在python中如何使用yolo
时间: 2024-06-08 12:05:31 浏览: 16
要在Python中使用YOLO,首先需要安装YOLO库。可以使用以下步骤:
1.安装OpenCV:
```
pip install opencv-python
```
2.使用以下命令从GitHub下载YOLO库:
```
git clone https://github.com/pjreddie/darknet.git
```
3.下载预先训练的权重文件,例如YOLOv3:
```
wget https://pjreddie.com/media/files/yolov3.weights
```
4.使用以下代码在Python中加载YOLO模型并进行目标检测:
```python
import cv2
import numpy as np
net = cv2.dnn.readNet("yolov3.weights", "darknet/cfg/yolov3.cfg")
classes = []
with open("darknet/data/coco.names", "r") as f:
classes = [line.strip() for line in f.readlines()]
layer_names = net.getLayerNames()
output_layers = [layer_names[i[0] - 1] for i in net.getUnconnectedOutLayers()]
img = cv2.imread("image.jpg")
img = cv2.resize(img, None, fx=0.4, fy=0.4)
height, width, channels = img.shape
blob = cv2.dnn.blobFromImage(img, 0.00392, (416, 416), (0, 0, 0), True, crop=False)
net.setInput(blob)
outs = net.forward(output_layers)
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)
indexes = cv2.dnn.NMSBoxes(boxes, confidences, 0.5, 0.4)
font = cv2.FONT_HERSHEY_PLAIN
colors = np.random.uniform(0, 255, size=(len(classes), 3))
for i in range(len(boxes)):
if i in indexes:
x, y, w, h = boxes[i]
label = str(classes[class_ids[i]])
color = colors[class_ids[i]]
cv2.rectangle(img, (x, y), (x + w, y + h), color, 2)
cv2.putText(img, label, (x, y + 30), font, 3, color, 3)
cv2.imshow("Image", img)
cv2.waitKey(0)
cv2.destroyAllWindows()
```
此代码将使用YOLOv3检测给定图像中的对象,并将其绘制在图像上。请注意,此代码假定您已将YOLO文件夹克隆到您的项目目录中。
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