mirror of
https://github.com/Zeyi-Lin/HivisionIDPhotos.git
synced 2025-09-15 14:58:34 +08:00
125 lines
3.7 KiB
Python
125 lines
3.7 KiB
Python
import os
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import cv2
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import ast
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import argparse
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import numpy as np
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import onnxruntime
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from utils import resize_image_to_kb
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from src.face_judgement_align import IDphotos_create
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from hivisionai.hycv.vision import add_background
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from src.layoutCreate import generate_layout_photo, generate_layout_image
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parser = argparse.ArgumentParser(description="HivisionIDPhotos 证件照制作推理程序。")
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parser.add_argument(
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"-t",
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"--type",
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help="请求 API 的种类，有 idphoto、add_background 和 generate_layout_photos 可选",
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default="idphoto",
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)
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parser.add_argument("-i", "--input_image_dir", help="输入图像路径", required=True)
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parser.add_argument("-o", "--output_image_dir", help="保存图像路径", required=True)
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parser.add_argument("-s", "--size", help="证件照尺寸", default="(413,295)")
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parser.add_argument("-c", "--color", help="证件照背景色", default="(255,255,255)")
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parser.add_argument(
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"-k", "--kb", help="输出照片的 KB 值，仅对换底和制作排版照生效", default=None
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)
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args = parser.parse_args()
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root_dir = os.path.dirname(os.path.abspath(__file__))
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# 预加载 ONNX 模型
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print("正在加载抠图模型...")
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HY_HUMAN_MATTING_WEIGHTS_PATH = os.path.join(root_dir, "hivision_modnet.onnx")
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sess = onnxruntime.InferenceSession(HY_HUMAN_MATTING_WEIGHTS_PATH)
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input_image = cv2.imread(args.input_image_dir, cv2.IMREAD_UNCHANGED)
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# 如果模式是生成证件照
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if args.type == "idphoto":
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# 将字符串转为元组
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size = ast.literal_eval(args.size)
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(
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result_image_hd,
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result_image_standard,
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typography_arr,
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typography_rotate,
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_,
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_,
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_,
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_,
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status,
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) = IDphotos_create(
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input_image,
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size=size,
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align=False,
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beauty=False,
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fd68=None,
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human_sess=sess,
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IS_DEBUG=False,
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)
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# 如果检测到人脸数量不等于 1（照片无人脸 or 多人脸）
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if status == 0:
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print("人脸数量不等于 1，请上传单张人脸的图像。")
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# 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）
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else:
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# 保存标准照
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cv2.imwrite(args.output_image_dir, result_image_standard)
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# 保存高清照
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file_name, file_extension = os.path.splitext(args.output_image_dir)
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new_file_name = file_name + "_hd" + file_extension
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cv2.imwrite(new_file_name, result_image_hd)
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# 如果模式是添加背景
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elif args.type == "add_background":
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# 将字符串转为元组
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color = ast.literal_eval(args.color)
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# 将元祖的 0 和 2 号数字交换
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color = (color[2], color[1], color[0])
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result_image = add_background(input_image, bgr=color)
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result_image = result_image.astype(np.uint8)
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if args.kb:
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result_image = cv2.cvtColor(result_image, cv2.COLOR_RGB2BGR)
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result_image = resize_image_to_kb(
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result_image, args.output_image_dir, int(args.kb)
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)
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else:
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cv2.imwrite(args.output_image_dir, result_image)
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# 如果模式是生成排版照
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elif args.type == "generate_layout_photos":
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# 将字符串转为元组
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size = ast.literal_eval(args.size)
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typography_arr, typography_rotate = generate_layout_photo(
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input_height=size[0], input_width=size[1]
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)
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result_layout_image = generate_layout_image(
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input_image,
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typography_arr,
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typography_rotate,
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height=size[0],
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width=size[1],
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)
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if args.kb:
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result_layout_image = cv2.cvtColor(result_layout_image, cv2.COLOR_RGB2BGR)
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result_layout_image = resize_image_to_kb(
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result_layout_image, args.output_image_dir, int(args.kb)
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)
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else:
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cv2.imwrite(args.output_image_dir, result_layout_image)
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