Upload app.py

app.py

@@ -0,0 +1,183 @@
+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
+
+TITLE = 'insightface Face Detection (SCRFD)'
+DESCRIPTION = 'This is an unofficial demo for https://github.com/deepinsight/insightface/tree/master/detection/scrfd.'
+ARTICLE = '<center><img src="https://visitor-badge.glitch.me/badge?page_id=hysts.insightface-scrfd" alt="visitor badge"/></center>'
+
+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')
+    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():
+    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_flagging=args.allow_flagging,
+        live=args.live,
+    ).launch(
+        enable_queue=args.enable_queue,
+        server_port=args.port,
+        share=args.share,
+    )
+
+
+if __name__ == '__main__':
+    main()