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from __future__ import annotations |
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import functools |
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import os |
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import pathlib |
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import sys |
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import tarfile |
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import urllib.request |
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from typing import Callable |
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import cv2 |
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import gradio as gr |
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import huggingface_hub |
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import numpy as np |
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import PIL.Image |
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import torch |
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import torchvision.transforms as T |
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sys.path.insert(0, "anime_face_landmark_detection") |
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from CFA import CFA |
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DESCRIPTION = "# [kanosawa/anime_face_landmark_detection](https://github.com/kanosawa/anime_face_landmark_detection)" |
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NUM_LANDMARK = 24 |
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CROP_SIZE = 128 |
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def load_sample_image_paths() -> list[pathlib.Path]: |
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image_dir = pathlib.Path("images") |
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if not image_dir.exists(): |
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dataset_repo = "hysts/sample-images-TADNE" |
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path = huggingface_hub.hf_hub_download(dataset_repo, "images.tar.gz", repo_type="dataset") |
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with tarfile.open(path) as f: |
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f.extractall() |
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return sorted(image_dir.glob("*")) |
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def load_face_detector() -> cv2.CascadeClassifier: |
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url = "https://raw.githubusercontent.com/nagadomi/lbpcascade_animeface/master/lbpcascade_animeface.xml" |
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path = pathlib.Path("lbpcascade_animeface.xml") |
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if not path.exists(): |
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urllib.request.urlretrieve(url, path.as_posix()) |
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return cv2.CascadeClassifier(path.as_posix()) |
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def load_landmark_detector(device: torch.device) -> torch.nn.Module: |
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path = huggingface_hub.hf_hub_download( |
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"public-data/anime_face_landmark_detection", "checkpoint_landmark_191116.pth" |
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) |
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model = CFA(output_channel_num=NUM_LANDMARK + 1, checkpoint_name=path) |
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model.to(device) |
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model.eval() |
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return model |
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@torch.inference_mode() |
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def detect( |
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image_path: str, |
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face_detector: cv2.CascadeClassifier, |
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device: torch.device, |
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transform: Callable, |
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landmark_detector: torch.nn.Module, |
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) -> np.ndarray: |
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image = cv2.imread(image_path) |
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gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) |
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preds = face_detector.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5, minSize=(24, 24)) |
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image_h, image_w = image.shape[:2] |
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pil_image = PIL.Image.fromarray(image[:, :, ::-1].copy()) |
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res = image.copy() |
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for x_orig, y_orig, w_orig, h_orig in preds: |
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x0 = round(max(x_orig - w_orig / 8, 0)) |
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x1 = round(min(x_orig + w_orig * 9 / 8, image_w)) |
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y0 = round(max(y_orig - h_orig / 4, 0)) |
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y1 = y_orig + h_orig |
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w = x1 - x0 |
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h = y1 - y0 |
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temp = pil_image.crop((x0, y0, x1, y1)) |
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temp = temp.resize((CROP_SIZE, CROP_SIZE), PIL.Image.BICUBIC) |
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data = transform(temp) |
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data = data.to(device).unsqueeze(0) |
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heatmaps = landmark_detector(data) |
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heatmaps = heatmaps[-1].cpu().numpy()[0] |
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cv2.rectangle(res, (x0, y0), (x1, y1), (0, 255, 0), 2) |
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for i in range(NUM_LANDMARK): |
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heatmap = cv2.resize(heatmaps[i], (CROP_SIZE, CROP_SIZE), interpolation=cv2.INTER_CUBIC) |
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pty, ptx = np.unravel_index(np.argmax(heatmap), heatmap.shape) |
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pt_crop = np.round(np.array([ptx * w, pty * h]) / CROP_SIZE).astype(int) |
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pt = np.array([x0, y0]) + pt_crop |
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cv2.circle(res, tuple(pt), 2, (0, 0, 255), cv2.FILLED) |
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return res[:, :, ::-1] |
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device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") |
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image_paths = load_sample_image_paths() |
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examples = [[path.as_posix()] for path in image_paths] |
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face_detector = load_face_detector() |
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landmark_detector = load_landmark_detector(device) |
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transform = T.Compose( |
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[ |
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T.ToTensor(), |
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T.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]), |
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] |
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) |
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fn = functools.partial( |
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detect, |
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face_detector=face_detector, |
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device=device, |
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transform=transform, |
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landmark_detector=landmark_detector, |
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) |
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with gr.Blocks(css="style.css") as demo: |
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gr.Markdown(DESCRIPTION) |
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with gr.Row(): |
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with gr.Column(): |
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image = gr.Image(label="Input", type="filepath") |
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run_button = gr.Button("Run") |
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with gr.Column(): |
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result = gr.Image(label="Result") |
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gr.Examples( |
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examples=examples, |
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inputs=image, |
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outputs=result, |
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fn=fn, |
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cache_examples=os.getenv("CACHE_EXAMPLES") == "1", |
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) |
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run_button.click( |
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fn=fn, |
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inputs=image, |
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outputs=result, |
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api_name="predict", |
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) |
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if __name__ == "__main__": |
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demo.queue(max_size=15).launch() |
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