Update app.py

app.py

@@ -1,48 +1,66 @@
 import os
 import gradio as gr
 import torch
+import cv2
 
-# Define your model architecture
 class YourModelArchitecture(torch.nn.Module):
+    # Remplacez ceci par votre architecture réelle
     def __init__(self):
         super(YourModelArchitecture, self).__init__()
-        # Initialize your model layers here
-        # Example: self.conv1 = torch.nn.Conv2d(in_channels, out_channels, kernel_size)
+        # Définissez votre architecture ici
 
     def forward(self, x):
-        # Define forward pass here
-        return x  # Change this to return the output of your model
+        # Implémentez la méthode forward
+        return x
 
-# Load model function
 def load_model(model_path):
-    checkpoint = torch.load(model_path, map_location=torch.device('cpu'))  # Load checkpoint
-    model = YourModelArchitecture()  # Initialize your model architecture
-    
-    # Load only model weights from checkpoint, ignoring unexpected keys
-    model.load_state_dict(checkpoint['model'], strict=False)  # Use strict=False
-    
-    model.eval()  # Set model to evaluation mode
+    model = YourModelArchitecture()
+    checkpoint = torch.load(model_path, map_location=torch.device('cpu'))
+    model.load_state_dict(checkpoint['model'])
+    model.eval()
     return model
 
-# Colorize video function
-def colorize_video(video):
-    model = load_model(MODEL_PATH)  # Load the model
-    # Add your video processing logic here
-    return "Processed video output"  # Replace with actual output
-
-# Gradio interface setup
-def create_interface():
-    interface = gr.Interface(
-        fn=colorize_video,
-        inputs=gr.Video(label="Upload Video"),
-        outputs=gr.Video(label="Colorized Video"),
-        title="Video Colorizer",
-        description="Upload a video to colorize it using a trained model.",
-    )
-    return interface
-
-if __name__ == "__main__":
-    MODEL_PATH = "ColorizeVideo_gen.pth" # Define your model path
-
-    interface = create_interface()
-    interface.launch()
+def colorize_frame(frame, model):
+    # Effectuer la colorisation sur une frame ici
+    with torch.no_grad():
+        # Transformez l'image et passez-la par le modèle
+        colorized_frame = model(frame)  # Assurez-vous que `frame` est correctement transformé
+        return colorized_frame
+
+def colorize_video(video_path, model):
+    cap = cv2.VideoCapture(video_path)
+    fourcc = cv2.VideoWriter_fourcc(*'mp4v')
+    output_path = "output_video.mp4"
+    out = cv2.VideoWriter(output_path, fourcc, 30, (int(cap.get(3)), int(cap.get(4))))
+
+    while cap.isOpened():
+        ret, frame = cap.read()
+        if not ret:
+            break
+        
+        # Convertir l'image au format approprié
+        input_tensor = preprocess_frame(frame)  # Implémentez cette fonction pour le prétraitement
+        colorized_frame = colorize_frame(input_tensor, model)
+
+        # Enregistrez chaque frame colorisée
+        out.write(colorized_frame.numpy())  # Assurez-vous que `colorized_frame` est converti en numpy array
+
+    cap.release()
+    out.release()
+    return output_path
+
+def preprocess_frame(frame):
+    # Convertir l'image de BGR à RGB et normaliser
+    frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+    frame = frame / 255.0  # Normaliser
+    # Convertir en tensor PyTorch
+    tensor_frame = torch.tensor(frame).permute(2, 0, 1).unsqueeze(0)  # Ajouter la dimension du batch
+    return tensor_frame
+
+def main(video_path):
+    model = load_model("model.pth")
+    output_video_path = colorize_video(video_path, model)
+    return output_video_path
+
+iface = gr.Interface(fn=main, inputs="video", outputs="video")
+iface.launch()