1.接口说明
证件照制作/检测 API:采用自研深度学习算法与图像处理技术,集成人脸检测、人像裁剪、背景抠除与背景色替换、美颜、换衣服、排版,以及普通/高级合规检测能力,实现普通生活照到标准证件照的自动化处理。
1.1主要功能
- 全规格支持:
- 支持自定义像素/毫米尺寸、DPI、背景色(纯色/渐变/透明)与输出大小控制(质量/文件大小)。
- 智能裁剪与人像调整:
- 按头顶、下巴、眼睛位置等约束裁剪与调整,满足各类证照规范。
- 抠图换底:
- 可抠图并替换背景色,也可返回透明底证件照。
- 美颜:
- 支持磨皮、瘦脸、调光、锐化等能力,提升成片观感。
- 换装:
- 支持男/女/儿童多套正装换装模板。
- 排版与预览:
- 支持 5 寸/6 寸排版输出与带水印预览(预览不扣积分)。
- 合规检测:
- 支持普通合规(尺寸/位置/角度等)与高级合规(遮挡/姿势/曝光/模糊/妆容等)检测结果返回。
1.2接入场景
支持小程序、APP、采集设备等接入,适用于护照/签证照片生成、身份证/社保卡/学生证/工作证等标准化证照制作与检测。
3.返回信息
3.1返回类型
JSON
3.2返回字段
| 参数 |
说明 |
| code |
错误码 |
| msg |
错误信息(英文) |
| msg_cn |
错误信息(中文) |
| id |
图片id,用于同一张图片的多次请求,id的有效时间为6小时,(当code==0时会有该返回值) |
| result_base64 |
证件照的base64编码,(当code==0时会有该返回值) |
| layout_base64 |
排版的base64编码,(当code==0且请求参数layout==1时会有该返回值) |
| info |
普通合规信息,(当code==0且请求参数qualify==1时会有该返回值) |
| advanced_info |
高级合规信息,(当code==0且请求参数advancedQualify==1时会有该返回值) |
| request_id |
请求ID,请求的唯一码 |
3.3返回示例
{
"code": 0,
"msg": "OK",
"msg_cn": "成功",
"id": "xxxxxx",
"result_base64": "/9j/4AAQSkZJRgABAQAAAQABAAD...(省略)",
"layout_base64": "/9j/4AAQSkZJRgABAQAAAQABAAD...(可选,layout==1 时返回)",
"info": {
"qualified": true,
"msg": "OK",
"msg_cn": "合规",
"width": 295,
"height": 413
},
"advanced_info": {
"qualified": true,
"msg": "OK",
"msg_cn": "高级合规"
},
"request_id": "req_xxxxxxxx"
}
3.4错误码
| 错误码 |
说明 |
| 0 |
成功 |
| 1 |
图片错误 |
| 2 |
抠图错误 |
| 3 |
服务器繁忙 |
| 4 |
参数错误(具体错误看 msg 或 msg_cn) |
| 5 |
换装时没检测到人脸 |
| 6 |
未知错误 |
| 7 |
图片 id 无效(可能已过期) |
| 101 |
API-KEY 不正确 |
| 102 |
未知用户 |
| 103 |
积分已用完 |
| 104 |
扣除积分失败 |
3.5 普通合规信息
| 分类 |
参数 |
类型 |
说明 |
| 综合 |
| qualified |
bool |
是否合规(默认由face_width, face_height, head_top, eyes_top, eyes_angle决定);(specID存在由thresholds里的阈值决定) |
| thresholds |
dict |
specID存在时返回:用来判断qualified的项目的阈值,有min/max_face_width, min/max_face_height等组成 |
| msg |
string |
合规消息(英文) |
| msg_cn |
string |
合规消息(中文) |
| 尺寸 |
| width |
int |
证件照宽度,单位为像素 |
| height |
int |
证件照高度,单位为像素 |
| 人脸 |
| has_face |
bool |
是否检测到人脸 |
| face_count |
int |
原图人脸数 |
| rotation |
int |
图片旋转角度(0度,90度,180度,或者270度) |
| 判断 |
| face_width_qualified |
bool |
人脸宽度是否合规 |
| face_height_qualified |
bool |
人脸长度是否合规 |
| face_left_qualified |
bool |
左侧脸颊到左侧边框距离是否合规 |
| face_right_qualified |
bool |
右侧脸颊到右侧边框距离是否合规 |
| head_top_qualified |
bool |
头顶至上边距离是否合规 |
| chin_top_qualified |
bool |
下巴至上边距离是否合规 |
| eyes_top_qualified |
bool |
眼睛至上边距离是否合规 |
| eyes_angle_qualified |
bool |
双眼连线角度是否合规 |
| 距离与角度 |
| face_width |
int |
生成证件照中人脸宽度,单位为像素 |
| face_left |
int |
生成证件照中左侧脸颊到左侧边框距离,单位为像素 |
| face_right |
int |
生成证件照中右侧脸颊到右侧边框距离,单位为像素 |
| face_height |
int |
人脸长度,单位为像素 |
| head_top |
int |
头顶至上边距离,单位为像素 |
| chin_top |
int |
下巴至上边距离,单位为像素 |
| eyes_top |
int |
眼睛至上边距离,单位为像素 |
| eyes_angle |
float |
[-pi/2, pi/2],双眼连线角度 |
| pupil_distance |
int |
瞳距,单位为像素 |
| eyes_center_from_left |
int |
双眼中心与左边距离,单位为像素 |
| eyes_center_from_bottom |
int |
双眼中心与下边距离,单位为像素 |
3.6 高级合规信息
| 分类 |
参数 |
类型 |
说明 |
| 综合 |
| qualified |
bool |
是否合规。(默认)如果无人脸,戴帽子,墨镜,耳机,手机,口罩,模糊,过曝或欠曝,眉毛遮挡,嘴巴张开,则不合规。 注意:其他项目不计入判断。(specID存在)由thresholds里的阈值决定 |
| thresholds |
dict |
specID存在时返回:用来判断qualified的项目的阈值,由specID决定 |
| msg |
string |
合规消息(英文) |
| msg_cn |
string |
合规消息(中文) |
| 人脸 |
| has_face |
bool |
是否检测到人脸 |
| face_count |
int |
原图人脸个数 |
| 姿势 |
| yaw |
float |
[-90, 90],人脸偏航角,决定pose_abnormal |
| pitch |
float |
[-90, 90],人脸俯仰角,参与 pose_abnormal 判断 |
| roll |
float |
[-90, 90],人脸翻滚角,参与 pose_abnormal 判断 |
| pose_abnormal |
bool |
人脸角度异常,如果abs(yaw)>10,或者abs(pitch)>15,或者abs(roll)>10,那么为True |
| 质量 |
| face_overexposed_score |
float |
[0, 1],脸部过曝程度,决定face_overexposed,默认阈值为0.5 |
| face_overexposed |
bool |
是否脸部过曝 |
| face_underexposed_score |
float |
[0, 1],脸部欠曝程度,决定face_underexposed,默认阈值为0.5 |
| face_underexposed |
bool |
是否脸部欠曝 |
| face_exposure_uneven_score |
float |
[0, 1],阴阳脸程度,决定face_exposure_uneven,默认阈值为0.2 |
| face_exposure_uneven |
bool |
是否阴阳脸 |
| face_blur_score |
float |
[0, 1],脸部模糊程度,决定face_blur,默认阈值为0.6 |
| face_blur |
bool |
是否脸部模糊 |
| face_dark_score |
float |
[0, 1],脸部过暗程度,决定face_blur,默认阈值为0.5 |
| face_dark |
bool |
是否脸部过暗 |
| face_color_abnormal_score |
float |
[0, 1],脸部肤色不正常程度,决定face_blur,默认阈值为0.5 |
| face_color_abnormal |
bool |
是否肤色不正常 |
| image_gray_score |
float |
[0, 1], 黑白照片程度,默认阈值为0.5 |
| image_gray |
bool |
是否黑白照片 |
| face_glare_score |
float |
[0, 1],脸部反光程度,默认阈值 0.5 |
| face_glare |
bool |
是否脸部反光 |
| 遮挡 |
| left_eye_occlusion_score |
float |
[0, 1],左眼遮挡程度,决定left_eye_occlusion,默认阈值为0.2 |
| left_eye_occlusion |
bool |
是否左眼遮挡 |
| right_eye_occlusion_score |
float |
[0, 1],右眼遮挡程度,决定right_eye_occlusion,默认阈值为0.2 |
| right_eye_occlusion |
bool |
是否右眼遮挡 |
| eye_occlusion |
bool |
是否眼睛遮挡 |
| mouth_occlusion_score |
float |
[0, 1],嘴巴遮挡程度,决定mouth_occlusion,默认阈值为0.2 |
| mouth_occlusion |
bool |
是否嘴巴遮挡 |
| nose_occlusion_score |
float |
[0, 1],鼻子遮挡程度,决定nose_occlusion,默认阈值为0.2 |
| nose_occlusion |
bool |
是否鼻子遮挡 |
| left_cheek_occlusion_score |
float |
[0, 1],左脸遮挡程度,决定left_cheek_occlusion,默认阈值为0.3 |
| left_cheek_occlusion |
bool |
是否左脸遮挡 |
| right_cheek_occlusion_score |
float |
[0, 1],右脸遮挡程度,决定right_cheek_occlusion,默认阈值为0.3 |
| right_cheek_occlusion |
bool |
是否右脸遮挡 |
| cheek_occlusion |
bool |
是否脸颊遮挡,当左脸或右脸遮挡时,该项为True |
| eyebrow_occlusion_score |
float |
[0, 1],眉毛遮挡程度,决定eyebrow_occlusion,默认阈值为0.3 |
| eyebrow_occlusion |
bool |
是否眉毛遮挡 |
| ear_occlusion_score |
float |
[0, 1],耳朵遮挡程度,决定ear_occlusion,默认阈值为0.8 |
| ear_occlusion |
bool |
是否耳朵遮挡 |
| 头发 |
| hair_incomplete_score |
float |
[0, 1],头发不完整程度,决定hair_incomplete,默认阈值为0.1 |
| hair_incomplete |
bool |
是否头发不完整 |
| hair_color_r |
float |
[0, 255],头发颜色红色分量 |
| hair_color_g |
float |
[0, 255],头发颜色绿色分量 |
| hair_color_b |
float |
[0, 255],头发颜色蓝色分量 |
| hair_dyed_score |
float |
[0, 1],染发程度,默认阈值 0.5 |
| hair_dyed |
bool |
是否染发 |
| 眼睛 |
| eye_close_score |
float |
[0, 1],闭眼程度,默认阈值 0.5 |
| eye_close |
bool |
是否闭眼 |
| gaze_horizontal_score |
float |
[0, 1],视线水平偏移程度,默认阈值 0.15 |
| gaze_horizontal |
bool |
是否视线水平偏移 |
| gaze_vertical_score |
float |
[0, 1],视线竖直偏移程度,默认阈值 0.1 |
| gaze_vertical |
bool |
是否视线竖直偏移 |
| gaze_abnormal |
bool |
视线是否未平视前方(水平或竖直偏移为真时为 True) |
| red_eye_score |
float |
[0, 1],红眼程度,默认阈值 0.5 |
| red_eye |
bool |
是否红眼 |
| colored_contacts_score |
float |
[0, 1],有色隐形眼镜得分,默认阈值 0.5 |
| colored_contacts |
bool |
是否佩戴有色隐形眼镜 |
| 耳朵 |
| ears_asymmetric_score |
float |
[0, 1],双耳不对称程度,默认阈值 0.5 |
| ears_asymmetric |
bool |
是否双耳不对称 |
| 嘴巴 |
| mouth_open_score |
float |
[0, 1],嘴巴张开程度,默认阈值 0.1 |
| mouth_open |
bool |
是否嘴巴张开 |
| 饰品 |
| hat |
int |
0 或 1,是否戴帽子 |
| glasses |
int |
0 或 1,是否戴眼镜 |
| sunglasses |
int |
0 或 1,是否戴墨镜 |
| headphone |
int |
0 或 1,是否戴头戴式耳机 |
| earphone |
int |
0 或 1,是否戴耳塞 |
| cellphone |
int |
0 或 1,是否使用手机 |
| earring |
int |
0 或 1,是否戴耳饰 |
| mask |
int |
0 或 1,是否戴口罩 |
| necklace |
int |
0 或 1,是否戴项链 |
| glasses_glare_score |
float |
[0, 1],眼镜反光程度,默认阈值 0.5 |
| glasses_glare |
bool |
是否眼镜反光 |
| heavy_makeup_score |
float |
[0, 1],浓妆程度,默认阈值 0.5 |
| heavy_makeup |
bool |
是否浓妆 |
| 肩膀 |
| shoulder_incomplete_score |
float |
[0, 1],肩膀不完整程度,默认阈值 0.5 |
| shoulder_incomplete |
bool |
是否肩膀不完整 |
| shoulder_imbalance_score |
float |
[0, 1],肩膀不平衡程度,默认阈值 0.5 |
| shoulder_imbalance |
bool |
是否肩膀不平衡 |
| 衣服 |
| clothes_color_r |
float |
[0, 255],衣服颜色红色分量 |
| clothes_color_g |
float |
[0, 255],衣服颜色绿色分量 |
| clothes_color_b |
float |
[0, 255],衣服颜色蓝色分量 |
| clothes_color_light_score |
float |
[0, 1],衣服浅色程度,默认阈值 0.75 |
| clothes_color_light |
bool |
是否浅色衣服 |
| clothes_bg_similar_score |
float |
[0, 1],衣服与背景颜色相近程度,默认阈值 0.8 |
| clothes_bg_similar |
bool |
是否衣服背景颜色相近 |
| shirtless_score |
float |
[0, 1],光膀程度,默认阈值 0.5 |
| shirtless |
bool |
是否光膀 |
| sleeveless_score |
float |
[0, 1],背心或吊带分数,默认阈值 0.5 |
| sleeveless |
bool |
是否穿背心或吊带 |
| big_clothes_score |
float |
[0, 1],臃肿外套分数,默认阈值 0.5 |
| big_clothes |
bool |
是否穿臃肿外套 |
| 背景 |
| bg_color_r |
float |
[0, 255],背景颜色红色分量 |
| bg_color_g |
float |
[0, 255],背景颜色绿色分量 |
| bg_color_b |
float |
[0, 255],背景颜色蓝色分量 |
| bg_color_wrong_score |
float |
[0, 1],背景颜色错误程度,默认阈值 0.5 |
| bg_color_wrong |
bool |
是否背景颜色错误 |
| 图片 |
| image_gray_score |
float |
[0, 1],黑白照片程度,默认阈值 0.5 |
| image_gray |
bool |
是否黑白照片 |
4.示例代码
4.1 Python
# -*- coding: utf-8 -*-
import requests
import base64
import cv2
import json
import numpy as np
api_key = '******' # 你的API KEY
file_path = '...' # 图片路径
with open(file_path, 'rb') as fp:
photo_base64 = base64.b64encode(fp.read()).decode('utf8')
url = 'https://api.shiliuai.com/api/id_photo/v1'
headers = {'APIKEY': api_key, "Content-type": "application/json"}
data = {
"base64": photo_base64,
"bgColor": "FFFFFF",
"dpi": 300,
"mmHeight": 35,
"mmWidth": 25
}
response = requests.post(url=url, headers=headers, json=data)
response = json.loads(response.content)
"""
成功:{'code': 0, 'msg': 'OK', 'msg_cn': '成功', 'id': id, 'result_base64': result_base64}
or
失败:{'code': error_code, 'msg': error_msg, 'msg_cn': 错误信息}
"""
result_base64 = response.get('result_base64', '')
img_id = response.get('id', '')
file_bytes = base64.b64decode(result_base64) if result_base64 else b''
if file_bytes:
with open('result.jpg', 'wb') as f:
f.write(file_bytes)
image = np.asarray(bytearray(file_bytes), dtype=np.uint8)
image = cv2.imdecode(image, cv2.IMREAD_COLOR)
cv2.imshow('result', image)
cv2.waitKey(0)
# 同一张图片,参数改变,再次请求(复用 id)
data2 = {
"id": img_id,
"bgColor": "FF0000",
"dpi": 300,
"pxHeight": 640,
"pxWidth": 480
}
response2 = requests.post(url=url, headers=headers, json=data2)
response2 = json.loads(response2.content)
4.2 PHP
<?php
$url = "https://api.shiliuai.com/api/id_photo/v1";
$method = "POST";
$apikey = "******";
$header = array();
array_push($header, "APIKEY:" . $apikey);
array_push($header, "Content-Type:application/json");
$file_path = "...";
$handle = fopen($file_path, "r");
$photo = fread($handle, filesize($file_path));
fclose($handle);
$photo_base64 = base64_encode($photo);
$data = array(
"base64"=> $photo_base64,
"bgColor"=>"FFFFFF",
"dpi"=>300,
"mmHeight"=>35,
"mmWidth"=>25
);
$post_data = json_encode($data);
$curl = curl_init();
curl_setopt($curl, CURLOPT_CUSTOMREQUEST, $method);
curl_setopt($curl, CURLOPT_URL, $url);
curl_setopt($curl, CURLOPT_HTTPHEADER, $header);
curl_setopt($curl, CURLOPT_POSTFIELDS, $post_data);
curl_setopt($curl, CURLOPT_RETURNTRANSFER, true);
curl_setopt($curl, CURLOPT_SSL_VERIFYPEER, false);
curl_setopt($curl, CURLOPT_SSL_VERIFYHOST, false);
$response = curl_exec($curl);
var_dump($response);
4.3 Java
import java.io.*;
import java.net.HttpURLConnection;
import java.net.URL;
import java.nio.file.Files;
import java.util.Base64;
import org.json.JSONObject;
public class IDPhotoAPIExample {
public static void main(String[] args) {
String apiKey = "******"; // 你的API KEY
String filePath = "path/to/your/image.jpg"; // 图片路径
try {
// 读取图片并编码为 Base64
byte[] fileBytes = Files.readAllBytes(new File(filePath).toPath());
String photoBase64 = Base64.getEncoder().encodeToString(fileBytes);
// API 请求的 URL
String apiUrl = "https://api.shiliuai.com/api/id_photo/v1";
// 请求参数 (初次请求)
JSONObject requestData = new JSONObject();
requestData.put("base64", photoBase64);
requestData.put("bgColor", "FFFFFF");
requestData.put("dpi", 300);
requestData.put("mmHeight", 35);
requestData.put("mmWidth", 25);
// 发送 POST 请求
JSONObject response = sendPostRequest(apiUrl, apiKey, requestData);
// 检查响应是否成功
if (response.getInt("code") == 0) {
String resultBase64 = response.getString("result_base64");
String id = response.getString("id");
// 解码并保存图片
byte[] resultBytes = Base64.getDecoder().decode(resultBase64);
try (FileOutputStream fos = new FileOutputStream("result.jpg")) {
fos.write(resultBytes);
}
System.out.println("图片生成成功,文件已保存为 result.jpg");
// 同一张图片,参数改变,再次请求
JSONObject newRequestData = new JSONObject();
newRequestData.put("id", id);
newRequestData.put("bgColor", "FF0000");
newRequestData.put("dpi", 300);
newRequestData.put("pxHeight", 640);
newRequestData.put("pxWidth", 480);
// 发送新的请求
JSONObject newResponse = sendPostRequest(apiUrl, apiKey, newRequestData);
if (newResponse.getInt("code") == 0) {
String newResultBase64 = newResponse.getString("result_base64");
byte[] newResultBytes = Base64.getDecoder().decode(newResultBase64);
try (FileOutputStream fos = new FileOutputStream("result_red_bg.jpg")) {
fos.write(newResultBytes);
}
System.out.println("参数改变后的图片生成成功,文件已保存为 result_red_bg.jpg");
} else {
System.out.println("新的请求失败: " + newResponse.getString("msg_cn"));
}
} else {
System.out.println("初次请求失败: " + response.getString("msg_cn"));
}
} catch (Exception e) {
e.printStackTrace();
}
}
// 发送 POST 请求
private static JSONObject sendPostRequest(String urlStr, String apiKey, JSONObject jsonData) throws IOException {
URL url = new URL(urlStr);
HttpURLConnection conn = (HttpURLConnection) url.openConnection();
conn.setRequestMethod("POST");
conn.setRequestProperty("APIKEY", apiKey);
conn.setRequestProperty("Content-Type", "application/json");
conn.setDoOutput(true);
// 写入请求数据
try (OutputStream os = conn.getOutputStream()) {
byte[] input = jsonData.toString().getBytes("utf-8");
os.write(input, 0, input.length);
}
// 读取响应
StringBuilder response = new StringBuilder();
try (BufferedReader br = new BufferedReader(new InputStreamReader(conn.getInputStream(), "utf-8"))) {
String responseLine;
while ((responseLine = br.readLine()) != null) {
response.append(responseLine.trim());
}
}
return new JSONObject(response.toString());
}
}
4.4 JavaScript
const fs = require('fs');
const fetch = require('node-fetch'); // 需安装:npm install node-fetch
const apiKey = '******'; // 你的API KEY
const filePath = 'path/to/your/image.jpg'; // 图片路径
(async () => {
try {
// 读取图片并编码为 Base64
const fileBuffer = fs.readFileSync(filePath);
const photoBase64 = fileBuffer.toString('base64');
// API 请求的 URL
const apiUrl = 'https://api.shiliuai.com/api/id_photo/v1';
// 请求参数 (初次请求)
const requestData = {
base64: photoBase64,
bgColor: "FFFFFF",
dpi: 300,
mmHeight: 35,
mmWidth: 25
};
// 发送 POST 请求
let response = await fetch(apiUrl, {
method: 'POST',
headers: {
'APIKEY': apiKey,
'Content-Type': 'application/json'
},
body: JSON.stringify(requestData)
});
let responseData = await response.json();
// 检查响应是否成功
if (responseData.code === 0) {
const resultBase64 = responseData.result_base64;
const id = responseData.id;
// 解码并保存图片
const resultBuffer = Buffer.from(resultBase64, 'base64');
fs.writeFileSync('result.jpg', resultBuffer);
console.log("图片生成成功,文件已保存为 result.jpg");
// 同一张图片,参数改变,再次请求
const newRequestData = {
id: id,
bgColor: "FF0000",
dpi: 300,
pxHeight: 640,
pxWidth: 480
};
response = await fetch(apiUrl, {
method: 'POST',
headers: {
'APIKEY': apiKey,
'Content-Type': 'application/json'
},
body: JSON.stringify(newRequestData)
});
responseData = await response.json();
if (responseData.code === 0) {
const newResultBase64 = responseData.result_base64;
const newResultBuffer = Buffer.from(newResultBase64, 'base64');
fs.writeFileSync('result_red_bg.jpg', newResultBuffer);
console.log("参数改变后的图片生成成功,文件已保存为 result_red_bg.jpg");
} else {
console.error("新的请求失败:", responseData.msg_cn);
}
} else {
console.error("初次请求失败:", responseData.msg_cn);
}
} catch (error) {
console.error("发生错误:", error);
}
})();
4.5 C#
using System;
using System.IO;
using System.Net.Http;
using System.Text;
using System.Text.Json;
using System.Threading.Tasks;
class Program
{
static async Task Main()
{
var apiKey = "******";
var filePath = "path/to/your/image.jpg";
var url = "https://api.shiliuai.com/api/id_photo/v1";
using var client = new HttpClient();
client.DefaultRequestHeaders.Add("APIKEY", apiKey);
var photoBase64 = Convert.ToBase64String(File.ReadAllBytes(filePath));
var request1 = new
{
base64 = photoBase64,
bgColor = "FFFFFF",
dpi = 300,
mmHeight = 35,
mmWidth = 25
};
var json1 = JsonSerializer.Serialize(request1);
var res1 = await client.PostAsync(url, new StringContent(json1, Encoding.UTF8, "application/json"));
var body1 = await res1.Content.ReadAsStringAsync();
using var doc1 = JsonDocument.Parse(body1);
var root1 = doc1.RootElement;
if (root1.GetProperty("code").GetInt32() != 0)
{
var msg = root1.TryGetProperty("msg_cn", out var msgCn) ? msgCn.GetString() : root1.GetProperty("msg").GetString();
Console.WriteLine("初次请求失败: " + msg);
return;
}
var resultBase64 = root1.GetProperty("result_base64").GetString();
var id = root1.GetProperty("id").GetString();
File.WriteAllBytes("result.jpg", Convert.FromBase64String(resultBase64));
Console.WriteLine("图片生成成功,文件已保存为 result.jpg");
var request2 = new
{
id = id,
bgColor = "FF0000",
dpi = 300,
pxHeight = 640,
pxWidth = 480
};
var json2 = JsonSerializer.Serialize(request2);
var res2 = await client.PostAsync(url, new StringContent(json2, Encoding.UTF8, "application/json"));
var body2 = await res2.Content.ReadAsStringAsync();
using var doc2 = JsonDocument.Parse(body2);
var root2 = doc2.RootElement;
if (root2.GetProperty("code").GetInt32() != 0)
{
var msg = root2.TryGetProperty("msg_cn", out var msgCn) ? msgCn.GetString() : root2.GetProperty("msg").GetString();
Console.WriteLine("二次请求失败: " + msg);
return;
}
var resultBase642 = root2.GetProperty("result_base64").GetString();
File.WriteAllBytes("result_red_bg.jpg", Convert.FromBase64String(resultBase642));
Console.WriteLine("参数改变后的图片生成成功,文件已保存为 result_red_bg.jpg");
}
}
4.6 易语言
版本 2
.支持库 spec
.支持库 dp1
.子程序 证件照_API_示例
.局部变量 局_网址, 文本型
.局部变量 局_方式, 整数型
.局部变量 局_提交数据, 文本型
.局部变量 局_提交协议头, 文本型
.局部变量 局_结果, 字节集
.局部变量 局_返回, 文本型
.局部变量 图片数据, 字节集
.局部变量 base64图片, 文本型
图片数据 = 读入文件 ("你的图片路径.jpg")
base64图片 = 编码_BASE64编码 (图片数据)
局_提交数据 = "{" + #引号 + "base64" + #引号 + ":" + #引号 + base64图片 + #引号 + "," + #引号 + "bgColor" + #引号 + ":" + #引号 + "FFFFFF" + #引号 + "," + #引号 + "dpi" + #引号 + ":300," + #引号 + "mmHeight" + #引号 + ":35," + #引号 + "mmWidth" + #引号 + ":25}"
局_网址 = "https://api.shiliuai.com/api/id_photo/v1"
局_方式 = 1
局_提交协议头 = "APIKEY: 你的APIKEY" + #换行符 + "Content-Type: application/json"
局_结果 = 网页_访问_对象 (局_网址, 局_方式, 局_提交数据, , , 局_提交协议头, , , , , , , , , , , , , )
局_返回 = 到文本 (编码_编码转换对象 (局_结果, , , ))
返回 (局_返回)
4.7 天诺
public static string Api_IdPhoto(Image image, string apiKey)
{
string url = "https://api.shiliuai.com/api/id_photo/v1";
var headers = new Dictionary
{
{"Authorization", "APPCODE " + appcode},
{"Content-Type", "application/json"}
};
string body = "{\"base64\":\"" + CustomHelp.ImageTobase64(image) + "\",\"bgColor\":\"FFFFFF\",\"dpi\":300,\"mmHeight\":35,\"mmWidth\":25}";
return CustomHelp.HttpPost(url, body, headers);
}
4.8 按键精灵-电脑版
Import "Encrypt.dll"
VBSBegin
Function Base64Encode(filePath)
Set inStream = CreateObject("ADODB.Stream")
inStream.Type = 1
inStream.Open
inStream.LoadFromFile filePath
inStream.Position = 0
Set dom = CreateObject("MSXML2.DOMDocument")
Set elem = dom.createElement("tmp")
elem.dataType = "bin.base64"
elem.nodeTypedValue = inStream.Read
Base64Encode = elem.Text
inStream.Close
End Function
Function api_id_photo(apiKey, imgPath)
url = "https://api.shiliuai.com/api/id_photo/v1"
jsonBody = "{""base64"":""" & Base64Encode(imgPath) & """,""bgColor"":""FFFFFF"",""dpi"":300,""mmHeight"":35,""mmWidth"":25}"
Set http = CreateObject("MSXML2.XMLHTTP")
http.Open "POST", url, False
http.setRequestHeader "APIKEY", apiKey
http.setRequestHeader "Content-Type", "application/json"
http.send jsonBody
api_id_photo = http.responseText
End Function
VBSEnd
apiKey = "你的APIKEY"
res = api_id_photo(apiKey, "你的图片路径.jpg")
TracePrint res
4.9 按键精灵-手机版
Import "yd.luae"
Import "zm.luae"
Dim imagePath = "/sdcard/Pictures/test.png"
SnapShotEx imagePath
Function api_id_photo(apiKey, imagePath)
Dim url = "https://api.shiliuai.com/api/id_photo/v1"
Dim body = "{""base64"":""" & yd.Base64EncodeFile(imagePath) & """,""bgColor"":""FFFFFF"",""dpi"":300,""mmHeight"":35,""mmWidth"":25}"
Dim headers = {null}
headers["APIKEY"] = apiKey
headers["Content-Type"] = "application/json"
Dim res = yd.HttpPost(url, body, headers)
api_id_photo = yd.JsonDecode(res)
End Function
Dim apiKey = "你的APIKEY"
Dim res = api_id_photo(apiKey, imagePath)
TracePrint res["code"]
4.10 触动精灵
require("tsnet")
require "TSLib"
local ts = require("ts")
local json = ts.json
function readFileBase64(path)
local f = io.open(path,"rb")
if not f then return nil end
local bytes = f:read("*all")
f:close()
return bytes:base64_encode()
end
function api_id_photo(apiKey, imagePath)
local url = "https://api.shiliuai.com/api/id_photo/v1"
local body = json.encode({ base64 = readFileBase64(imagePath), bgColor = 'FFFFFF', dpi = 300, mmHeight = 35, mmWidth = 25 })
local headers = {}
headers["APIKEY"] = apiKey
headers["Content-Type"] = "application/json"
local resp = httpPost(url, body, { headers = headers })
return json.decode(resp)
end
4.11 懒人精灵
function api_id_photo(apiKey, imagePath)
local url = "https://api.shiliuai.com/api/id_photo/v1"
local body = jsonLib.encode({ base64 = getFileBase64(imagePath), bgColor = 'FFFFFF', dpi = 300, mmHeight = 35, mmWidth = 25 })
local headers = {}
headers["APIKEY"] = apiKey
headers["Content-Type"] = "application/json"
local resp = httpPost(url, body, { headers = headers })
return jsonLib.decode(resp)
end
4.12 EasyClick
function main()
local request = image.requestScreenCapture(10000, 0)
if not request then
request = image.requestScreenCapture(10000, 0)
end
local apiKey = "你的APIKEY"
local img = image.captureFullScreenEx()
console.time("t")
local res = api_id_photo(apiKey, img)
logd(console.timeEnd("t"))
logd(res.code)
end
function api_id_photo(apiKey, img)
local url = "https://api.shiliuai.com/api/id_photo/v1"
local imgBase64 = image.toBase64Format(img, "jpg", 100)
image.recycle(img)
local body = JSON.stringify({ base64 = imgBase64, bgColor = 'FFFFFF', dpi = 300, mmHeight = 35, mmWidth = 25 })
local params = {
url = url,
method = "POST",
headers = {
["APIKEY"] = apiKey,
["Content-Type"] = "application/json"
},
requestBody = body
}
local res = http.request(params)
return JSON.parse(res.body)
end