release v1.1.1

Author: Koldim2001

README.md

@@ -84,7 +84,7 @@ element_crops = MakeCropsDetectThem(
     iou=0.7,
     resize_initial_size=True,
 )
-result = CombineDetections(element_crops, nms_threshold=0.05, match_metric='IOS')  
+result = CombineDetections(element_crops, nms_threshold=0.25, match_metric='IOS')  
 
 # Final Results:
 img=result.image
@@ -122,7 +122,7 @@ Class implementing combining masks/boxes from multiple crops + NMS (Non-Maximum
 - **nms_threshold** (*float*): IoU/IoS threshold for non-maximum suppression.
 - **match_metric** (*str*): Matching metric, either 'IOU' or 'IOS'.
 - **intelligent_sorter** (*bool*): Enable sorting by area and rounded confidence parameter. 
-            If False, sorting will be done only by confidence (usual nms). (Dafault is False)
+            If False, sorting will be done only by confidence (usual nms). (Dafault is True)
 
 
 

examples/example_patch_based_inference.ipynb

@@ -90,6 +90,7 @@
     "    element_crops (MakeCropsDetectThem): Object containing crop information.\n",
     "    nms_threshold (float): IoU/IoS threshold for non-maximum suppression.\n",
     "    match_metric (str): Matching metric, either 'IOU' or 'IOS'.\n",
+    "    intelligent_sorter (bool): Enable sorting by area and rounded confidence parameter. If False, sorting will be done only by confidence (usual nms). (Dafault is True)\n",
     "```"
    ]
   },
@@ -1008,7 +1009,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.13"
+   "version": "3.11.8"
   }
  },
  "nbformat": 4,

patched_yolo_infer/README.md

@@ -71,7 +71,7 @@ element_crops = MakeCropsDetectThem(
     iou=0.7,
     resize_initial_size=True,
 )
-result = CombineDetections(element_crops, nms_threshold=0.05, match_metric='IOS')  
+result = CombineDetections(element_crops, nms_threshold=0.25, match_metric='IOS')  
 
 # Final Results:
 img=result.image
@@ -108,7 +108,7 @@ Class implementing combining masks/boxes from multiple crops + NMS (Non-Maximum
 - **element_crops** (*MakeCropsDetectThem*): Object containing crop information.
 - **nms_threshold** (*float*): IoU/IoS threshold for non-maximum suppression.
 - **match_metric** (*str*): Matching metric, either 'IOU' or 'IOS'.
-- **intelligent_sorter** (*bool*): Enable sorting by area and rounded confidence parameter. If False, sorting will be done only by confidence (usual nms). (Dafault is False)
+- **intelligent_sorter** (*bool*): Enable sorting by area and rounded confidence parameter. If False, sorting will be done only by confidence (usual nms). (Dafault is True)
 
 ---
 ### 2. Custom inference visualization:

patched_yolo_infer/nodes/CombineDetections.py

@@ -12,7 +12,7 @@ class CombineDetections:
         nms_threshold (float): IoU/IoS threshold for non-maximum suppression.
         match_metric (str): Matching metric, either 'IOU' or 'IOS'.
         intelligent_sorter (bool): Enable sorting by area and rounded confidence parameter. 
-            If False, sorting will be done only by confidence (usual nms). (Dafault False)
+            If False, sorting will be done only by confidence (usual nms). (Dafault True)
 
     Attributes:
         conf_treshold (float): Confidence threshold of yolov8.
@@ -40,7 +40,7 @@ def __init__(
         element_crops: MakeCropsDetectThem,
         nms_threshold=0.3,
         match_metric='IOS',
-        intelligent_sorter=False
+        intelligent_sorter=True
     ) -> None:
         self.conf_treshold = element_crops.conf
         self.class_names = element_crops.class_names_dict 

setup.py

@@ -8,13 +8,13 @@
     long_description = "\n" + fh.read()
 
 
-VERSION = '1.1.0'
+VERSION = '1.1.1'
 DESCRIPTION = '''YOLO-Patch-Based-Inference for detection/segmentation of small objects in images.'''
 
 setup(
     name="patched_yolo_infer",
     version=VERSION,
-    license="MIT",
+    license="AGPL-3.0 license",
     url="https://github.com/Koldim2001/YOLO-Patch-Based-Inference",
     author="Koldim2001",
     description=DESCRIPTION,
@@ -46,12 +46,14 @@
         "slicing inference",
         "inference visualization",
         "patchify",
+        "ultralytics",
+        "SAHI",
     ],
     classifiers=[
         "Development Status :: 5 - Production/Stable",
         "Intended Audience :: Developers",
         "Programming Language :: Python :: 3",
-        "License :: OSI Approved :: MIT License",
+        "License :: OSI Approved :: GNU Affero General Public License v3 or later (AGPLv3+) (AGPL-3.0)",
         "Operating System :: OS Independent",
     ],
 )