2025年使用python-opencv检测图片中的人像

科技前沿 • 2025-03-23 14:27 • 阅读 43

大家好，我是讯享网，很高兴认识大家。

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

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2、下载yolo模型文件和配置文件：

下载地址：

https://download.csdn.net/download/mldxs/y
讯享网https://download.csdn.net/download/mldxs/

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

讯享网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, crop=False) # 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"person"（0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）

2025年使用python-opencv检测图片中的人像

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