mirror of
https://github.com/deepinsight/insightface.git
synced 2025-12-30 08:02:27 +00:00
721 lines
26 KiB
Python
721 lines
26 KiB
Python
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os
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import argparse
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import requests
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import logging
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import imghdr
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import pickle
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import tarfile
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from functools import partial
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import cv2
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import numpy as np
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from sklearn.metrics.pairwise import cosine_similarity
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from tqdm import tqdm
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from prettytable import PrettyTable
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from PIL import Image, ImageDraw, ImageFont
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import paddle
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from paddle.inference import Config
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from paddle.inference import create_predictor
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__all__ = ["InsightFace", "parser"]
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BASE_INFERENCE_MODEL_DIR = os.path.expanduser("~/.insightface/ppmodels/")
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BASE_DOWNLOAD_URL = "https://paddle-model-ecology.bj.bcebos.com/model/insight-face/{}.tar"
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def parser(add_help=True):
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def str2bool(v):
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return v.lower() in ("true", "t", "1")
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parser = argparse.ArgumentParser(add_help=add_help)
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parser.add_argument(
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"--det", action="store_true", help="Whether to detect.")
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parser.add_argument(
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"--rec", action="store_true", help="Whether to recognize.")
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parser.add_argument(
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"--det_model",
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type=str,
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default="BlazeFace",
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help="The detection model.")
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parser.add_argument(
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"--rec_model",
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type=str,
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default="MobileFace",
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help="The recognition model.")
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parser.add_argument(
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"--use_gpu",
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type=str2bool,
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default=True,
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help="Whether use GPU to predict. Default by True.")
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parser.add_argument(
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"--enable_mkldnn",
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type=str2bool,
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default=True,
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help="Whether use MKLDNN to predict, valid only when --use_gpu is False. Default by False."
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)
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parser.add_argument(
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"--cpu_threads",
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type=int,
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default=1,
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help="The num of threads with CPU, valid only when --use_gpu is False. Default by 1."
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)
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parser.add_argument(
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"--input",
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type=str,
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help="The path or directory of image(s) or video to be predicted.")
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parser.add_argument(
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"--output", type=str, default="./output/", help="The directory of prediction result.")
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parser.add_argument(
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"--det_thresh",
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type=float,
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default=0.8,
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help="The threshold of detection postprocess. Default by 0.8.")
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parser.add_argument(
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"--index", type=str, default=None, help="The path of index file.")
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parser.add_argument(
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"--cdd_num",
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type=int,
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default=5,
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help="The number of candidates in the recognition retrieval. Default by 10."
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)
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parser.add_argument(
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"--rec_thresh",
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type=float,
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default=0.45,
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help="The threshold of recognition postprocess. Default by 0.45.")
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parser.add_argument(
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"--max_batch_size",
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type=int,
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default=1,
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help="The maxium of batch_size to recognize. Default by 1.")
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return parser
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def print_config(args):
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args = vars(args)
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table = PrettyTable(['Param', 'Value'])
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for param in args:
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table.add_row([param, args[param]])
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width = len(str(table).split("\n")[0])
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print("{}".format("-" * width))
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print("PaddleFace".center(width))
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print(table)
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print("Powered by PaddlePaddle!".rjust(width))
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print("{}".format("-" * width))
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def download_with_progressbar(url, save_path):
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"""Download from url with progressbar.
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"""
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if os.path.isfile(save_path):
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os.remove(save_path)
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response = requests.get(url, stream=True)
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total_size_in_bytes = int(response.headers.get("content-length", 0))
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block_size = 1024 # 1 Kibibyte
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progress_bar = tqdm(total=total_size_in_bytes, unit="iB", unit_scale=True)
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with open(save_path, "wb") as file:
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for data in response.iter_content(block_size):
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progress_bar.update(len(data))
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file.write(data)
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progress_bar.close()
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if total_size_in_bytes == 0 or progress_bar.n != total_size_in_bytes or not os.path.isfile(
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save_path):
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raise Exception(
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f"Something went wrong while downloading model/image from {url}")
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def check_model_file(model):
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"""Check the model files exist and download and untar when no exist.
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"""
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model_map = {
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"ArcFace": "arcface_iresnet50_v1.0_infer",
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"BlazeFace": "blazeface_fpn_ssh_1000e_v1.0_infer",
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"MobileFace": "mobileface_v1.0_infer"
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}
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if os.path.isdir(model):
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model_file_path = os.path.join(model, "inference.pdmodel")
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params_file_path = os.path.join(model, "inference.pdiparams")
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if not os.path.exists(model_file_path) or not os.path.exists(
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params_file_path):
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raise Exception(
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f"The specifed model directory error. The drectory must include 'inference.pdmodel' and 'inference.pdiparams'."
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)
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elif model in model_map:
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storage_directory = partial(os.path.join, BASE_INFERENCE_MODEL_DIR,
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model)
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url = BASE_DOWNLOAD_URL.format(model_map[model])
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tar_file_name_list = [
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"inference.pdiparams", "inference.pdiparams.info",
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"inference.pdmodel"
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]
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model_file_path = storage_directory("inference.pdmodel")
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params_file_path = storage_directory("inference.pdiparams")
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if not os.path.exists(model_file_path) or not os.path.exists(
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params_file_path):
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tmp_path = storage_directory(url.split("/")[-1])
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logging.info(f"Download {url} to {tmp_path}")
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os.makedirs(storage_directory(), exist_ok=True)
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download_with_progressbar(url, tmp_path)
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with tarfile.open(tmp_path, "r") as tarObj:
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for member in tarObj.getmembers():
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filename = None
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for tar_file_name in tar_file_name_list:
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if tar_file_name in member.name:
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filename = tar_file_name
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if filename is None:
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continue
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file = tarObj.extractfile(member)
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with open(storage_directory(filename), "wb") as f:
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f.write(file.read())
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os.remove(tmp_path)
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if not os.path.exists(model_file_path) or not os.path.exists(
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params_file_path):
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raise Exception(
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f"Something went wrong while downloading and unzip the model[{model}] files!"
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)
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else:
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raise Exception(
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f"The specifed model name error. Support 'BlazeFace' for detection and 'ArcFace' and 'MobileFace' for recognition. And support local directory that include model files ('inference.pdmodel' and 'inference.pdiparams')."
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)
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return model_file_path, params_file_path
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def normalize_image(img, scale=None, mean=None, std=None, order='chw'):
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if isinstance(scale, str):
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scale = eval(scale)
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scale = np.float32(scale if scale is not None else 1.0 / 255.0)
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mean = mean if mean is not None else [0.485, 0.456, 0.406]
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std = std if std is not None else [0.229, 0.224, 0.225]
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shape = (3, 1, 1) if order == 'chw' else (1, 1, 3)
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mean = np.array(mean).reshape(shape).astype('float32')
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std = np.array(std).reshape(shape).astype('float32')
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if isinstance(img, Image.Image):
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img = np.array(img)
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assert isinstance(img, np.ndarray), "invalid input 'img' in NormalizeImage"
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return (img.astype('float32') * scale - mean) / std
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def to_CHW_image(img):
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if isinstance(img, Image.Image):
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img = np.array(img)
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return img.transpose((2, 0, 1))
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class ColorMap(object):
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def __init__(self, num):
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super().__init__()
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self.get_color_map_list(num)
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self.color_map = {}
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self.ptr = 0
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def __getitem__(self, key):
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return self.color_map[key]
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def update(self, keys):
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for key in keys:
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if key not in self.color_map:
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i = self.ptr % len(self.color_list)
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self.color_map[key] = self.color_list[i]
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self.ptr += 1
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def get_color_map_list(self, num_classes):
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color_map = num_classes * [0, 0, 0]
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for i in range(0, num_classes):
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j = 0
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lab = i
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while lab:
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color_map[i * 3] |= (((lab >> 0) & 1) << (7 - j))
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color_map[i * 3 + 1] |= (((lab >> 1) & 1) << (7 - j))
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color_map[i * 3 + 2] |= (((lab >> 2) & 1) << (7 - j))
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j += 1
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lab >>= 3
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self.color_list = [
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color_map[i:i + 3] for i in range(0, len(color_map), 3)
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]
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class ImageReader(object):
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def __init__(self, inputs):
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super().__init__()
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self.idx = 0
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if isinstance(inputs, np.ndarray):
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self.image_list = [inputs]
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else:
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imgtype_list = {'jpg', 'bmp', 'png', 'jpeg', 'rgb', 'tif', 'tiff'}
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self.image_list = []
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if os.path.isfile(inputs):
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if imghdr.what(inputs) not in imgtype_list:
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raise Exception(
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f"Error type of input path, only support: {imgtype_list}"
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)
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self.image_list.append(inputs)
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elif os.path.isdir(inputs):
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tmp_file_list = os.listdir(inputs)
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warn_tag = False
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for file_name in tmp_file_list:
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file_path = os.path.join(inputs, file_name)
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if not os.path.isfile(file_path):
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warn_tag = True
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continue
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if imghdr.what(file_path) in imgtype_list:
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self.image_list.append(file_path)
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else:
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warn_tag = True
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if warn_tag:
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logging.warning(
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f"The directory of input contine directory or not supported file type, only support: {imgtype_list}"
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)
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else:
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raise Exception(
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f"The file of input path not exist! Please check input: {inputs}"
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)
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def __iter__(self):
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return self
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def __next__(self):
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if self.idx >= len(self.image_list):
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raise StopIteration
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data = self.image_list[self.idx]
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if isinstance(data, np.ndarray):
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self.idx += 1
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return data, "tmp.png"
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path = data
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_, file_name = os.path.split(path)
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img = cv2.imread(path)
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if img is None:
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logging.warning(f"Error in reading image: {path}! Ignored.")
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self.idx += 1
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return self.__next__()
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img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
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self.idx += 1
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return img, file_name
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def __len__(self):
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return len(self.image_list)
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class VideoReader(object):
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def __init__(self, inputs):
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super().__init__()
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videotype_list = {"mp4"}
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if os.path.splitext(inputs)[-1][1:] not in videotype_list:
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raise Exception(
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f"The input file is not supported, only support: {videotype_list}"
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)
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if not os.path.isfile(inputs):
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raise Exception(
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f"The file of input path not exist! Please check input: {inputs}"
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)
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self.capture = cv2.VideoCapture(inputs)
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self.file_name = os.path.split(inputs)[-1]
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def get_info(self):
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info = {}
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width = int(self.capture.get(cv2.CAP_PROP_FRAME_WIDTH))
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height = int(self.capture.get(cv2.CAP_PROP_FRAME_HEIGHT))
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fourcc = cv2.VideoWriter_fourcc(* 'mp4v')
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info["file_name"] = self.file_name
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info["fps"] = 30
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info["shape"] = (width, height)
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info["fourcc"] = cv2.VideoWriter_fourcc(* 'mp4v')
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return info
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def __iter__(self):
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return self
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def __next__(self):
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ret, frame = self.capture.read()
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if not ret:
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raise StopIteration
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return frame, self.file_name
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class ImageWriter(object):
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def __init__(self, output_dir):
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super().__init__()
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if output_dir is None:
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raise Exception(
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"Please specify the directory of saving prediction results by --output."
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)
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if not os.path.exists(output_dir):
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os.makedirs(output_dir)
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self.output_dir = output_dir
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def write(self, image, file_name):
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path = os.path.join(self.output_dir, file_name)
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cv2.imwrite(path, cv2.cvtColor(image, cv2.COLOR_RGB2BGR))
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class VideoWriter(object):
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def __init__(self, output_dir, video_info):
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super().__init__()
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if output_dir is None:
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raise Exception(
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"Please specify the directory of saving prediction results by --output."
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)
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if not os.path.exists(output_dir):
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os.makedirs(output_dir)
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output_path = os.path.join(output_dir, video_info["file_name"])
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fourcc = cv2.VideoWriter_fourcc(* 'mp4v')
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self.writer = cv2.VideoWriter(output_path, video_info["fourcc"],
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video_info["fps"], video_info["shape"])
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def write(self, frame, file_name):
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self.writer.write(frame)
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def __del__(self):
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if hasattr(self, "writer"):
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self.writer.release()
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class BasePredictor(object):
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def __init__(self, predictor_config):
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super().__init__()
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self.predictor_config = predictor_config
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self.predictor, self.input_names, self.output_names = self.load_predictor(
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predictor_config["model_file"], predictor_config["params_file"])
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def load_predictor(self, model_file, params_file):
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config = Config(model_file, params_file)
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if self.predictor_config["use_gpu"]:
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config.enable_use_gpu(200, 0)
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config.switch_ir_optim(True)
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else:
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config.disable_gpu()
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config.set_cpu_math_library_num_threads(self.predictor_config[
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"cpu_threads"])
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if self.predictor_config["enable_mkldnn"]:
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try:
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# cache 10 different shapes for mkldnn to avoid memory leak
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config.set_mkldnn_cache_capacity(10)
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config.enable_mkldnn()
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except Exception as e:
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logging.error(
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"The current environment does not support `mkldnn`, so disable mkldnn."
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)
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config.disable_glog_info()
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config.enable_memory_optim()
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# use zero copy
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config.switch_use_feed_fetch_ops(False)
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predictor = create_predictor(config)
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input_names = predictor.get_input_names()
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output_names = predictor.get_output_names()
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return predictor, input_names, output_names
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def preprocess(self):
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raise NotImplementedError
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def postprocess(self):
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raise NotImplementedError
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def predict(self, img):
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raise NotImplementedError
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class Detector(BasePredictor):
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def __init__(self, det_config, predictor_config):
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super().__init__(predictor_config)
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self.det_config = det_config
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self.target_size = self.det_config["target_size"]
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self.thresh = self.det_config["thresh"]
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def preprocess(self, img):
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resize_h, resize_w = self.target_size
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img_shape = img.shape
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img_scale_x = resize_w / img_shape[1]
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img_scale_y = resize_h / img_shape[0]
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img = cv2.resize(
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img, None, None, fx=img_scale_x, fy=img_scale_y, interpolation=1)
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img = normalize_image(
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img,
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scale=1. / 255.,
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mean=[0.485, 0.456, 0.406],
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std=[0.229, 0.224, 0.225],
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order='hwc')
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img_info = {}
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img_info["im_shape"] = np.array(
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img.shape[:2], dtype=np.float32)[np.newaxis, :]
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img_info["scale_factor"] = np.array(
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[img_scale_y, img_scale_x], dtype=np.float32)[np.newaxis, :]
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img = img.transpose((2, 0, 1)).copy()
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img_info["image"] = img[np.newaxis, :, :, :]
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return img_info
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def postprocess(self, np_boxes):
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expect_boxes = (np_boxes[:, 1] > self.thresh) & (np_boxes[:, 0] > -1)
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return np_boxes[expect_boxes, :]
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def predict(self, img):
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inputs = self.preprocess(img)
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for input_name in self.input_names:
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input_tensor = self.predictor.get_input_handle(input_name)
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input_tensor.copy_from_cpu(inputs[input_name])
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self.predictor.run()
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output_tensor = self.predictor.get_output_handle(self.output_names[0])
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np_boxes = output_tensor.copy_to_cpu()
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# boxes_num = self.detector.get_output_handle(self.detector_output_names[1])
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# np_boxes_num = boxes_num.copy_to_cpu()
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box_list = self.postprocess(np_boxes)
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return box_list
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class Recognizer(BasePredictor):
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def __init__(self, rec_config, predictor_config):
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super().__init__(predictor_config)
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if rec_config["index"] is not None:
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self.load_index(rec_config["index"])
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self.rec_config = rec_config
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self.cdd_num = self.rec_config["cdd_num"]
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self.thresh = self.rec_config["thresh"]
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self.max_batch_size = self.rec_config["max_batch_size"]
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|
|
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def preprocess(self, img, box_list=None):
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img = normalize_image(
|
|
img,
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|
scale=1. / 255.,
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mean=[0.5, 0.5, 0.5],
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|
std=[0.5, 0.5, 0.5],
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|
order='hwc')
|
|
if box_list is None:
|
|
height, width = img.shape[:2]
|
|
box_list = [np.array([0, 0, 0, 0, width, height])]
|
|
batch = []
|
|
input_batches = []
|
|
cnt = 0
|
|
for idx, box in enumerate(box_list):
|
|
box[box < 0] = 0
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xmin, ymin, xmax, ymax = list(map(int, box[2:]))
|
|
face_img = img[ymin:ymax, xmin:xmax, :]
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|
face_img = cv2.resize(face_img, (112, 112)).transpose(
|
|
(2, 0, 1)).copy()
|
|
batch.append(face_img)
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|
cnt += 1
|
|
if cnt % self.max_batch_size == 0 or (idx + 1) == len(box_list):
|
|
input_batches.append(np.array(batch))
|
|
batch = []
|
|
return input_batches
|
|
|
|
def postprocess(self):
|
|
pass
|
|
|
|
def retrieval(self, np_feature):
|
|
labels = []
|
|
for feature in np_feature:
|
|
similarity = cosine_similarity(self.index_feature,
|
|
feature).squeeze()
|
|
abs_similarity = np.abs(similarity)
|
|
candidate_idx = np.argpartition(abs_similarity,
|
|
-self.cdd_num)[-self.cdd_num:]
|
|
remove_idx = np.where(abs_similarity[candidate_idx] < self.thresh)
|
|
candidate_idx = np.delete(candidate_idx, remove_idx)
|
|
candidate_label_list = list(np.array(self.label)[candidate_idx])
|
|
if len(candidate_label_list) == 0:
|
|
maxlabel = ""
|
|
else:
|
|
maxlabel = max(candidate_label_list,
|
|
key=candidate_label_list.count)
|
|
labels.append(maxlabel)
|
|
return labels
|
|
|
|
def load_index(self, file_path):
|
|
with open(file_path, "rb") as f:
|
|
index = pickle.load(f)
|
|
self.label = index["label"]
|
|
self.index_feature = np.array(index["feature"]).squeeze()
|
|
|
|
def predict(self, img, box_list=None):
|
|
batch_list = self.preprocess(img, box_list)
|
|
feature_list = []
|
|
for batch in batch_list:
|
|
for input_name in self.input_names:
|
|
input_tensor = self.predictor.get_input_handle(input_name)
|
|
input_tensor.copy_from_cpu(batch)
|
|
self.predictor.run()
|
|
output_tensor = self.predictor.get_output_handle(self.output_names[
|
|
0])
|
|
np_feature = output_tensor.copy_to_cpu()
|
|
feature_list.append(np_feature)
|
|
return np.array(feature_list)
|
|
|
|
|
|
class InsightFace(object):
|
|
def __init__(self, args, print_info=True):
|
|
super().__init__()
|
|
if print_info:
|
|
print_config(args)
|
|
|
|
self.font_path = os.path.join(
|
|
os.path.abspath(os.path.dirname(__file__)),
|
|
"SourceHanSansCN-Medium.otf")
|
|
self.args = args
|
|
|
|
predictor_config = {
|
|
"use_gpu": args.use_gpu,
|
|
"enable_mkldnn": args.enable_mkldnn,
|
|
"cpu_threads": args.cpu_threads
|
|
}
|
|
if args.det:
|
|
model_file_path, params_file_path = check_model_file(
|
|
args.det_model)
|
|
det_config = {"thresh": args.det_thresh, "target_size": [640, 640]}
|
|
predictor_config["model_file"] = model_file_path
|
|
predictor_config["params_file"] = params_file_path
|
|
self.det_predictor = Detector(det_config, predictor_config)
|
|
self.color_map = ColorMap(100)
|
|
|
|
if args.rec:
|
|
model_file_path, params_file_path = check_model_file(
|
|
args.rec_model)
|
|
rec_config = {
|
|
"max_batch_size": args.max_batch_size,
|
|
"resize": 112,
|
|
"thresh": args.rec_thresh,
|
|
"index": args.index,
|
|
"cdd_num": args.cdd_num
|
|
}
|
|
predictor_config["model_file"] = model_file_path
|
|
predictor_config["params_file"] = params_file_path
|
|
self.rec_predictor = Recognizer(rec_config, predictor_config)
|
|
|
|
def preprocess(self, img):
|
|
img = img.astype(np.float32, copy=False)
|
|
return img
|
|
|
|
def draw(self, img, box_list, labels):
|
|
self.color_map.update(labels)
|
|
im = Image.fromarray(img)
|
|
draw = ImageDraw.Draw(im)
|
|
|
|
for i, dt in enumerate(box_list):
|
|
bbox, score = dt[2:], dt[1]
|
|
label = labels[i]
|
|
color = tuple(self.color_map[label])
|
|
|
|
xmin, ymin, xmax, ymax = bbox
|
|
|
|
font_size = max(int((xmax - xmin) // 6), 10)
|
|
font = ImageFont.truetype(self.font_path, font_size)
|
|
|
|
text = "{} {:.4f}".format(label, score)
|
|
th = sum(font.getmetrics())
|
|
tw = font.getsize(text)[0]
|
|
start_y = max(0, ymin - th)
|
|
|
|
draw.rectangle(
|
|
[(xmin, start_y), (xmin + tw + 1, start_y + th)], fill=color)
|
|
draw.text(
|
|
(xmin + 1, start_y),
|
|
text,
|
|
fill=(255, 255, 255),
|
|
font=font,
|
|
anchor="la")
|
|
draw.rectangle(
|
|
[(xmin, ymin), (xmax, ymax)], width=2, outline=color)
|
|
return np.array(im)
|
|
|
|
def predict_np_img(self, img):
|
|
input_img = self.preprocess(img)
|
|
box_list = None
|
|
np_feature = None
|
|
if hasattr(self, "det_predictor"):
|
|
box_list = self.det_predictor.predict(input_img)
|
|
if hasattr(self, "rec_predictor"):
|
|
np_feature = self.rec_predictor.predict(input_img, box_list)
|
|
return box_list, np_feature
|
|
|
|
def init_reader_writer(self, input_data):
|
|
if isinstance(input_data, np.ndarray):
|
|
self.input_reader = ImageReader(input_data)
|
|
if hasattr(self, "det_predictor"):
|
|
self.output_writer = ImageWriter(self.args.output)
|
|
elif isinstance(input_data, str):
|
|
if input_data.endswith('mp4'):
|
|
self.input_reader = VideoReader(input_data)
|
|
info = self.input_reader.get_info()
|
|
self.output_writer = VideoWriter(self.args.output, info)
|
|
else:
|
|
self.input_reader = ImageReader(input_data)
|
|
if hasattr(self, "det_predictor"):
|
|
self.output_writer = ImageWriter(self.args.output)
|
|
else:
|
|
raise Exception(
|
|
f"The input data error. Only support path of image or video(.mp4) and dirctory that include images."
|
|
)
|
|
|
|
def predict(self, input_data, print_info=False):
|
|
"""Predict input_data.
|
|
|
|
Args:
|
|
input_data (str | NumPy.array): The path of image, or the derectory including images, or the image data in NumPy.array format.
|
|
print_info (bool, optional): Wheather to print the prediction results. Defaults to False.
|
|
|
|
Yields:
|
|
dict: {
|
|
"box_list": The prediction results of detection.
|
|
"features": The output of recognition.
|
|
"labels": The results of retrieval.
|
|
}
|
|
"""
|
|
self.init_reader_writer(input_data)
|
|
for img, file_name in self.input_reader:
|
|
if img is None:
|
|
logging.warning(f"Error in reading img {file_name}! Ignored.")
|
|
continue
|
|
box_list, np_feature = self.predict_np_img(img)
|
|
if np_feature is not None:
|
|
labels = self.rec_predictor.retrieval(np_feature)
|
|
else:
|
|
labels = ["face"] * len(box_list)
|
|
if box_list is not None:
|
|
result = self.draw(img, box_list, labels=labels)
|
|
self.output_writer.write(result, file_name)
|
|
if print_info:
|
|
logging.info(f"File: {file_name}, predict label(s): {labels}")
|
|
yield {
|
|
"box_list": box_list,
|
|
"features": np_feature,
|
|
"labels": labels
|
|
}
|
|
logging.info(f"Predict complete!")
|
|
|
|
|
|
# for CLI
|
|
def main(args=None):
|
|
logging.basicConfig(level=logging.INFO)
|
|
|
|
args = parser().parse_args()
|
|
predictor = InsightFace(args)
|
|
res = predictor.predict(args.input, print_info=True)
|
|
for _ in res:
|
|
pass
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|