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import gradio as gr
import cv2
import numpy as np
import matplotlib.pyplot as plt
from sklearn.cluster import MeanShift
import io
from PIL import Image
def segment_image(image):
img = np.array(image)
twoDim = img.reshape((-1, 3))
twoDim = np.float32(twoDim)
mean_shift = MeanShift(bandwidth=30, bin_seeding=True)
mean_shift.fit(twoDim)
labels = mean_shift.labels_
segmented_image = mean_shift.cluster_centers_[labels].astype(np.uint8)
segmented_image = segmented_image.reshape(img.shape)
segmented_image_pil = Image.fromarray(segmented_image)
return segmented_image_pil
def analyze_health(image):
return "Plante en bonne santé, pas de signes de stress ou de maladie."
# Interface Gradio
def plant_diagnosis(image):
segmented = segment_image(image)
diagnosis = analyze_health(segmented)
return segmented, diagnosis
# Créer l'interface Gradio
iface = gr.Interface(
fn=plant_diagnosis,
inputs=gr.Image(type="pil", label="Téléchargez une image de votre plante"),
outputs=[gr.Image(label="Image Segmentée"), gr.Textbox(label="Diagnostic")],
title="AppleHealth",
description="Téléchargez une image de votre plante pour effectuer une analyse de segmentation et obtenir un diagnostic de sa santé.",
live=True
)
# Lancer l'application Gradio
iface.launch()
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