Upload 3 files for sample code use

.gitattributes

@@ -594,3 +594,5 @@ images/val/FrenchHermit1_toplit.jpg filter=lfs diff=lfs merge=lfs -text
 images/val/holyground_77.jpg filter=lfs diff=lfs merge=lfs -text
 images/val/oysters_14Feb2024_3.jpg filter=lfs diff=lfs merge=lfs -text
 images/val/real_disks_2.jpg filter=lfs diff=lfs merge=lfs -text
+oyster_sample_annotated.jpg filter=lfs diff=lfs merge=lfs -text
+oyster_sample.jpg filter=lfs diff=lfs merge=lfs -text

oysters.py

@@ -0,0 +1,74 @@
+"""
+Python3 script for YOLO prediction and sizing of  oyster objects.
+
+ To ensure compatible dependencies, set up a new venv virtual environment
+ and install  all required dependencies with just one installation after updating
+  the PIP installer.
+ $ pip install -U pip
+ $ pip install ultralytics
+"""
+
+from ultralytics import YOLO
+
+results_dir = 'oyster_yolo_results'
+predict_name = 'example_oyster_results'
+model = YOLO( 'aquaculture_oysters.pt')
+oyster_img =  'oyster_sample.jpg'
+
+# The size standard disk diameter. In this case, as millimeters, 
+#  using a cutout from a 3 inch hockey puck.
+std_mm = 76.2  
+
+# Run inference with these arguments
+prediction_kwargs = dict(
+    save=True, # True Saves image to project/name.
+    save_txt=True,  # Relative bounding box coordinates. up to conf limit.
+    save_conf=True,  # Adds conf scores to bbox coordinates
+    augment=False,
+    show_labels=True,  # True shows class labels in the saved image.
+    line_width=2, 
+    project=results_dir,   # bbox txt goes into runs/detect/labels.
+    name=predict_name,
+    exist_ok=False,  # True, overwrites existing project/name
+)
+
+results = model.predict(
+    source=oyster_img,
+    half=False,  # False is much faster than True
+    imgsz=960,
+    conf=0.7,
+    device='cpu',
+    iou=0.7,
+    max_det=400,
+    classes=[0,1],  #  0 is disks, 1 is oysters.
+    **prediction_kwargs
+)
+
+# Display detection bounding boxes with labels and confidence values.
+results[0].show(line_width=2)
+
+# Calculate and print size metrics, Are based on longest bounding box dimension.
+# Does not account for longer  lengths of oysters oriented on the diagonal.
+#  An empirically determined correction factor is recommended for better accuracy.
+#  A good correction function is used in the companion GitHub repository.
+boxes_tensor = results[0].boxes.xywh
+boxes_numpy = boxes_tensor.cpu().numpy().astype(int)
+w_h = boxes_numpy[:, 2:4]
+px_lengths = w_h.max(axis=1)  
+
+classes = results[0].boxes.cls
+class_distribution = classes.cpu().numpy().astype(int)
+num_disks = len(class_distribution[class_distribution == 0])
+num_oysters = len(class_distribution[class_distribution == 1])
+oyster_px_sizes = px_lengths[class_distribution == 1]
+disk_px_sizes = px_lengths[class_distribution == 0]
+px2mm_factor = std_mm / disk_px_sizes.mean()
+oyster_mean_mm = round(oyster_px_sizes.mean() * px2mm_factor, 2)
+smallest = round(oyster_px_sizes.min() * px2mm_factor, 2)
+largest = round(oyster_px_sizes.max() * px2mm_factor, 2)
+
+print(f'Mean oyster size, mm: {oyster_mean_mm}')
+print(f'Oyster size range, mm: [{smallest} -- {largest}]')
+
+
+