在OpenCV中读取YOLOv3或YOLOv4模型进行目标检测涉及几个关键步骤。以下是详细的解释和示例代码:
YOLO(You Only Look Once)是一种流行的实时目标检测系统。YOLOv3和YOLOv4是其后续版本,分别改进了速度和准确性。
以下是一个使用OpenCV读取YOLOv4模型并进行目标检测的示例代码:
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()]
# 读取图像
img = cv2.imread("image.jpg")
img = cv2.resize(img, None, fx=1.0/255.0, fy=1.0/255.0)
height, width, channels = img.shape
# 预处理图像
blob = cv2.dnn.blobFromImage(img, 1/255.0, (416, 416), swapRB=True, crop=False)
net.setInput(blob)
# 进行前向传播
outs = net.forward(output_layers)
# 解析检测结果
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:
# 目标检测
center_x = int(detection[0] * width)
center_y = int(detection[1] * height)
w = int(detection[2] * width)
h = int(detection[3] * height)
# 矩形框
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)
# 非极大值抑制
indexes = cv2.dnn.NMSBoxes(boxes, confidences, 0.5, 0.4)
# 绘制矩形框和标签
for i in range(len(boxes)):
if i in indexes:
x, y, w, h = boxes[i]
label = str(class_ids[i])
cv2.rectangle(img, (x, y), (x + w, y + h), (0, 255, 0), 2)
cv2.putText(img, label, (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
# 显示结果
cv2.imshow("Image", img)
cv2.waitKey(0)
cv2.destroyAllWindows()
通过以上步骤和代码示例,你应该能够在OpenCV中成功读取YOLOv3或YOLOv4模型并进行目标检测。
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