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| from gradio.outputs import Label | |
| from icevision.all import * | |
| from icevision.models.checkpoint import * | |
| import PIL | |
| import gradio as gr | |
| import os | |
| # Load model | |
| checkpoint_path = "models/model_checkpoint.pth" | |
| checkpoint_and_model = model_from_checkpoint(checkpoint_path) | |
| model = checkpoint_and_model["model"] | |
| model_type = checkpoint_and_model["model_type"] | |
| class_map = checkpoint_and_model["class_map"] | |
| # Transforms | |
| img_size = checkpoint_and_model["img_size"] | |
| valid_tfms = tfms.A.Adapter([*tfms.A.resize_and_pad(img_size), tfms.A.Normalize()]) | |
| examples = [['sample_images/IMG_20191212_151351.jpg'],['sample_images/IMG_20191212_153420.jpg'],['sample_images/IMG_20191212_154100.jpg']] | |
| def show_preds(input_image): | |
| img = PIL.Image.fromarray(input_image, "RGB") | |
| pred_dict = model_type.end2end_detect(img, valid_tfms, model, class_map=class_map, detection_threshold=0.5, | |
| display_label=False, display_bbox=True, return_img=True, | |
| font_size=16, label_color="#FF59D6") | |
| return pred_dict["img"], len(pred_dict["detection"]["bboxes"]) | |
| gr_interface = gr.Interface( | |
| fn=show_preds, | |
| inputs=["image"], | |
| outputs=[gr.outputs.Image(type="pil", label="RetinaNet Inference"), gr.outputs.Textbox(type="number", label="Microalgae Count")], | |
| title="Microalgae Detector with RetinaNet", | |
| description="This RetinaNet model counts microalgaes on a given image. Upload an image or click an example image below to use.", | |
| article="<p style='text-align: center'><a href='https://dicksonneoh.com/portfolio/how_to_deploy_od_models_on_android_with_flutter/' target='_blank'>Blog post</a></p>", | |
| examples=examples, | |
| theme="dark-grass", | |
| enable_queue=True | |
| ) | |
| gr_interface.launch() |