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e5ba844 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 | # coding: utf-8
import os.path as osp
import torch
import numpy as np
import cv2
from .utils.prior_box import PriorBox
from .utils.nms_wrapper import nms
from .utils.box_utils import decode
from .utils.timer import Timer
from .utils.functions import check_keys, remove_prefix, load_model
from .utils.config import cfg
from .models.faceboxes import FaceBoxesNet
# some global configs
confidence_threshold = 0.05
top_k = 5000
keep_top_k = 750
nms_threshold = 0.3
vis_thres = 0.5
resize = 1
scale_flag = True
HEIGHT, WIDTH = 720, 1080
def make_abs_path(fn): return osp.join(osp.dirname(osp.realpath(__file__)), fn)
pretrained_path = make_abs_path('weights/FaceBoxesProd.pth')
def viz_bbox(img, dets, wfp='out.jpg'):
# show
for b in dets:
if b[4] < vis_thres:
continue
text = "{:.4f}".format(b[4])
b = list(map(int, b))
cv2.rectangle(img, (b[0], b[1]), (b[2], b[3]), (0, 0, 255), 2)
cx = b[0]
cy = b[1] + 12
cv2.putText(img, text, (cx, cy), cv2.FONT_HERSHEY_DUPLEX,
0.5, (255, 255, 255))
cv2.imwrite(wfp, img)
print(f'Viz bbox to {wfp}')
class FaceBoxes:
def __init__(self, timer_flag=False):
torch.set_grad_enabled(False)
net = FaceBoxesNet(phase='test', size=None,
num_classes=2) # initialize detector
self.net = load_model(
net, pretrained_path=pretrained_path, load_to_cpu=True)
self.net.eval()
# print('Finished loading model!')
self.timer_flag = timer_flag
def __call__(self, img_):
img_raw = img_.copy()
# scaling to speed up
scale = 1
if scale_flag:
h, w = img_raw.shape[:2]
if h > HEIGHT:
scale = HEIGHT / h
if w * scale > WIDTH:
scale *= WIDTH / (w * scale)
# print(scale)
if scale == 1:
img_raw_scale = img_raw
else:
h_s = int(scale * h)
w_s = int(scale * w)
# print(h_s, w_s)
img_raw_scale = cv2.resize(img_raw, dsize=(w_s, h_s))
# print(img_raw_scale.shape)
img = np.float32(img_raw_scale)
else:
img = np.float32(img_raw)
# forward
_t = {'forward_pass': Timer(), 'misc': Timer()}
im_height, im_width, _ = img.shape
scale_bbox = 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)
_t['forward_pass'].tic()
loc, conf = self.net(img) # forward pass
_t['forward_pass'].toc()
_t['misc'].tic()
priorbox = PriorBox(image_size=(im_height, im_width))
priors = priorbox.forward()
prior_data = priors.data
boxes = decode(loc.data.squeeze(0), prior_data, cfg['variance'])
if scale_flag:
boxes = boxes * scale_bbox / scale / resize
else:
boxes = boxes * scale_bbox / 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, args.nms_threshold)
keep = nms(dets, nms_threshold)
dets = dets[keep, :]
# keep top-K faster NMS
dets = dets[:keep_top_k, :]
_t['misc'].toc()
if self.timer_flag:
print('Detection: {:d}/{:d} forward_pass_time: {:.4f}s misc: {:.4f}s'.format(1, 1, _t[
'forward_pass'].average_time, _t['misc'].average_time))
# filter using vis_thres
det_bboxes = []
for b in dets:
if b[4] > vis_thres:
xmin, ymin, xmax, ymax, score = b[0], b[1], b[2], b[3], b[4]
w = xmax - xmin + 1
h = ymax - ymin + 1
bbox = [xmin, ymin, xmax, ymax, score]
det_bboxes.append(bbox)
return det_bboxes
def main():
face_boxes = FaceBoxes(timer_flag=True)
fn = 'trump_hillary.jpg'
img_fp = f'../examples/inputs/{fn}'
img = cv2.imread(img_fp)
dets = face_boxes(img) # xmin, ymin, w, h
# print(dets)
wfn = fn.replace('.jpg', '_det.jpg')
wfp = osp.join('../examples/results', wfn)
viz_bbox(img, dets, wfp)
if __name__ == '__main__':
main()
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