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""" |
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Gradio app to showcase the pyronear model for early forest fire detection. |
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""" |
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from pathlib import Path |
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from typing import Tuple |
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import gradio as gr |
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import numpy as np |
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from PIL import Image |
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from ultralytics import YOLO |
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def bgr_to_rgb(a: np.ndarray) -> np.ndarray: |
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""" |
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Turn a BGR numpy array into a RGB numpy array when the array `a` represents |
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an image. |
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""" |
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return a[:, :, ::-1] |
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def prediction_to_str(yolo_prediction) -> str: |
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""" |
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Turn the yolo_prediction into a human friendly string. |
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""" |
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boxes = yolo_prediction.boxes |
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classes = boxes.cls.cpu().numpy().astype(np.int8) |
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n_hard_coral = len([c for c in classes if c == 0]) |
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n_soft_coral = len([c for c in classes if c == 1]) |
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return f"""{len(boxes.conf)} corals detected:\n- {n_hard_coral} hard corals\n- {n_soft_coral} soft corals""" |
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def predict(model: YOLO, pil_image: Image.Image) -> Tuple[Image.Image, str]: |
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""" |
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Main interface function that runs the model on the provided pil_image and |
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returns the exepected tuple to populate the gradio interface. |
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Args: |
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model (YOLO): Loaded ultralytics YOLO model. |
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pil_image (PIL): image to run inference on. |
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Returns: |
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pil_image_with_prediction (PIL): image with prediction from the model. |
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raw_prediction_str (str): string representing the raw prediction from the |
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model. |
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""" |
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predictions = model(pil_image) |
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prediction = predictions[0] |
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pil_image_with_prediction = Image.fromarray(bgr_to_rgb(prediction.plot())) |
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raw_prediction_str = prediction_to_str(prediction) |
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return (pil_image_with_prediction, raw_prediction_str) |
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def examples(dir_examples: Path) -> list[Path]: |
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""" |
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List the images from the dir_examples directory. |
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Returns: |
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filepaths (list[Path]): list of image filepaths. |
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""" |
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return list(dir_examples.glob("*.jpg")) |
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def load_model(filepath_weights: Path) -> YOLO: |
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""" |
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Load the YOLO model given the filepath_weights. |
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""" |
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return YOLO(filepath_weights) |
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MODEL_FILEPATH_WEIGHTS = Path("data/model/best.pt") |
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DIR_EXAMPLES = Path("data/images/") |
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DEFAULT_IMAGE_INDEX = 3 |
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with gr.Blocks() as demo: |
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model = load_model(MODEL_FILEPATH_WEIGHTS) |
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image_filepaths = examples(dir_examples=DIR_EXAMPLES) |
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default_value_input = Image.open(image_filepaths[DEFAULT_IMAGE_INDEX]) |
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input = gr.Image( |
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value=default_value_input, |
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type="pil", |
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label="input image", |
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sources=["upload", "clipboard"], |
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) |
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output_image = gr.Image(type="pil", label="model prediction") |
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output_raw = gr.Text(label="raw prediction") |
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fn = lambda pil_image: predict(model=model, pil_image=pil_image) |
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gr.Interface( |
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title="ML model for benthic imagery segmentation 🪸", |
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fn=fn, |
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inputs=input, |
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outputs=[output_image, output_raw], |
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examples=image_filepaths, |
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allow_flagging="never", |
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) |
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demo.launch() |
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