显示无法识别命令行中的代码

抱歉，我误解了您的问题。如果您想在命令行中显示代码，可以使用以下命令：

- Windows：`type filename.extension` 或 `more filename.extension`
- Mac/Linux：`cat filename.extension` 或 `more filename.extension`

其中，`filename.extension` 是您要显示的代码文件名和扩展名。

相关问题

人脸识别性别年龄python代码

以下是使用Python和OpenCV库实现人脸识别、性别识别和年龄识别的代码：

import cv2
import math
import argparse

# 加载人脸识别模型和性别识别模型
face_model = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
gender_model = cv2.dnn.readNetFromCaffe('deploy_gender.prototxt', 'gender_net.caffemodel')
age_model = cv2.dnn.readNetFromCaffe('deploy_age.prototxt', 'age_net.caffemodel')

# 解析命令行参数
parser = argparse.ArgumentParser()
parser.add_argument('--image', help='输入图片路径')
args = parser.parse_args()

# 加载输入图片
img = cv2.imread(args.image)

# 缩放图片
scale_percent = 60
width = int(img.shape[1] * scale_percent / 100)
height = int(img.shape[0] * scale_percent / 100)
dim = (width, height)
img = cv2.resize(img, dim, interpolation = cv2.INTER_AREA)

# 转换为灰度图像
gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

# 检测人脸
faces = face_model.detectMultiScale(gray_img, scaleFactor=1.1, minNeighbors=5)

# 对每个人脸进行性别识别和年龄识别
for (x, y, w, h) in faces:
    # 提取人脸图像
    face_img = img[y:y+h, x:x+w].copy()

    # 进行性别识别
    gender_blob = cv2.dnn.blobFromImage(face_img, 1, (227, 227), (78.4263377603, 87.7689143744, 114.895847746), swapRB=False)
    gender_model.setInput(gender_blob)
    gender_preds = gender_model.forward()
    gender = 'Male' if gender_preds[0][0] > gender_preds[0][1] else 'Female'

    # 进行年龄识别
    age_blob = cv2.dnn.blobFromImage(face_img, 1, (227, 227), (78.4263377603, 87.7689143744, 114.895847746), swapRB=False)
    age_model.setInput(age_blob)
    age_preds = age_model.forward()
    age = int(age_preds[0][0] * 100)

    # 在图像上绘制人脸框和性别、年龄信息
    cv2.rectangle(img, (x, y), (x+w, y+h), (0, 255, 0), 2)
    label = "{} {}".format(gender, age)
    cv2.putText(img, label, (x, y), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)

# 显示输出图像
cv2.imshow('Output', img)
cv2.waitKey(0)

说明：

- 代码中使用了OpenCV和argparse两个Python库。
- 人脸识别模型使用了OpenCV自带的哈尔级联分类器。
- 性别识别和年龄识别模型使用了Caffe深度学习框架。
- 代码中使用了命令行参数，可以通过命令行传入输入图片路径。

python opencv人脸性别年龄识别代码

以下是一个基于OpenCV和深度学习的人脸性别年龄识别代码示例：

import cv2
import math
import argparse

# 加载模型文件
face_caffe_model = "deploy.prototxt"
face_caffe_weight = "res10_300x300_ssd_iter_140000.caffemodel"
gender_caffe_model = "gender_net.caffemodel"
gender_caffe_weight = "gender_deploy.prototxt"
age_caffe_model = "age_net.caffemodel"
age_caffe_weight = "age_deploy.prototxt"

# 定义性别和年龄标签
gender_labels = ['Male', 'Female']
age_labels = ['(0-2)', '(4-6)', '(8-12)', '(15-20)', '(25-32)', '(38-43)', '(48-53)', '(60-100)']

# 加载人脸检测模型和性别、年龄识别模型
face_net = cv2.dnn.readNetFromCaffe(face_caffe_model, face_caffe_weight)
gender_net = cv2.dnn.readNetFromCaffe(gender_caffe_model, gender_caffe_weight)
age_net = cv2.dnn.readNetFromCaffe(age_caffe_model, age_caffe_weight)

# 处理人脸图像
def process_face_image(image):
    # 提取图像大小
    (h, w) = image.shape[:2]

    # 构建人脸检测的输入blob
    blob = cv2.dnn.blobFromImage(cv2.resize(image, (300, 300)), 1.0,
        (300, 300), (104.0, 177.0, 123.0))

    # 通过人脸检测网络检测人脸
    face_net.setInput(blob)
    detections = face_net.forward()

    # 遍历检测结果
    for i in range(0, detections.shape[2]):
        # 提取置信度
        confidence = detections[0, 0, i, 2]

        # 过滤掉低置信度的检测结果
        if confidence > 0.5:
            # 计算人脸位置坐标
            box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
            (startX, startY, endX, endY) = box.astype("int")

            # 提取人脸ROI区域
            face = image[startY:endY, startX:endX]

            # 构建性别和年龄检测的输入blob
            face_blob = cv2.dnn.blobFromImage(face, 1.0, (227, 227),
                (78.4263377603, 87.7689143744, 114.895847746), swapRB=False)

            # 通过性别和年龄检测网络进行性别和年龄识别
            gender_net.setInput(face_blob)
            gender_preds = gender_net.forward()
            age_net.setInput(face_blob)
            age_preds = age_net.forward()

            # 提取性别和年龄标签
            gender_label = gender_labels[gender_preds[0].argmax()]
            age_label = age_labels[age_preds[0].argmax()]

            # 计算年龄和性别的置信度
            gender_confidence = gender_preds[0].max()
            age_confidence = age_preds[0].max()

            # 在图像中绘制人脸框、性别和年龄标签
            cv2.rectangle(image, (startX, startY), (endX, endY), (0, 0, 255), 2)
            label = "{}, {:.2f}%".format(gender_label, gender_confidence * 100)
            cv2.putText(image, label, (startX, startY - 10),
                cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 0, 255), 2)
            label = "{}, {:.2f}%".format(age_label, age_confidence * 100)
            cv2.putText(image, label, (startX, startY + (endY - startY) + 30),
                cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 0, 255), 2)

    return image

# 主函数
if __name__ == '__main__':
    # 解析命令行参数
    parser = argparse.ArgumentParser()
    parser.add_argument('--image', required=True, help='path to input image')
    args = parser.parse_args()

    # 加载输入图像
    image = cv2.imread(args.image)

    # 处理人脸图像并显示结果
    result = process_face_image(image)
    cv2.imshow("Output", result)
    cv2.waitKey(0)

注意：在运行代码之前，你需要下载相应的模型文件并将其放置在正确的路径下，才能正常运行代码。