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| #!/usr/bin/env python | |
| 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 | |
| DESCRIPTION = "# [Age Estimation](https://github.com/yu4u/age-estimation-pytorch)" | |
| 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("public-data/yu4u-age-estimation-pytorch", "pretrained.pth") | |
| 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) | |
| def predict(image, model, face_detector, device, margin=0.4, input_size=224): | |
| image = cv2.imread(image, 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 | |
| device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") | |
| model = load_model(device) | |
| face_detector = dlib.get_frontal_face_detector() | |
| fn = functools.partial(predict, model=model, face_detector=face_detector, device=device) | |
| image_dir = pathlib.Path("sample_images") | |
| examples = [path.as_posix() for path in sorted(image_dir.glob("*.jpg"))] | |
| with gr.Blocks(css="style.css") as demo: | |
| gr.Markdown(DESCRIPTION) | |
| with gr.Row(): | |
| with gr.Column(): | |
| image = gr.Image(label="Input", type="filepath") | |
| run_button = gr.Button("Run") | |
| with gr.Column(): | |
| result = gr.Image(label="Result") | |
| gr.Examples( | |
| examples=examples, | |
| inputs=image, | |
| outputs=result, | |
| fn=fn, | |
| cache_examples=os.getenv("CACHE_EXAMPLES") == "1", | |
| ) | |
| run_button.click( | |
| fn=fn, | |
| inputs=image, | |
| outputs=result, | |
| api_name="predict", | |
| ) | |
| if __name__ == "__main__": | |
| demo.queue(max_size=15).launch() | |