From a42d2d41e1a29f6dcf1162831ceaf02aa96e36cc Mon Sep 17 00:00:00 2001
From: vladmandic <mandic00@live.com>
Date: Thu, 12 Mar 2026 14:16:50 +0100
Subject: [PATCH] merge: modules/control/proc/depth_anything_v2/__init__.py

---
 .../proc/depth_anything_v2/__init__.py        | 48 +++++++++++++++++++
 1 file changed, 48 insertions(+)
 create mode 100644 modules/control/proc/depth_anything_v2/__init__.py

diff --git a/modules/control/proc/depth_anything_v2/__init__.py b/modules/control/proc/depth_anything_v2/__init__.py
new file mode 100644
index 000000000..7f995fc8d
--- /dev/null
+++ b/modules/control/proc/depth_anything_v2/__init__.py
@@ -0,0 +1,48 @@
+import cv2
+import torch
+import torch.nn.functional as F
+import numpy as np
+from PIL import Image
+from modules import devices, masking
+from modules.shared import opts
+
+
+class DepthAnythingV2Detector:
+    def __init__(self, model, processor):
+        self.model = model
+        self.processor = processor
+
+    @classmethod
+    def from_pretrained(cls, pretrained_model_or_path="depth-anything/Depth-Anything-V2-Small-hf", cache_dir=None, local_files_only=False):
+        from transformers import AutoImageProcessor, AutoModelForDepthEstimation
+        processor = AutoImageProcessor.from_pretrained(pretrained_model_or_path, cache_dir=cache_dir, local_files_only=local_files_only)
+        model = AutoModelForDepthEstimation.from_pretrained(pretrained_model_or_path, cache_dir=cache_dir, local_files_only=local_files_only, use_safetensors=True).to(devices.device).eval()
+        return cls(model, processor)
+
+    def __call__(self, image, color_map="none", output_type="pil"):
+        self.model.to(devices.device)
+        if isinstance(image, Image.Image):
+            image = np.array(image)
+        h, w = image.shape[:2]
+        image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+        pil_image = Image.fromarray(image_rgb)
+        inputs = self.processor(images=pil_image, return_tensors="pt")
+        inputs = {k: v.to(devices.device) if isinstance(v, torch.Tensor) else v for k, v in inputs.items()}
+        with devices.inference_context():
+            outputs = self.model(**inputs)
+        predicted_depth = outputs.predicted_depth
+        depth = F.interpolate(predicted_depth.unsqueeze(1), size=(h, w), mode="bicubic", align_corners=False).squeeze()
+        if opts.control_move_processor:
+            self.model.to("cpu")
+        depth = depth - depth.min()
+        depth_max = depth.max()
+        if depth_max > 0:
+            depth = depth / depth_max
+        depth = (depth * 255.0).clamp(0, 255).to(torch.uint8).cpu().numpy()
+        if color_map != "none":
+            colormap_key = color_map if color_map in masking.COLORMAP else "inferno"
+            depth = cv2.applyColorMap(depth, masking.COLORMAP.index(colormap_key))[:, :, ::-1]
+        if output_type == "pil":
+            mode = "RGB" if depth.ndim == 3 else "L"
+            depth = Image.fromarray(depth, mode=mode)
+        return depth