Add docformatter to pre-commit (#5279)

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Burhan <62214284+Burhan-Q@users.noreply.github.com>

examples/YOLOv8-ONNXRuntime/main.py

@@ -9,11 +9,12 @@ from ultralytics.utils import ASSETS, yaml_load
 from ultralytics.utils.checks import check_requirements, check_yaml
 
 
-class Yolov8:
+class YOLOv8:
+    """YOLOv8 object detection model class for handling inference and visualization."""
 
     def __init__(self, onnx_model, input_image, confidence_thres, iou_thres):
         """
-        Initializes an instance of the Yolov8 class.
+        Initializes an instance of the YOLOv8 class.
 
         Args:
             onnx_model: Path to the ONNX model.
@@ -213,8 +214,8 @@ if __name__ == '__main__':
     # Check the requirements and select the appropriate backend (CPU or GPU)
     check_requirements('onnxruntime-gpu' if torch.cuda.is_available() else 'onnxruntime')
 
-    # Create an instance of the Yolov8 class with the specified arguments
-    detection = Yolov8(args.model, args.img, args.conf_thres, args.iou_thres)
+    # Create an instance of the YOLOv8 class with the specified arguments
+    detection = YOLOv8(args.model, args.img, args.conf_thres, args.iou_thres)
 
     # Perform object detection and obtain the output image
     output_image = detection.main()

examples/YOLOv8-OpenCV-ONNX-Python/main.py

@@ -7,11 +7,22 @@ from ultralytics.utils import ASSETS, yaml_load
 from ultralytics.utils.checks import check_yaml
 
 CLASSES = yaml_load(check_yaml('coco128.yaml'))['names']
-
 colors = np.random.uniform(0, 255, size=(len(CLASSES), 3))
 
 
 def draw_bounding_box(img, class_id, confidence, x, y, x_plus_w, y_plus_h):
+    """
+    Draws bounding boxes on the input image based on the provided arguments.
+
+    Args:
+        img (numpy.ndarray): The input image to draw the bounding box on.
+        class_id (int): Class ID of the detected object.
+        confidence (float): Confidence score of the detected object.
+        x (int): X-coordinate of the top-left corner of the bounding box.
+        y (int): Y-coordinate of the top-left corner of the bounding box.
+        x_plus_w (int): X-coordinate of the bottom-right corner of the bounding box.
+        y_plus_h (int): Y-coordinate of the bottom-right corner of the bounding box.
+    """
     label = f'{CLASSES[class_id]} ({confidence:.2f})'
     color = colors[class_id]
     cv2.rectangle(img, (x, y), (x_plus_w, y_plus_h), color, 2)
@@ -19,18 +30,39 @@ def draw_bounding_box(img, class_id, confidence, x, y, x_plus_w, y_plus_h):
 
 
 def main(onnx_model, input_image):
+    """
+    Main function to load ONNX model, perform inference, draw bounding boxes, and display the output image.
+
+    Args:
+        onnx_model (str): Path to the ONNX model.
+        input_image (str): Path to the input image.
+
+    Returns:
+        list: List of dictionaries containing detection information such as class_id, class_name, confidence, etc.
+    """
+    # Load the ONNX model
     model: cv2.dnn.Net = cv2.dnn.readNetFromONNX(onnx_model)
+
+    # Read the input image
     original_image: np.ndarray = cv2.imread(input_image)
     [height, width, _] = original_image.shape
+
+    # Prepare a square image for inference
     length = max((height, width))
     image = np.zeros((length, length, 3), np.uint8)
     image[0:height, 0:width] = original_image
+
+    # Calculate scale factor
     scale = length / 640
 
+    # Preprocess the image and prepare blob for model
     blob = cv2.dnn.blobFromImage(image, scalefactor=1 / 255, size=(640, 640), swapRB=True)
     model.setInput(blob)
+
+    # Perform inference
     outputs = model.forward()
 
+    # Prepare output array
     outputs = np.array([cv2.transpose(outputs[0])])
     rows = outputs.shape[1]
 
@@ -38,6 +70,7 @@ def main(onnx_model, input_image):
     scores = []
     class_ids = []
 
+    # Iterate through output to collect bounding boxes, confidence scores, and class IDs
     for i in range(rows):
         classes_scores = outputs[0][i][4:]
         (minScore, maxScore, minClassLoc, (x, maxClassIndex)) = cv2.minMaxLoc(classes_scores)
@@ -49,9 +82,12 @@ def main(onnx_model, input_image):
             scores.append(maxScore)
             class_ids.append(maxClassIndex)
 
+    # Apply NMS (Non-maximum suppression)
     result_boxes = cv2.dnn.NMSBoxes(boxes, scores, 0.25, 0.45, 0.5)
 
     detections = []
+
+    # Iterate through NMS results to draw bounding boxes and labels
     for i in range(len(result_boxes)):
         index = result_boxes[i]
         box = boxes[index]
@@ -65,6 +101,7 @@ def main(onnx_model, input_image):
         draw_bounding_box(original_image, class_ids[index], scores[index], round(box[0] * scale), round(box[1] * scale),
                           round((box[0] + box[2]) * scale), round((box[1] + box[3]) * scale))
 
+    # Display the image with bounding boxes
     cv2.imshow('image', original_image)
     cv2.waitKey(0)
     cv2.destroyAllWindows()
@@ -74,7 +111,7 @@ def main(onnx_model, input_image):
 
 if __name__ == '__main__':
     parser = argparse.ArgumentParser()
-    parser.add_argument('--model', default='yolov8n.onnx', help='Input your onnx model.')
+    parser.add_argument('--model', default='yolov8n.onnx', help='Input your ONNX model.')
     parser.add_argument('--img', default=str(ASSETS / 'bus.jpg'), help='Path to input image.')
     args = parser.parse_args()
     main(args.model, args.img)

examples/YOLOv8-Region-Counter/yolov8_region_counter.py

@@ -33,10 +33,6 @@ counting_regions = [
     }, ]
 
 
-def is_inside_polygon(point, polygon):
-    return polygon.contains(Point(point))
-
-
 def mouse_callback(event, x, y, flags, param):
     """Mouse call back event."""
     global current_region
@@ -44,7 +40,7 @@ def mouse_callback(event, x, y, flags, param):
     # Mouse left button down event
     if event == cv2.EVENT_LBUTTONDOWN:
         for region in counting_regions:
-            if is_inside_polygon((x, y), region['polygon']):
+            if region['polygon'].contains(Point((x, y))):
                 current_region = region
                 current_region['dragging'] = True
                 current_region['offset_x'] = x
@@ -150,7 +146,7 @@ def run(
 
             # Check if detection inside region
             for region in counting_regions:
-                if is_inside_polygon((x, y), region['polygon']):
+                if region['polygon'].contains(Point((x, y))):
                     region['counts'] += 1
 
         # Draw regions (Polygons/Rectangles)