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