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# Ultralytics YOLO 🚀, AGPL-3.0 license
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import argparse
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import cv2.dnn
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import numpy as np
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from ultralytics.utils import ASSETS, yaml_load
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from ultralytics.utils.checks import check_yaml
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CLASSES = yaml_load(check_yaml("coco128.yaml"))["names"]
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colors = np.random.uniform(0, 255, size=(len(CLASSES), 3))
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def draw_bounding_box(img, class_id, confidence, x, y, x_plus_w, y_plus_h):
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"""
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Draws bounding boxes on the input image based on the provided arguments.
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Args:
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img (numpy.ndarray): The input image to draw the bounding box on.
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class_id (int): Class ID of the detected object.
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confidence (float): Confidence score of the detected object.
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x (int): X-coordinate of the top-left corner of the bounding box.
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y (int): Y-coordinate of the top-left corner of the bounding box.
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x_plus_w (int): X-coordinate of the bottom-right corner of the bounding box.
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y_plus_h (int): Y-coordinate of the bottom-right corner of the bounding box.
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"""
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label = f"{CLASSES[class_id]} ({confidence:.2f})"
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color = colors[class_id]
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cv2.rectangle(img, (x, y), (x_plus_w, y_plus_h), color, 2)
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cv2.putText(img, label, (x - 10, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)
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def main(onnx_model, input_image):
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"""
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Main function to load ONNX model, perform inference, draw bounding boxes, and display the output image.
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Args:
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onnx_model (str): Path to the ONNX model.
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input_image (str): Path to the input image.
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Returns:
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list: List of dictionaries containing detection information such as class_id, class_name, confidence, etc.
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"""
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# Load the ONNX model
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model: cv2.dnn.Net = cv2.dnn.readNetFromONNX(onnx_model)
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# Read the input image
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original_image: np.ndarray = cv2.imread(input_image)
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[height, width, _] = original_image.shape
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# Prepare a square image for inference
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length = max((height, width))
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image = np.zeros((length, length, 3), np.uint8)
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image[0:height, 0:width] = original_image
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# Calculate scale factor
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scale = length / 640
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# Preprocess the image and prepare blob for model
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blob = cv2.dnn.blobFromImage(image, scalefactor=1 / 255, size=(640, 640), swapRB=True)
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model.setInput(blob)
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# Perform inference
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outputs = model.forward()
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# Prepare output array
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outputs = np.array([cv2.transpose(outputs[0])])
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rows = outputs.shape[1]
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boxes = []
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scores = []
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class_ids = []
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# Iterate through output to collect bounding boxes, confidence scores, and class IDs
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for i in range(rows):
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classes_scores = outputs[0][i][4:]
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(minScore, maxScore, minClassLoc, (x, maxClassIndex)) = cv2.minMaxLoc(classes_scores)
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if maxScore >= 0.25:
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box = [
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outputs[0][i][0] - (0.5 * outputs[0][i][2]),
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outputs[0][i][1] - (0.5 * outputs[0][i][3]),
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outputs[0][i][2],
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outputs[0][i][3],
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]
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boxes.append(box)
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scores.append(maxScore)
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class_ids.append(maxClassIndex)
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# Apply NMS (Non-maximum suppression)
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result_boxes = cv2.dnn.NMSBoxes(boxes, scores, 0.25, 0.45, 0.5)
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detections = []
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# Iterate through NMS results to draw bounding boxes and labels
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for i in range(len(result_boxes)):
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index = result_boxes[i]
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box = boxes[index]
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detection = {
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"class_id": class_ids[index],
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"class_name": CLASSES[class_ids[index]],
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"confidence": scores[index],
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"box": box,
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"scale": scale,
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}
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detections.append(detection)
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draw_bounding_box(
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original_image,
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class_ids[index],
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scores[index],
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round(box[0] * scale),
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round(box[1] * scale),
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round((box[0] + box[2]) * scale),
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round((box[1] + box[3]) * scale),
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)
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# Display the image with bounding boxes
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cv2.imshow("image", original_image)
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cv2.waitKey(0)
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cv2.destroyAllWindows()
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return detections
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument("--model", default="yolov8n.onnx", help="Input your ONNX model.")
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parser.add_argument("--img", default=str(ASSETS / "bus.jpg"), help="Path to input image.")
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args = parser.parse_args()
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main(args.model, args.img)
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