def genOutput(img_path, output_dir, bname):
I = cv2.imread(img_path)
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,
[int(cv2.IMWRITE_PNG_COMPRESSION), 9])
def draw_without_car(self, img, Llp_shapes, Llp_str, wh, tl):
for i in range(len(Llp_shapes)):
if Llp_shapes[i] is not None:
pts = Llp_shapes[i].pts * wh.reshape(2, 1) + tl.reshape(2, 1)
ptspx = pts * np.array(img.shape[1::-1], dtype=float).reshape(
2, 1)
draw_losangle(img, ptspx, RED, 3)
if Llp_str[i] is not None:
llp = Label(0, tl=pts.min(1), br=pts.max(1))
write2img(img, llp, Llp_str[i])
return img
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 draw(self, img, Lcars, Llp_shapes, Llp_str):
for i, lcar in enumerate(Lcars):
draw_label(img, lcar, color=YELLOW, thickness=3)
if Llp_shapes[i] is not None:
# print("Llp.shapes[i].pts: ", Llp_shapes[i].pts)
pts = Llp_shapes[i].pts * lcar.wh().reshape(
2, 1) + lcar.tl().reshape(2, 1)
# print("pts: ", pts)
ptspx = pts * np.array(img.shape[1::-1], dtype=float).reshape(
2, 1)
# print("ptspx: ", ptspx)
draw_losangle(img, ptspx, RED, 3)
if Llp_str[i] is not None:
llp = Label(0, tl=pts.min(1), br=pts.max(1))
write2img(img, llp, Llp_str[i])
return img
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
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', encoding='utf-8') as f:
lp_str = f.read().strip()
llp = Label(0, tl=pts.min(1), br=pts.max(1))
I = write2img(I, llp, lp_str)
sys.stdout.write(',%s' % lp_str)
cv2.imwrite('%s/%s_output.png' % (output_dir, bname), I)
sys.stdout.write('\n')
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)
sys.exit(0)
print(width, height)
if len(R2):
L = dknet_label_conversion(R2, width, height)
L = nms(L, .45)
L.sort(key=lambda x: x.tl()[0])
lp_str = ''.join([chr(l.cl())
for l in L]) # ocr识别出的车牌字符串
#with open('%s/%s_str.txt' % (output_dir,bname),'w') as f:
# f.write(lp_str + '\n')
print('\t\tLP: %s' % lp_str)
label_lp = Label(0, tl=lpts.min(1), br=lpts.max(1))
write2img(Iorig, label_lp, lp_str)
else:
print('No characters found')
cv2.imshow("test.vedio", Iorig)
cv2.waitKey(10)
#lwrite('%s/%s_cars.txt' % (output_dir,bname),Lcars)
except:
traceback.print_exc()
sys.exit(1)
sys.exit(0)
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)
sys.stdout.write(',%s' % lp_str)
cv2.imwrite('%s/%s_output.png' % (output_dir, bname), I)
sys.stdout.write('\n')