YOLOv5 pyqt计数结果显示

基于YOLOv5和pyqt的项目可以实现目标检测并显示计数结果。以下是一个简单的示例代码：

import sys
from PyQt5.QtWidgets import QApplication, QMainWindow, QLabel
from PyQt5.QtGui import QPixmap
import cv2
import torch
from models.experimental import attempt_load
from utils.general import non_max_suppression, scale_coords
from utils.plots import plot_one_box

class MainWindow(QMainWindow):
    def __init__(self):
        super().__init__()
        self.setWindowTitle("YOLOv5 Object Detection")
        self.setGeometry(100, 100, 800, 600)
        self.image_label = QLabel(self)
        self.image_label.setGeometry(10, 10, 780, 580)

    def detect_objects(self, image_path):
        # 加载模型
        model = attempt_load('yolov5s.pt', map_location=torch.device('cpu'))
        stride = int(model.stride.max())
        imgsz = 640

        # 读取图像
        img0 = cv2.imread(image_path)  # BGR
        img = img0.copy()

        # 图像预处理
        img = torch.from_numpy(img).to(torch.device('cpu')).unsqueeze(0).permute(0, 3, 1, 2).float() / 255.0

        # 模型推理
        pred = model(img)[0]
        pred = non_max_suppression(pred, 0.4, 0.5)

        # 绘制边界框和计数结果
        for det in pred[0]:
            if det is not None and len(det):
                det[:, :4] = scale_coords(img.shape[2:], det[:, :4], img0.shape).round()
                for *xyxy, conf, cls in reversed(det):
                    label = f'{model.names[int(cls)]} {conf:.2f}'
                    plot_one_box(xyxy, img0, label=label, color=(0, 255, 0), line_thickness=3)

        # 显示图像
        img_rgb = cv2.cvtColor(img0, cv2.COLOR_BGR2RGB)
        h, w, ch = img_rgb.shape
        bytes_per_line = ch * w
        q_img = QImage(img_rgb.data, w, h, bytes_per_line, QImage.Format_RGB888)
        pixmap = QPixmap.fromImage(q_img)
        self.image_label.setPixmap(pixmap)

if __name__ == '__main__':
    app = QApplication(sys.argv)
    window = MainWindow()
    window.detect_objects('image.jpg')  # 替换为你的图像路径
    window.show()
    sys.exit(app.exec_())

这段代码创建了一个基于pyqt的GUI窗口，可以显示目标检测的结果。你需要将YOLOv5的模型文件`yolov5s.pt`放在同一目录下，并将`image.jpg`替换为你要检测的图像路径。运行代码后，窗口将显示带有边界框和计数结果的图像。