# 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)