Update app.py

app.py

@@ -1,200 +1,209 @@
-import gradio as gr
-import torch
-import numpy as np
-import cv2
-from PIL import Image
-from transformers import DPTForDepthEstimation, DPTImageProcessor
-from huggingface_hub import hf_hub_download
-import os
-
-# === DEVICE ===
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-print(f"Running on device: {device}")
-
-# === LOAD MODELS ===
-def load_models():
-    print("Loading Depth Model...")
-    # 1. Depth Model
-    depth_model = DPTForDepthEstimation.from_pretrained("Intel/dpt-hybrid-midas").to(device)
-    depth_processor = DPTImageProcessor.from_pretrained("Intel/dpt-hybrid-midas")
-    
-    print("Loading LaMa Inpainting Model...")
-    # 2. LaMa Inpainting Model (TorchScript)
-    # We download the JIT traced model which is self-contained
-    model_path = hf_hub_download(repo_id="smartywu/big-lama", filename="big-lama.pt")
-    lama_model = torch.jit.load(model_path).to(device)
-    lama_model.eval()
-    
-    return depth_model, depth_processor, lama_model
-
-# Load models once at startup
-depth_model, depth_processor, lama_model = load_models()
-
-# === DEPTH ESTIMATION ===
-@torch.no_grad()
-def estimate_depth(image_pil, model, processor):
-    original_size = image_pil.size
-    inputs = processor(images=image_pil, return_tensors="pt").to(device)
-    depth = model(**inputs).predicted_depth
-    
-    depth = torch.nn.functional.interpolate(
-        depth.unsqueeze(1),
-        size=(original_size[1], original_size[0]),
-        mode="bicubic",
-        align_corners=False,
-    ).squeeze().detach().cpu().numpy()
-    
-    depth_min, depth_max = depth.min(), depth.max()
-    if depth_max - depth_min > 0:
-        return (depth - depth_min) / (depth_max - depth_min)
-    return depth
-
-# === STEREO GENERATION LOGIC ===
-def generate_right_and_mask(image, shift_map):
-    height, width = image.shape[:2]
-    x_coords, y_coords = np.meshgrid(np.arange(width), np.arange(height))
-    shift = shift_map.astype(int)
-    target_x = x_coords - shift
-    
-    right = np.zeros_like(image)
-    # Mask: 1 (or 255) means HOLE/MISSING info. 
-    # Initialize as all holes (255)
-    mask = np.ones((height, width), dtype=np.float32) 
-    
-    valid_mask = (target_x >= 0) & (target_x < width)
-    flat_y = y_coords[valid_mask]
-    flat_x_target = target_x[valid_mask]
-    flat_x_source = x_coords[valid_mask]
-    
-    right[flat_y, flat_x_target] = image[flat_y, flat_x_source]
-    # Mark written pixels as valid (0)
-    mask[flat_y, flat_x_target] = 0.0
-    
-    return right, mask
-
-# === LOCAL INPAINTING ===
-@torch.no_grad()
-def run_local_lama(image_bgr, mask_float):
-    """
-    Runs LaMa locally.
-    image_bgr: HxWx3 uint8 numpy array
-    mask_float: HxW float32 numpy array (1.0 = hole, 0.0 = valid)
-    """
-    # 1. Resize to be divisible by 8 (LaMa requirement)
-    h, w = image_bgr.shape[:2]
-    new_h = (h // 8) * 8
-    new_w = (w // 8) * 8
-    
-    img_resized = cv2.resize(image_bgr, (new_w, new_h))
-    mask_resized = cv2.resize(mask_float, (new_w, new_h), interpolation=cv2.INTER_NEAREST)
-    
-    # 2. Convert to Torch Tensors
-    # Image: (1, 3, H, W), RGB, 0-1
-    img_t = torch.from_numpy(img_resized).float().permute(2, 0, 1).unsqueeze(0) / 255.0
-    # Swap BGR to RGB
-    img_t = img_t[:, [2, 1, 0], :, :]
-    
-    # Mask: (1, 1, H, W), 0-1
-    mask_t = torch.from_numpy(mask_resized).float().unsqueeze(0).unsqueeze(0)
-    # Binary threshold just in case
-    mask_t = (mask_t > 0.5).float()
-    
-    img_t = img_t.to(device)
-    mask_t = mask_t.to(device)
-    
-    # 3. Inference
-    inpainted_t = lama_model(img_t, mask_t)
-    
-    # 4. Post-process
-    inpainted = inpainted_t[0].permute(1, 2, 0).cpu().numpy()
-    inpainted = np.clip(inpainted * 255, 0, 255).astype(np.uint8)
-    
-    # Swap back RGB to BGR
-    inpainted = cv2.cvtColor(inpainted, cv2.COLOR_RGB2BGR)
-    
-    # Resize back to original if needed
-    if new_h != h or new_w != w:
-        inpainted = cv2.resize(inpainted, (w, h))
-        
-    return inpainted
-
-def make_anaglyph(left, right):
-    l_arr = np.array(left)
-    r_arr = np.array(right)
-    anaglyph = np.zeros_like(l_arr)
-    anaglyph[:, :, 0] = l_arr[:, :, 0]
-    anaglyph[:, :, 1] = r_arr[:, :, 1]
-    anaglyph[:, :, 2] = r_arr[:, :, 2]
-    return Image.fromarray(anaglyph)
-
-# === PIPELINE ===
-def stereo_pipeline(image_pil, divergence, convergence):
-    if image_pil is None:
-        return None, None
-        
-    # Convert to BGR for OpenCV processing
-    image_cv = cv2.cvtColor(np.array(image_pil), cv2.COLOR_RGB2BGR)
-
-    # 1. Depth
-    depth = estimate_depth(image_pil, depth_model, depth_processor)
-    
-    # 2. Shift Map
-    shift = (depth - convergence) * divergence
-    
-    # 3. Warping
-    right_img, mask = generate_right_and_mask(image_cv, shift)
-    
-    # 4. Inpainting (Local)
-    right_filled = run_local_lama(right_img, mask)
-
-    left = image_pil
-    right = Image.fromarray(cv2.cvtColor(right_filled, cv2.COLOR_BGR2RGB))
-
-    # 5. Composition
-    width, height = left.size
-    combined_image = Image.new('RGB', (width * 2, height))
-    combined_image.paste(left, (0, 0))
-    combined_image.paste(right, (width, 0))
-    
-    anaglyph_image = make_anaglyph(left, right)
-    
-    return combined_image, anaglyph_image
-
-# === GRADIO UI ===
-with gr.Blocks(title="2D to 3D Stereo") as demo:
-    gr.Markdown("## 2D to 3D Stereo Generator (Fully Local)")
-    gr.Markdown("Generates stereo pairs using Depth Estimation and **Local LaMa Inpainting**. No external APIs required.")
-    
-    with gr.Row():
-        with gr.Column(scale=1):
-            input_img = gr.Image(type="pil", label="Input Image", height=480)
-            
-            with gr.Group():
-                gr.Markdown("### 3D Controls")
-                divergence_slider = gr.Slider(
-                    minimum=0, maximum=100, value=30, step=1, 
-                    label="3D Strength (Divergence)", 
-                    info="Max pixel separation."
-                )
-                convergence_slider = gr.Slider(
-                    minimum=0.0, maximum=1.0, value=0.1, step=0.05, 
-                    label="Focus Plane (Convergence)", 
-                    info="0.0 = Background at screen. 1.0 = Foreground at screen."
-                )
-            
-            btn = gr.Button("Generate 3D", variant="primary")
-            
-        with gr.Column(scale=1):
-            out_anaglyph = gr.Image(label="Anaglyph (Red/Cyan)", height=480)
-    
-    with gr.Row():
-        out_stereo = gr.Image(label="Side-by-Side Stereo Pair", height=400)
-        
-    btn.click(
-        fn=stereo_pipeline, 
-        inputs=[input_img, divergence_slider, convergence_slider], 
-        outputs=[out_stereo, out_anaglyph]
-    )
-
-if __name__ == "__main__":
+import gradio as gr
+import torch
+import numpy as np
+import cv2
+from PIL import Image
+from transformers import DPTForDepthEstimation, DPTImageProcessor
+from huggingface_hub import hf_hub_download
+import os
+
+# === DEVICE ===
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+print(f"Running on device: {device}")
+
+# === LOAD MODELS ===
+def load_models():
+    print("Loading Depth Model...")
+    # 1. Depth Model
+    depth_model = DPTForDepthEstimation.from_pretrained("Intel/dpt-hybrid-midas").to(device)
+    depth_processor = DPTImageProcessor.from_pretrained("Intel/dpt-hybrid-midas")
+    
+    print("Loading LaMa Inpainting Model...")
+    # 2. LaMa Inpainting Model (TorchScript)
+    # We download the .pt file directly from a repository that hosts the compiled JIT version.
+    # This avoids dealing with .ckpt files and source code dependencies.
+    try:
+        model_path = hf_hub_download(repo_id="fashn-ai/LaMa", filename="big-lama.pt")
+        
+        print(f"Loading LaMa from: {model_path}")
+        # Load the TorchScript model
+        lama_model = torch.jit.load(model_path, map_location=device)
+        lama_model.eval()
+        
+    except Exception as e:
+        print(f"Error loading LaMa model: {e}")
+        raise e
+    
+    return depth_model, depth_processor, lama_model
+
+# Load models once at startup
+depth_model, depth_processor, lama_model = load_models()
+
+# === DEPTH ESTIMATION ===
+@torch.no_grad()
+def estimate_depth(image_pil, model, processor):
+    original_size = image_pil.size
+    inputs = processor(images=image_pil, return_tensors="pt").to(device)
+    depth = model(**inputs).predicted_depth
+    
+    depth = torch.nn.functional.interpolate(
+        depth.unsqueeze(1),
+        size=(original_size[1], original_size[0]),
+        mode="bicubic",
+        align_corners=False,
+    ).squeeze().detach().cpu().numpy()
+    
+    depth_min, depth_max = depth.min(), depth.max()
+    if depth_max - depth_min > 0:
+        return (depth - depth_min) / (depth_max - depth_min)
+    return depth
+
+# === STEREO GENERATION LOGIC ===
+def generate_right_and_mask(image, shift_map):
+    height, width = image.shape[:2]
+    x_coords, y_coords = np.meshgrid(np.arange(width), np.arange(height))
+    shift = shift_map.astype(int)
+    target_x = x_coords - shift
+    
+    right = np.zeros_like(image)
+    # Mask: 1 (or 255) means HOLE/MISSING info. 
+    # Initialize as all holes (255)
+    mask = np.ones((height, width), dtype=np.float32) 
+    
+    valid_mask = (target_x >= 0) & (target_x < width)
+    flat_y = y_coords[valid_mask]
+    flat_x_target = target_x[valid_mask]
+    flat_x_source = x_coords[valid_mask]
+    
+    right[flat_y, flat_x_target] = image[flat_y, flat_x_source]
+    # Mark written pixels as valid (0)
+    mask[flat_y, flat_x_target] = 0.0
+    
+    return right, mask
+
+# === LOCAL INPAINTING ===
+@torch.no_grad()
+def run_local_lama(image_bgr, mask_float):
+    """
+    Runs LaMa locally.
+    image_bgr: HxWx3 uint8 numpy array
+    mask_float: HxW float32 numpy array (1.0 = hole, 0.0 = valid)
+    """
+    # 1. Resize to be divisible by 8 (LaMa requirement)
+    h, w = image_bgr.shape[:2]
+    new_h = (h // 8) * 8
+    new_w = (w // 8) * 8
+    
+    img_resized = cv2.resize(image_bgr, (new_w, new_h))
+    mask_resized = cv2.resize(mask_float, (new_w, new_h), interpolation=cv2.INTER_NEAREST)
+    
+    # 2. Convert to Torch Tensors
+    # Image: (1, 3, H, W), RGB, 0-1
+    img_t = torch.from_numpy(img_resized).float().permute(2, 0, 1).unsqueeze(0) / 255.0
+    # Swap BGR to RGB
+    img_t = img_t[:, [2, 1, 0], :, :]
+    
+    # Mask: (1, 1, H, W), 0-1
+    mask_t = torch.from_numpy(mask_resized).float().unsqueeze(0).unsqueeze(0)
+    # Binary threshold just in case
+    mask_t = (mask_t > 0.5).float()
+    
+    img_t = img_t.to(device)
+    mask_t = mask_t.to(device)
+    
+    # 3. Inference
+    inpainted_t = lama_model(img_t, mask_t)
+    
+    # 4. Post-process
+    inpainted = inpainted_t[0].permute(1, 2, 0).cpu().numpy()
+    inpainted = np.clip(inpainted * 255, 0, 255).astype(np.uint8)
+    
+    # Swap back RGB to BGR
+    inpainted = cv2.cvtColor(inpainted, cv2.COLOR_RGB2BGR)
+    
+    # Resize back to original if needed
+    if new_h != h or new_w != w:
+        inpainted = cv2.resize(inpainted, (w, h))
+        
+    return inpainted
+
+def make_anaglyph(left, right):
+    l_arr = np.array(left)
+    r_arr = np.array(right)
+    anaglyph = np.zeros_like(l_arr)
+    anaglyph[:, :, 0] = l_arr[:, :, 0]
+    anaglyph[:, :, 1] = r_arr[:, :, 1]
+    anaglyph[:, :, 2] = r_arr[:, :, 2]
+    return Image.fromarray(anaglyph)
+
+# === PIPELINE ===
+def stereo_pipeline(image_pil, divergence, convergence):
+    if image_pil is None:
+        return None, None
+        
+    # Convert to BGR for OpenCV processing
+    image_cv = cv2.cvtColor(np.array(image_pil), cv2.COLOR_RGB2BGR)
+
+    # 1. Depth
+    depth = estimate_depth(image_pil, depth_model, depth_processor)
+    
+    # 2. Shift Map
+    shift = (depth - convergence) * divergence
+    
+    # 3. Warping
+    right_img, mask = generate_right_and_mask(image_cv, shift)
+    
+    # 4. Inpainting (Local)
+    right_filled = run_local_lama(right_img, mask)
+
+    left = image_pil
+    right = Image.fromarray(cv2.cvtColor(right_filled, cv2.COLOR_BGR2RGB))
+
+    # 5. Composition
+    width, height = left.size
+    combined_image = Image.new('RGB', (width * 2, height))
+    combined_image.paste(left, (0, 0))
+    combined_image.paste(right, (width, 0))
+    
+    anaglyph_image = make_anaglyph(left, right)
+    
+    return combined_image, anaglyph_image
+
+# === GRADIO UI ===
+with gr.Blocks(title="2D to 3D Stereo") as demo:
+    gr.Markdown("## 2D to 3D Stereo Generator (Fully Local)")
+    gr.Markdown("Generates stereo pairs using Depth Estimation and **Local LaMa Inpainting**. No external APIs required.")
+    
+    with gr.Row():
+        with gr.Column(scale=1):
+            input_img = gr.Image(type="pil", label="Input Image", height=480)
+            
+            with gr.Group():
+                gr.Markdown("### 3D Controls")
+                divergence_slider = gr.Slider(
+                    minimum=0, maximum=100, value=30, step=1, 
+                    label="3D Strength (Divergence)", 
+                    info="Max pixel separation."
+                )
+                convergence_slider = gr.Slider(
+                    minimum=0.0, maximum=1.0, value=0.1, step=0.05, 
+                    label="Focus Plane (Convergence)", 
+                    info="0.0 = Background at screen. 1.0 = Foreground at screen."
+                )
+            
+            btn = gr.Button("Generate 3D", variant="primary")
+            
+        with gr.Column(scale=1):
+            out_anaglyph = gr.Image(label="Anaglyph (Red/Cyan)", height=480)
+    
+    with gr.Row():
+        out_stereo = gr.Image(label="Side-by-Side Stereo Pair", height=400)
+        
+    btn.click(
+        fn=stereo_pipeline, 
+        inputs=[input_img, divergence_slider, convergence_slider], 
+        outputs=[out_stereo, out_anaglyph]
+    )
+
+if __name__ == "__main__":
     demo.launch()