Update app.py

app.py

@@ -16,7 +16,8 @@ def process_image(image_path):
     image_raw = Image.open(image_path)
     image = image_raw.resize(
         (800, int(800 * image_raw.size[1] / image_raw.size[0])),
-        Image.Resampling.LANCZOS)
+        Image.Resampling.LANCZOS,
+    )
 
     # prepare image for the model
     encoding = feature_extractor(image, return_tensors="pt")
@@ -34,14 +35,13 @@ def process_image(image_path):
         align_corners=False,
     ).squeeze()
     output = prediction.cpu().numpy()
-    depth_image = (output * 255 / np.max(output)).astype('uint8')
+    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)
+        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:
@@ -53,50 +53,48 @@ 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)
+        image_o3d, depth_o3d, convert_rgb_to_intensity=False
+    )
     w = int(depth_image.shape[1])
     h = int(depth_image.shape[0])
 
     camera_intrinsic = o3d.camera.PinholeCameraIntrinsic()
-    camera_intrinsic.set_intrinsics(w, h, 500, 500, w/2, h/2)
+    camera_intrinsic.set_intrinsics(w, h, 500, 500, w / 2, h / 2)
 
-    pcd = o3d.geometry.PointCloud.create_from_rgbd_image(
-        rgbd_image, camera_intrinsic)
+    pcd = o3d.geometry.PointCloud.create_from_rgbd_image(rgbd_image, camera_intrinsic)
 
-    print('normals')
+    print("normals")
     pcd.normals = o3d.utility.Vector3dVector(
-        np.zeros((1, 3)))  # invalidate existing normals
+        np.zeros((1, 3))
+    )  # invalidate existing normals
     pcd.estimate_normals(
-        search_param=o3d.geometry.KDTreeSearchParamHybrid(radius=0.01, max_nn=30))
+        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')
+        camera_location=np.array([0.0, 0.0, 1000.0])
+    )
+    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, depth=depth, width=0, scale=1.1, linear_fit=True)
+            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}')
+    print(f"voxel_size = {voxel_size:e}")
     mesh = mesh_raw.simplify_vertex_clustering(
         voxel_size=voxel_size,
-        contraction=o3d.geometry.SimplificationContraction.Average)
+        contraction=o3d.geometry.SimplificationContraction.Average,
+    )
 
     # vertices_to_remove = densities < np.quantile(densities, 0.001)
     # mesh.remove_vertices_by_mask(vertices_to_remove)
     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)
+    gltf_path = f"./{image_path.stem}.gltf"
+    o3d.io.write_triangle_mesh(gltf_path, mesh_crop, write_triangle_uvs=True)
     return gltf_path
 
 
@@ -104,16 +102,19 @@ title = "Demo: zero-shot depth estimation with DPT + 3D Point Cloud"
 description = "This demo is a variation from the original <a href='https://huggingface.co/spaces/nielsr/dpt-depth-estimation' target='_blank'>DPT Demo</a>. It uses the DPT model to predict the depth of an image and then uses 3D Point Cloud to create a 3D object."
 examples = [["examples/" + img] for img in os.listdir("examples/")]
 
-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)
-iface.launch(debug=True, enable_queue=False)
+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,
+    api_open=false
+)
+iface.launch(debug=True, show_api=False)