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* 重构insightface代码 * 重写insightface example代码 * 重写insightface example代码 * 删除多余代码 * 修改预处理代码 * 修改文档 * 修改文档 * 恢复误删除的文件 * 修改cpp example * 修改cpp example * 测试python代码 * 测试python代码 * 测试python代码 * 测试python代码 * 测试python代码 * 测试python代码 * 测试python代码 * 跑通python代码 * 修复重复初始化的bug * 更新adaface的python代码 * 修复c++重复初始化的问题 * 修复c++重复初始化的问题 * 修复Python重复初始化的问题 * 新增preprocess的几个参数的获取方式 * 修复注释的错误 * 按照要求修改 * 修改文档中的图片为图片压缩包 * 修改编译完成后程序的提示 * 更新错误include * 删除无用文件 * 更新文档
91 lines
2.3 KiB
Python
91 lines
2.3 KiB
Python
import fastdeploy as fd
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import cv2
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import numpy as np
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# 余弦相似度
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def cosine_similarity(a, b):
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a = np.array(a)
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b = np.array(b)
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mul_a = np.linalg.norm(a, ord=2)
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mul_b = np.linalg.norm(b, ord=2)
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mul_ab = np.dot(a, b)
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return mul_ab / (mul_a * mul_b)
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def parse_arguments():
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import argparse
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import ast
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"--model", required=True, help="Path of insightface onnx model.")
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parser.add_argument(
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"--face", required=True, help="Path of test face image file.")
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parser.add_argument(
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"--face_positive",
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required=True,
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help="Path of test face_positive image file.")
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parser.add_argument(
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"--face_negative",
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required=True,
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help="Path of test face_negative image file.")
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parser.add_argument(
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"--device",
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type=str,
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default='cpu',
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help="Type of inference device, support 'cpu' or 'gpu'.")
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parser.add_argument(
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"--use_trt",
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type=ast.literal_eval,
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default=False,
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help="Wether to use tensorrt.")
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return parser.parse_args()
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def build_option(args):
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option = fd.RuntimeOption()
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if args.device.lower() == "gpu":
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option.use_gpu()
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if args.use_trt:
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option.use_trt_backend()
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option.set_trt_input_shape("data", [1, 3, 112, 112])
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return option
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args = parse_arguments()
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# 配置runtime，加载模型
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runtime_option = build_option(args)
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model = fd.vision.faceid.VPL(args.model, runtime_option=runtime_option)
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# 加载图片
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face0 = cv2.imread(args.face) # 0,1 同一个人
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face1 = cv2.imread(args.face_positive)
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face2 = cv2.imread(args.face_negative) # 0,2 不同的人
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# 设置 l2 normalize
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model.postprocessor.l2_normalize = True
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# 预测图片检测结果
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result0 = model.predict(face0)
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result1 = model.predict(face1)
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result2 = model.predict(face2)
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# 计算余弦相似度
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embedding0 = result0.embedding
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embedding1 = result1.embedding
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embedding2 = result2.embedding
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cosine01 = cosine_similarity(embedding0, embedding1)
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cosine02 = cosine_similarity(embedding0, embedding2)
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# 打印结果
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print(result0, end="")
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print(result1, end="")
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print(result2, end="")
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print("Cosine 01: ", cosine01)
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print("Cosine 02: ", cosine02)
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print(model.runtime_option)
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