python下使用cv2调用darknet

要使用OpenCV调用Darknet，需要先编译Darknet库，然后使用OpenCV调用Darknet库中的函数。

以下是一个简单的示例代码，演示了如何使用OpenCV调用Darknet进行目标检测：

import cv2
import numpy as np

# 加载Darknet库
net = cv2.dnn.readNetFromDarknet("yolov3.cfg", "yolov3.weights")

# 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
img = cv2.imread("test.jpg")

# 将图像传递给Darknet进行目标检测
blob = cv2.dnn.blobFromImage(img, 1/255.0, (416, 416), swapRB=True, crop=False)
net.setInput(blob)
layer_names = net.getLayerNames()
output_layers = [layer_names[i[0] - 1] for i in net.getUnconnectedOutLayers()]
outputs = net.forward(output_layers)

# 处理检测结果
boxes = []
confidences = []
class_ids = []
for output in outputs:
    for detection in output:
        scores = detection[5:]
        class_id = np.argmax(scores)
        confidence = scores[class_id]
        if confidence > 0.5:
            center_x = int(detection[0] * img.shape[1])
            center_y = int(detection[1] * img.shape[0])
            w = int(detection[2] * img.shape[1])
            h = int(detection[3] * img.shape[0])
            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)

# 绘制检测结果
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))
if len(indexes) > 0:
    for i in indexes.flatten():
        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-5), font, 1, color, 2)

# 显示图像
cv2.imshow("Image", img)
cv2.waitKey(0)
cv2.destroyAllWindows()

在这个例子中，我们首先使用`cv2.dnn.readNetFromDarknet()`函数加载了预训练的Darknet模型。然后，我们使用`cv2.dnn.blobFromImage()`函数将图像转换为网络可接受的格式，并将其传递给网络进行目标检测。最后，我们使用`cv2.dnn.NMSBoxes()`函数对检测结果进行筛选，然后使用OpenCV的绘图函数将检测结果绘制在图像上。