在save_annotation方法中,你需要根据YOLOv8模型的输出结果进行标注,并保存相应的标注信息。以下是一个简单示例,假设你已经有了YOLOv8模型和相关代码:
def save_annotation(self):
image = self.image_label.pixmap().toImage()
# 将QImage转换为OpenCV图像
cv_image = cv2.cvtColor(np.array(image), cv2.COLOR_RGBA2BGR)
# 在这里添加YOLOv8模型推理代码,得到目标检测结果
detections = yolo_inference(cv_image) # 假设yolo_inference是进行目标检测的函数
# 遍历每个检测到的目标
for detection in detections:
class_name = detection['class_name']
confidence = detection['confidence']
bbox = detection['bbox'] # (x, y, width, height)
# 在图像上绘制边界框和类别信息
x, y, w, h = bbox
cv2.rectangle(cv_image, (x, y), (x+w, y+h), (255, 0, 0), 2)
cv2.putText(cv_image, f"{class_name}: {confidence:.2f}", (x, y-10),
cv2.FONT_HERSHEY_SIMPLEX, 0.9, (255, 0 ,0), 2)
# 显示带有标注的图像在界面上
q_image_with_annotation = QImage(cv_image.data,
cv_image.shape[1],
cv_image.shape[0],
QImage.Format_RGB888)
self.image_label.setPixmap(QPixmap.fromImage(q_image_with_annotation))
# 保存标注信息到文件(这里仅作示例,可以根据需要自定义保存格式)
annotation_file = "annotation.txt"
with open(annotation_file, "a") as f:
for detection in detections:
class_name = detection['class_name']
confidence = detection['confidence']
bbox = detection['bbox']
x, y, w, h = bbox
line = f"{class_name} {x} {y} {w} {h} {confidence}\n"
f.write(line)
请注意,以上代码是一个简化的示例,你需要根据你实际的YOLOv8模型和需求进行相应的修改。在示例中,我们假设yolo_inference
函数接受一张OpenCV图像并返回目标检测结果列表。你可以将此示例作为参考,并根据YOLOv8模型的输出结果进行相应的处理和保存操作。
希望对你有所帮助!
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