Switch to opencv-python-headless to avoid conflict.

Also fixed bug with Path

pyproject.toml

@@ -22,7 +22,7 @@ readme = "README.md"
 license = { file="LICENSE" }
 requires-python = ">=3.7"
 dependencies = [
-    "opencv-python",
+    "opencv-python-headless",
     "numpy",
     "torch",
     "scikit-learn",

src/defect_detection/functions.py

@@ -4,6 +4,7 @@
 
 import torch
 from .deep_AE import AE_cls
+from pathlib import Path
 
 
 # Error map using mean
@@ -44,7 +45,7 @@ def deepAE_load(path, use_only=True, loss_fn=None, opt=None, opt_param=None):
             If None, a empty dict is assumed.
     """
     # Load the model hyperparameter
-    with open(path + "AE_config.txt") as f:
+    with Path.open(Path(path + "AE_config.txt")) as f:
         param = eval(f.read())
 
     # Initilize the model

src/defect_detection/preprocessing.py

@@ -9,6 +9,7 @@
 import cv2 as cv
 import torch
 import os
+from pathlib import Path
 import concurrent.futures as thd
 
 
@@ -103,7 +104,7 @@ def generate_dataset(
         "Ncore": Ncore,
         "seed": seed,
     }
-    with open(param["opath"] + param["name"] + ".txt", "w") as f:
+    with Path.open(Path(param["opath"] + param["name"] + ".txt"), "w") as f:
         print(param, file=f)
 
     print("Preprocessing parameters :")
@@ -112,7 +113,7 @@ def generate_dataset(
 
     # Load initial croping if needed
     if param["fcrop"] is not None:
-        with open(param["fcrop"]) as f:
+        with Path.open(Path(param["fcrop"]), "r") as f:
             cr = eval(f.read())
     else:
         cr = None
@@ -124,7 +125,7 @@ def generate_dataset(
 
     # Get the image list from file
     fl = []
-    with open(param["Flist"]) as f:
+    with Path.open(Path(param["Flist"])) as f:
         for li in f.readlines():
             if li[0] == "#":
                 continue