yolov8 flask部署

以下是使用Flask框架进行YOLOv8模型部署的示例代码：

from flask import Flask, request, jsonify
import cv2
import numpy as np

app = Flask(__name__)

# 加载YOLOv8模型
net = cv2.dnn.readNetFromDarknet('yolov8.cfg', 'yolov8.weights')
layer_names = net.getLayerNames()
output_layers = [layer_names[i[0] - 1] for i in net.getUnconnectedOutLayers()]

# 定义目标类别
classes = ['class1', 'class2', 'class3']

@app.route('/predict', methods=['POST'])
def predict():
    # 获取上传的图片
    image = request.files['image']
    image.save('input.jpg')

    # 进行目标检测
    img = cv2.imread('input.jpg')
    height, width, channels = img.shape

    blob = cv2.dnn.blobFromImage(img, 0.00392, (416, 416), (0, 0, 0), True, crop=False)
    net.setInput(blob)
    outs = net.forward(output_layers)

    class_ids = []
    confidences = []
    boxes = []

    for out in outs:
        for detection in out:
            scores = detection[5:]
            class_id = np.argmax(scores)
            confidence = scores[class_id]

            if confidence > 0.5:
                center_x = int(detection[0] * width)
                center_y = int(detection[1] * height)
                w = int(detection[2] * width)
                h = int(detection[3] * height)

                x = int(center_x - w / 2)
                y = int(center_y - h / 2)

                boxes.append([x, y, w, h])
                confidences.append(float(confidence))
                class_ids.append(class_id)

    # 绘制边界框和标签
    indexes = cv2.dnn.NMSBoxes(boxes, confidences, 0.5, 0.4)
    font = cv2.FONT_HERSHEY_PLAIN
    colors = np.random.uniform(0, 255, size=(len(classes), 3))

    for i in range(len(boxes)):
        if i in indexes:
            x, y, w, h = boxes[i]
            label = str(classes[class_ids[i]])
            color = colors[class_ids[i]]
            cv2.rectangle(img, (x, y), (x + w, y + h), color, 2)
            cv2.putText(img, label, (x, y + 30), font, 3, color, 3)

    cv2.imwrite('output.jpg', img)

    # 返回结果
    result = {
        'image': 'output.jpg',
        'detections': len(boxes)
    }

    return jsonify(result)

if __name__ == '__main__':
    app.run()

请确保已经安装了Flask和OpenCV库，并将YOLOv8的配置文件（yolov8.cfg）和权重文件（yolov8.weights）放在同一目录下。运行上述代码后，可以通过发送POST请求到`http://localhost:5000/predict`来进行目标检测。请求中需要包含一个名为`image`的文件字段，值为待检测的图片文件。