Update app.py

app.py

@@ -5,12 +5,10 @@ import numpy as np
 from PIL import Image
 import open3d as o3d
 from pathlib import Path
-import os
 
 feature_extractor = DPTFeatureExtractor.from_pretrained("Intel/dpt-large")
 model = DPTForDepthEstimation.from_pretrained("Intel/dpt-large")
 
-
 def process_image(image_path):
     image_path = Path(image_path)
     image_raw = Image.open(image_path)
@@ -18,15 +16,12 @@ def process_image(image_path):
         (800, int(800 * image_raw.size[1] / image_raw.size[0])),
         Image.Resampling.LANCZOS)
 
-    # prepare image for the model
     encoding = feature_extractor(image, return_tensors="pt")
-
-    # forward pass
+    
     with torch.no_grad():
         outputs = model(**encoding)
         predicted_depth = outputs.predicted_depth
 
-    # interpolate to original size
     prediction = torch.nn.functional.interpolate(
         predicted_depth.unsqueeze(1),
         size=image.size[::-1],
@@ -35,97 +30,57 @@ def process_image(image_path):
     ).squeeze()
     output = prediction.cpu().numpy()
     depth_image = (output * 255 / np.max(output)).astype('uint8')
+    
     try:
         gltf_path = create_3d_obj(np.array(image), depth_image, image_path)
-        img = Image.fromarray(depth_image)
-        return [img, gltf_path, gltf_path]
-    except Exception as e:
-        gltf_path = create_3d_obj(
-            np.array(image), depth_image, image_path, depth=8)
-        img = Image.fromarray(depth_image)
-        return [img, gltf_path, gltf_path]
-    except:
-        print("Error reconstructing 3D model")
-        raise Exception("Error reconstructing 3D model")
-
+    except Exception:
+        gltf_path = create_3d_obj(np.array(image), depth_image, image_path, depth=8)
+    
+    return [Image.fromarray(depth_image), gltf_path, gltf_path]
 
 def create_3d_obj(rgb_image, depth_image, image_path, depth=10):
     depth_o3d = o3d.geometry.Image(depth_image)
     image_o3d = o3d.geometry.Image(rgb_image)
     rgbd_image = o3d.geometry.RGBDImage.create_from_color_and_depth(
         image_o3d, depth_o3d, convert_rgb_to_intensity=False)
-    w = int(depth_image.shape[1])
-    h = int(depth_image.shape[0])
+    w, h = depth_image.shape[1], depth_image.shape[0]
 
     camera_intrinsic = o3d.camera.PinholeCameraIntrinsic()
     camera_intrinsic.set_intrinsics(w, h, 500, 500, w/2, h/2)
 
-    pcd = o3d.geometry.PointCloud.create_from_rgbd_image(
-        rgbd_image, camera_intrinsic)
-
-    print('normals')
-    pcd.normals = o3d.utility.Vector3dVector(
-        np.zeros((1, 3)))  # invalidate existing normals
+    pcd = o3d.geometry.PointCloud.create_from_rgbd_image(rgbd_image, camera_intrinsic)
     pcd.estimate_normals(
         search_param=o3d.geometry.KDTreeSearchParamHybrid(radius=0.01, max_nn=30))
-    pcd.orient_normals_towards_camera_location(
-        camera_location=np.array([0., 0., 1000.]))
-    pcd.transform([[1, 0, 0, 0],
-                   [0, -1, 0, 0],
-                   [0, 0, -1, 0],
-                   [0, 0, 0, 1]])
-    pcd.transform([[-1, 0, 0, 0],
-                   [0, 1, 0, 0],
-                   [0, 0, 1, 0],
-                   [0, 0, 0, 1]])
-
-    print('run Poisson surface reconstruction')
-    with o3d.utility.VerbosityContextManager(o3d.utility.VerbosityLevel.Debug) as cm:
-        mesh_raw, densities = o3d.geometry.TriangleMesh.create_from_point_cloud_poisson(
+    pcd.orient_normals_towards_camera_location(camera_location=np.array([0., 0., 1000.]))
+    
+    with o3d.utility.VerbosityContextManager(o3d.utility.VerbosityLevel.Debug):
+        mesh_raw, _ = o3d.geometry.TriangleMesh.create_from_point_cloud_poisson(
             pcd, depth=depth, width=0, scale=1.1, linear_fit=True)
 
     voxel_size = max(mesh_raw.get_max_bound() - mesh_raw.get_min_bound()) / 256
-    print(f'voxel_size = {voxel_size:e}')
-    mesh = mesh_raw.simplify_vertex_clustering(
-        voxel_size=voxel_size,
-        contraction=o3d.geometry.SimplificationContraction.Average)
-
-    # vertices_to_remove = densities < np.quantile(densities, 0.001)
-    # mesh.remove_vertices_by_mask(vertices_to_remove)
+    mesh = mesh_raw.simplify_vertex_clustering(voxel_size=voxel_size)
+    
     bbox = pcd.get_axis_aligned_bounding_box()
     mesh_crop = mesh.crop(bbox)
     gltf_path = f'./{image_path.stem}.gltf'
-    o3d.io.write_triangle_mesh(
-        gltf_path, mesh_crop, write_triangle_uvs=True)
+    o3d.io.write_triangle_mesh(gltf_path, mesh_crop, write_triangle_uvs=True)
     return gltf_path
 
-
-title = "zero-shot depth estimation with DPT + 3D Point Cloud"
-description = "DPT model to predict the depth of an image and then 3D Point Cloud to create a 3D object."
-
-# Add both image and model examples
-examples = [
-    ["examples/" + img] for img in os.listdir("files/")
-] + [
-    [os.path.join(os.path.dirname(__file__), "files/model1.glb")],
-    [os.path.join(os.path.dirname(__file__), "files/model2.glb")],
-    [os.path.join(os.path.dirname(__file__), "files/model3.glb")],
-    [os.path.join(os.path.dirname(__file__), "files/model4.glb")],
-    ["https://huggingface.co/datasets/dylanebert/3dgs/resolve/main/bonsai/bonsai-7k-mini.splat"],
-]
-
-iface = gr.Interface(fn=process_image,
-                     inputs=[gr.Image(
-                         type="filepath", label="Input Image")],
-                     outputs=[gr.Image(label="predicted depth", type="pil"),
-                              gr.Model3D(label="3d mesh reconstruction", clear_color=[
-                                                 1.0, 1.0, 1.0, 1.0]),
-                              gr.File(label="3d gLTF")],
-                     title=title,
-                     description=description,
-                     examples=examples,
-                     allow_flagging="never",
-                     cache_examples=False)
+title = "Zero-shot Depth Estimation with DPT + 3D Point Cloud"
+description = "DPT model predicts depth from an image, followed by 3D Point Cloud reconstruction."
+
+iface = gr.Interface(
+    fn=process_image,
+    inputs=[gr.Image(type="filepath", label="Input Image")],
+    outputs=[
+        gr.Image(label="Predicted Depth", type="pil"),
+        gr.Model3D(label="3D Mesh Reconstruction", clear_color=[1.0, 1.0, 1.0, 1.0]),
+        gr.File(label="3D gLTF")
+    ],
+    title=title,
+    description=description,
+    allow_flagging="never"
+)
 
 if __name__ == "__main__":
     iface.launch()
@@ -134,3 +89,4 @@ if __name__ == "__main__":
 
 
 
+