ComfyUI version of depth flow

README.md

@@ -1,2 +1,8 @@
 # ComfyUI_DepthFlow
 comfyui custom node for depthflow
+
+original depthflow website: https://github.com/BrokenSource/DepthFlow
+
+check this for installation: https://brokensrc.dev/get/
+I believe run commands below is enough: 
+python -m pip install depthflow 

__init__.py

@@ -0,0 +1,9 @@
+from . import nodes as nodes
+
+NODE_CLASS_MAPPINGS = {
+    "DepthFlowSimple":nodes.DepthFlow,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "DepthFlow Simple": "DepthFlowSimple"
+}

nodes.py

@@ -0,0 +1,70 @@
+from DepthFlow import DepthScene
+from attr import Factory, define
+from Broken.Externals.Depthmap import DepthAnythingV2, DepthEstimator
+import random
+import numpy as np
+import torch
+import os, shutil
+from PIL import Image
+   
+class DepthFlow:
+
+    NAME = "DepthFlow"
+    CATEGORY = "utils"
+    def __init__(self):
+        self.glob_estimator = None
+
+    def tensor2pil(self, image):
+        return Image.fromarray(np.clip(255. * image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8))
+    
+    # PIL to Tensor
+    def pil2tensor(self, image):
+        return torch.from_numpy(np.array(image).astype(np.float32) / 255.0).unsqueeze(0)
+    
+    @classmethod
+    def INPUT_TYPES(s):
+        
+        return {
+      "required": {
+        "images": ("IMAGE",),
+        "fps": ("INT",{"default": 24, "min": 8, "max": 100, "step": 1}), 
+        "width": ("INT",  {"default": 20, "min": 20, "max": 9999, "step": 1}), 
+        "height": ("INT",  {"default": 20, "min": 20, "max": 9999, "step": 1}), 
+        "filename_prefix": ("STRING", {"default": "depthflow"}),
+      },    
+        }
+
+    RETURN_TYPES = ("STRING", )
+    RETURN_NAMES = ("filepath",)
+
+    FUNCTION = "doit"
+
+    CATEGORY = "DepthFlow"
+
+    def IS_CHANGED(s):
+        return False
+
+    def doit(self, images, fps, width, height, filename_prefix):
+        
+        depthflow = DepthScene(backend='headless')
+        
+        if self.glob_estimator == None:   # trick 1 to avoid vram leak
+            self.glob_estimator = depthflow.estimator
+        else:
+            depthflow.estimator = self.glob_estimator
+        
+        img = self.tensor2pil(images[0])
+        frame = img.convert('RGB')
+
+        random_number = random.randint(0, 1073741824)
+        tmpfile_path = os.path.join(filename_prefix, 'in{}.png'.format(random_number))
+        frame.save(tmpfile_path)
+        depthflow.input(image=tmpfile_path)
+        
+        save_path = os.path.join(filename_prefix, str(random_number))
+        depthflow.main(output=save_path, fps=fps, width=width, height=height)
+        depthflow.window.destroy()  # trick 2 to avoid vram leak
+        shutil.os.remove(tmpfile_path)
+        #del depthflow
+
+        return (filename_prefix)