fixed circular import

inference.py

@@ -10,9 +10,30 @@ import open3d as o3d
 import pandas as pd
 import plotly.express as px
 import matplotlib.pyplot as plt
-from utils import map_image_range
 
+def map_image_range(image, min_value, max_value):
+    """
+    Maps the values of a numpy image array to a specified range.
 
+    Args:
+        image (numpy.ndarray): Input image array with values ranging from 0 to 1.
+        min_value (float): Minimum value of the new range.
+        max_value (float): Maximum value of the new range.
+
+    Returns:
+        numpy.ndarray: Image array with values mapped to the specified range.
+    """
+    # Ensure the input image is a numpy array
+    if not isinstance(image, np.ndarray):
+        raise ValueError("Input image must be a numpy array.")
+
+    # Ensure the image values are within the valid range (0 to 1)
+    if not (0 <= np.min(image) <= 1) or not (0 <= np.max(image) <= 1):
+        raise ValueError("Input image values must be in the range [0, 1].")
+
+    # Map the values to the specified range
+    mapped_image = (image - 0) * (max_value - min_value) / (1 - 0) + min_value
+    return mapped_image
 
 def PCL(mask, depth):
     assert mask.shape == depth.shape
@@ -205,4 +226,10 @@ class SegmentPredictor:
         sam_masks = []
         for i,mask in enumerate(sam_result):
             sam_masks.append((mask["segmentation"], str(i)))
-        return sam_masks
+        return sam_masks
+    
+
+
+
+
+    

utils.py

@@ -196,27 +196,3 @@ def PCL3(image):
     return fig
 
 import numpy as np
-
-def map_image_range(image, min_value, max_value):
-    """
-    Maps the values of a numpy image array to a specified range.
-
-    Args:
-        image (numpy.ndarray): Input image array with values ranging from 0 to 1.
-        min_value (float): Minimum value of the new range.
-        max_value (float): Maximum value of the new range.
-
-    Returns:
-        numpy.ndarray: Image array with values mapped to the specified range.
-    """
-    # Ensure the input image is a numpy array
-    if not isinstance(image, np.ndarray):
-        raise ValueError("Input image must be a numpy array.")
-
-    # Ensure the image values are within the valid range (0 to 1)
-    if not (0 <= np.min(image) <= 1) or not (0 <= np.max(image) <= 1):
-        raise ValueError("Input image values must be in the range [0, 1].")
-
-    # Map the values to the specified range
-    mapped_image = (image - 0) * (max_value - min_value) / (1 - 0) + min_value
-    return mapped_image