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opencv、dlib、paddlehub人脸检测

opencv、dlib、paddlehub检测效果对比。dlib和paddlehub的效果相对好一点。

说明：本文只做人脸检测不识别，找识别的不用看本文。

## 部署说明
# 1. 安装python或conda
# 2. 安装依赖，pip install -r requirements.txt
# 3. 192.168.1.41 修改为你部署机器的IP
# 4. python app_dlib.py启动
# 5. 试验，http://192.168.1.41:7049
# 6. 接口，http://192.168.1.41:7049/run/predict/
接口参数，post请求，body传1个包含base64图片的JSON，替换图片就行
{
    fn_index: 0, 
    data: ["data:image/jpeg;base64,/9j/4AAQSkZJtXlnut7A8QOeSpiTO/DNIrhn3HpugKCATj590EhqShGP8VInOz6TrioYTyGR0oyiMh/dnEpkQ0Pu+Yy+QWamDMkbve9U6MyWdEa+MqHDn1zUtpCT4f/AC//2Q=="], 
    session_hash: "s1oy98lial"
}

依赖（用1个就行）

dlib需要C++编译器（gcc 或 vs）

gradio
opencv-python
dlib
paddlehub

opencv检测

import gradio as gr
import cv2

# 加载人脸检测器
face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
# face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_eye_tree_eyeglasses.xml')

# UGC: Define the inference fn() for your models
def model_inference(image):
    # 加载图像
    # image = cv2.imread(image)
    # 将图像转换为灰度图像
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    # 进行人脸检测
    faces = face_cascade.detectMultiScale(gray, scaleFactor=1.2, minNeighbors=3, minSize=(32, 32))
    # 在图像上标记人脸
    for (x, y, w, h) in faces:
        cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 3)
    # 显示结果
    # cv2.imshow('Face Detection', image)
    # cv2.waitKey(0)
    # cv2.destroyAllWindows()
    json_out = {"result": len(faces)}
    return image,json_out


def clear_all():
    return None, None, None

with gr.Blocks() as demo:
    gr.Markdown("人脸检测")

    with gr.Column(scale=1, min_width=100):
        img_in = gr.Image(value="1.png",
                          label="Input")

        with gr.Row():
            btn1 = gr.Button("Clear")
            btn2 = gr.Button("Submit")
        img_out = gr.Image(label="Output").style(height=400)
        json_out = gr.JSON(label="jsonOutput")

    btn2.click(fn=model_inference, inputs=img_in, outputs=[img_out, json_out])
    btn1.click(fn=clear_all, inputs=None, outputs=[img_in, img_out, json_out])
    gr.Button.style(1)

demo.launch(server_name='192.168.1.41', share=True, server_port=7048)

 

 

dlib检测

import gradio as gr
import cv2
import dlib

detector = dlib.get_frontal_face_detector()
# predictor = dlib.shape_predictor(
#     "dlib_model/shape_predictor_68_face_landmarks.dat"
# )

# UGC: Define the inference fn() for your models
def model_inference(image):
    # 加载图像
    # image = cv2.imread(image)
    # 将图像转换为灰度图像
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    # 进行人脸检测
    faces = detector(gray, 1)
    for face in faces:
        # 在图片中标注人脸，并显示
        left = face.left()
        top = face.top()
        right = face.right()
        bottom = face.bottom()
        cv2.rectangle(image, (left, top), (right, bottom), (0, 255, 0), 2)

        # shape = predictor(image, face)  # 寻找人脸的68个标定点
        # # 遍历所有点，打印出其坐标，并圈出来
        # for pt in shape.parts():
        #     pt_pos = (pt.x, pt.y)
        #     cv2.circle(image, pt_pos, 1, (0, 255, 0), 2)
    json_out = {"result": len(faces)}
    return image,json_out


def clear_all():
    return None, None, None

with gr.Blocks() as demo:
    gr.Markdown("人脸检测")

    with gr.Column(scale=1, min_width=100):
        img_in = gr.Image(value="1.png",
                          label="Input")

        with gr.Row():
            btn1 = gr.Button("Clear")
            btn2 = gr.Button("Submit")
        img_out = gr.Image(label="Output").style(height=400)
        json_out = gr.JSON(label="jsonOutput")

    btn2.click(fn=model_inference, inputs=img_in, outputs=[img_out, json_out])
    btn1.click(fn=clear_all, inputs=None, outputs=[img_in, img_out, json_out])
    gr.Button.style(1)

demo.launch(server_name='192.168.1.41', share=True, server_port=7049)

 PaddleHub检测

import gradio as gr
import paddlehub as hub
import cv2

#直接调用PaddleHub中的人脸检测
module = hub.Module(name="ultra_light_fast_generic_face_detector_1mb_640")

def model_inference(image):
    # images(list[numpy.ndarray]): 图片数据，ndarray.shape为[H, W, C]，BGR格式；
    # paths(list[str]): 图片的路径；
    # batch_size(int): batch的大小；
    # use_gpu(bool): 是否使用GPU；
    # visualization(bool): 是否将识别结果保存为图片文件；
    # output_dir(str): 图片的保存路径，当为None时，默认设为face_detector_640_predict_output；
    # confs_threshold(float): 置信度的阈值。
    faces = module.face_detection([image], visualization=False)[0]["data"]
    for face in faces:
        # 在图片中标注人脸，并显示
        left = int(face["left"])
        top = int(face["top"])
        right = int(face["right"])
        bottom = int(face["bottom"])
        cv2.rectangle(image, (left, top), (right, bottom), (0, 255, 0), 2)

    json_out = {"result": len(faces)}
    return image,json_out


def clear_all():
    return None, None, None

with gr.Blocks() as demo:
    gr.Markdown("人脸检测")

    with gr.Column(scale=1, min_width=100):
        img_in = gr.Image(value="1.png",
                          label="Input")

        with gr.Row():
            btn1 = gr.Button("Clear")
            btn2 = gr.Button("Submit")
        img_out = gr.Image(label="Output").style(height=400)
        json_out = gr.JSON(label="jsonOutput")

    btn2.click(fn=model_inference, inputs=img_in, outputs=[img_out, json_out])
    btn1.click(fn=clear_all, inputs=None, outputs=[img_in, img_out, json_out])
    gr.Button.style(1)

demo.launch(server_name='192.168.1.41', share=True, server_port=7050)

APIPOST调接口测试

 

 

axios调用示例

var axios = require("axios").default;

var options = {
  method: 'POST',
  url: 'http://192.168.1.41:7050/run/predict/',
  headers: {'content-type': 'application/json'},
  data: '{\r\n    fn_index: 0, \r\n    data: ["data:image/jpeg;base64,/9j/4gM5jj4ihEoiOUxSpDKSBjsPFBYRtXlnut7A8QOeSpiTO/DNIrhn3HpugKCATj590EhqShGP8VInOz6TrioYTyGR0oyiMh/dnEpkQ0Pu+Yy+QWamDMkbve9U6MyWdEa+MqHDn1zUtpCT4f/AC//2Q=="], \r\n    session_hash: "s1oy98lial"\r\n}'
};

axios.request(options).then(function (response) {
  console.log(response.data);
}).catch(function (error) {
  console.error(error);
});

jquery调用示例

const settings = {
  "async": true,
  "crossDomain": true,
  "url": "http://192.168.1.41:7050/run/predict/",
  "method": "POST",
  "headers": {
    "content-type": "application/json"
  },
  "data": "{\r\n    fn_index: 0, \r\n    data: [\"data:image/jpeg;base64,/9j/4AAQSkZJUWYgM5jj4ihEoiOUxSpDKSBjsPFBYRtXlnut7A8QOeSpiTO/DNIrhn3HpugKCATj590EhqShGP8VInOz6TrioYTyGR0oyiMh/dnEpkQ0Pu+Yy+QWamDMkbve9U6MyWdEa+MqHDn1zUtpCT4f/AC//2Q==\"], \r\n    session_hash: \"s1oy98lial\"\r\n}"
};

$.ajax(settings).done(function (response) {
  console.log(response);
});