太原建设厅网站,吉林企业网络推广方法,产品推广建议,wordpress入门主题最简单的方法进行图片中的人像检测
使用python-opencv配合yolov3模型进行图片中的人像检测
1、安装python-opencv、numpy
pip install opencv-python
pip install numpy
2、下载yolo模型文件和配置文件#xff1a;
下载地址#xff1a; https://download.csdn.net/down…最简单的方法进行图片中的人像检测
使用python-opencv配合yolov3模型进行图片中的人像检测
1、安装python-opencv、numpy
pip install opencv-python
pip install numpy
2、下载yolo模型文件和配置文件
下载地址 https://download.csdn.net/download/mldxs/88396654yhttps://download.csdn.net/download/mldxs/88396654
yolo官网
YOLO: Real-Time Object DetectionYou only look once (YOLO) is a state-of-the-art, real-time object detection system.https://pjreddie.com/darknet/yolo/3、搬砖代码比较简单并且带注释不过多介绍
import cv2
import numpy as np# 读取输入图像
image cv2.imread(input.jpeg)# 加载YOLOv3模型和类别标签
net cv2.dnn.readNet(yolov3.weights, yolov3.cfg)
classes []
with open(coco.data, r) as f:classes f.read().strip().split(\n)# 获取YOLO模型的输出层名称
layer_names net.getLayerNames()
output_layers []
unconnected_layers net.getUnconnectedOutLayers()# 根据输出层索引获取输出层名称
for i in unconnected_layers:output_layers.append(layer_names[i - 1])# 为每个类别生成随机颜色
colors np.random.uniform(0, 255, size(len(classes), 3))# 获取图像的尺寸
height, width, channels image.shape# 创建YOLO模型的输入blob
blob cv2.dnn.blobFromImage(image, 0.00392, (416, 416), (0, 0, 0), True, cropFalse)# 将blob设置为模型的输入
net.setInput(blob)
outs net.forward(output_layers)class_ids []
confidences []
boxes []# 处理YOLO模型的输出
for out in outs:for detection in out:scores detection[5:]class_id np.argmax(scores)confidence scores[class_id]# 如果置信度大于0.5并且类别是person0对应COCO数据集中的person类if confidence 0.5 and class_id 0: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)# 使用非极大值抑制获取最终的检测结果
indexes cv2.dnn.NMSBoxes(boxes, confidences, 0.5, 0.4)
margin 30 # 定义边框扩展的边距大小# 绘制边框和类别标签
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]# 扩展边框的坐标x_new max(0, x - margin)y_new max(0, y - margin)w_new min(image.shape[1], w 2 * margin)h_new min(image.shape[0], h 2 * margin)# 绘制扩展后的边框cv2.rectangle(image, (x_new, y_new), (x_new w_new, y_new h_new), color, 2)cv2.putText(image, label, (x_new, y_new - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)# 显示带有边框的图像
cv2.imshow(Detected Bodies, image)
cv2.waitKey(0)
cv2.destroyAllWindows() 最终效果 yolo有很多检测类别上述代码只对人像进行检测就是检测类别里的第一项person
