modif image en amont

app.py

@@ -1,19 +1,13 @@
-# import torch
-# gpu_id = torch.cuda.current_device()
-# print("GPU", gpu_id)
-# print(torch.__path__)
-# print(torch.cuda.is_available())
-# print(torch.cuda.get_arch_list())
-# print(torch.cuda.device_count())
-
+from PIL import Image, ExifTags
 import streamlit as st
 from transformers import pipeline
-from PIL import Image
+
 import torch
 # Assurez-vous que models.py est correctement défini et dans votre PATH
 from models import vgg19  
 from torchvision import transforms
 
+
 # Initialisation des pipelines Hugging Face
 caption_pipeline = pipeline("image-to-text", model="Salesforce/blip-image-captioning-large")
 emotion_pipeline = pipeline("image-classification", model="RickyIG/emotion_face_image_classification_v3")
@@ -28,6 +22,7 @@ model.to(device)
 model.load_state_dict(torch.load(model_path, map_location=device))
 model.eval()
 
+
 def predict_count(image):
     # Prétraitement de l'image
     trans = transforms.Compose([
@@ -75,10 +70,41 @@ def main():
     # Upload d'image
     uploaded_image = st.file_uploader("Choisissez une image...", type=["jpg", "jpeg", "png"])
     if uploaded_image is not None:
+
+
+
+
         image = Image.open(uploaded_image)
 
-        # Affichage de l'image uploadée
-        st.image(image, caption='Image Uploadée', use_column_width=True)
+        # Obtenir et afficher la taille de l'image
+        width, height = image.size
+        st.write(f"Largeur: {width} pixels, Hauteur: {height} pixels")
+        if width>1200 or height > 1200:
+            try:
+                exif = image._getexif()
+                if exif is not None:
+                    orientation_key = next((key for key, value in ExifTags.TAGS.items() if value == 'Orientation'), None)
+                    if orientation_key is not None and orientation_key in exif:
+                        orientation = exif[orientation_key]
+                        if orientation == 3:
+                            image = image.rotate(180, expand=True)
+                        elif orientation == 6:
+                            image = image.rotate(270, expand=True)
+                        elif orientation == 8:
+                            image = image.rotate(90, expand=True)
+                        # Affichage de l'image uploadée
+                        st.image(image, caption='Image Uploadée', use_column_width=True)
+            except Exception as e:
+                st.write(f"Erreur lors de la correction de l'orientation: {e}")
+                pass
+            image = image.resize((224, 224))
+        else:
+            # Affichage de l'image uploadée
+            st.image(image, caption='Image Uploadée', use_column_width=True)
+        
+        
+        
+
 
         # Placeholder pour le bouton ou le message de chargement
         button_placeholder = st.empty()