目标检测yolov8daima
时间: 2023-10-02 11:05:57 浏览: 32
你可以在以下代码片段中找到使用YOLOv4进行目标检测的示例代码:
```python
import cv2
import numpy as np
# 加载YOLOv4模型
net = cv2.dnn.readNet("yolov4.weights", "yolov4.cfg")
layer_names = net.getLayerNames()
output_layers = [layer_names[i[0] - 1] for i in net.getUnconnectedOutLayers()]
# 加载类别标签
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")
height, width, channels = image.shape
# 对图像进行预处理
blob = cv2.dnn.blobFromImage(image, 0.00392, (416, 416), (0, 0, 0), True, crop=False)
net.setInput(blob)
outs = net.forward(output_layers)
# 解析输出结果
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:
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)
# 使用非极大值抑制去除重叠的边界框
indexes = cv2.dnn.NMSBoxes(boxes, confidences, 0.5, 0.4)
# 在图像上绘制边界框和类