yolov8-物体检测代码示例

import os.path

import cv2
import requests
import numpy as np
from ultralytics.utils import yaml_load
from ultralytics.utils.checks import check_yaml


class ImageDetect:
    """图片检测"""

    def __init__(self):
        self.MODEL_BASE_DIR = os.path.dirname(__file__)
        self.CLASSES = yaml_load(check_yaml("coco128.yaml"))["names"]
        self.colors = np.random.uniform(0, 255, size=(len(self.CLASSES), 3))
        self.model_path = os.path.join(self.MODEL_BASE_DIR, "model/yolov8n.onnx")
        self.onnx_model = None

    def load_model(self):
        """加载模型"""
        # Load the ONNX model
        self.onnx_model: cv2.dnn.Net = cv2.dnn.readNetFromONNX(self.model_path)

    def draw_bounding_box(self, 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"{self.CLASSES[class_id]} ({confidence:.2f})"
        color = self.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 parse_image(self, image: str, show: bool = False):
        """
        parse_image function to load ONNX model, perform inference, draw bounding boxes, and display the output image.

        Args:
            image (str): 图片路径
            show (bool): 是否展示识别后的结果
        Returns:
            list: List of dictionaries containing detection information such as class_id, class_name, confidence, etc.
        """
        if not self.onnx_model:
            self.load_model()

        if not image.startswith("http"):
            # 读取本地图片
            original_image: np.ndarray = cv2.imread(image)
        else:
            # 读取网络图片
            response = requests.get(image)
            image_array = np.frombuffer(response.content, dtype=np.uint8)
            original_image: np.ndarray = cv2.imdecode(image_array, cv2.IMREAD_COLOR)
        [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)
        self.onnx_model.setInput(blob)

        # Perform inference
        outputs = self.onnx_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],  # 分类id
                "class_name": self.CLASSES[class_ids[index]],  # 分类名称
                "confidence": scores[index],  # 置信度
                "box": box,
                "scale": scale}
            detections.append(detection)
            # print(detection)
            if show:
                self.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)
                )
        if show:
            # Display the image with bounding boxes
            cv2.imshow('image', original_image)

            cv2.waitKey(0)
            cv2.destroyAllWindows()

        return detections


detect = ImageDetect()


if __name__ == '__main__':
    for _ in range(1):
        detect.parse_image(image="image/bus.jpg")

服务器占用资源小，默认数据集不太好用。建议自己针对场景进行训练。

推荐一个占用资源搭，比较好用的：https://github.com/xinyu1205/recognize-anything