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import os
import sys
sys.path.append(os.path.dirname(__file__))
import cv2
import math
import torch
import torch.backends.cudnn as cudnn
import numpy as np
from data.config import cfg
from layers.functions.prior_box import PriorBox
from utils.nms_wrapper import nms
from models.faceboxes import FaceBoxes
from utils.box_utils import decode
from utils.timer import Timer
trained_model = os.path.join(os.path.dirname(__file__), './checkpoints/FaceBoxesProd.pth')
save_folder = 'eval'
dataset = 'Custom'
confidence_threshold = 0.2
top_k = 5000
nms_threshold = 0.3
keep_top_k = 750
show_image = True
vis_thres = 0.5
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, device):
print('Loading pretrained model from {}'.format(pretrained_path))
pretrained_dict = torch.load(pretrained_path, map_location=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
torch.set_grad_enabled(False)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
net = FaceBoxes(phase='test', size=None, num_classes=2)
net = load_model(net, trained_model, device)
net.eval()
cudnn.benchmark = True
net = net.to(device)
def get_bbox(orig_image):
# testing scale
resize = 0.5
_t = {'forward_pass': Timer(), 'misc': Timer()}
img_raw = orig_image
img = np.float32(img_raw)
if resize != 1:
img = cv2.resize(img, None, None, fx=resize, fy=resize, interpolation=cv2.INTER_LINEAR)
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)
_t['forward_pass'].tic()
loc, conf = net(img) # forward pass
_t['forward_pass'].toc()
_t['misc'].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 / resize
boxes = boxes.cpu().numpy()
scores = conf.squeeze(0).data.cpu().numpy()[:, 1]
# ignore low scores
inds = np.where(scores > confidence_threshold)[0]
boxes = boxes[inds]
scores = scores[inds]
# keep top-K before NMS
order = scores.argsort()[::-1][:top_k]
boxes = boxes[order]
scores = scores[order]
# do NMS
dets = np.hstack((boxes, scores[:, np.newaxis])).astype(np.float32, copy=False)
#keep = py_cpu_nms(dets, nms_threshold)
keep = nms(dets, nms_threshold, force_cpu=True)
dets = dets[keep, :]
# keep top-K faster NMS
dets = dets[:keep_top_k, :]
_t['misc'].toc()
boxes, scores = [], []
for k in range(dets.shape[0]):
xmin = dets[k, 0]
ymin = dets[k, 1]
xmax = dets[k, 2]
ymax = dets[k, 3]
ymin += 0.2 * (ymax - ymin + 1)
score = dets[k, 4]
boxes.append([int(xmin), int(ymin), int(xmax - xmin), int(ymax - ymin)])
scores.append(score)
max_score = 0.0
final_box = None
for i, score in enumerate(scores):
if max_score < score:
max_score = score
final_box = boxes[i]
return final_box
class Detection:
def __init__(self):
src_dir = os.path.dirname(__file__)
if not os.path.exists(os.path.join(src_dir, "checkpoints")):
os.makedirs(os.path.join(src_dir, "checkpoints"))
caffemodel = os.path.join(src_dir, "checkpoints/Widerface-RetinaFace.caffemodel")
deploy = os.path.join(src_dir, "checkpoints/deploy.prototxt")
self.detector = cv2.dnn.readNetFromCaffe(deploy, caffemodel)
self.detector_confidence = 0.6
def get_bbox(self, img):
height, width = img.shape[0], img.shape[1]
aspect_ratio = width / height
if img.shape[1] * img.shape[0] >= 192 * 192:
img = cv2.resize(img,
(int(192 * math.sqrt(aspect_ratio)),
int(192 / math.sqrt(aspect_ratio))), interpolation=cv2.INTER_LINEAR)
blob = cv2.dnn.blobFromImage(img, 1, mean=(104, 117, 123))
self.detector.setInput(blob, 'data')
out = self.detector.forward('detection_out').squeeze()
max_conf_index = np.argmax(out[:, 2])
left, top, right, bottom = out[max_conf_index, 3]*width, out[max_conf_index, 4]*height, \
out[max_conf_index, 5]*width, out[max_conf_index, 6]*height
if right == left or bottom == top:
return None
bbox = [int(left), int(top), int(right-left+1), int(bottom-top+1)]
return bbox
def check_face(self):
pass
if __name__ == '__main__':
# image = cv2.imread('arun_2.jpg')
# box = get_bbox(image)
# cv2.rectangle(image, (box[0], box[1]), (box[2], box[3]), (0, 0, 255), 2)
#
src_dir = 'D:/19.Database/office_angled_db'
dst_dir = 'D:/19.Database/office_angled_db_result'
detector = Detection()
for file in os.listdir(src_dir):
image1 = cv2.imread(os.path.join(src_dir, file))
box = detector.get_bbox(image1)
if box:
cv2.rectangle(image1, (box[0], box[1]), (box[0] + box[2], box[1] + box[3]), (0, 0, 255), 5)
cv2.imwrite(os.path.join(dst_dir, file), image1)
# cv2.waitKey(0)
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