Upload __init__.py

controlnet_aux/anyline/__init__.py

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+# code based in https://github.com/TheMistoAI/ComfyUI-Anyline/blob/main/anyline.py
+import os
+
+import cv2
+import numpy as np
+import torch
+from einops import rearrange
+from huggingface_hub import hf_hub_download
+from PIL import Image
+from skimage import morphology
+
+from ..teed.ted import TED
+from ..util import HWC3, resize_image, safe_step
+
+
+class AnylineDetector:
+    def __init__(self, model):
+        self.model = model
+
+    @classmethod
+    def from_pretrained(cls, pretrained_model_or_path, filename=None, subfolder=None):
+        if os.path.isdir(pretrained_model_or_path):
+            model_path = os.path.join(pretrained_model_or_path, filename)
+        else:
+            model_path = hf_hub_download(
+                pretrained_model_or_path, filename, subfolder=subfolder
+            )
+
+        model = TED()
+        model.load_state_dict(torch.load(model_path, map_location="cpu"))
+
+        return cls(model)
+
+    def to(self, device):
+        self.model.to(device)
+        return self
+
+    def __call__(
+        self,
+        input_image,
+        detect_resolution=1280,
+        guassian_sigma=2.0,
+        intensity_threshold=3,
+        output_type="pil",
+    ):
+        device = next(iter(self.model.parameters())).device
+
+        if not isinstance(input_image, np.ndarray):
+            input_image = np.array(input_image, dtype=np.uint8)
+            output_type = output_type or "pil"
+        else:
+            output_type = output_type or "np"
+
+        original_height, original_width, _ = input_image.shape
+
+        input_image = HWC3(input_image)
+        input_image = resize_image(input_image, detect_resolution)
+
+        assert input_image.ndim == 3
+        height, width, _ = input_image.shape
+        with torch.no_grad():
+            image_teed = torch.from_numpy(input_image.copy()).float().to(device)
+            image_teed = rearrange(image_teed, "h w c -> 1 c h w")
+            edges = self.model(image_teed)
+            edges = [e.detach().cpu().numpy().astype(np.float32)[0, 0] for e in edges]
+            edges = [
+                cv2.resize(e, (width, height), interpolation=cv2.INTER_LINEAR)
+                for e in edges
+            ]
+            edges = np.stack(edges, axis=2)
+            edge = 1 / (1 + np.exp(-np.mean(edges, axis=2).astype(np.float64)))
+            edge = safe_step(edge, 2)
+            edge = (edge * 255.0).clip(0, 255).astype(np.uint8)
+
+        mteed_result = edge
+        mteed_result = HWC3(mteed_result)
+
+        x = input_image.astype(np.float32)
+        g = cv2.GaussianBlur(x, (0, 0), guassian_sigma)
+        intensity = np.min(g - x, axis=2).clip(0, 255)
+        intensity /= max(16, np.median(intensity[intensity > intensity_threshold]))
+        intensity *= 127
+        lineart_result = intensity.clip(0, 255).astype(np.uint8)
+
+        lineart_result = HWC3(lineart_result)
+
+        lineart_result = self.get_intensity_mask(
+            lineart_result, lower_bound=0, upper_bound=255
+        )
+
+        cleaned = morphology.remove_small_objects(
+            lineart_result.astype(bool), min_size=36, connectivity=1
+        )
+        lineart_result = lineart_result * cleaned
+        final_result = self.combine_layers(mteed_result, lineart_result)
+
+        final_result = cv2.resize(
+            final_result,
+            (original_width, original_height),
+            interpolation=cv2.INTER_LINEAR,
+        )
+
+        if output_type == "pil":
+            final_result = Image.fromarray(final_result)
+
+        return final_result
+
+    def get_intensity_mask(self, image_array, lower_bound, upper_bound):
+        mask = image_array[:, :, 0]
+        mask = np.where((mask >= lower_bound) & (mask <= upper_bound), mask, 0)
+        mask = np.expand_dims(mask, 2).repeat(3, axis=2)
+        return mask
+
+    def combine_layers(self, base_layer, top_layer):
+        mask = top_layer.astype(bool)
+        temp = 1 - (1 - top_layer) * (1 - base_layer)
+        result = base_layer * (~mask) + temp * mask
+        return result