mirror of
https://github.com/Zeyi-Lin/HivisionIDPhotos.git
synced 2025-09-15 14:58:34 +08:00
Ze-Yi LIN
GitHub
parent
0712b789cb
commit
530481b137
13
README.md
13
README.md
@ -191,7 +191,7 @@ python app.py
|
||||
输入 1 张照片，获得 1 张标准证件照和 1 张高清证件照的 4 通道透明 png
|
||||
|
||||
```python
|
||||
python inference.py -i demo/images/test.jpg -o ./idphoto.png --height 413 --width 295
|
||||
python inference.py -i demo/images/test0.jpg -o ./idphoto.png --height 413 --width 295
|
||||
```
|
||||
|
||||
## 2. 人像抠图
|
||||
@ -199,7 +199,7 @@ python inference.py -i demo/images/test.jpg -o ./idphoto.png --height 413 --widt
|
||||
输入 1 张照片，获得 1张 4 通道透明 png
|
||||
|
||||
```python
|
||||
python inference.py -t human_matting -i demo/images/test.jpg -o ./idphoto_matting.png --matting_model hivision_modnet
|
||||
python inference.py -t human_matting -i demo/images/test0.jpg -o ./idphoto_matting.png --matting_model hivision_modnet
|
||||
```
|
||||
|
||||
## 3. 透明图增加底色
|
||||
@ -218,6 +218,15 @@ python inference.py -t add_background -i ./idphoto.png -o ./idphoto_ab.jpg -c 4
|
||||
python inference.py -t generate_layout_photos -i ./idphoto_ab.jpg -o ./idphoto_layout.jpg --height 413 --width 295 -k 200
|
||||
```
|
||||
|
||||
## 5. 证件照裁剪
|
||||
|
||||
输入 1 张 4 通道照片（抠图好的图像），获得 1 张标准证件照和 1 张高清证件照的 4 通道透明 png
|
||||
|
||||
```python
|
||||
python inference.py -t idphoto_crop -i ./idphoto_matting.png -o ./idphoto_crop.png --height 413 --width 295
|
||||
```
|
||||
|
||||
|
||||
<br>
|
||||
|
||||
# ⚡️ 部署 API 服务
|
||||
|
||||
@ -252,6 +252,54 @@ async def set_kb(
|
||||
return result_messgae
|
||||
|
||||
|
||||
# 证件照智能裁剪接口
|
||||
@app.post("/idphoto_crop")
|
||||
async def idphoto_crop_inference(
|
||||
input_image: UploadFile,
|
||||
height: int = Form(413),
|
||||
width: int = Form(295),
|
||||
face_detect_model: str = Form("mtcnn"),
|
||||
hd: bool = Form(True),
|
||||
head_measure_ratio: float = 0.2,
|
||||
head_height_ratio: float = 0.45,
|
||||
top_distance_max: float = 0.12,
|
||||
top_distance_min: float = 0.10,
|
||||
):
|
||||
|
||||
image_bytes = await input_image.read()
|
||||
nparr = np.frombuffer(image_bytes, np.uint8)
|
||||
img = cv2.imdecode(nparr, cv2.IMREAD_UNCHANGED) # 读取图像(4通道)
|
||||
|
||||
# ------------------- 选择抠图与人脸检测模型 -------------------
|
||||
choose_handler(creator, face_detect_option=face_detect_model)
|
||||
|
||||
# 将字符串转为元组
|
||||
size = (int(height), int(width))
|
||||
try:
|
||||
result = creator(
|
||||
img,
|
||||
size=size,
|
||||
head_measure_ratio=head_measure_ratio,
|
||||
head_height_ratio=head_height_ratio,
|
||||
head_top_range=(top_distance_max, top_distance_min),
|
||||
crop_only=True,
|
||||
)
|
||||
except FaceError:
|
||||
result_message = {"status": False}
|
||||
# 如果检测到人脸数量等于1, 则返回标准证和高清照结果（png 4通道图像）
|
||||
else:
|
||||
result_message = {
|
||||
"status": True,
|
||||
"image_base64_standard": numpy_2_base64(result.standard),
|
||||
}
|
||||
|
||||
# 如果hd为True, 则增加高清照结果（png 4通道图像）
|
||||
if hd:
|
||||
result_message["image_base64_hd"] = numpy_2_base64(result.hd)
|
||||
|
||||
return result_message
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
import uvicorn
|
||||
|
||||
|
||||
@ -42,19 +42,19 @@ python deploy_api.py
|
||||
|
||||
接口名：`add_background`
|
||||
|
||||
`添加背景色`接口的逻辑是发送一张 RGBA 图像，根据`color`添加背景色，合成一张 JPG 图像。
|
||||
`添加背景色`接口的逻辑是接收一张 RGBA 图像（透明图），根据`color`添加背景色，合成一张 JPG 图像。
|
||||
|
||||
### 3.生成六寸排版照
|
||||
|
||||
接口名：`generate_layout_photos`
|
||||
|
||||
`生成六寸排版照`接口的逻辑是发送一张 RGB 图像（一般为添加背景色之后的证件照），根据`size`进行照片排布，然后生成一张六寸排版照。
|
||||
`生成六寸排版照`接口的逻辑是接收一张 RGB 图像（一般为添加背景色之后的证件照），根据`size`进行照片排布，然后生成一张六寸排版照。
|
||||
|
||||
### 4.人像抠图
|
||||
|
||||
接口名：`human_matting`
|
||||
|
||||
`人像抠图`接口的逻辑是发送一张 RGB 图像，输出一张标准抠图人像照和高清抠图人像照（无任何背景填充）。
|
||||
`人像抠图`接口的逻辑是接收一张 RGB 图像，输出一张标准抠图人像照和高清抠图人像照（无任何背景填充）。
|
||||
|
||||
### 5.图像加水印
|
||||
|
||||
@ -69,6 +69,12 @@ python deploy_api.py
|
||||
|
||||
`设置图像KB大小`接口的功能是接收一张图像和目标文件大小（以KB为单位），如果设置的KB值小于原文件，则调整压缩率；如果设置的KB值大于源文件，则通过给文件头添加信息的方式调大KB值，目标是让图像的最终大小与设置的KB值一致。
|
||||
|
||||
### 7.证件照裁切
|
||||
|
||||
接口名：`idphoto_crop`
|
||||
|
||||
`证件照裁切`接口的功能是接收一张 RBGA 图像（透明图），输出一张标准证件照和一张高清证件照。
|
||||
|
||||
<br>
|
||||
|
||||
## cURL 请求示例
|
||||
@ -135,6 +141,18 @@ curl -X 'POST' \
|
||||
-F 'kb=50'
|
||||
```
|
||||
|
||||
### 7. 证件照裁切
|
||||
```bash
|
||||
curl -X 'POST' \
|
||||
'http://127.0.0.1:8080/idphoto_crop?head_measure_ratio=0.2&head_height_ratio=0.45&top_distance_max=0.12&top_distance_min=0.1' \
|
||||
-H 'accept: application/json' \
|
||||
-H 'Content-Type: multipart/form-data' \
|
||||
-F 'input_image=@idphoto_matting.png;type=image/png' \
|
||||
-F 'height=413' \
|
||||
-F 'width=295' \
|
||||
-F 'face_detect_model=mtcnn' \
|
||||
-F 'hd=true'
|
||||
```
|
||||
|
||||
<br>
|
||||
|
||||
@ -236,7 +254,8 @@ else:
|
||||
```
|
||||
|
||||
### 6. 设置图像KB大小
|
||||
```bash
|
||||
|
||||
```python
|
||||
import requests
|
||||
|
||||
# 设置请求的 URL
|
||||
@ -259,6 +278,44 @@ else:
|
||||
print(f"Request failed with status code {response.status_code}: {response.text}")
|
||||
```
|
||||
|
||||
### 7. 证件照裁切
|
||||
|
||||
```python
|
||||
import requests
|
||||
|
||||
# 设置请求的 URL
|
||||
url = "http://127.0.0.1:8080/idphoto_crop"
|
||||
|
||||
# 设置请求参数
|
||||
params = {
|
||||
"head_measure_ratio": 0.2,
|
||||
"head_height_ratio": 0.45,
|
||||
"top_distance_max": 0.12,
|
||||
"top_distance_min": 0.1,
|
||||
}
|
||||
|
||||
# 设置文件和其他表单数据
|
||||
input_image_path = "idphoto_matting.png"
|
||||
files = {"input_image": ("idphoto_matting.png", open(input_image_path, "rb"), "image/png")}
|
||||
data = {
|
||||
"height": 413,
|
||||
"width": 295,
|
||||
"face_detect_model": "mtcnn",
|
||||
"hd": "true"
|
||||
}
|
||||
|
||||
# 发送 POST 请求
|
||||
response = requests.post(url, params=params, files=files, data=data)
|
||||
|
||||
# 检查响应
|
||||
if response.ok:
|
||||
# 输出响应内容
|
||||
print(response.json())
|
||||
else:
|
||||
# 输出错误信息
|
||||
print(f"Request failed with status code {response.status_code}: {response.text}")
|
||||
```
|
||||
|
||||
<br>
|
||||
|
||||
## Java 请求示例
|
||||
|
||||
18
inference.py
18
inference.py
@ -19,6 +19,7 @@ INFERENCE_TYPE = [
|
||||
"add_background",
|
||||
"generate_layout_photos",
|
||||
"watermark",
|
||||
"idphoto_crop",
|
||||
]
|
||||
MATTING_MODEL = [
|
||||
"hivision_modnet",
|
||||
@ -149,3 +150,20 @@ elif args.type == "generate_layout_photos":
|
||||
)
|
||||
else:
|
||||
cv2.imwrite(args.output_image_dir, result_layout_image)
|
||||
|
||||
# 如果模式是证件照裁切
|
||||
elif args.type == "idphoto_crop":
|
||||
# 将字符串转为元组
|
||||
size = (int(args.height), int(args.width))
|
||||
try:
|
||||
result = creator(input_image, size=size, crop_only=True)
|
||||
except FaceError:
|
||||
print("人脸数量不等于 1，请上传单张人脸的图像。")
|
||||
else:
|
||||
# 保存标准照
|
||||
cv2.imwrite(args.output_image_dir, result.standard)
|
||||
|
||||
# 保存高清照
|
||||
file_name, file_extension = os.path.splitext(args.output_image_dir)
|
||||
new_file_name = file_name + "_hd" + file_extension
|
||||
cv2.imwrite(new_file_name, result.hd)
|
||||
|
||||
Loading…
Reference in New Issue
Block a user