给我写一个YOLO模型的垃圾分类的代码
时间: 2023-03-28 19:00:17 浏览: 63
以下是一个简单的YOLO模型的垃圾分类代码示例:
```
import cv2
import numpy as np
# 加载模型
net = cv2.dnn.readNet("yolov3.weights", "yolov3.cfg")
# 加载类别标签
classes = []
with open("coco.names", "r") as f:
classes = [line.strip() for line in f.readlines()]
# 设置输入图像尺寸和缩放因子
input_size = 416
scale_factor = 1/255.
# 加载图像
img = cv2.imread("garbage.jpg")
# 获取图像尺寸
height, width, channels = img.shape
# 创建输入图像的blob
blob = cv2.dnn.blobFromImage(img, scale_factor, (input_size, input_size), swapRB=True, crop=False)
# 设置输入blob
net.setInput(blob)
# 运行前向传递
outs = net.forward(net.getUnconnectedOutLayersNames())
# 解析输出
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 > .5:
center_x = int(detection[] * 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)
class_ids.append(class_id)
confidences.append(float(confidence))
boxes.append([x, y, w, h])
# 非最大抑制
indices = cv2.dnn.NMSBoxes(boxes, confidences, .5, .4)
# 绘制边界框和类别标签
for i in indices:
i = i[]
box = boxes[i]
x = box[]
y = box[1]
w = box[2]
h = box[3]
cv2.rectangle(img, (x, y), (x + w, y + h), (, 255, ), 2)
label = f"{classes[class_ids[i]]}: {confidences[i]:.2f}"
cv2.putText(img, label, (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, .5, (, 255, ), 2)
# 显示结果
cv2.imshow("Image", img)
cv2.waitKey()
cv2.destroyAllWindows()
```
请注意,这只是一个简单的示例代码,实际应用中需要根据具体情况进行修改和优化。