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Create app.py
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app.py
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import gradio as gr
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from ultralytics import YOLO
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import cv2
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import tempfile
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# Charger le modèle de segmentation
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model = YOLO("yolov8n-seg.pt")
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# 🔹 Fonction de détection par segmentation sur image
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def detect_segmentation_image(img):
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results = model(img)
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annotated_img = results[0].plot()
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return annotated_img
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# 🔹 Fonction de détection par segmentation sur vidéo
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def detect_segmentation_video(video_path):
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cap = cv2.VideoCapture(video_path)
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fps = cap.get(cv2.CAP_PROP_FPS)
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width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
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height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
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# Créer un fichier vidéo temporaire pour la sortie
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temp_output = tempfile.NamedTemporaryFile(suffix=".mp4", delete=False)
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output_path = temp_output.name
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temp_output.close()
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# Définir le codec et le writer
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fourcc = cv2.VideoWriter_fourcc(*'mp4v')
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out = cv2.VideoWriter(output_path, fourcc, fps, (width, height))
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while True:
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ret, frame = cap.read()
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if not ret:
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break
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results = model(frame)
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annotated_frame = results[0].plot()
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out.write(annotated_frame)
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cap.release()
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out.release()
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return output_path
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# 🔧 Interface Gradio combinée avec onglets
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with gr.Blocks(title="YOLOv8n-seg - Détection par Segmentation") as app:
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gr.Markdown("## 🧠 YOLOv8n Segmentation - Image & Vidéo")
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gr.Markdown("Détection par segmentation automatique avec le modèle **YOLOv8n-seg**.")
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with gr.Tab("📷 Segmentation sur Image"):
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image_input = gr.Image(type="numpy", label="Importer une image")
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image_output = gr.Image(type="numpy", label="Image segmentée")
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image_button = gr.Button("Lancer la détection")
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image_button.click(fn=detect_segmentation_image, inputs=image_input, outputs=image_output)
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with gr.Tab("🎥 Segmentation sur Vidéo"):
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video_input = gr.Video(label="Importer une vidéo")
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video_output = gr.Video(label="Vidéo segmentée")
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video_button = gr.Button("Lancer la détection")
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video_button.click(fn=detect_segmentation_video, inputs=video_input, outputs=video_output)
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# 🚀 Lancement de l'application Gradio
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app.launch()
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