def do_fr_after_YOLO(imgs_paths):
output_dir = 'output_txt/fr_after_YOLO_kr'
# using yolo v2 - voc
YOLO_weights = 'data/vehicle-detector/yolov3.weights'
YOLO_netcfg = 'data/vehicle-detector/yolov3.cfg'
YOLO_data = 'data/vehicle-detector/coco.data'
print 'YOLOv3 weights pre-loading...'
YOLO_net = dn.load_net(YOLO_netcfg, YOLO_weights, 0)
YOLO_meta = dn.load_meta(YOLO_data)
threshold = 0.5
for img_path in imgs_paths:
print 'detecting cars in', img_path
img = cv2.imread(img_path)
results, wh = dn.detect(YOLO_net, YOLO_meta, img, threshold)
txt_file = open(
join(output_dir,
basename(splitext(img_path)[0]) + '.txt'), 'w')
if len(results) == 0:
txt_file.write('')
continue
for result in results:
if result[0] in ['car', 'bus']:
WH = np.array(img.shape[1::-1], dtype=float)
cx, cy, w, h = (np.array(result[2]) / np.concatenate(
(WH, WH))).tolist()
tl = np.array([cx - w / 2., cy - h / 2.])
br = np.array([cx + w / 2., cy + h / 2.])
label_sub = Label(tl=tl, br=br)
sub_img = crop_region(img, label_sub)
# sub_image FRD, only process the highest prob one
print '\tFRD processing...'
frd, _ = dn.detect(FR_net, FR_meta, sub_img, threshold)
if len(frd) == 0:
continue
WH_sub = np.array(sub_img.shape[1::-1], dtype=float)
cx, cy, w, h = (np.array(frd[0][2]) / np.concatenate(
(WH_sub, WH_sub))).tolist()
tl = np.array([cx - w / 2., cy - h / 2.])
br = np.array([cx + w / 2., cy + h / 2.])
label_FR = Label(tl=tl, br=br)
label_scale_up = Label(
tl=label_sub.tl() * WH + label_FR.tl() * WH_sub,
br=label_sub.tl() * WH + label_FR.br() * WH_sub)
tl = label_scale_up.tl().astype(int)
br = label_scale_up.br().astype(int)
txt_file.write(frd[0][0] + ' ' + str('%.2f' % frd[0][1]) +
' ' + str(tl[0]) + ' ' + str(tl[1]) + ' ' +
str(br[0]) + ' ' + str(br[1]) + '\n')
print '\twrote result to', join(
output_dir,
basename(splitext(img_path)[0]) + '.txt')
txt_file.close()
def do_fr(imgs_paths):
modelmame = basename(splitext(FR_netcfg)[0])
weight = basename(splitext(FR_weights)[0])
model_dir = join('output_txt/', modelmame + '_' + valid_dataset)
final_dir = join(model_dir, weight)
if not isdir(model_dir):
mkdir(model_dir)
if not isdir(final_dir):
mkdir(final_dir)
for img_path in imgs_paths:
print 'FRD processing on', img_path
img = cv2.imread(img_path)
frds, wh = dn.detect(FR_net, FR_meta, img, threshold)
txt_file = open(
join(final_dir,
basename(splitext(img_path)[0]) + '.txt'), 'w')
if len(frds) == 0:
txt_file.write('')
continue
for frd in frds:
cx, cy, w, h = (np.array(frd[2])).tolist()
tl = np.array([cx - w / 2., cy - h / 2.])
br = np.array([cx + w / 2., cy + h / 2.])
label_FR = Label(tl=tl, br=br)
tl = label_FR.tl().astype(int)
br = label_FR.br().astype(int)
txt_file.write(frd[0] + ' ' + str('%.2f' % frd[1]) + ' ' +
str(tl[0]) + ' ' + str(tl[1]) + ' ' + str(br[0]) +
' ' + str(br[1]) + '\n')
print '\twrote result to', join(
final_dir,
basename(splitext(img_path)[0]) + '.txt')
txt_file.close()