def gen_output(output_dir):
YELLOW = (0, 255, 255)
RED = (0, 0, 255)
bname = splitext(basename(img_path))[0]
I = cv2.imread(img_path)
detected_cars_labels = '%s/%s_cars.txt' % (output_dir, bname)
Lcar = lread(detected_cars_labels)
if Lcar:
for i, lcar in enumerate(Lcar):
draw_label(I, lcar, color=YELLOW, thickness=3)
lp_label = '%s/%s_%dcar_lp.txt' % (output_dir, bname, i)
lp_label_str = '%s/%s_%dcar_lp_str.txt' % (output_dir, bname, i)
C = cv2.imread('%s/%s_%dcar.png' % (output_dir, bname, i))
if isfile(lp_label):
Llp = lread(lp_label)
for j, llp in enumerate(Llp):
draw_label(I,
llp,
color=RED,
thickness=3,
lp=True,
lcar=lcar,
C=C)
if isfile(lp_label_str):
with open(lp_label_str, 'r') as f:
lp_str = f.read().strip()
cwh = np.array(C.shape[1::-1]).astype(float)
iwh = np.array(I.shape[1::-1]).astype(float)
tl = tuple(
np.add((llp.tl() * cwh).tolist(),
(lcar.tl() * iwh).tolist()) / iwh)
br = tuple(
np.add((llp.br() * cwh).tolist(),
(lcar.tl() * iwh).tolist()) / iwh)
temp_label = Label(0, tl=tl, br=br)
write2img(I, temp_label, lp_str)
return I
def generate_outputs(input_dir, output_dir):
img_files = image_files_from_folder(input_dir)
for img_file in img_files:
bname = splitext(basename(img_file))[0]
I = cv2.imread(img_file)
detected_cars_labels = '%s/%s_cars.txt' % (output_dir, bname)
Lcar = lread(detected_cars_labels)
sys.stdout.write('%s' % bname)
if Lcar:
for i, lcar in enumerate(Lcar):
draw_label(I, lcar, color=YELLOW, thickness=3)
lp_label = '%s/%s_%dcar_lp.txt' % (output_dir, bname, i)
lp_label_str = '%s/%s_%dcar_lp_str.txt' % (output_dir, bname, i)
if isfile(lp_label):
Llp_shapes = readShapes(lp_label)
pts = Llp_shapes[0].pts * lcar.wh().reshape(2, 1) + lcar.tl().reshape(2, 1)
ptspx = pts * np.array(I.shape[1::-1], dtype=float).reshape(2, 1)
draw_losangle(I, ptspx, RED, 3)
if isfile(lp_label_str):
with open(lp_label_str, 'r') as f:
lp_str = f.read().strip()
llp = Label(0, tl=pts.min(1), br=pts.max(1))
write2img(I, llp, lp_str)
sys.stdout.write(',%s' % lp_str)
cv2.imwrite('%s/%s_output.png' % (output_dir, bname), I)
def draw_car(img, Lcars, Tcars, Ccars, IDcars, Llps, lp_strs):
print ('Performing outputting car ...')
YELLOW = ( 0,255,255)
RED = ( 0, 0,255)
WHITE = (255,255,255)
I = img
if Lcars:
for i,(lcar, tcar, ccar, idcar) in enumerate(zip(Lcars, Tcars, Ccars, IDcars)):
draw_label(I,lcar,color=YELLOW,thickness=3)
if (ccar is not np.nan):
car_cat = str(tcar.decode("utf-8")) + " " + ccar + " " + str(idcar)
else:
car_cat = str(tcar.decode("utf-8")) + " " + str(idcar)
draw_text(I,lcar,car_cat,color=YELLOW,thickness=1)
lp_label = Llps[i]
lp_label_str = lp_strs[i]
if (lp_label is not np.nan):
pts = lp_label.pts*lcar.wh().reshape(2,1) + lcar.tl().reshape(2,1)
ptspx = pts*np.array(I.shape[1::-1],dtype=float).reshape(2,1)
draw_losangle(I,ptspx,RED,3)
if (lp_label_str is not np.nan):
lp_str = lp_label_str.strip()
llp = Label(0,tl=pts.min(1),br=pts.max(1))
write2img(I,llp,lp_str)
sys.stdout.write('%s\n' % lp_str)
else:
if (lp_label_str is not np.nan):
lp_str = lp_label_str.strip()
draw_text2(I,lcar,lp_str,color=WHITE,thickness=3)
sys.stdout.write('%s\n' % lp_str)
return I
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))
Llp, LlpImgs, elapse = detect_lp(wpod_net, im2single(Icar),
bound_dim, 2**4,
(240, 80), lp_threshold)
print('\tld detection used %d s time' % elapse)
if len(LlpImgs):
bname = splitext(basename(img_file))[0]
I = cv2.imread(img_file)
detected_cars_labels = '%s/%s_cars.txt' % (output_dir, bname)
Lcar = lread(detected_cars_labels)
sys.stdout.write('%s' % bname)
if Lcar:
for i, lcar in enumerate(Lcar):
draw_label(I, lcar, color=YELLOW, thickness=3)
lp_label = '%s/%s_%dcar_lp.txt' % (output_dir, bname, i)
lp_label_str = '%s/%s_%dcar_lp_str.txt' % (output_dir, bname, i)
imgs_path = glob('%s/%s_%dcar.png' % (input_dir, bname, i))
C = cv2.imread('%s/%s_%dcar.png' % (output_dir, bname, i))
if isfile(lp_label):
Llp = lread(lp_label)
for j, llp in enumerate(Llp):
draw_label(I,
llp,
color=RED,
thickness=3,
bname = splitext(basename(img_file))[0]
I = cv2.imread(img_file)
detected_cars_labels = '%s/%s_cars.txt' % (output_dir, bname)
Lcar = lread(detected_cars_labels)
track_file.write(str(f_count))
if Lcar:
for i, lcar in enumerate(Lcar):
draw_label(I, lcar, color=GREEN, thickness=3)
lp_label = '%s/%s_%dcar_lp.txt' % (output_dir, bname, i)
lp_label_str = '%s/%s_%dcar_lp_str.txt' % (output_dir, bname,
i)
if isfile(lp_label):
Llp_shapes = readShapes(lp_label)
pts = Llp_shapes[0].pts * lcar.wh().reshape(
2, 1) + lcar.tl().reshape(2, 1)
ptspx = pts * np.array(I.shape[1::-1],
dtype=float).reshape(2, 1)
draw_losangle(I, ptspx, RED, 3)
if isfile(lp_label_str):