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| #!/usr/bin/env python | |
| from __future__ import annotations | |
| import functools | |
| import os | |
| import pathlib | |
| import shlex | |
| import subprocess | |
| import sys | |
| import urllib.request | |
| if os.environ.get('SYSTEM') == 'spaces': | |
| import mim | |
| mim.install('mmcv-full==1.4', is_yes=True) | |
| subprocess.call(shlex.split('pip uninstall -y opencv-python')) | |
| subprocess.call(shlex.split('pip uninstall -y opencv-python-headless')) | |
| subprocess.call( | |
| shlex.split('pip install opencv-python-headless==4.5.5.64')) | |
| subprocess.call(shlex.split('pip install terminaltables==3.1.0')) | |
| subprocess.call(shlex.split('pip install mmpycocotools==12.0.3')) | |
| subprocess.call(shlex.split('pip install insightface==0.6.2')) | |
| subprocess.call(shlex.split('sed -i 23,26d __init__.py'), | |
| cwd='insightface/detection/scrfd/mmdet') | |
| import cv2 | |
| import gradio as gr | |
| import huggingface_hub | |
| import numpy as np | |
| import torch | |
| import torch.nn as nn | |
| sys.path.insert(0, 'insightface/detection/scrfd') | |
| from mmdet.apis import inference_detector, init_detector, show_result_pyplot | |
| TITLE = 'insightface Face Detection (SCRFD)' | |
| DESCRIPTION = 'This is an unofficial demo for https://github.com/deepinsight/insightface/tree/master/detection/scrfd.' | |
| HF_TOKEN = os.getenv('HF_TOKEN') | |
| def load_model(model_size: str, device) -> nn.Module: | |
| ckpt_path = huggingface_hub.hf_hub_download( | |
| 'hysts/insightface', | |
| f'models/scrfd_{model_size}/model.pth', | |
| use_auth_token=HF_TOKEN) | |
| scrfd_dir = 'insightface/detection/scrfd' | |
| config_path = f'{scrfd_dir}/configs/scrfd/scrfd_{model_size}.py' | |
| model = init_detector(config_path, ckpt_path, device.type) | |
| return model | |
| def update_test_pipeline(model: nn.Module, mode: int): | |
| cfg = model.cfg | |
| pipelines = cfg.data.test.pipeline | |
| for pipeline in pipelines: | |
| if pipeline.type == 'MultiScaleFlipAug': | |
| if mode == 0: # 640 scale | |
| pipeline.img_scale = (640, 640) | |
| if hasattr(pipeline, 'scale_factor'): | |
| del pipeline.scale_factor | |
| elif mode == 1: # for single scale in other pages | |
| pipeline.img_scale = (1100, 1650) | |
| if hasattr(pipeline, 'scale_factor'): | |
| del pipeline.scale_factor | |
| elif mode == 2: # original scale | |
| pipeline.img_scale = None | |
| pipeline.scale_factor = 1.0 | |
| transforms = pipeline.transforms | |
| for transform in transforms: | |
| if transform.type == 'Pad': | |
| if mode != 2: | |
| transform.size = pipeline.img_scale | |
| if hasattr(transform, 'size_divisor'): | |
| del transform.size_divisor | |
| else: | |
| transform.size = None | |
| transform.size_divisor = 32 | |
| def detect(image: np.ndarray, model_size: str, mode: int, | |
| face_score_threshold: float, | |
| detectors: dict[str, nn.Module]) -> np.ndarray: | |
| model = detectors[model_size] | |
| update_test_pipeline(model, mode) | |
| # RGB -> BGR | |
| image = image[:, :, ::-1] | |
| preds = inference_detector(model, image) | |
| boxes = preds[0] | |
| res = image.copy() | |
| for box in boxes: | |
| box, score = box[:4], box[4] | |
| if score < face_score_threshold: | |
| continue | |
| box = np.round(box).astype(int) | |
| line_width = max(2, int(3 * (box[2:] - box[:2]).max() / 256)) | |
| cv2.rectangle(res, tuple(box[:2]), tuple(box[2:]), (0, 255, 0), | |
| line_width) | |
| res = cv2.cvtColor(res, cv2.COLOR_BGR2RGB) | |
| return res | |
| device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu') | |
| model_sizes = [ | |
| '500m', | |
| '1g', | |
| '2.5g', | |
| '10g', | |
| '34g', | |
| ] | |
| detectors = { | |
| model_size: load_model(model_size, device=device) | |
| for model_size in model_sizes | |
| } | |
| modes = [ | |
| '(640, 640)', | |
| '(1100, 1650)', | |
| 'original', | |
| ] | |
| func = functools.partial(detect, detectors=detectors) | |
| image_path = pathlib.Path('selfie.jpg') | |
| if not image_path.exists(): | |
| url = 'https://raw.githubusercontent.com/peiyunh/tiny/master/data/demo/selfie.jpg' | |
| urllib.request.urlretrieve(url, image_path) | |
| examples = [[image_path.as_posix(), '10g', modes[0], 0.3]] | |
| gr.Interface( | |
| fn=func, | |
| inputs=[ | |
| gr.Image(label='Input', type='numpy'), | |
| gr.Radio(label='Model', choices=model_sizes, type='value', | |
| value='10g'), | |
| gr.Radio(label='Mode', choices=modes, type='index', value=modes[0]), | |
| gr.Slider(label='Face Score Threshold', | |
| minimum=0, | |
| maximum=1, | |
| step=0.05, | |
| default=0.3), | |
| ], | |
| outputs=gr.Image(label='Output', type='numpy'), | |
| examples=examples, | |
| title=TITLE, | |
| description=DESCRIPTION, | |
| ).queue().launch(show_api=False) | |