Add files

app.py

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+#!/usr/bin/env python
+
+import argparse
+import functools
+import os
+import pathlib
+
+import cv2
+import dlib
+import gradio as gr
+import huggingface_hub
+import numpy as np
+import pretrainedmodels
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+TOKEN = os.environ['TOKEN']
+
+MODEL_REPO = 'hysts/yu4u-age-estimation-pytorch'
+MODEL_FILENAME = 'pretrained.pth'
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser()
+    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 get_model(model_name='se_resnext50_32x4d',
+              num_classes=101,
+              pretrained='imagenet'):
+    model = pretrainedmodels.__dict__[model_name](pretrained=pretrained)
+    dim_feats = model.last_linear.in_features
+    model.last_linear = nn.Linear(dim_feats, num_classes)
+    model.avg_pool = nn.AdaptiveAvgPool2d(1)
+    return model
+
+
+def load_model(device):
+    model = get_model(model_name='se_resnext50_32x4d', pretrained=None)
+    path = huggingface_hub.hf_hub_download(MODEL_REPO,
+                                           MODEL_FILENAME,
+                                           use_auth_token=TOKEN)
+    model.load_state_dict(torch.load(path))
+    model = model.to(device)
+    model.eval()
+    return model
+
+
+def load_image(path):
+    image = cv2.imread(path)
+    h_orig, w_orig = image.shape[:2]
+    size = max(h_orig, w_orig)
+    scale = 640 / size
+    w, h = int(w_orig * scale), int(h_orig * scale)
+    image = cv2.resize(image, (w, h))
+    return image
+
+
+def draw_label(image,
+               point,
+               label,
+               font=cv2.FONT_HERSHEY_SIMPLEX,
+               font_scale=0.8,
+               thickness=1):
+    size = cv2.getTextSize(label, font, font_scale, thickness)[0]
+    x, y = point
+    cv2.rectangle(image, (x, y - size[1]), (x + size[0], y), (255, 0, 0),
+                  cv2.FILLED)
+    cv2.putText(image,
+                label,
+                point,
+                font,
+                font_scale, (255, 255, 255),
+                thickness,
+                lineType=cv2.LINE_AA)
+
+
+@torch.inference_mode()
+def predict(image, model, face_detector, device, margin=0.4, input_size=224):
+    image = cv2.imread(image.name, cv2.IMREAD_COLOR)[:, :, ::-1].copy()
+    image_h, image_w = image.shape[:2]
+
+    # detect faces using dlib detector
+    detected = face_detector(image, 1)
+    faces = np.empty((len(detected), input_size, input_size, 3))
+
+    if len(detected) > 0:
+        for i, d in enumerate(detected):
+            x1, y1, x2, y2, w, h = d.left(), d.top(
+            ), d.right() + 1, d.bottom() + 1, d.width(), d.height()
+            xw1 = max(int(x1 - margin * w), 0)
+            yw1 = max(int(y1 - margin * h), 0)
+            xw2 = min(int(x2 + margin * w), image_w - 1)
+            yw2 = min(int(y2 + margin * h), image_h - 1)
+            faces[i] = cv2.resize(image[yw1:yw2 + 1, xw1:xw2 + 1],
+                                  (input_size, input_size))
+
+            cv2.rectangle(image, (x1, y1), (x2, y2), (255, 255, 255), 2)
+            cv2.rectangle(image, (xw1, yw1), (xw2, yw2), (255, 0, 0), 2)
+
+        # predict ages
+        inputs = torch.from_numpy(
+            np.transpose(faces.astype(np.float32), (0, 3, 1, 2))).to(device)
+        outputs = F.softmax(model(inputs), dim=-1).cpu().numpy()
+        ages = np.arange(0, 101)
+        predicted_ages = (outputs * ages).sum(axis=-1)
+
+        # draw results
+        for age, d in zip(predicted_ages, detected):
+            draw_label(image, (d.left(), d.top()), f'{int(age)}')
+    return image
+
+
+def main():
+    gr.close_all()
+
+    args = parse_args()
+    device = torch.device(args.device)
+
+    model = load_model(device)
+    face_detector = dlib.get_frontal_face_detector()
+
+    func = functools.partial(predict,
+                             model=model,
+                             face_detector=face_detector,
+                             device=device)
+    func = functools.update_wrapper(func, predict)
+
+    image_dir = pathlib.Path('sample_images')
+    examples = [path.as_posix() for path in sorted(image_dir.glob('*.jpg'))]
+
+    repo_url = 'https://github.com/yu4u/age-estimation-pytorch'
+    title = 'yu4u/age-estimation-pytorch'
+    description = f'A demo for {repo_url}'
+    article = None
+
+    gr.Interface(
+        func,
+        gr.inputs.Image(type='file', label='Input'),
+        gr.outputs.Image(label='Output'),
+        theme=args.theme,
+        title=title,
+        description=description,
+        article=article,
+        examples=examples,
+        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()

packages.txt

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+cmake

requirements.txt

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+dlib>=19.23
+numpy>=1.22.2
+opencv-python-headless>=4.5.5.62
+pretrainedmodels>=0.7.4
+torch>=1.10.2
+torchvision>=0.11.3

sample_images/README.md

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+These images are from the following public domain:
+- https://www.pexels.com/photo/2-women-sitting-on-rock-during-daytime-214576/
+- https://www.pexels.com/photo/boy-in-yellow-crew-neck-t-shirt-and-gray-bottoms-929436/
+- https://www.pexels.com/photo/group-of-people-standing-beside-body-of-water-2672979/
+- https://www.pexels.com/photo/man-sitting-on-chair-beside-table-834863/
+- https://www.pexels.com/photo/man-wearing-white-dress-shirt-and-black-blazer-2182970/
+- https://www.pexels.com/photo/shallow-focus-photography-of-woman-in-white-shirt-and-blue-denim-shorts-on-street-near-green-trees-937416/
+- https://www.pexels.com/photo/woman-in-collared-shirt-774909/
+