Add VideoGenerator

app.py

@@ -3,6 +3,9 @@ import tempfile
 import os
 from SDXLImageGenerator import SDXLImageGenerator  # Import your existing class
 import sys
+from Image3DProcessor import Image3DProcessor  # Import your 3D processing class
+
+# Ensure setup.sh runs and paths are appended
 os.system('bash setup.sh')
 sys.path.append('./splatter-image')
 sys.path.append('./diff-gaussian-rasterization')
@@ -10,26 +13,30 @@ sys.path.append('./diff-gaussian-rasterization')
 class ControlNetProcessor:
     def controlnet_image(self, image):
         # Placeholder for ControlNet processing (e.g., returning a processed image or placeholder text)
-        return "Placeholder for ControlNet Output Image"
+        return image  # Returning the image for further processing
 
 class VideoGenerator:
-    def generate_3d_video(self, controlled_image):
-        # Creating a temporary video with a placeholder for demonstration purposes.
-        with tempfile.NamedTemporaryFile(delete=False, suffix='.mp4') as tmp:
-            # Generates a sample video with FFmpeg using a solid color and overlay text
-            os.system(
-                f"ffmpeg -f lavfi -i color=c=blue:s=320x240:d=5 "
-                f"-vf drawtext=fontfile=/path/to/font.ttf:text='3D Model':fontsize=24:fontcolor=white:x=(w-text_w)/2:y=(h-text_h)/2 "
-                f"{tmp.name}"
-            )
-            video_path = tmp.name
+    def __init__(self, model_cfg_path, model_repo_id, model_filename):
+        # Initialize the Image3DProcessor
+        self.processor = Image3DProcessor(model_cfg_path, model_repo_id, model_filename)
+
+    def generate_3d_video(self, image):
+        # Process the image and create a 3D video
+        processed_image = self.processor.preprocess(image, preprocess_background=False)
+        mesh_path, video_path = self.processor.reconstruct_and_export(processed_image)
+
         return video_path
 
 class GradioApp:
     def __init__(self):
         self.sdxl_generator = SDXLImageGenerator()  # Use your existing class
         self.controlnet_processor = ControlNetProcessor()
-        self.video_generator = VideoGenerator()
+        # Initialize VideoGenerator with required paths and details
+        self.video_generator = VideoGenerator(
+            model_cfg_path="path/to/gradio_config.yaml",
+            model_repo_id="szymanowiczs/splatter-image-multi-category-v1",
+            model_filename="model_latest.pth"
+        )
 
     def full_pipeline(self, prompt):
         initial_image = self.sdxl_generator.generate_images([prompt])[0]
@@ -43,7 +50,7 @@ class GradioApp:
             inputs=gr.Textbox(label="Input Prompt"),
             outputs=[
                 gr.Image(label="Generated Image"),
-                gr.Textbox(label="ControlNet Output Image Placeholder"),
+                gr.Image(label="ControlNet Processed Image"),
                 gr.Video(label="3D Model Video")
             ],
             title="SDXL to ControlNet to 3D Pipeline",
@@ -53,4 +60,4 @@ class GradioApp:
 
 if __name__ == "__main__":
     app = GradioApp()
-    app.launch()
+    app.launch()