Update utils/depth_utils.py

utils/depth_utils.py

@@ -1,25 +1,23 @@
-import torch
-import cv2
-import numpy as np
-from transformers import DPTFeatureExtractor, DPTForDepthEstimation
-
-feature_extractor = DPTFeatureExtractor.from_pretrained("Intel/dpt-large")
-model = DPTForDepthEstimation.from_pretrained("Intel/dpt-large")
-model.eval()
-
-def estimate_depth(frame_paths):
-    depth_maps = []
-
-    for path in frame_paths:
-        image = cv2.imread(path)
-        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
-
-        inputs = feature_extractor(images=image, return_tensors="pt")
-        with torch.no_grad():
-            outputs = model(**inputs)
-            depth = outputs.predicted_depth[0].cpu().numpy()
-
-        depth = cv2.resize(depth, (image.shape[1], image.shape[0]))
-        depth_maps.append(depth)
-
-    return depth_maps
+import torch
+import cv2
+from transformers import DPTFeatureExtractor, DPTForDepthEstimation
+
+extractor = DPTFeatureExtractor.from_pretrained("Intel/dpt-large")
+model = DPTForDepthEstimation.from_pretrained("Intel/dpt-large")
+model.eval()
+
+def estimate_depth(frame_paths):
+    depth_maps = []
+
+    for path in frame_paths:
+        image = cv2.imread(path)
+        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+
+        inputs = extractor(images=image, return_tensors="pt")
+        with torch.no_grad():
+            outputs = model(**inputs)
+            depth = outputs.predicted_depth[0].cpu().numpy()
+
+        depth_maps.append(depth)
+
+    return depth_maps