Created image_annotate.py

computer_vision/image_annotate.py

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+import supervision as sv
+from ultralytics import YOLO
+from PIL import Image
+import numpy as np
+import cv2
+
+def image_annotate(image:str) -> Image.Image:
+  # load the input image
+  image = cv2.imread(image)
+
+  # load pre-trained vision model
+  model = YOLO("yolo12s.pt")
+
+  # run object detection on the image
+  result = model(image)[0]
+
+  # convert YOLO output to a Supervision-compatible detections format
+  detections = sv.Detections.from_ultralytics(result)
+
+  # initialize a box annotator for drawing detection bounding boxes
+  box_annotator = sv.BoxAnnotator()
+
+  # annotate the image with detected objects
+  annotated_image = box_annotator.annotate(
+    scene=image.copy(),
+    detections=detections)
+
+  # convert BGR to RGB for correct display
+  annotated_image_rgb = cv2.cvtColor(annotated_image, cv2.COLOR_BGR2RGB)
+
+  # convert the annotated NumPy array (RGB) to a PIL Image object
+  annotated_pil_image = Image.fromarray(annotated_image_rgb)
+
+  # return annotated image
+  return annotated_pil_image
+
+if __name__ == "__main__":
+  annotate_img = image_annotate("image_file_path")
+  annotate_img.show()