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examples/YOLOv8-OpenCV-ONNX-Python/README.md

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+# YOLOv8 - OpenCV
+
+Implementation YOLOv8 on OpenCV using ONNX Format.
+
+Just simply clone and run
+
+```bash
+pip install -r requirements.txt
+python main.py --model yolov8n.onnx --img image.jpg
+```
+
+If you start from scratch:
+
+```bash
+pip install ultralytics
+yolo export model=yolov8n.pt imgsz=640 format=onnx opset=12
+```
+
+_\*Make sure to include "opset=12"_

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

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+# Ultralytics YOLO 🚀, AGPL-3.0 license
+
+import argparse
+
+import cv2.dnn
+import numpy as np
+
+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)
+    cv2.putText(img, label, (x - 10, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)
+
+
+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]
+
+    boxes = []
+    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)
+        if maxScore >= 0.25:
+            box = [
+                outputs[0][i][0] - (0.5 * outputs[0][i][2]),
+                outputs[0][i][1] - (0.5 * outputs[0][i][3]),
+                outputs[0][i][2],
+                outputs[0][i][3],
+            ]
+            boxes.append(box)
+            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]
+        detection = {
+            "class_id": class_ids[index],
+            "class_name": CLASSES[class_ids[index]],
+            "confidence": scores[index],
+            "box": box,
+            "scale": scale,
+        }
+        detections.append(detection)
+        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()
+
+    return detections
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    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)