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detect.py
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detect.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
import numpy as np
import os
import cv2
import numpy as np
import os
from os.path import isfile, join
import time
import sys
from os.path import isfile, join
from toolbox.makedir import make
from toolbox.videoMaker import imagesToVideo
parser = argparse.ArgumentParser(description='Retinaface')
parser.add_argument('-m', '--trained_model', default='Resnet50_epoch_28_noGrad_FT_Adam_lre3',
type=str, help='Trained state_dict file path to open')
parser.add_argument('--network', default='resnet50', help='Backbone network mobile0.25 or resnet50')
parser.add_argument('--cpu', action="store_true", default=False, help='Use cpu inference')
parser.add_argument('--confidence_threshold', default=0.055, type=float, help='confidence_threshold')
parser.add_argument('--top_k', default=5000, type=int, help='top_k')
parser.add_argument('--nms_threshold', default=0.4, type=float, help='nms_threshold')
parser.add_argument('--keep_top_k', default=750, type=int, help='keep_top_k')
parser.add_argument('-s', '--save_image', action="store_true", default=True, help='show detection results')
parser.add_argument('--vis_thres', default=0.055, type=float, help='visualization_threshold')
parser.add_argument('--area_thres', default=225, type=float, help='visualization_threshold')
parser.add_argument('--fps', default=2, type=int, help='visualization_threshold')
parser.add_argument('--mode', default="images", type=str, help='images: for image inference, video: for video inference')
parser.add_argument('--convert_to_video', default="False", type=str, help='Save to video')
parser.add_argument('--save_name', default="test", type=str, help='folder in which you inference will be saved in inference/outputs/<save_name>')
args = parser.parse_args()
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
def infer(net,img_raw):
# print(sys.getsizeof(img_raw))
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)
loc, conf, landms = net(img) # forward pass
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 / resize
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 / resize
landms = landms.cpu().numpy()
# ignore low scores
inds = np.where(scores > args.confidence_threshold)[0]
boxes = boxes[inds]
landms = landms[inds]
scores = scores[inds]
# keep top-K before NMS
order = scores.argsort()[::-1][:args.top_k]
boxes = boxes[order]
landms = landms[order]
scores = scores[order]
# do NMS
dets = np.hstack((boxes, scores[:, np.newaxis])).astype(np.float32, copy=False)
keep = py_cpu_nms(dets, args.nms_threshold)
# keep = nms(dets, args.nms_threshold,force_cpu=args.cpu)
dets = dets[keep, :]
landms = landms[keep]
# keep top-K faster NMS
dets = dets[:args.keep_top_k, :]
landms = landms[:args.keep_top_k, :]
dets = np.concatenate((dets, landms), axis=1)
#removing small face predictions
area_thresh=args.area_thres
dets=dets[np.where(np.multiply(dets[:,2],dets[:,3])>=area_thresh)[0]]
# show image
for b in dets:
if b[4] < args.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)
return img_raw
if __name__ == '__main__':
torch.set_grad_enabled(False)
cfg = None
if args.network == "mobile0.25":
cfg = cfg_mnet
elif args.network == "resnet50":
cfg = cfg_re50
# net and model
net = RetinaFace(cfg=cfg, phase = 'test')
modelPath=join(os.getcwd(),"weights",(args.trained_model+".pth"))
net = load_model(net, modelPath, args.cpu)
net.eval()
print('Finished loading model!')
print(net)
cudnn.benchmark = True
device = torch.device("cpu" if args.cpu else "cuda")
net = net.to(device)
resize = 1
saveName=args.save_name
#now loading images
if(args.mode=="images"):
pathIn=join(os.getcwd(),"inference","inputImages")
files = [f for f in os.listdir(pathIn) if isfile(join(pathIn, f))]
print("inferning on {} image files".format(len(files)))
files.sort()
beginTime=time.time()
for i,file in enumerate(files):
print(file)
img_raw = cv2.imread(join(pathIn,file), cv2.IMREAD_COLOR)
updatedImg=infer(net,img_raw)
if(i%100==0):
print("===========================================")
print("Time taken for 100 image inference and savings= {} sec".format(time.time()-beginTime))
print("===========================================")
beginTime=time.time()
saveFolder=join(os.getcwd(),"inference","output",args.save_name,f"visConf={args.vis_thres}")
make(saveFolder)
cv2.imwrite(join(saveFolder,file), img_raw)
if(args.convert_to_video=="True"):
imagesToVideo(saveFolder,args.save_name,args.fps)
elif(args.mode=="videos"):
pathIn=join(os.getcwd(),"inference","inputVideos")
files = [f for f in os.listdir(pathIn) if isfile(join(pathIn, f))]
print("inferning on {} video files".format(len(files)))
for i,video in enumerate(files):
print(video)
# reading from my video
realvideo=cv2.VideoCapture(join(pathIn,video))
#setting up for new video
saveFolder=join(os.getcwd(),"inference","output",args.save_name,f"visConf={args.vis_thres}","video")
make(saveFolder)
fps=args.fps
pathOut=join(saveFolder,"output-{}_fps={}.avi".format(video.split(".")[0],fps))
print(pathOut)
# frame size (width,height)
size=(int(realvideo.get(cv2.CAP_PROP_FRAME_WIDTH)),int(realvideo.get(cv2.CAP_PROP_FRAME_HEIGHT)))
# fps=int(realvideo.get(cv2.CAP_PROP_FPS))
out=cv2.VideoWriter(pathOut,cv2.VideoWriter_fourcc(*'DIVX'),fps,size)
print("Total frames= {}".format(realvideo.get(cv2.CAP_PROP_FRAME_COUNT)))
counter=0
while(True):
print(counter)
counter+=1
ret,img_raw=realvideo.read()
if(ret):
out.write(infer(net,img_raw))
else:
break
out.release()