feat: added data classes

secure_pixelation/__main__.py

@@ -1,7 +1,7 @@
-from .detect_humans import detect_humans
+from .get_bounding_boxes import select_bounding_boxes
 
 
 def cli():
     print(f"Running secure_pixelation")
 
-    detect_humans("assets/human_detection/humans.png")
+    select_bounding_boxes("assets/human_detection/test.png")

secure_pixelation/data_classes.py

@@ -0,0 +1,39 @@
+from __future__ import annotations
+
+from typing import Union
+from pathlib import Path
+import json
+
+import cv2
+import numpy as np
+
+
+class RawImage:
+    def __init__(self, file: Union[Path, str]):
+        self.file = Path(file)
+        self.name = self.file.name
+
+        self.meta_file = self._get_path("boxes.json")
+        self.meta_data = self.read_meta()
+        self.image = self.get_image()
+    
+    def _get_path(self, ending: str, original_suffix: bool = False) -> Path:
+        if original_suffix:
+            return self.file.with_name(self.file.stem + "_" + ending + self.file.suffix)
+        else:
+            return self.file.with_name(self.file.stem + "_" + ending)
+
+    def read_meta(self) -> dict:
+        if not self.meta_file.exists():
+            return {}
+
+        with self.meta_file.open("r") as f:
+            self.meta_data = json.load(f)
+        return self.meta_data
+
+    def write_meta(self):
+        with self.meta_file.open("w") as f:
+            json.dump(self.meta_data, f)
+
+    def get_image(self) -> np.ndarray:
+        return cv2.imread(str(self.file))

secure_pixelation/get_bounding_boxes.py

@@ -1 +1,21 @@
-# https://learnopencv.com/how-to-select-a-bounding-box-roi-in-opencv-cpp-python/
+from __future__ import annotations
+
+import cv2
+import numpy as np
+
+from .data_classes import RawImage
+
+
+# https://learnopencv.com/how-to-select-a-bounding-box-roi-in-opencv-cpp-python/
+
+
+def select_bounding_boxes(to_detect: str):
+    raw_image = RawImage(to_detect)
+
+    bounding_boxes = cv2.selectROIs(
+        windowName=raw_image.name, 
+        img=raw_image.image, 
+        fromCenter=False
+    )
+
+    print(bounding_boxes)