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Update app.py
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app.py
CHANGED
@@ -16,31 +16,26 @@ class_map = checkpoint_and_model["class_map"]
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img_size = checkpoint_and_model["img_size"]
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valid_tfms = tfms.A.Adapter([*tfms.A.resize_and_pad(img_size), tfms.A.Normalize()])
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examples = [
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['./1.jpg'],
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['./2.jpg'],
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['./3.jpg']
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]
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def show_preds(input_image):
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img = PIL.Image.fromarray(input_image, "RGB")
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pred_dict
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font_size=16,
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label_color="#FF59D6")
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return pred_dict["img"]
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gr_interface = gr.Interface(
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fn=show_preds,
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inputs=["image"],
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outputs=[gr.outputs.Image(type="pil", label="
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title="
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description="
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examples=examples,
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)
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gr_interface.launch(
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img_size = checkpoint_and_model["img_size"]
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valid_tfms = tfms.A.Adapter([*tfms.A.resize_and_pad(img_size), tfms.A.Normalize()])
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examples = [['1.jpg'],['2.jpg'],['3.jpg']]
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def show_preds(input_image):
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img = PIL.Image.fromarray(input_image, "RGB")
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pred_dict = model_type.end2end_detect(img, valid_tfms, model, class_map=class_map, detection_threshold=0.5,
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display_label=False, display_bbox=True, return_img=True,
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font_size=16, label_color="#FF59D6")
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return pred_dict["img"], len(pred_dict["detection"]["bboxes"])
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gr_interface = gr.Interface(
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fn=show_preds,
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inputs=["image"],
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outputs=[gr.outputs.Image(type="pil", label="RetinaNet Inference"), gr.outputs.Textbox(type="number", label="Microalgae Count")],
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title="Microalgae Detector with RetinaNet",
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description="This RetinaNet model counts microalgaes on a given image. Upload an image or click an example image below to use.",
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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>",
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examples=examples,
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theme="dark-grass",
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enable_queue=True
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)
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gr_interface.launch()
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