Update app.py

app.py

@@ -275,6 +275,40 @@ def start_tryon(dict, garm_img, garment_des, is_checked, is_checked_crop, denois
     else:
         return images[0], mask_gray
 
+        
+@app.route('/tryon-v2', methods=['POST'])
+def tryon_v2():
+
+    data = request.json
+    human_image_data = data['human_image']
+    garment_image_data = data['garment_image']
+
+    # Process images (base64 ou URL)
+    human_image = process_image(human_image_data)
+    garment_image = process_image(garment_image_data)
+
+    description = data.get('description')
+    use_auto_mask = data.get('use_auto_mask', True)
+    use_auto_crop = data.get('use_auto_crop', False)
+    denoise_steps = int(data.get('denoise_steps', 30))
+    seed = int(data.get('seed', random.randint(0, 9999999)))
+    categorie = data.get('categorie', 'upper_body')
+
+        # Vérifie si 'mask_image' est présent dans les données
+    mask_image = None
+    if 'mask_image' in data:
+        mask_image_data = data['mask_image']
+        mask_image = process_image(mask_image_data)
+    
+    human_dict = {
+        'background': human_image,
+        'layers': [mask_image] if not use_auto_mask else None,
+        'composite': None
+    }
+    output_image, mask_image = start_tryon(human_dict, garment_image, description, use_auto_mask, use_auto_crop, denoise_steps, seed , categorie)
+    return jsonify({
+        'image_id': save_image(output_image)
+    })
 
 def clear_gpu_memory():
     torch.cuda.empty_cache()
@@ -315,87 +349,7 @@ def tryon():
         'mask_image': mask_base64
     })
 
-@app.route('/tryon-v2', methods=['POST'])
-def tryon_v2():
-
-    data = request.json
-    human_image_data = data['human_image']
-    garment_image_data = data['garment_image']
 
-    # Process images (base64 ou URL)
-    human_image = process_image(human_image_data)
-    garment_image = process_image(garment_image_data)
-
-    description = data.get('description')
-    use_auto_mask = data.get('use_auto_mask', True)
-    use_auto_crop = data.get('use_auto_crop', False)
-    denoise_steps = int(data.get('denoise_steps', 30))
-    seed = int(data.get('seed', random.randint(0, 9999999)))
-    categorie = data.get('categorie', 'upper_body')
-
-        # Vérifie si 'mask_image' est présent dans les données
-    mask_image = None
-    if 'mask_image' in data:
-        mask_image_data = data['mask_image']
-        mask_image = process_image(mask_image_data)
-    
-    human_dict = {
-        'background': human_image,
-        'layers': [mask_image] if not use_auto_mask else None,
-        'composite': None
-    }
-    output_image, mask_image = start_tryon(human_dict, garment_image, description, use_auto_mask, use_auto_crop, denoise_steps, seed , categorie)
-    return jsonify({
-        'image_id': save_image(output_image)
-    })
-
-@spaces.GPU 
-def generate_mask(human_img, categorie='upper_body'):
-    device = "cuda"
-    openpose_model.preprocessor.body_estimation.model.to(device)
-    pipe.to(device)
-
-    try:
-        # Redimensionner l'image pour le modèle
-        human_img_resized = human_img.convert("RGB").resize((384, 512))
-
-        # Générer les points clés et le masque
-        keypoints = openpose_model(human_img_resized)
-        model_parse, _ = parsing_model(human_img_resized)
-        mask, _ = get_mask_location('hd', categorie, model_parse, keypoints)
-
-        # Redimensionner le masque à la taille d'origine de l'image
-        mask_resized = mask.resize(human_img.size)
-        
-        return mask_resized
-    except Exception as e:
-        logging.error(f"Error generating mask: {e}")
-        raise e
-
-   
-@app.route('/generate_mask', methods=['POST'])
-def generate_mask_api():
-    try:
-        # Récupérer les données de l'image à partir de la requête
-        data = request.json
-        base64_image = data.get('human_image')
-        categorie = data.get('categorie', 'upper_body')
-        
-        # Décodage de l'image à partir de base64
-        human_img = process_image(base64_image)
-        
-        # Appeler la fonction pour générer le masque
-        mask_resized = generate_mask(human_img, categorie)
-        
-        # Encodage du masque en base64 pour la réponse
-        mask_base64 = encode_image_to_base64(mask_resized)
-        
-        return jsonify({
-            'mask_image': mask_base64
-        }), 200
-    except Exception as e:
-        logging.error(f"Error generating mask: {e}")
-        return jsonify({'error': str(e)}), 500
 
 # Route pour récupérer l'image générée
 @app.route('/api/get_image/<image_id>', methods=['GET'])