2_camera_predictor(
     apply_postprocessing=True,
 ):
     if GlobalHydra.instance().is_initialized():
-        GlobalHydra.instance().clear()
+    GlobalHydra.instance().clear()
 
     # Initialize Hydra to load the configuration
     config_path = "sam2_configs"

sam2_realtime/sam2_tensor_predictor.py

@@ -14,6 +14,7 @@ from tqdm import tqdm
 from sam2_realtime.modeling.sam2_base import NO_OBJ_SCORE, SAM2Base
 from sam2_realtime.utils.misc import concat_points, fill_holes_in_mask_scores, load_video_frames
 
+
 class SAM2TensorPredictor(SAM2Base):
     """The predictor class to handle user interactions and manage inference states."""
 
@@ -54,9 +55,7 @@ class SAM2TensorPredictor(SAM2Base):
             img = img.float()
         else:
             raise ValueError("Input must be a numpy array or a PyTorch tensor")
-        #save original height/width
-        orig_h, orig_w = img.shape[1:]
-        
+
         # Resize to the target size (supports tensor resizing)
         img = torch.nn.functional.interpolate(
             img.unsqueeze(0), size=(image_size, image_size), mode="bilinear", align_corners=False
@@ -69,29 +68,28 @@ class SAM2TensorPredictor(SAM2Base):
         img /= img_std
 
         height, width = img.shape[1:]  # CHW format
-        return img, width, height, orig_w, orig_h
+        return img, width, height
 
     @torch.inference_mode()
     def load_first_frame(self, img):
         if isinstance(img, torch.Tensor):
             img = img.to(self.device)  # Ensure the tensor is on the correct device
-            
+
         self.condition_state = self._init_state(
             offload_video_to_cpu=False, offload_state_to_cpu=False
         )
-        img, width, height, orig_w, orig_h = self.prepare_data(img, image_size=self.image_size)
-        self._orig_hw = (orig_w, orig_h)
+        img, width, height = self.prepare_data(img, image_size=self.image_size)
         self.condition_state["images"] = [img]
         self.condition_state["num_frames"] = len(self.condition_state["images"])
         self.condition_state["video_height"] = height
         self.condition_state["video_width"] = width
         self._get_image_feature(frame_idx=0, batch_size=1)
-    
+
     def add_conditioning_frame(self, img):
         if isinstance(img, torch.Tensor):
             img = img.to(self.device)  # Ensure the tensor is on the correct device
 
-        img, width, height, _, _ = self.prepare_data(img, image_size=self.image_size)
+        img, width, height = self.prepare_data(img, image_size=self.image_size)
         self.condition_state["images"].append(img)
         self.condition_state["num_frames"] = len(self.condition_state["images"])
         self._get_image_feature(
@@ -237,15 +235,14 @@ class SAM2TensorPredictor(SAM2Base):
                     points = torch.cat([box_coords, points], dim=1)
                     labels = torch.cat([box_labels, labels], dim=1)
         if normalize_coords:
-            #video_H = self.condition_state["video_height"]
-            #video_W = self.condition_state["video_width"]
-            orig_w, orig_h = self._orig_hw
-            
-            points = points / torch.tensor([orig_w, orig_h]).to(points.device)
+            video_H = self.condition_state["video_height"]
+            video_W = self.condition_state["video_width"]
+            points = points / torch.tensor([video_W, video_H]).to(points.device)
         # scale the (normalized) coordinates by the model's internal image size
         points = points * self.image_size
         points = points.to(self.condition_state["device"])
         labels = labels.to(self.condition_state["device"])
+
         if not clear_old_points:
             point_inputs = point_inputs_per_frame.get(frame_idx, None)
         else:
@@ -345,16 +342,14 @@ class SAM2TensorPredictor(SAM2Base):
         if labels.dim() == 1:
             labels = labels.unsqueeze(0)  # add batch dimension
         if normalize_coords:
-            #video_H = self.condition_state["video_height"]
-            #video_W = self.condition_state["video_width"]
-            orig_w, orig_h = self._orig_hw
-            
-            points = points / torch.tensor([orig_w, orig_h]).to(points.device)
-            
+            video_H = self.condition_state["video_height"]
+            video_W = self.condition_state["video_width"]
+            points = points / torch.tensor([video_W, video_H]).to(points.device)
         # scale the (normalized) coordinates by the model's internal image size
         points = points * self.image_size
         points = points.to(self.condition_state["device"])
         labels = labels.to(self.condition_state["device"])
+
         if not clear_old_points:
             point_inputs = point_inputs_per_frame.get(frame_idx, None)
         else:
@@ -774,7 +769,7 @@ class SAM2TensorPredictor(SAM2Base):
         if isinstance(img, torch.Tensor):
             img = img.to(self.device)  # Ensure the tensor is on the correct device
 
-        img, _, _ , _, _ = self.prepare_data(img, image_size=self.image_size)
+        img, _, _ = self.prepare_data(img, image_size=self.image_size)
 
         output_dict = self.condition_state["output_dict"]
         obj_ids = self.condition_state["obj_ids"]

sam2_realtime/utils/misc.py

@@ -58,7 +58,7 @@ def get_connected_components(mask):
     - counts: A tensor of shape (N, 1, H, W) containing the area of the connected
               components for foreground pixels and 0 for background pixels.
     """
-    from sam2_realtime import _C
+    from sam2 import _C
 
     return _C.get_connected_componnets(mask.to(torch.uint8).contiguous())
 

setup.py

@@ -4,10 +4,6 @@
 # This source code is licensed under the license found in the
 # LICENSE file in the root directory of this source tree.
 
-import os
-# Set the CUDA architecture list
-os.environ["TORCH_CUDA_ARCH_LIST"] = "8.0 8.6+PTX 8.7 9.0 9.0a"
-
 from setuptools import find_packages, setup
 from torch.utils.cpp_extension import BuildExtension, CUDAExtension
 
@@ -32,7 +28,7 @@ REQUIRED_PACKAGES = [
 ]
 
 def get_extensions():
-    srcs = ["sam2_realtime/csrc/connected_components.cu"]
+    srcs = ["sam2/csrc/connected_components.cu"]
     compile_args = {
         "cxx": [],
         "nvcc": [
@@ -42,7 +38,7 @@ def get_extensions():
             "-D__CUDA_NO_HALF2_OPERATORS__",
         ],
     }
-    ext_modules = [CUDAExtension("sam2_realtime._C", srcs, extra_compile_args=compile_args)]
+    ext_modules = [CUDAExtension("sam2._C", srcs, extra_compile_args=compile_args)]
     return ext_modules
 
 
@@ -57,7 +53,7 @@ setup(
     license=LICENSE,
     packages=find_packages(),
     install_requires=REQUIRED_PACKAGES,
-    python_requires=">=3.10.15",
+    python_requires=">=3.11.10",
     ext_modules=get_extensions(),
     cmdclass={"build_ext": BuildExtension.with_options(no_python_abi_suffix=True)},
 )