1

app.py

@@ -103,7 +103,7 @@ def generate_monocular_depth_maps(img_list, depth_prior_name):
           prediction = model.infer(image, f_px=f_px)
           depth = prediction["depth"].cpu().numpy()  # Depth in [m].
           focallength_px=prediction["focallength_px"].cpu()
-          depth = cv2.resize(depth[0], image.size, interpolation=cv2.INTER_CUBIC)
+          #depth = cv2.resize(depth[0], image.size, interpolation=cv2.INTER_CUBIC)
           depth_list.append(depth)
           focallength_px_list.append(focallength_px)
           #np.savez_compressed(path_depthpro, depth=depth, focallength_px=prediction["focallength_px"].cpu())  
@@ -114,10 +114,9 @@ def generate_monocular_depth_maps(img_list, depth_prior_name):
           image = Image.open(image_path)
           #print(image.size)
           depth = pipe(image)["predicted_depth"].numpy()
-          print(depth.max(),depth.min())
+          #print(depth.max(),depth.min())
           #depth = cv2.resize(depth[0], image.size, interpolation=cv2.INTER_CUBIC)
           focallength_px = 200
-          print(depth.max(),depth.min())
           depth_list.append(depth)
           focallength_px_list.append(focallength_px)
           #np.savez_compressed(path_depthanything, depth=depth)  
@@ -194,6 +193,13 @@ with gradio.Blocks(css=css, title=title, delete_cache=(gradio_delete_cache, grad
                      [os.path.join(HERE_PATH, 'example/bear/00000.jpg'),
                       os.path.join(HERE_PATH, 'example/bear/00001.jpg'),
                       os.path.join(HERE_PATH, 'example/bear/00002.jpg'),
+                      os.path.join(HERE_PATH, 'example/bear/00003.jpg'),
+                      os.path.join(HERE_PATH, 'example/bear/00004.jpg'),
+                      os.path.join(HERE_PATH, 'example/bear/00005.jpg'),
+                      os.path.join(HERE_PATH, 'example/bear/00006.jpg'),
+                      os.path.join(HERE_PATH, 'example/bear/00007.jpg'),
+                      os.path.join(HERE_PATH, 'example/bear/00008.jpg'),
+                      os.path.join(HERE_PATH, 'example/bear/00009.jpg'),
                       ]
                 ],
                 [

dust3r/cloud_opt_flow/base_opt.py

@@ -25,6 +25,8 @@ from dust3r.utils.vo_eval import save_trajectory_tum_format
 import os
 import matplotlib.pyplot as plt
 from PIL import Image
+HERE_PATH = path.normpath(path.dirname(__file__)) 
+print('**',HERE_PATH)
 
 def c2w_to_tumpose(c2w):
     """