Initial commit with custom depth estimation node for ComfyUI

depth_estimation_node.py

@@ -0,0 +1,108 @@
+import os
+import numpy as np
+import torch
+from transformers import pipeline
+from PIL import Image, ImageFilter, ImageOps
+from comfy.nodes import Node, register_node
+
+def ensure_odd(value):
+    """Ensure the value is an odd integer."""
+    value = int(value)
+    return value if value % 2 == 1 else value + 1
+
+def convert_path(path):
+    """Convert path for compatibility between Windows and WSL."""
+    if os.name == 'nt':  # If running on Windows
+        return path.replace('\\', '/')
+    return path
+
+def gamma_correction(img, gamma=1.0):
+    """Apply gamma correction to the image."""
+    inv_gamma = 1.0 / gamma
+    table = [((i / 255.0) ** inv_gamma) * 255 for i in range(256)]
+    table = np.array(table, np.uint8)
+    return Image.fromarray(np.array(img).astype(np.uint8)).point(lambda i: table[i])
+
+def auto_gamma_correction(image):
+    """Automatically adjust gamma correction for the image."""
+    image_array = np.array(image).astype(np.float32) / 255.0
+    mean_luminance = np.mean(image_array)
+    gamma = np.log(0.5) / np.log(mean_luminance)
+    return gamma_correction(image, gamma=gamma)
+
+def auto_contrast(image):
+    """Apply automatic contrast adjustment to the image."""
+    return ImageOps.autocontrast(image)
+
+class DepthEstimationNode(Node):
+    def __init__(self, blur_radius=2.0, median_size=5, device="cpu"):
+        super().__init__()
+        self.blur_radius = blur_radius
+        self.median_size = ensure_odd(median_size)
+        self.device = 0 if device == "gpu" and torch.cuda.is_available() else -1
+        self.pipe = pipeline(task="depth-estimation", model="LiheYoung/depth-anything-large-hf", device=self.device)
+
+    def process_image(self, image):
+        if self.device == 0:
+            image = image.convert("RGB")  # Ensure image is in RGB format
+            inputs = self.pipe.feature_extractor(images=image, return_tensors="pt").to(self.device)
+            with torch.no_grad():
+                outputs = self.pipe.model(**inputs)
+            result = self.pipe.post_process(outputs, (image.height, image.width))
+        else:
+            result = self.pipe(image)
+
+        # Convert depth data to a NumPy array if not already one
+        depth_data = np.array(result["depth"])
+
+        # Normalize and convert to uint8
+        depth_normalized = (depth_data - depth_data.min()) / (depth_data.max() - depth_data.min() + 1e-8)  # Avoid zero division
+        depth_uint8 = (255 * depth_normalized).astype(np.uint8)
+
+        # Create an image from the processed depth data
+        depth_image = Image.fromarray(depth_uint8)
+
+        # Apply a median filter to reduce noise
+        depth_image = depth_image.filter(ImageFilter.MedianFilter(size=self.median_size))
+
+        # Enhanced edge detection with more feathering
+        edges = depth_image.filter(ImageFilter.FIND_EDGES)
+        edges = edges.filter(ImageFilter.GaussianBlur(radius=2 * self.blur_radius))
+        edges = edges.point(lambda x: 255 if x > 20 else 0)  # Adjusted threshold
+
+        # Create a mask from the edges
+        mask = edges.convert("L")
+
+        # Blur only the edges using the mask
+        blurred_edges = depth_image.filter(ImageFilter.GaussianBlur(radius=self.blur_radius * 2))
+
+        # Combine the blurred edges with the original depth image using the mask
+        combined_image = Image.composite(blurred_edges, depth_image, mask)
+
+        # Apply auto gamma correction with a lower gamma to darken the image
+        gamma_corrected_image = gamma_correction(combined_image, gamma=0.7)
+
+        # Apply auto contrast
+        final_image = auto_contrast(gamma_corrected_image)
+
+        # Additional post-processing: Sharpen the final image
+        final_image = final_image.filter(ImageFilter.SHARPEN)
+
+        return final_image
+
+    def forward(self, image_path: str):
+        if not os.path.exists(image_path):
+            raise FileNotFoundError(f"The input image path does not exist: {image_path}")
+
+        image = Image.open(image_path)
+        return self.process_image(image)
+
+@register_node
+class ComfyUIDepthEstimationNode(DepthEstimationNode):
+    def __init__(self):
+        super().__init__(blur_radius=2.0, median_size=5, device="cpu")
+
+    def execute(self, image_path: str, output_path: str):
+        final_image = self.forward(image_path)
+        final_image.save(output_path)
+        print(f"Processed and saved: {output_path}")

requirements.txt

@@ -0,0 +1,4 @@
+transformers==4.12.3
+torch==1.10.0
+Pillow==8.4.0
+numpy==1.21.2