for i, r in enumerate(R):
cx, cy, w, h = (np.array(r[2]) / np.concatenate(
(WH, WH))).tolist()
tl = np.array([cx - w / 2., cy - h / 2.])
print(tl.shape)
br = np.array([cx + w / 2., cy + h / 2.])
label = Label(0, tl, br)
Icar = crop_region(Iorig, label)
Lcars.append(label)
I = Iorig
wh = np.array(I.shape[1::-1])
ch = I.shape[2] if len(I.shape) == 3 else 1
tl = np.floor(label.tl() * wh).astype(int)
br = np.ceil(label.br() * wh).astype(int)
outwh = br - tl
# if np.prod(outwh) == 0.:
# return None
outsize = (outwh[1], outwh[0], ch) if ch > 1 else (outwh[1],
outwh[0])
if (np.array(outsize) < 0).any():
pause()
Iout = np.zeros(outsize, dtype=I.dtype) + 0.5
offset = np.minimum(tl, 0) * (-1)
tl = np.maximum(tl, 0)
br = np.minimum(br, wh)
print('\t\t%d cars found' % len(R))
if len(R):
Iorig = frame # 原始帧图片
WH = np.array(Iorig.shape[1::-1], dtype=float)
Lcars = []
for i, r in enumerate(R):
cx, cy, w, h = (np.array(r[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 = Label(0, tl, br)
print(label.wh())
print(label.tl())
Icar = crop_region(Iorig,
label) # 截取车辆区域 Icar 车辆 label 车辆坐标信息
Icar = Icar.astype('uint8')
Lcars.append(label)
draw_label(Iorig, label, color=YELLOW, thickness=3)
# lp detector
print('Searching for license plates using WPOD-NET')
ratio = float(max(Icar.shape[:2])) / min(Icar.shape[:2])
side = int(ratio * 288.)
bound_dim = min(side + (side % (2**4)), 608)
print("\t\tBound dim: %d, ratio: %f" % (bound_dim, ratio))
br = np.array([cx + w / 2., cy + h / 2.])
label = Label(0, tl, br)
Lcars.append(label)
Icar = crop_region(arr, label)
# print('Searching for license plates using WPOD-NET')
ratio = float(max(Icar.shape[:2])) / min(Icar.shape[:2])
side = int(ratio * 288.)
bound_dim = min(side + (side % (2 ** 4)), 608)
# print("\t\tBound dim: %d, ratio: %f" % (bound_dim, ratio))
Llp, LlpImgs, _ = detect_lp(wpod_net, Icar / 255, bound_dim, 2 ** 4, (240, 80),
0.5)
if len(LlpImgs):
Ilp = LlpImgs[0]
res, confidence = ocrmodel.recognizeOneframe(Ilp * 255.)
pts = Llp[0].pts * label.wh().reshape(2, 1) + label.tl().reshape(2, 1)
ptspx = pts * np.array(arr.shape[1::-1], dtype=float).reshape(2, 1)
draw_losangle(arr, ptspx, RED, 3)
if confidence > 0.5:
llp = Label(0, tl=pts.min(1), br=pts.max(1))
arr = write2img(arr, llp, res)
for i, lcar in enumerate(Lcars):
draw_label(arr, lcar, color=YELLOW, thickness=3)
videoWriter.write(arr)
print('finish writing %d frame!' % frame)
frame = frame + 1
videoWriter.release()
except:
traceback.print_exc()
sys.exit(1)
def main(indir, lpdir, imgdir, name_file, outdir):
print("##Convert annotation function")
indir = os.path.expanduser(indir)
lpdir = os.path.expanduser(lpdir)
imgdir = os.path.expanduser(imgdir)
outdir = os.path.expanduser(outdir)
if os.path.exists(outdir):
os.system('rm -rf ' + outdir)
os.system('mkdir -p ' + outdir)
name_file = os.path.expanduser(name_file)
with open(name_file) as f:
names = f.readlines()
print(names)
print('sorted(names)', sorted(names))
name2idx = {
name.rstrip().upper(): i
for i, name in enumerate(sorted(names))
}
print('name2idx = ', name2idx)
for root, _, file_names in os.walk(indir):
print('#Root:', root)
print('#file_names: ', file_names)
root_part_path = os.path.relpath(root, indir)
print('#root_part_path: ', root_part_path)
print(file_names)
new_root = os.path.join(outdir, root_part_path)
try:
os.mkdir(new_root)
except:
pass
des_file_names = list(filter(lambda x: x.endswith('.txt'), file_names))
des_file_paths = [os.path.join(root, e) for e in des_file_names]
for des_file_name, des_file_path in zip(des_file_names,
des_file_paths):
bfile_name = os.path.splitext(des_file_name)[0]
print('bfile_name: ', bfile_name)
actual_strgs = []
with open(des_file_path) as f:
lines = f.readlines()
print(lines)
for line in lines:
line = line.replace("-", "")
print('line=', line)
colums = line.split(',')
raw_strg = colums[9]
actual_strg = raw_strg.split('.')[0]
print('actual_strg: ', actual_strg)
actual_strgs.append(actual_strg)
xml_file_paths = sorted(
glob.glob(
os.path.join(lpdir, root_part_path,
bfile_name + '_?.xml')))
xml_file_names = [
os.path.basename(path) for path in xml_file_paths
]
print('nxml_file_names', len(xml_file_names))
for xml_file_name, xml_file_path, actual_strg in zip(
xml_file_names, xml_file_paths, actual_strgs):
xml_bfile_name = os.path.splitext(xml_file_name)[0]
print('xml_bfile_name: ', xml_bfile_name)
print(
"os.path.join(imgdir, root_part_path, xml_bfile_name + '.jpg' = ",
os.path.join(imgdir, root_part_path,
xml_bfile_name + '.jpg'))
print("os.path.join(outdir, root_part_path) = ",
os.path.join(outdir, root_part_path))
os.system('cp ' +
os.path.join(imgdir, root_part_path, xml_bfile_name +
'.jpg') + ' ' +
os.path.join(outdir, root_part_path))
tree = ET.parse(xml_file_path)
root = tree.getroot()
size = root[4]
width, height = [int(e.text) for e in size[:2]]
yolo_file_name = os.path.join(outdir, root_part_path,
xml_bfile_name + '.txt')
with open(yolo_file_name, 'w+') as f:
L = []
for i, ob in enumerate(root.findall('object')):
name, bndbox = ob[0], ob[4]
print('name.text, bndbox', name.text, bndbox)
xmin, ymin, xmax, ymax = [
int(e.text) for e in bndbox[:]
]
print('xmin, ymin, xmax, ymax: ', xmin, ymin, xmax,
ymax)
l = Label()
l.set_tl(np.array([xmin, ymin]))
l.set_br(np.array([xmax, ymax]))
L.append(l)
print('len(L): ', len(L))
L_pair = list(zip(L, [(width, height)] * len(L)))
L_pair = sorted(L_pair, cmp=numeric_compare)
L, _ = zip(*L_pair)
print('len(L): ', len(L))
for i, l in enumerate(L):
print('i: ', i)
(xmin, ymin), (xmax, ymax) = l.tl(), l.br()
yolo_location = [(xmin + xmax) / 2 / width,
(ymin + ymax) / 2 / height,
(xmax - xmin) / width,
(ymax - ymin) / height]
yolo_strg = str(
name2idx[actual_strg[i]]) + ' ' + ' '.join(
["%.6f" % round(e, 6) for e in yolo_location])
print('i, yolo_strg: ', i, yolo_strg)
if i < len(root.findall('object')) - 1:
yolo_strg += '\n'
f.write(yolo_strg)