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#!/usr/bin/env python | |
from __future__ import annotations | |
import argparse | |
import functools | |
import os | |
import pathlib | |
import subprocess | |
import sys | |
import urllib.request | |
if os.environ.get('SYSTEM') == 'spaces': | |
import mim | |
mim.install('mmcv-full==1.3.3', is_yes=True) | |
subprocess.call('pip uninstall -y opencv-python'.split()) | |
subprocess.call('pip uninstall -y opencv-python-headless'.split()) | |
subprocess.call('pip install opencv-python-headless==4.5.5.64'.split()) | |
subprocess.call('pip install terminaltables==3.1.0'.split()) | |
subprocess.call('pip install mmpycocotools==12.0.3'.split()) | |
subprocess.call('pip install insightface==0.6.2'.split()) | |
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 | |
REPO_URL = 'https://github.com/deepinsight/insightface/tree/master/detection/scrfd' | |
TITLE = 'insightface Face Detection (SCRFD)' | |
DESCRIPTION = f'This is a demo for {REPO_URL}.' | |
ARTICLE = None | |
TOKEN = os.environ['TOKEN'] | |
def parse_args() -> argparse.Namespace: | |
parser = argparse.ArgumentParser() | |
parser.add_argument('--face-score-slider-step', type=float, default=0.05) | |
parser.add_argument('--face-score-threshold', type=float, default=0.3) | |
parser.add_argument('--device', type=str, default='cpu') | |
parser.add_argument('--theme', type=str) | |
parser.add_argument('--live', action='store_true') | |
parser.add_argument('--share', action='store_true') | |
parser.add_argument('--port', type=int) | |
parser.add_argument('--disable-queue', | |
dest='enable_queue', | |
action='store_false') | |
parser.add_argument('--allow-flagging', type=str, default='never') | |
parser.add_argument('--allow-screenshot', action='store_true') | |
return parser.parse_args() | |
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=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 | |
def main(): | |
gr.close_all() | |
args = parse_args() | |
device = torch.device(args.device) | |
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) | |
func = functools.update_wrapper(func, detect) | |
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( | |
func, | |
[ | |
gr.inputs.Image(type='numpy', label='Input'), | |
gr.inputs.Radio( | |
model_sizes, type='value', default='10g', label='Model'), | |
gr.inputs.Radio( | |
modes, type='index', default=modes[0], label='Mode'), | |
gr.inputs.Slider(0, | |
1, | |
step=args.face_score_slider_step, | |
default=args.face_score_threshold, | |
label='Face Score Threshold'), | |
], | |
gr.outputs.Image(type='numpy', label='Output'), | |
examples=examples, | |
title=TITLE, | |
description=DESCRIPTION, | |
article=ARTICLE, | |
theme=args.theme, | |
allow_screenshot=args.allow_screenshot, | |
allow_flagging=args.allow_flagging, | |
live=args.live, | |
).launch( | |
enable_queue=args.enable_queue, | |
server_port=args.port, | |
share=args.share, | |
) | |
if __name__ == '__main__': | |
main() | |