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retinaface.py
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retinaface.py
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from __future__ import print_function
import os
import argparse
import torch
import torch.backends.cudnn as cudnn
import numpy as np
# from data import cfg_mnet, cfg_re50
from layers.functions.prior_box import PriorBox
from utils.nms.py_cpu_nms import py_cpu_nms
import cv2
from models.retinaface import RetinaFace
from utils.box_utils import decode, decode_landm
import time
# def check_keys(model, pretrained_state_dict):
# ckpt_keys = set(pretrained_state_dict.keys())
# model_keys = set(model.state_dict().keys())
# used_pretrained_keys = model_keys & ckpt_keys
# unused_pretrained_keys = ckpt_keys - model_keys
# missing_keys = model_keys - ckpt_keys
# print('Missing keys:{}'.format(len(missing_keys)))
# print('Unused checkpoint keys:{}'.format(len(unused_pretrained_keys)))
# print('Used keys:{}'.format(len(used_pretrained_keys)))
# assert len(used_pretrained_keys) > 0, 'load NONE from pretrained checkpoint'
# return True
def remove_prefix(state_dict, prefix):
''' Old style model is stored with all names of parameters sharing common prefix 'module.' '''
# print('remove prefix \'{}\''.format(prefix))
f = lambda x: x.split(prefix, 1)[-1] if x.startswith(prefix) else x
return {f(key): value for key, value in state_dict.items()}
def load_model(model, pretrained_path, load_to_cpu):
# print('Loading pretrained model from {}'.format(pretrained_path))
if load_to_cpu:
pretrained_dict = torch.load(pretrained_path, map_location=lambda storage, loc: storage)
else:
device = torch.cuda.current_device()
pretrained_dict = torch.load(pretrained_path, map_location=lambda storage, loc: storage.cuda(device))
if "state_dict" in pretrained_dict.keys():
pretrained_dict = remove_prefix(pretrained_dict['state_dict'], 'module.')
else:
pretrained_dict = remove_prefix(pretrained_dict, 'module.')
# check_keys(model, pretrained_dict)
model.load_state_dict(pretrained_dict, strict=False)
return model
if __name__ == '__main__':
cfg_mnet = {
'name': 'mobilenet0.25',
'min_sizes': [[16, 32], [64, 128], [256, 512]],
'steps': [8, 16, 32],
'variance': [0.1, 0.2],
'clip': False,
'loc_weight': 2.0,
'gpu_train': True,
'batch_size': 32,
'ngpu': 1,
'epoch': 250,
'decay1': 190,
'decay2': 220,
'image_size': 640,
'pretrain': True,
'return_layers': {'stage1': 1, 'stage2': 2, 'stage3': 3},
'in_channel': 32,
'out_channel': 64
}
cfg_re50 = {
'name': 'Resnet50',
'min_sizes': [[16, 32], [64, 128], [256, 512]],
'steps': [8, 16, 32],
'variance': [0.1, 0.2],
'clip': False,
'loc_weight': 2.0,
'gpu_train': True,
'batch_size': 24,
'ngpu': 4,
'epoch': 100,
'decay1': 70,
'decay2': 90,
'image_size': 840,
'pretrain': True,
'return_layers': {'layer2': 1, 'layer3': 2, 'layer4': 3},
'in_channel': 256,
'out_channel': 256
}
trained_model = '/home/intec/Downloads/Pytorch_Retinaface-master/Resnet50_Final.pth' #help='Trained state_dict file path to open')
network = 'resnet50' #help='Backbone network mobile0.25 or resnet50')
cpu = False
torch.set_grad_enabled(False)
cfg = None
if network == "mobile0.25":
cfg = cfg_mnet
elif network == "resnet50":
cfg = cfg_re50
# net and model
net = RetinaFace(cfg=cfg, phase='test')
net = load_model(net, trained_model, cpu)
net.eval()
# print('Finished loading model!')
# print(net)
cudnn.benchmark = True
device = torch.device("cpu" if cpu else "cuda")
net = net.to(device)
# resize = 1
# testing begin
# for i in range(100):
v = cv2.VideoCapture(0)
# フォーマット・解像度・FPSの設定
# v.set(cv2.CAP_PROP_FOURCC, cv2.VideoWriter_fourcc('M','J','P','G'))
v.set(cv2.CAP_PROP_FOURCC, cv2.VideoWriter_fourcc('Y', 'U', 'Y', 'V'))
v.set(cv2.CAP_PROP_FRAME_WIDTH, 640)
v.set(cv2.CAP_PROP_FRAME_HEIGHT, 360)
v.set(cv2.CAP_PROP_FPS, 25)
# v = cv2.VideoCapture("/home/intec/Videos/【白い怪物、爆誕。】最高にイカつい自作PCが完成しました。.mp4")
while (v.isOpened()):
ret, img_raw = v.read()
if (ret == False):
break
# image_path = "/home/intec/Downloads/ivan.jpg"
# img_raw = cv2.imread(image_path, cv2.IMREAD_COLOR)
img = np.float32(img_raw)
im_height, im_width, _ = img.shape
scale = torch.Tensor([img.shape[1], img.shape[0], img.shape[1], img.shape[0]])
img -= (104, 117, 123)
img = img.transpose(2, 0, 1)
img = torch.from_numpy(img).unsqueeze(0)
img = img.to(device)
scale = scale.to(device)
tic = time.time()
loc, conf, landms = net(img) # forward pass
print('net forward time: {:.4f}'.format(time.time() - tic))
priorbox = PriorBox(cfg, image_size=(im_height, im_width))
priors = priorbox.forward()
priors = priors.to(device)
prior_data = priors.data
boxes = decode(loc.data.squeeze(0), prior_data, cfg['variance'])
boxes = boxes * scale
boxes = boxes.cpu().numpy()
scores = conf.squeeze(0).data.cpu().numpy()[:, 1]
landms = decode_landm(landms.data.squeeze(0), prior_data, cfg['variance'])
scale1 = torch.Tensor([img.shape[3], img.shape[2], img.shape[3], img.shape[2],
img.shape[3], img.shape[2], img.shape[3], img.shape[2],
img.shape[3], img.shape[2]])
scale1 = scale1.to(device)
landms = landms * scale1
landms = landms.cpu().numpy()
# ignore low scores
confidence_threshold = 0.02
inds = np.where(scores > confidence_threshold)[0]
boxes = boxes[inds]
landms = landms[inds]
scores = scores[inds]
# keep top-K before NMS
top_k = 5000
order = scores.argsort()[::-1][:top_k]
boxes = boxes[order]
landms = landms[order]
scores = scores[order]
# do NMS
nms_threshold = 0.4
dets = np.hstack((boxes, scores[:, np.newaxis])).astype(np.float32, copy=False)
keep = py_cpu_nms(dets, nms_threshold)
# keep = nms(dets, args.nms_threshold,force_cpu=args.cpu)
dets = dets[keep, :]
landms = landms[keep]
# keep top-K faster NMS
keep_top_k = 750
dets = dets[:keep_top_k, :]
landms = landms[:keep_top_k, :]
dets = np.concatenate((dets, landms), axis=1)
# show image
# if args.save_image:
vis_thres = 0.6
for b in dets:
if b[4] < vis_thres:
continue
# text = "{:.4f}".format(b[4])
b = list(map(int, b))
cv2.rectangle(img_raw, (b[0], b[1]), (b[2], b[3]), (0, 0, 255), 2)
# cx = b[0]
# cy = b[1] + 12
# cv2.putText(img_raw, text, (cx, cy),
# cv2.FONT_HERSHEY_DUPLEX, 0.5, (255, 255, 255))
# landms
cv2.circle(img_raw, (b[5], b[6]), 1, (0, 0, 255), 4)
cv2.circle(img_raw, (b[7], b[8]), 1, (0, 255, 255), 4)
cv2.circle(img_raw, (b[9], b[10]), 1, (255, 0, 255), 4)
cv2.circle(img_raw, (b[11], b[12]), 1, (0, 255, 0), 4)
cv2.circle(img_raw, (b[13], b[14]), 1, (255, 0, 0), 4)
# save image
name = "/home/intec/Downloads/test.jpg"
# cv2.imwrite(name, img_raw)
cv2.imshow("test", img_raw)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
v.release()
cv2.destroyAllWindows()