# Ultralytics YOLO 🚀, AGPL-3.0 license import argparse import cv2 import numpy as np import onnxruntime as ort import torch from ultralytics.utils import ASSETS, yaml_load from ultralytics.utils.checks import check_requirements, check_yaml 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. Args: onnx_model: Path to the ONNX model. input_image: Path to the input image. confidence_thres: Confidence threshold for filtering detections. iou_thres: IoU (Intersection over Union) threshold for non-maximum suppression. """ self.onnx_model = onnx_model self.input_image = input_image self.confidence_thres = confidence_thres self.iou_thres = iou_thres # Load the class names from the COCO dataset self.classes = yaml_load(check_yaml("coco8.yaml"))["names"] # Generate a color palette for the classes self.color_palette = np.random.uniform(0, 255, size=(len(self.classes), 3)) def draw_detections(self, img, box, score, class_id): """ Draws bounding boxes and labels on the input image based on the detected objects. Args: img: The input image to draw detections on. box: Detected bounding box. score: Corresponding detection score. class_id: Class ID for the detected object. Returns: None """ # Extract the coordinates of the bounding box x1, y1, w, h = box # Retrieve the color for the class ID color = self.color_palette[class_id] # Draw the bounding box on the image cv2.rectangle(img, (int(x1), int(y1)), (int(x1 + w), int(y1 + h)), color, 2) # Create the label text with class name and score label = f"{self.classes[class_id]}: {score:.2f}" # Calculate the dimensions of the label text (label_width, label_height), _ = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, 0.5, 1) # Calculate the position of the label text label_x = x1 label_y = y1 - 10 if y1 - 10 > label_height else y1 + 10 # Draw a filled rectangle as the background for the label text cv2.rectangle( img, (label_x, label_y - label_height), (label_x + label_width, label_y + label_height), color, cv2.FILLED ) # Draw the label text on the image cv2.putText(img, label, (label_x, label_y), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0), 1, cv2.LINE_AA) def preprocess(self): """ Preprocesses the input image before performing inference. Returns: image_data: Preprocessed image data ready for inference. """ # Read the input image using OpenCV self.img = cv2.imread(self.input_image) # Get the height and width of the input image self.img_height, self.img_width = self.img.shape[:2] # Convert the image color space from BGR to RGB img = cv2.cvtColor(self.img, cv2.COLOR_BGR2RGB) # Resize the image to match the input shape img = cv2.resize(img, (self.input_width, self.input_height)) # Normalize the image data by dividing it by 255.0 image_data = np.array(img) / 255.0 # Transpose the image to have the channel dimension as the first dimension image_data = np.transpose(image_data, (2, 0, 1)) # Channel first # Expand the dimensions of the image data to match the expected input shape image_data = np.expand_dims(image_data, axis=0).astype(np.float32) # Return the preprocessed image data return image_data def postprocess(self, input_image, output): """ Performs post-processing on the model's output to extract bounding boxes, scores, and class IDs. Args: input_image (numpy.ndarray): The input image. output (numpy.ndarray): The output of the model. Returns: numpy.ndarray: The input image with detections drawn on it. """ # Transpose and squeeze the output to match the expected shape outputs = np.transpose(np.squeeze(output[0])) # Get the number of rows in the outputs array rows = outputs.shape[0] # Lists to store the bounding boxes, scores, and class IDs of the detections boxes = [] scores = [] class_ids = [] # Calculate the scaling factors for the bounding box coordinates x_factor = self.img_width / self.input_width y_factor = self.img_height / self.input_height # Iterate over each row in the outputs array for i in range(rows): # Extract the class scores from the current row classes_scores = outputs[i][4:] # Find the maximum score among the class scores max_score = np.amax(classes_scores) # If the maximum score is above the confidence threshold if max_score >= self.confidence_thres: # Get the class ID with the highest score class_id = np.argmax(classes_scores) # Extract the bounding box coordinates from the current row x, y, w, h = outputs[i][0], outputs[i][1], outputs[i][2], outputs[i][3] # Calculate the scaled coordinates of the bounding box left = int((x - w / 2) * x_factor) top = int((y - h / 2) * y_factor) width = int(w * x_factor) height = int(h * y_factor) # Add the class ID, score, and box coordinates to the respective lists class_ids.append(class_id) scores.append(max_score) boxes.append([left, top, width, height]) # Apply non-maximum suppression to filter out overlapping bounding boxes indices = cv2.dnn.NMSBoxes(boxes, scores, self.confidence_thres, self.iou_thres) # Iterate over the selected indices after non-maximum suppression for i in indices: # Get the box, score, and class ID corresponding to the index box = boxes[i] score = scores[i] class_id = class_ids[i] # Draw the detection on the input image self.draw_detections(input_image, box, score, class_id) # Return the modified input image return input_image def main(self): """ Performs inference using an ONNX model and returns the output image with drawn detections. Returns: output_img: The output image with drawn detections. """ # Create an inference session using the ONNX model and specify execution providers session = ort.InferenceSession(self.onnx_model, providers=["CUDAExecutionProvider", "CPUExecutionProvider"]) # Get the model inputs model_inputs = session.get_inputs() # Store the shape of the input for later use input_shape = model_inputs[0].shape self.input_width = input_shape[2] self.input_height = input_shape[3] # Preprocess the image data img_data = self.preprocess() # Run inference using the preprocessed image data outputs = session.run(None, {model_inputs[0].name: img_data}) # Perform post-processing on the outputs to obtain output image. return self.postprocess(self.img, outputs) # output image if __name__ == "__main__": # Create an argument parser to handle command-line arguments parser = argparse.ArgumentParser() parser.add_argument("--model", type=str, default="yolov8n.onnx", help="Input your ONNX model.") parser.add_argument("--img", type=str, default=str(ASSETS / "bus.jpg"), help="Path to input image.") parser.add_argument("--conf-thres", type=float, default=0.5, help="Confidence threshold") parser.add_argument("--iou-thres", type=float, default=0.5, help="NMS IoU threshold") args = parser.parse_args() # 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) # Perform object detection and obtain the output image output_image = detection.main() # Display the output image in a window cv2.namedWindow("Output", cv2.WINDOW_NORMAL) cv2.imshow("Output", output_image) # Wait for a key press to exit cv2.waitKey(0)