yolov8实例分割代码解读
时间: 2023-10-12 15:05:57 浏览: 84
YOLOv8实例分割代码的解读可以从以下几个方面入手。
首先,在代码中可以看到使用了"-seg"后缀的模型文件,即"yolov8n-seg.pt"。这说明该代码使用的是YOLOv8分割模型。<span class="em">1</span><span class="em">2</span><span class="em">3</span>
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相关问题
yolov8实例分割代码
以下是一个使用YOLOv8进行实例分割的示例代码:
```python
import cv2
import numpy as np
# 加载模型和权重
net = cv2.dnn.readNetFromDarknet('yolov3.cfg', 'yolov3.weights')
# 获取输出层的名称
layer_names = net.getLayerNames()
output_layers = [layer_names[i[0] - 1] for i in net.getUnconnectedOutLayers()]
# 加载类标签
classes = []
with open('coco.names', 'r') as f:
classes = [line.strip() for line in f.readlines()]
# 加载图像
image = cv2.imread('image.jpg')
# 图像预处理
blob = cv2.dnn.blobFromImage(image, 0.00392, (416, 416), (0, 0, 0), True, crop=False)
net.setInput(blob)
outs = net.forward(output_layers)
# 解析结果
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:
# 检测到目标物体
center_x = int(detection[0] * image.shape[1])
center_y = int(detection[1] * image.shape[0])
width = int(detection[2] * image.shape[1])
height = int(detection[3] * image.shape[0])
# 边框坐标
x = int(center_x - width / 2)
y = int(center_y - height / 2)
boxes.append([x, y, width, height])
confidences.append(float(confidence))
class_ids.append(class_id)
# NMS抑制
indexes = cv2.dnn.NMSBoxes(boxes, confidences, 0.5, 0.4)
# 绘制边框和类标签
font = cv2.FONT_HERSHEY_PLAIN
colors = np.random.uniform(0, 255, size=(len(classes), 3))
if len(indexes) > 0:
for i in indexes.flatten():
x, y, w, h = boxes[i]
label = str(classes[class_ids[i]])
confidence = str(round(confidences[i], 2))
color = colors[i]
cv2.rectangle(image, (x, y), (x+w, y+h), color, 2)
cv2.putText(image, label + ' ' + confidence, (x, y+30), font, 3, color, 3)
# 显示结果
cv2.imshow("Image", image)
cv2.waitKey(0)
cv2.destroyAllWindows()
```
请确保将YOLOv8的配置文件(.cfg)、权重文件(.weights)和类标签文件(.names)放在正确的路径下,并将图像文件命名为`image.jpg`。
这段代码使用了OpenCV库加载YOLOv8模型和权重,对图像进行预处理,然后解析模型输出,应用非最大抑制(NMS)来去除重叠的边框,最后绘制边框和类标签并显示结果图像。
ros yolov5实例分割 代码
以下是使用ROS和YOLOv5进行实例分割的示例代码:
首先,需要安装ROS和YOLOv5的依赖项。在Ubuntu中,可以使用以下命令安装:
```
sudo apt-get install ros-<your_ros_version>-cv-bridge ros-<your_ros_version>-image-transport
```
然后,下载YOLOv5的代码并将其放置在ROS包中。在终端中,进入ROS包的目录并运行以下命令:
```
git clone https://github.com/ultralytics/yolov5.git
```
接下来,创建一个ROS节点并在其中运行YOLOv5。在ROS包的`src`目录下创建一个Python脚本,例如`yolov5_node.py`,并添加以下代码:
```python
#!/usr/bin/env python
import rospy
from sensor_msgs.msg import Image
from cv_bridge import CvBridge
import cv2
from yolov5.models.experimental import attempt_load
from yolov5.utils.general import non_max_suppression, scale_coords
from yolov5.utils.torch_utils import select_device
class YOLOv5Node:
def __init__(self):
# Load YOLOv5 model
self.device = select_device('')
self.model = attempt_load('yolov5s.pt', map_location=self.device)
self.names = self.model.module.names if hasattr(self.model, 'module') else self.model.names
self.colors = [[0, 255, 0]]
# Set up ROS node
rospy.init_node('yolov5_node')
self.bridge = CvBridge()
self.image_sub = rospy.Subscriber('/camera/image_raw', Image, self.image_callback)
self.image_pub = rospy.Publisher('/camera/image_processed', Image, queue_size=1)
def image_callback(self, data):
# Convert ROS image message to OpenCV image
cv_image = self.bridge.imgmsg_to_cv2(data, 'bgr8')
# Run YOLOv5 inference on image
img = self.model.preprocess(cv_image)
img = img.to(self.device)
pred = self.model(img, augment=False)[0]
pred = non_max_suppression(pred, conf_thres=0.25, iou_thres=0.45, agnostic=False)
# Draw bounding boxes on image
for i, det in enumerate(pred):
if len(det):
det[:, :4] = scale_coords(img.shape[2:], det[:, :4], cv_image.shape).round()
for *xyxy, conf, cls in reversed(det):
label = f'{self.names[int(cls)]} {conf:.2f}'
color = self.colors[int(cls) % len(self.colors)]
cv2.rectangle(cv_image, (int(xyxy[0]), int(xyxy[1])), (int(xyxy[2]), int(xyxy[3])), color, thickness=2)
cv2.putText(cv_image, label, (int(xyxy[0]), int(xyxy[1]) - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, thickness=2)
# Convert OpenCV image to ROS image message and publish
image_msg = self.bridge.cv2_to_imgmsg(cv_image, 'bgr8')
self.image_pub.publish(image_msg)
if __name__ == '__main__':
try:
node = YOLOv5Node()
rospy.spin()
except rospy.ROSInterruptException:
pass
```
在`__init__`方法中,YOLOv5模型被加载并初始化ROS节点。`image_callback`方法会在每次接收到ROS图像消息时被调用,并使用YOLOv5进行实例分割和边框绘制。最后,绘制的图像被转换为ROS图像消息并发布到`/camera/image_processed`话题。
在终端中,运行以下命令启动ROS节点:
```
rosrun <your_package_name> yolov5_node.py
```
接下来,使用ROS的`image_view`节点查看分割后的图像。在终端中运行以下命令:
```
rosrun image_view image_view image:=/camera/image_processed
```
应该可以看到实例分割和边框绘制后的图像了。