Supprimer backend_huggingfacemodel.py

backend_huggingfacemodel.py

@@ -1,186 +0,0 @@
-from fastapi import FastAPI, HTTPException
-from fastapi.responses import JSONResponse
-from pydantic import BaseModel
-from contextlib import asynccontextmanager
-import torch
-import numpy as np
-from PIL import Image
-import cv2
-import base64
-import io
-import logging
-from transformers import SegformerForSemanticSegmentation, SegformerImageProcessor
-import rasterio
-import matplotlib.pyplot as plt
-
-#app = FastAPI() #delete when charging the model 
-
-# Configure logging
-logging.basicConfig(level=logging.INFO)
-
-# Chemin vers le dossier contenant les fichiers de votre modèle
-MODEL_DIR = "AndreaLeylavergne/segformer_B0_B5"
-
-@asynccontextmanager
-async def lifespan(app: FastAPI):
-    global model, processor
-    processor = SegformerImageProcessor.from_pretrained(MODEL_DIR)
-    model = SegformerForSemanticSegmentation.from_pretrained(MODEL_DIR)
-    logging.info("Model loaded successfully")
-    
-    yield
-
-app = FastAPI(lifespan=lifespan)
-
-class ImageID(BaseModel):
-    image_id: str
-
-
-# Paths of images and annotated masks
-images_paths = {
-    "image1": "./dataset/images_prepped/val/0000FT_000294.png",
-    "image2": "./dataset/images_prepped/val/0000FT_000576.png",
-    "image3": "./dataset/images_prepped/val/0000FT_001016.png"
-}
-
-annotated_masks_paths = {
-    "image1": "./dataset/annotations_prepped_grouped/val/0000FT_000294.png",
-    "image2": "./dataset/annotations_prepped_grouped/val/0000FT_000576.png",
-    "image3": "./dataset/annotations_prepped_grouped/val/0000FT_001016.png"
-}
-
-# Utility functions
-def read_img(raster_file: str) -> np.ndarray:
-    print(f"Reading image from {raster_file}")
-    with rasterio.open(raster_file) as src_img:
-        rgb = src_img.read([1, 2, 3]).transpose(1, 2, 0)
-        rgb = rgb.astype(np.float32)
-        return rgb
-
-def read_msk(raster_file: str) -> np.ndarray:
-    print(f"Reading mask from {raster_file}")
-    with rasterio.open(raster_file) as src_msk:
-        array = src_msk.read(1)
-        array = np.squeeze(array)
-        return array
-
-def map_colors(mask):
-    # Create a color map with high contrast colors
-    color_map = np.array([
-        [0, 0, 0],       # Class 0 - background
-        [128, 0, 0],     # Class 1 - red
-        [0, 128, 0],     # Class 2 - green
-        [128, 128, 0],   # Class 3 - yellow
-        [0, 0, 128],     # Class 4 - blue
-        [128, 0, 128],   # Class 5 - magenta
-        [0, 128, 128],   # Class 6 - cyan
-        [128, 128, 128], # Class 7 - white
-        # Add more colors if you have more classes
-    ])
-    
-    # Apply the color map to the mask
-    color_mask = color_map[mask]
-    
-    return color_mask
-
-def predict(model, processor, image_path):
-    image = read_img(image_path)
-    inputs = processor(images=image, return_tensors="pt")
-
-    with torch.no_grad():
-        outputs = model(**inputs)
-        logits = outputs.logits
-        upsampled_logits = torch.nn.functional.interpolate(
-            logits,
-            size=image.shape[:2],
-            mode='bilinear',
-            align_corners=False
-        )
-        pred_seg = upsampled_logits.argmax(dim=1).squeeze().cpu().numpy()
-
-    return pred_seg, image
-
-def calculate_iou(array1, array2):
-    assert array1.shape == array2.shape, "Arrays must have the same shape"
-
-    array1_binary = (array1 > 0).astype(int)
-    array2_binary = (array2 > 0).astype(int)
-
-    intersection = np.sum(array1_binary * array2_binary)
-    union = np.sum(array1_binary) + np.sum(array2_binary) - intersection
-
-    return intersection / union if union > 0 else 0
-
-@app.post("/predict/")
-async def predict_mask(data: ImageID):
-    image_id = data.image_id
-    if image_id not in images_paths:
-        raise HTTPException(status_code=404, detail="Image ID not found")
-    
-    image_path = images_paths[image_id]
-    #pr, overlay_image = predict(model, processor, image_path)
-    pred_seg, image = predict(model, processor, image_path)
-
-    # Convert the predicted mask to a color image for better visualization
-    color_pred_mask = map_colors(pred_seg)
-    color_pred_mask_image = Image.fromarray(color_pred_mask.astype(np.uint8))
-
-    annotated_mask_path = annotated_masks_paths[image_id]
-    annotated_mask_image = cv2.imread(annotated_mask_path, cv2.IMREAD_GRAYSCALE)
-    annotated_mask_image = (annotated_mask_image / annotated_mask_image.max()) * 255
-    annotated_mask_image = Image.fromarray(annotated_mask_image.astype(np.uint8)).resize((256, 128))
-
-    color_pred_mask_stream = io.BytesIO()
-    annotated_mask_stream = io.BytesIO()
-        
-    color_pred_mask_image.save(color_pred_mask_stream, format='PNG')
-    annotated_mask_image.save(annotated_mask_stream, format='PNG')
-    
-    color_pred_mask_stream.seek(0)
-    annotated_mask_stream.seek(0)
-        
-    color_pred_mask_data_url = base64.b64encode(color_pred_mask_stream.read()).decode('utf8')
-    annotated_data_url = base64.b64encode(annotated_mask_stream.read()).decode('utf8')
-        
-    return JSONResponse(content={
-        "annotated_mask": "data:image/png;base64," + annotated_data_url,
-        "predicted_mask": "data:image/png;base64," + color_pred_mask_data_url
-    })
-
-@app.post("/evaluate/")
-async def evaluate_masks(data: dict):
-    annotated_mask_data = data['annotated_mask']
-    predicted_mask_data = data['predicted_mask']
-    
-    try:
-        annotated_mask = Image.open(io.BytesIO(base64.b64decode(annotated_mask_data.split(',')[1]))).resize((256, 128))
-        predicted_mask = Image.open(io.BytesIO(base64.b64decode(predicted_mask_data.split(',')[1]))).resize((256, 128))
-        predicted_mask = predicted_mask.convert("L") #Covert "L" converts en grayscale the mask before evaluating. Important for obtainig IoU
-    except Exception as e:
-        raise HTTPException(status_code=400, detail=f"Error decoding images: {str(e)}")
-    
-    annotated_mask_array = np.array(annotated_mask)
-    predicted_mask_array = np.array(predicted_mask)
-    iou_score = calculate_iou(annotated_mask_array, predicted_mask_array)
-    
-    annotated_mask_stream = io.BytesIO()
-    mask_image_stream = io.BytesIO()
-    
-    annotated_mask.save(annotated_mask_stream, format='PNG')
-    predicted_mask.save(mask_image_stream, format='PNG')
-    
-    annotated_mask_stream.seek(0)
-    mask_image_stream.seek(0)
-    
-    annotated_data_url = base64.b64encode(annotated_mask_stream.read()).decode('utf8')
-    predicted_data_url = base64.b64encode(mask_image_stream.read()).decode('utf8')
-    
-    return JSONResponse(content={
-        "iou_score": iou_score,
-        "annotated_mask": "data:image/png;base64," + annotated_data_url,
-        "predicted_mask": "data:image/png;base64," + predicted_data_url
-    })
-
-@app.get("/")
-def root():
-    return {"Greeting": "Hello, World!"}