| import gradio as gr | |
| from image_resizer import ImageResizer | |
| MODEL_PATH = "face_detection_yunet_2023mar.onnx" | |
| image_resizer = ImageResizer(modelPath=MODEL_PATH) | |
| def face_detector(input_image, target_size=512): | |
| return image_resizer.resize(input_image, target_size) | |
| inputs = [ | |
| gr.Image(sources=["upload", "clipboard"], type="numpy"), | |
| gr.Dropdown( | |
| choices=[512, 768, 1024], | |
| value=512, | |
| allow_custom_value=True, | |
| info="Target size of images", | |
| ), | |
| ] | |
| outputs = [ | |
| gr.Image(label="face detection", format="JPEG"), | |
| gr.Image(label="focused resized", format="JPEG"), | |
| ] | |
| demo = gr.Interface( | |
| fn=face_detector, | |
| inputs=inputs, | |
| outputs=outputs, | |
| title="Image Resizer", | |
| theme="gradio/monochrome", | |
| api_name="resize", | |
| submit_btn=gr.Button("Resize", variant="primary"), | |
| allow_flagging="never", | |
| ) | |
| demo.queue( | |
| max_size=10, | |
| ) | |
| if __name__ == "__main__": | |
| demo.launch() | |