python用yolo查看模型代码

以下是使用Python代码加载和运行YOLOv3模型的示例：

import cv2
import numpy as np

# 加载 YOLOv3 模型
net = cv2.dnn.readNet("yolov3.weights", "yolov3.cfg")

# 获取输出层名称
layer_names = net.getLayerNames()
output_layers = [layer_names[i[0] - 1] for i in net.getUnconnectedOutLayers()]

# 加载图像
img = cv2.imread("image.jpg")

# 预处理图像
height, width, channels = img.shape
blob = cv2.dnn.blobFromImage(img, 0.00392, (416, 416), (0, 0, 0), True, crop=False)

# 将图像输入到网络中
net.setInput(blob)

# 运行前向传递
outs = net.forward(output_layers)

# 解析输出并绘制边界框
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:
            # 边界框坐标是相对于图像宽度和高度的比例
            center_x = int(detection[0] * width)
            center_y = int(detection[1] * height)
            w = int(detection[2] * width)
            h = int(detection[3] * height)
            x = center_x - w // 2
            y = center_y - h // 2
            boxes.append([x, y, w, h])
            confidences.append(float(confidence))
            class_ids.append(class_id)

# 非极大值抑制
indexes = cv2.dnn.NMSBoxes(boxes, confidences, 0.5, 0.4)

# 绘制边界框和类别标签
font = cv2.FONT_HERSHEY_PLAIN
colors = np.random.uniform(0, 255, size=(len(class_ids), 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[i]
        cv2.rectangle(img, (x, y), (x + w, y + h), color, 2)
        cv2.putText(img, label, (x, y + 30), font, 2, color, 3)

# 显示输出图像
cv2.imshow("Image", img)
cv2.waitKey(0)
cv2.destroyAllWindows()

在上面的代码中，我们通过`cv2.dnn.readNet()`函数加载了YOLOv3模型，并通过`net.getLayerNames()`获得了输出层的名称。接下来，我们加载了一张图像，将其预处理成网络所需的输入格式，并将其输入到网络中进行前向传递。然后，我们解析了输出并绘制了边界框，最后将输出图像显示出来。