mirror of
https://github.com/deepinsight/insightface.git
synced 2026-05-15 12:52:47 +00:00
tiny
This commit is contained in:
Jia Guo
@@ -1,239 +0,0 @@
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import os
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#os.environ["CUDA_VISIBLE_DEVICES"] = "7"
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from datetime import datetime
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import os.path
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from easydict import EasyDict as edict
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import time
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import json
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import sys
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import numpy as np
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import importlib
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import itertools
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import argparse
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import struct
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import cv2
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sys.path.append(os.path.join(os.path.dirname(__file__), '..', 'common'))
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import face_preprocess
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from sklearn.preprocessing import normalize
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#import facenet
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#import lfw
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import mxnet as mx
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from mxnet import ndarray as nd
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#from caffe.proto import caffe_pb2
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facescrub_aligned_root = '/raid5data/dplearn/megaface/facescrub_aligned_mtcnn'
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facescrub_aligned_root = None
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megaface_out = '/raid5data/dplearn/megaface/MegaFace_Features'
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#facescrub_out = '/raid5data/dplearn/megaface/FaceScrubSubset_Features'
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facescrub_out = '/raid5data/dplearn/megaface/FaceScrub_Features'
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def do_flip(data):
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for idx in xrange(data.shape[0]):
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data[idx,:,:] = np.fliplr(data[idx,:,:])
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def ch_dev(arg_params, aux_params, ctx):
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new_args = dict()
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new_auxs = dict()
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for k, v in arg_params.items():
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new_args[k] = v.as_in_context(ctx)
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for k, v in aux_params.items():
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new_auxs[k] = v.as_in_context(ctx)
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return new_args, new_auxs
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def get_feature(image_path, bbox, landmark, nets, image_shape, use_align, aligned, use_mean):
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img = face_preprocess.read_image(image_path, mode='rgb')
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#print(img.shape)
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if img is None:
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print('parse image',image_path,'error')
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return None
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if not aligned:
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_landmark = landmark
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if not use_align:
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_landmark = None
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#cv2.imwrite("./align/origin_%s"%image_path.split('/')[-1], img)
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img = face_preprocess.preprocess(img, bbox=bbox, landmark=_landmark, image_size='%d,%d'%(image_shape[1], image_shape[2]))
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else:
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assert img.shape==(image_shape[1],image_shape[2],image_shape[0])
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#print('already aligned', image_path, img.shape)
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#img = cv2.resize(img, (image_shape[2], image_shape[1]))
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#cv2.imwrite("./align/%s"%image_path.split('/')[-1], img)
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if use_mean>0:
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v_mean = np.array([127.5,127.5,127.5], dtype=np.float32).reshape( (1,1,3) )
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img = img.astype(np.float32) - v_mean
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img *= 0.0078125
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img = np.transpose( img, (2,0,1) )
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F = None
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for net in nets:
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embedding = None
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#ppatch = net.patch
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for flipid in [0,1]:
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_img = np.copy(img)
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if flipid==1:
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do_flip(_img)
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#nimg = np.zeros(_img.shape, dtype=np.float32)
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#nimg[:,ppatch[1]:ppatch[3],ppatch[0]:ppatch[2]] = _img[:, ppatch[1]:ppatch[3], ppatch[0]:ppatch[2]]
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#_img = nimg
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input_blob = np.expand_dims(_img, axis=0)
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net.arg_params["data"] = mx.nd.array(input_blob, net.ctx)
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net.arg_params["softmax_label"] = mx.nd.empty((1,), net.ctx)
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exe = net.sym.bind(net.ctx, net.arg_params ,args_grad=None, grad_req="null", aux_states=net.aux_params)
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exe.forward(is_train=False)
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_embedding = exe.outputs[0].asnumpy().flatten()
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#print(_embedding.shape)
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if embedding is None:
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embedding = _embedding
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else:
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embedding += _embedding
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_norm=np.linalg.norm(embedding)
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embedding /= _norm
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if F is None:
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F = embedding
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else:
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#F += embedding
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F = np.concatenate((F,embedding), axis=0)
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_norm=np.linalg.norm(F)
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F /= _norm
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#print(F.shape)
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return F
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def write_bin(path, feature):
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feature = list(feature)
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with open(path, 'wb') as f:
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f.write(struct.pack('4i', len(feature),1,4,5))
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f.write(struct.pack("%df"%len(feature), *feature))
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def main(args):
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print(args)
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gpuid = args.gpu
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ctx = mx.gpu(gpuid)
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nets = []
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for model in args.model.split('|'):
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vec = model.split(',')
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assert len(vec)>1
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prefix = vec[0]
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epoch = int(vec[1])
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print('loading',prefix, epoch)
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net = edict()
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net.ctx = ctx
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net.sym, net.arg_params, net.aux_params = mx.model.load_checkpoint(prefix, epoch)
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net.arg_params, net.aux_params = ch_dev(net.arg_params, net.aux_params, net.ctx)
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all_layers = net.sym.get_internals()
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net.sym = all_layers['fc1_output']
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#_pp = prefix.rfind('p')+1
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#_pp = prefix[_pp:]
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#net.patch = [int(x) for x in _pp.split('_')]
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#assert len(net.patch)==5
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#print('patch', net.patch)
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nets.append(net)
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image_shape = [int(x) for x in args.image_size.split(',')]
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#megaface_lst = "/raid5data/dplearn/faceinsight_align_megaface.lst"
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megaface_lst = "/raid5data/dplearn/megaface/megaface_mtcnn_112x112/lst"
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facescrub_lst = "/raid5data/dplearn/faceinsight_align_facescrub.lst"
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if args.skip==0:
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i = 0
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succ = 0
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for line in open(facescrub_lst, 'r'):
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if i%1000==0:
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print("writing fs",i, succ)
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i+=1
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image_path, label, bbox, landmark, aligned = face_preprocess.parse_lst_line(line)
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_path = image_path.split('/')
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a,b = _path[-2], _path[-1]
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#a = a.replace(' ', '_')
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#b = b.replace(' ', '_')
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file, ext = os.path.splitext(b)
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image_id = int(file.split('_')[-1])
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if image_id==40499 or image_id==10788 or image_id==2367:
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b = file
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#b = file+".png"
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out_dir = os.path.join(facescrub_out, a)
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#print(out_dir)
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if not os.path.exists(out_dir):
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os.makedirs(out_dir)
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#continue
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if len(ext)==0:
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print(image_path)
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image_path = image_path+".jpg"
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if facescrub_aligned_root is not None:
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_vec = image_path.split('/')
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_image_path = os.path.join(facescrub_aligned_root, _vec[-2], _vec[-1])
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_base, _ext = os.path.splitext(_image_path)
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if _ext=='.gif':
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_image_path = _base+".jpg"
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print('changing', _image_path)
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if os.path.exists(_image_path):
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image_path = _image_path
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bbox = None
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landmark = None
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aligned = True
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else:
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print("not aligned:",_image_path)
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feature = get_feature(image_path, bbox, landmark, nets, image_shape, True, aligned, args.mean)
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if feature is None:
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continue
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#print(np.linalg.norm(feature))
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out_path = os.path.join(out_dir, b+"_%s_%dx%d.bin"%(args.algo, image_shape[1], image_shape[2]))
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write_bin(out_path, feature)
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succ+=1
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print('fs stat',i, succ)
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#return
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i = 0
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succ = 0
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for line in open(megaface_lst, 'r'):
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if i%1000==0:
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print("writing mf",i, succ)
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i+=1
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if i<=args.skip:
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continue
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image_path, label, bbox, landmark, aligned = face_preprocess.parse_lst_line(line)
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assert aligned==True
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_path = image_path.split('/')
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a1, a2, b = _path[-3], _path[-2], _path[-1]
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out_dir = os.path.join(megaface_out, a1, a2)
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if not os.path.exists(out_dir):
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os.makedirs(out_dir)
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#continue
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#print(landmark)
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feature = get_feature(image_path, bbox, landmark, nets, image_shape, True, aligned, args.mean)
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if feature is None:
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continue
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out_path = os.path.join(out_dir, b+"_%s_%dx%d.bin"%(args.algo, image_shape[1], image_shape[2]))
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#print(out_path)
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write_bin(out_path, feature)
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succ+=1
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print('mf stat',i, succ)
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def parse_arguments(argv):
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parser = argparse.ArgumentParser()
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parser.add_argument('--batch_size', type=int, help='', default=100)
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parser.add_argument('--image_size', type=str, help='', default='3,112,112')
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parser.add_argument('--gpu', type=int, help='', default=0)
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parser.add_argument('--mean', type=int, help='', default=0)
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parser.add_argument('--seed', type=int, help='', default=727)
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parser.add_argument('--skip', type=int, help='', default=0)
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parser.add_argument('--algo', type=str, help='', default='mxsphereface20c')
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#parser.add_argument('--model', type=str, help='', default='../model/sphereface-20-p0_0_96_112_0,22|../model/sphereface-20-p0_0_96_95_0,21|../model/sphereface-20-p0_0_80_95_0,21')
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#parser.add_argument('--model', type=str, help='', default='../model/sphereface-s60-p0_0_96_112_0,31|../model/sphereface-s60-p0_0_96_95_0,21|../model/sphereface2-s60-p0_0_96_112_0,21|../model/sphereface3-s60-p0_0_96_95_0,23')
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#parser.add_argument('--model', type=str, help='', default='../model/sphereface-s60-p0_0_96_112_0,31|../model/sphereface-s60-p0_0_96_95_0,21|../model/sphereface2-s60-p0_0_96_112_0,21|../model/sphereface3-s60-p0_0_96_95_0,23|../model/sphereface-20-p0_0_96_112_0,22|../model/sphereface-20-p0_0_96_95_0,21|../model/sphereface-20-p0_0_80_95_0,21')
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#parser.add_argument('--model', type=str, help='', default='../model/spherefacei-s60-p0_0_96_112_0,135')
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#parser.add_argument('--model', type=str, help='', default='../model/spherefacei-s60-p0_0_96_95_0,95')
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parser.add_argument('--model', type=str, help='', default='../model/spherefacei-s60-p0_15_96_112_0,95')
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return parser.parse_args(argv)
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if __name__ == '__main__':
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main(parse_arguments(sys.argv[1:]))
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