帮我写一个pyqt5的界面来实现yolov5的测试功能,并可以实时显示在窗口内
时间: 2024-04-16 17:26:48 浏览: 15
当然可以!下面是一个简单的示例代码,演示如何使用PyQt5创建一个界面来实现Yolov5的测试功能,并实时显示在窗口内:
```python
import sys
from PyQt5.QtWidgets import QApplication, QMainWindow, QLabel, QVBoxLayout, QWidget
from PyQt5.QtGui import QImage, QPixmap
from PyQt5.QtCore import Qt
import cv2
import torch
from torchvision import transforms
from PIL import Image
from models.experimental import attempt_load
from utils.general import non_max_suppression, scale_coords
from utils.datasets import letterbox
class YoloWindow(QMainWindow):
def __init__(self):
super().__init__()
self.setWindowTitle("Yolov5 Test")
self.setGeometry(100, 100, 800, 600)
self.image_label = QLabel(self)
self.image_label.setAlignment(Qt.AlignCenter)
layout = QVBoxLayout()
layout.addWidget(self.image_label)
widget = QWidget()
widget.setLayout(layout)
self.setCentralWidget(widget)
self.model = None
def load_model(self, path):
self.model = attempt_load(path)
self.model.to(torch.device('cpu')).eval()
def detect_objects(self, image_path):
img0 = cv2.imread(image_path)
img = letterbox(img0, new_shape=640)[0]
img = img[:, :, ::-1].transpose(2, 0, 1)
img = np.ascontiguousarray(img)
img = torch.from_numpy(img).unsqueeze(0)
img /= 255.0
if img.ndimension() == 3:
img = img.unsqueeze(0)
outputs = self.model(img)[0]
outputs = non_max_suppression(outputs, conf_thres=0.5, iou_thres=0.5)
for output in outputs:
if output is not None and len(output):
output[:, :4] = scale_coords(img.shape[2:], output[:, :4], img0.shape).round()
for x1, y1, x2, y2, conf, cls in output:
cv2.rectangle(img0, (int(x1), int(y1)), (int(x2), int(y2)), (255, 0, 0), 2)
cv2.putText(img0, str(cls.item()), (int(x1), int(y1) - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (255, 0, 0), 2)
img0 = cv2.cvtColor(img0, cv2.COLOR_BGR2RGB)
height, width, channel = img0.shape
bytes_per_line = channel * width
q_img = QImage(img0.data, width, height, bytes_per_line, QImage.Format_RGB888)
pixmap = QPixmap.fromImage(q_img)
pixmap = pixmap.scaledToWidth(self.image_label.width())
self.image_label.setPixmap(pixmap)
if __name__ == '__main__':
app = QApplication(sys.argv)
window = YoloWindow()
window.load_model("yolov5.pt")
window.detect_objects("test.jpg")
window.show()
sys.exit(app.exec_())
```
这个代码创建了一个简单的窗口,并在窗口中实时显示检测到的物体。你需要将`yolov5.pt`和`test.jpg`替换为你自己的模型和测试图片。
请确保你已经安装了必要的依赖库,比如`torch`, `torchvision`, `pyqt5`, `opencv-python`等。你可以使用`pip`进行安装。
这只是一个简单的示例,你可以根据自己的需求进行修改和扩展。希望对你有所帮助!