def detect(model, input_dir, output_dir):
lp_threshold = .5
imgs_paths = glob('%s/*car.jpg' % input_dir)
print 'Searching for license plates using WPOD-NET'
for i, img_path in enumerate(imgs_paths):
print '\t Processing %s' % img_path
bname = splitext(basename(img_path))[0]
Ivehicle = cv2.imread(img_path)
ratio = float(max(Ivehicle.shape[:2])) / min(Ivehicle.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(model, im2single(Ivehicle), bound_dim, 2 ** 4, (240, 80), lp_threshold)
if len(LlpImgs):
Ilp = LlpImgs[0]
Ilp = cv2.cvtColor(Ilp, cv2.COLOR_BGR2GRAY)
Ilp = cv2.cvtColor(Ilp, cv2.COLOR_GRAY2BGR)
s = Shape(Llp[0].pts)
cv2.imwrite('%s/%s_lp.png' % (output_dir, bname), Ilp * 255.)
writeShapes('%s/%s_lp.txt' % (output_dir, bname), [s])
def predict(self, img, bname):
output_dir = "output/tmp"
# bname = splitext(basename(img_path))[0]
# Ivehicle = cv2.imread(img_path)
Ivehicle = cv2.normalize(src=img, dst=None, alpha=0, beta=255, norm_type=cv2.NORM_MINMAX, dtype=cv2.CV_8U)
ratio = float(max(Ivehicle.shape[:2]))/min(Ivehicle.shape[:2])
side = int(ratio*288.)
bound_dim = min(side + (side % (2**4)), 608)
# print("\t\tBound dim: {}, ratio: {}".format(bound_dim, ratio))
Llp, LlpImgs, _ = detect_lp(self.wpod_net, im2single(Ivehicle), bound_dim, 2**4, (240, 80), self.lp_threshold)
if len(LlpImgs):
Ilp = LlpImgs[0]
Ilp = cv2.cvtColor(Ilp, cv2.COLOR_BGR2GRAY)
Ilp = cv2.cvtColor(Ilp, cv2.COLOR_GRAY2BGR)
s = Shape(Llp[0].pts)
# cv2.imwrite('{}/{}_lp.png'.format(output_dir, bname), Ilp * 255.)
# writeShapes('{}/{}_lp.txt'.format(output_dir, bname), [s])
Ilp = cv2.normalize(src=(Ilp * 255.), dst=None, alpha=0, beta=255, norm_type=cv2.NORM_MINMAX, dtype=cv2.CV_8U)
return Ilp, s
return None, None
def LPDection(carImagePath):
wpod_net = load_model(wpod_net_path)
print carImagePath
Ivehicle = cv2.imread(carImagePath)
ratio = float(max(Ivehicle.shape[:2])) / min(Ivehicle.shape[:2])
side = int(ratio * 288.)
bound_dim = min(side + (side % (2**4)), 608)
Llp, LlpImgs, _ = detect_lp(wpod_net, im2single(Ivehicle), bound_dim, 2**4,
(240, 80), lp_threshold)
if len(LlpImgs):
bname = basename(splitext(carImagePath)[0])
dname = dirname(carImagePath)
Ilp = LlpImgs[0]
cv2.imwrite('%s/%s_lp_raw.png' % (dname, bname), Ilp * 255.)
Ilp = cv2.cvtColor(Ilp, cv2.COLOR_BGR2GRAY)
Ilp = cv2.cvtColor(Ilp, cv2.COLOR_GRAY2BGR)
s = Shape(Llp[0].pts)
LPImagePath = '%s/%s_lp.png' % (dname, bname)
cv2.imwrite(LPImagePath, Ilp * 255.)
LPTextPath = '%s/%s_lp.txt' % (dname, bname)
writeShapes(LPTextPath, [s])
return LPImagePath
else:
return ""
def detect_lp(self, img, filename, i):
ratio = float(max(img.shape[:2])) / min(img.shape[:2])
side = int(ratio*288.)
bound_dim = min(side + (side % (2**4)), 608)
Llp, LlpImgs, _ = detect_lp(self.wpod_net, im2single(img), bound_dim, 2**4, (240, 80), self.lp_threshold)
if len(LlpImgs):
Ilp = LlpImgs[0]
Ilp = cv2.cvtColor(Ilp, cv2.COLOR_BGR2GRAY)
Ilp = cv2.cvtColor(Ilp, cv2.COLOR_GRAY2BGR)
s = Shape(Llp[0].pts)
cv2.imwrite('%s/%s-lp-%s.png' % (self.output_dir, filename, i), Ilp*255.)
# LP OCR
R, idxs, boxes, confidences, class_ids = self.lp.detect_objects(Ilp*255., filename)
print('\t\t%d lps found' % len(R))
if len(R):
L = dknet_label_conversion(R, 240, 80)
L = nms(L, .45)
L.sort(key=lambda x: x.tl()[0])
lp_str = ''.join([chr(l.cl()) for l in L])
if (len(lp_str) <= 3):
return "No license found"
return lp_str
return "No license found"
def plate_detect(wpod_net, lp_threshold, Icars, Lcars):
print ('Searching for license plates using WPOD-NET')
Ilps = []
Llps = []
for i, (Icar, Lcar) in enumerate(zip(Icars, Lcars)):
print ('\t Processing car %s' % i)
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))
Icar = Icar.astype('uint8')
Llp,LlpImgs,_ = detect_lp(wpod_net,im2single(Icar),bound_dim,2**4,(240,80),lp_threshold)
if len(LlpImgs):
print ("\t\tLP found")
Ilp = LlpImgs[0]
Ilp = cv2.cvtColor(Ilp, cv2.COLOR_BGR2GRAY)
Ilp = cv2.cvtColor(Ilp, cv2.COLOR_GRAY2BGR)
s = Shape(Llp[0].pts)
#cv2.imwrite('%s/car%s_lp.png' % (plate_img_dir, i),Ilp*255.)
#writeShapes('%s/car%s_lp.txt' % (plate_img_dir, i),[s])
Ilps.append(Ilp*255.)
Llps.append(s)
else:
Ilps.append(np.nan)
Llps.append(np.nan)
return Ilps, Llps
def lpd(img, boxes, frame_count):
print("inside-1")
output_dir = 'output'
detected_cars_dir="detected_cars"
detected_plates_dir="detected_plates"
lp_threshold = .5
# lp_model="data/lp-detector/wpod-net_update1.h5"
# wpod_net = load_model(lp_model)
plates=[]
for i, box in enumerate(boxes):
if min(box) >0:
# print(box)
print("inside-2")
bname = 'frame{}_{}.png'.format(frame_count,i)
Ivehicle=img[box[0]:box[2], box[1]:box[3]]
cv2.imwrite("%s/%s/%s" %(output_dir,detected_cars_dir,bname),Ivehicle)
# print(Ivehicle.shape[:2])
ratio = float(max(Ivehicle.shape[:2]))/min(Ivehicle.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(self.wpod_net,im2single(Ivehicle),bound_dim,2**4,(240,80),lp_threshold)
if len(LlpImgs):
print("inside-3")
Ilp = LlpImgs[0]
Ilp = cv2.cvtColor(Ilp, cv2.COLOR_BGR2GRAY)
Ilp = cv2.cvtColor(Ilp, cv2.COLOR_GRAY2BGR)
s = Shape(Llp[0].pts)
# print(Llp[0].pts)
plates.append(Llp[0].pts)
# cv2.imwrite('%s/%s_lp.png' % (output_dir,bname),Ilp*255.)
cv2.imwrite("%s/%s/%s" % (output_dir,detected_plates_dir,bname),Ilp*255.)
# writeShapes('%s/%s_lp.txt' % (output_dir,bname),[s])
return plates
def find_lp_one_img(img_path, lp_trans_net, out_dir, lp_thd):
st = time.time()
print '\t Processing %s' % img_path
bname = splitext(basename(img_path))[0]
Ivehicle = cv2.imread(img_path)
ratio = float(max(Ivehicle.shape[:2])) / min(Ivehicle.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(lp_trans_net, im2single(Ivehicle), bound_dim,
2**4, (240, 80), lp_thd)
lp_img = ""
lp_label_f = ""
if len(LlpImgs):
Ilp = LlpImgs[0]
Ilp = cv2.cvtColor(Ilp, cv2.COLOR_BGR2GRAY)
Ilp = cv2.cvtColor(Ilp, cv2.COLOR_GRAY2BGR)
s = Shape(Llp[0].pts)
lp_img = '%s/%s_lp.png' % (out_dir, bname)
lp_label_f = '%s/%s_lp.txt' % (out_dir, bname)
cv2.imwrite(lp_img, Ilp * 255.)
writeShapes(lp_label_f, [s])
print "runtime: %.1fs" % (time.time() - st)
return lp_img, lp_label_f
print('\t Processing %s' % img_path)
start = datetime.datetime.now()
bname = splitext(basename(img_path))[0]
Ivehicle = cv2.imread(img_path)
ratio = float(max(Ivehicle.shape[:2]))/min(Ivehicle.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, im2single(
Ivehicle), bound_dim, 2**4, (240, 80), lp_threshold)
if len(LlpImgs):
Ilp = LlpImgs[0]
Ilp = cv2.cvtColor(Ilp, cv2.COLOR_BGR2GRAY)
Ilp = cv2.cvtColor(Ilp, cv2.COLOR_GRAY2BGR)
s = Shape(Llp[0].pts)
cv2.imwrite('%s/%s_lp.png' % (output_dir, bname), Ilp*255.)
writeShapes('%s/%s_lp.txt' % (output_dir, bname), [s])
stop = datetime.datetime.now()
print(stop-start)
except:
traceback.print_exc()
sys.exit(1)
sleep(10)
sys.exit(0)
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):
# 检测的车牌图像信息
Ilp = LlpImgs[0]
Ilp = cv2.cvtColor(Ilp, cv2.COLOR_BGR2GRAY)
Ilp = cv2.cvtColor(Ilp, cv2.COLOR_GRAY2BGR)
s = Shape(Llp[0].pts) # 车牌坐标信息
#cv2.imwrite('%s/%s_lp.png' % (output_dir,bname),Ilp*255.)
#writeShapes('%s/%s_lp.txt' % (output_dir,bname),[s])
lpts = Llp[0].pts * label.wh().reshape(
2, 1) + label.tl().reshape(2, 1)
lptspx = lpts * np.array(Iorig.shape[1::-1],
dtype=float).reshape(2, 1)
draw_losangle(Iorig, lptspx, RED, 3)
Ilp = Ilp * 255.
Ilp = Ilp.astype('uint8') # 十分重要，不做这个转换，就无法识别
'''
cv2.imshow("img", Ilp)
cv2.waitKey(3000)
cv2.imwrite('%s/%s_new_lp.png' % (output_dir,bname),Ilp)
def validar_lp_model(entrada_diretorio_validacao, diretorio_saida, wpod_net):
print('iniciando validacao modelo')
lp_threshold = .4
imgs_paths = glob('%s/*.jpg' % entrada_diretorio_validacao)
print('Searching for license plates using WPOD-NET')
for i, img_path in enumerate(imgs_paths):
print('\t Processing %s' % img_path)
bname_image_file = splitext(basename(img_path))[0]
name_file_gt = bname_image_file + '.txt'
name_file = basename(img_path)
Ivehicle = cv2.imread(img_path)
height, width = Ivehicle.shape[:2]
ratio = float(max(Ivehicle.shape[:2])) / min(Ivehicle.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, im2single(Ivehicle), bound_dim,
2**4, (240, 80), lp_threshold)
if len(LlpImgs):
lista_preds_frame = []
for indice_bbox, Ilp in enumerate(LlpImgs):
# Ilp = LlpImgs[0]
Ilp = cv2.cvtColor(Ilp, cv2.COLOR_BGR2GRAY)
Ilp = cv2.cvtColor(Ilp, cv2.COLOR_GRAY2BGR)
license_plate_bouding_box_relativo = Llp[indice_bbox]
probability = license_plate_bouding_box_relativo._Label__prob
top_left_rl_tp = tuple(
license_plate_bouding_box_relativo._Label__tl.tolist())
top_left_x = int(top_left_rl_tp[0] * width)
top_left_y = int(top_left_rl_tp[1] * height)
bottom_right_rl = license_plate_bouding_box_relativo._Label__br.tolist(
)
bottom_right_x = int(bottom_right_rl[0] * width)
bottom_right_y = int(bottom_right_rl[1] * height)
plate_width = bottom_right_x - top_left_x
plate_height = bottom_right_y - top_left_y
red_color = (255, 0, 0)
bbox_color = red_color
# car_rgb_np = cv2.cvtColor(Ivehicle, cv2.COLOR_BGR2RGB)
car_rgb_np = Ivehicle
thickness = 1
# [xmin, ymin, xmax, ymax, class_id, confidence]
if top_left_x > 0 and top_left_y > 0 and bottom_right_x > 0 and bottom_right_y > 0:
preds_frame = [
int(top_left_x),
int(top_left_y),
int(bottom_right_x),
int(bottom_right_y), 0, probability
]
lista_preds_frame.append(preds_frame)
cv2.rectangle(car_rgb_np, (top_left_x, top_left_y),
(bottom_right_x, bottom_right_y), bbox_color,
thickness) # filled
s = Shape(Llp[0].pts)
# cv2.imwrite('%s/%s_lp_car.png' % (output_dir,bname), car_rgb_np)
# cv2.imwrite('%s/%s_lp.png' % (output_dir,bname),Ilp*255.)
writeShapes(
'%s/%s_lp.txt' % (diretorio_saida, bname_image_file), [s])
gt_img_path = img_path.replace('.jpg', '.txt')
ground_truth_frame = []
with open(gt_img_path) as f:
lines = f.readlines()
for linha in lines:
pontos = linha.split(',')[1:9]
ground_truth = [
int(float(pontos[0]) * width),
int(float(pontos[4]) * height),
int(float(pontos[2]) * width),
int(float(pontos[6]) * height), 0, 0, 0
]
ground_truth_frame.append(ground_truth)
metric_fn.add(np.array(lista_preds_frame),
np.array(ground_truth_frame))
if is_exibir_gt and len(LlpImgs):
for bbox_gt in ground_truth_frame:
cv2.rectangle(car_rgb_np, (bbox_gt[0], bbox_gt[1]),
(bbox_gt[2], bbox_gt[3]), bbox_color_gt,
thickness) # filled
cv2.imwrite(
'%s/%s_lp_car.png' % (diretorio_saida, bname_image_file),
car_rgb_np)
print('calculando mAP')
# compute PASCAL VOC metric
mAP_pascal = metric_fn.value(iou_thresholds=0.5,
recall_thresholds=np.arange(0., 1.1,
0.1))['mAP']
print('VOC PASCAL mAP:' + str(mAP_pascal))
# compute PASCAL VOC metric at the all points
mAP_pascal_all_points = metric_fn.value(iou_thresholds=0.5)['mAP']
print('VOC PASCAL mAP in all points:' + str(mAP_pascal_all_points))
# compute metric COCO metric
mAP_coco = metric_fn.value(iou_thresholds=np.arange(0.5, 1.0, 0.05),
recall_thresholds=np.arange(0., 1.01, 0.01),
mpolicy='soft')['mAP']
print('COCO mAP: ' + str(mAP_coco))
return mAP_pascal, mAP_pascal_all_points, mAP_coco
def validar_lp(entrada_diretorio_validacao, nome_arquivo_gt, diretorio_saida,
wpod_net):
print('iniciando validacao')
caminho_completo_arquivo = os.path.abspath(
os.path.join(entrada_diretorio_validacao, nome_arquivo_gt))
annotation_tree = ET.parse(caminho_completo_arquivo)
tag_raiz = annotation_tree.getroot()
qtd_imagens_rotuladas = 0
qtd_imagens = 0
# [xmin, ymin, xmax, ymax, class_id, difficult, crowd]
dict_bbox_gt = {}
for indice_tag_image, image_tag in enumerate(tag_raiz.findall('image')):
nome_frame = image_tag.get('name')
boxes = image_tag.findall('polygon')
lista_bbox_gt_frame = []
for box in boxes:
if 'plate' in box.get('label'):
# forma de obtencao do bbox qdo o shape e retangulo
# bbox_plate = [int(float(box.get('xtl'))), int(float(box.get('ytl'))), int(float(box.get('xbr'))), int(float(box.get('ybr'))), 0, 0, 0]
# forma de obtencao do bbox qdo o shape e poligon 4 pontos
pontos_str = box.get('points').split(';')
pontos = [point_str_to_tuple(p) for p in pontos_str]
ponto_top_left, ponto_bottom_right = get_pontos_extremos(
pontos)
bbox_plate = [
ponto_top_left[0], ponto_top_left[1],
ponto_bottom_right[0], ponto_bottom_right[1], 0, 0, 0
]
lista_bbox_gt_frame.append(bbox_plate)
print(image_tag.tag, image_tag.attrib, box.get('label'))
dict_bbox_gt[nome_frame] = np.array(lista_bbox_gt_frame)
lp_threshold = .4
imgs_paths = glob('%s/*.jpg' % entrada_diretorio_validacao)
print('Searching for license plates using WPOD-NET')
for i, img_path in enumerate(imgs_paths):
print('\t Processing %s' % img_path)
bname = splitext(basename(img_path))[0]
name_file = basename(img_path)
Ivehicle = cv2.imread(img_path)
height, width = Ivehicle.shape[:2]
ratio = float(max(Ivehicle.shape[:2])) / min(Ivehicle.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, im2single(Ivehicle), bound_dim,
2**4, (240, 80), lp_threshold)
ground_truth_frame = dict_bbox_gt[name_file]
if len(LlpImgs):
lista_preds_frame = []
for indice_bbox, Ilp in enumerate(LlpImgs):
# Ilp = LlpImgs[0]
Ilp = cv2.cvtColor(Ilp, cv2.COLOR_BGR2GRAY)
Ilp = cv2.cvtColor(Ilp, cv2.COLOR_GRAY2BGR)
license_plate_bouding_box_relativo = Llp[indice_bbox]
probability = license_plate_bouding_box_relativo._Label__prob
top_left_rl_tp = tuple(
license_plate_bouding_box_relativo._Label__tl.tolist())
top_left_x = int(top_left_rl_tp[0] * width)
top_left_y = int(top_left_rl_tp[1] * height)
bottom_right_rl = license_plate_bouding_box_relativo._Label__br.tolist(
)
bottom_right_x = int(bottom_right_rl[0] * width)
bottom_right_y = int(bottom_right_rl[1] * height)
plate_width = bottom_right_x - top_left_x
plate_height = bottom_right_y - top_left_y
red_color = (255, 0, 0)
bbox_color = red_color
# car_rgb_np = cv2.cvtColor(Ivehicle, cv2.COLOR_BGR2RGB)
car_rgb_np = Ivehicle
thickness = 1
# [xmin, ymin, xmax, ymax, class_id, confidence]
if top_left_x > 0 and top_left_y > 0 and bottom_right_x > 0 and bottom_right_y > 0:
preds_frame = [
int(top_left_x),
int(top_left_y),
int(bottom_right_x),
int(bottom_right_y), 0, probability
]
lista_preds_frame.append(preds_frame)
cv2.rectangle(car_rgb_np, (top_left_x, top_left_y),
(bottom_right_x, bottom_right_y), bbox_color,
thickness) # filled
s = Shape(Llp[0].pts)
# cv2.imwrite('%s/%s_lp_car.png' % (output_dir,bname), car_rgb_np)
# cv2.imwrite('%s/%s_lp.png' % (output_dir,bname),Ilp*255.)
writeShapes('%s/%s_lp.txt' % (diretorio_saida, bname), [s])
metric_fn.add(np.array(lista_preds_frame), ground_truth_frame)
else:
gt_frame = dict_bbox_gt[name_file]
# metric_fn.add(np.empty(()), gt_frame)
if is_exibir_gt and len(LlpImgs):
for bbox_gt in ground_truth_frame:
cv2.rectangle(car_rgb_np, (bbox_gt[0], bbox_gt[1]),
(bbox_gt[2], bbox_gt[3]), bbox_color_gt,
thickness) # filled
cv2.imwrite('%s/%s_lp_car.png' % (diretorio_saida, bname),
car_rgb_np)
print('calculando mAP')
# compute PASCAL VOC metric
mAP_pascal = metric_fn.value(iou_thresholds=0.5,
recall_thresholds=np.arange(0., 1.1,
0.1))['mAP']
print('VOC PASCAL mAP:' + str(mAP_pascal))
# compute PASCAL VOC metric at the all points
mAP_pascal_all_points = metric_fn.value(iou_thresholds=0.5)['mAP']
print('VOC PASCAL mAP in all points:' + str(mAP_pascal_all_points))
# compute metric COCO metric
mAP_coco = metric_fn.value(iou_thresholds=np.arange(0.5, 1.0, 0.05),
recall_thresholds=np.arange(0., 1.01, 0.01),
mpolicy='soft')['mAP']
print('COCO mAP: ' + str(mAP_coco))
return mAP_pascal, mAP_pascal_all_points, mAP_coco
def validar_lp_model(entrada_diretorio_validacao, diretorio_saida, wpod_net):
print('iniciando validacao modelo')
lp_threshold = .4
imgs_paths = glob('%s/*.jpg' % entrada_diretorio_validacao)
# if not os.path.exists(diretorio_saida):
# os.makedirs(diretorio_saida)
print('Searching for license plates using WPOD-NET')
indicadores_validacao = IndicadoresValidacao()
for i, img_path in enumerate(imgs_paths):
# print('\t Processing %s' % img_path)
bname_image_file = splitext(basename(img_path))[0]
name_file_gt = bname_image_file + '.txt'
name_file = basename(img_path)
Ivehicle = cv2.imread(img_path)
height, width = Ivehicle.shape[:2]
ratio = float(max(Ivehicle.shape[:2])) / min(Ivehicle.shape[:2])
# side = int(ratio*288.)
# bound_dim = min(side + (side%(2**4)),608)
side = int(ratio * 288.)
bound_dim = min(side + (side % (2**4)), 608)
# print("\t\tBound dim: %d, ratio: %f" % (bound_dim,ratio))
ground_truth_frame = []
ground_truth_car = []
ground_truth_moto = []
class_object = None
gt_img_path = img_path.replace('.jpg', '.txt')
with open(gt_img_path) as f:
lines = f.readlines()
for linha in lines:
if len(linha) < 5:
continue
indicadores_validacao.labeled_samples_total += 1
pontos = linha.split(',')[1:9]
class_object = int(linha.split(',')[-2])
Llp, LlpImgs, _ = detect_lp(wpod_net, im2single(Ivehicle),
bound_dim, 2**4, (240, 80),
lp_threshold, class_object)
lista_preds_frame = []
pontos = [
int(float(ponto) *
width) if indice < 4 else int(float(ponto) * height)
for indice, ponto in enumerate(pontos)
]
# ground_truth = [int(float(pontos[0]) * width), int(float(pontos[4]) * height),
# int(float(pontos[2]) * width), int(float(pontos[6]) * height), 0, 0, 0]
x_points = pontos[0:4] * width
y_points = pontos[4:8] * height
# top_left_plate = int(float(pontos[0]) * width), int(float(pontos[4]) * height)
top_left_plate_x = min(x_points)
top_left_plate_y = min(y_points)
bottom_right_plate_x = max(x_points)
bottom_right_plate_y = max(y_points)
top_left_plate = top_left_plate_x, top_left_plate_y
# bottom_right_plate = int(float(pontos[2]) * width), int(float(pontos[6]) * height)
bottom_right_plate = bottom_right_plate_x, bottom_right_plate_y
# gt = [xmin, ymin, xmax, ymax, class_id, difficult, crowd]
ground_truth_frame.append([
top_left_plate[0], top_left_plate[1],
bottom_right_plate[0], bottom_right_plate[1], 0, 0, 0
])
if class_object == 0:
indicadores_validacao.labeled_samples_total_car += 1
ground_truth_car.append([
top_left_plate[0], top_left_plate[1],
bottom_right_plate[0], bottom_right_plate[1],
class_object, 0, 0
])
else:
indicadores_validacao.labeled_samples_total_moto += 1
ground_truth_moto.append([
top_left_plate[0], top_left_plate[1],
bottom_right_plate[0], bottom_right_plate[1],
class_object, 0, 0
])
if len(LlpImgs):
for indice_bbox, Ilp in enumerate(LlpImgs):
# Ilp = LlpImgs[0]
Ilp = cv2.cvtColor(Ilp, cv2.COLOR_BGR2GRAY)
# Ilp = cv2.cvtColor(Ilp, cv2.COLOR_GRAY2BGR)
license_plate_bouding_box_relativo = Llp[indice_bbox]
probability = license_plate_bouding_box_relativo._Label__prob
top_left_rl_tp = tuple(
license_plate_bouding_box_relativo._Label__tl.tolist())
top_left_x = int(top_left_rl_tp[0] * width)
top_left_y = int(top_left_rl_tp[1] * height)
bottom_right_rl = license_plate_bouding_box_relativo._Label__br.tolist(
)
bottom_right_x = int(bottom_right_rl[0] * width)
bottom_right_y = int(bottom_right_rl[1] * height)
plate_width = bottom_right_x - top_left_x
plate_height = bottom_right_y - top_left_y
red_color = (255, 0, 0)
bbox_color = red_color
# car_rgb_np = cv2.cvtColor(Ivehicle, cv2.COLOR_BGR2RGB)
car_rgb_np = Ivehicle
thickness = 1
# [xmin, ymin, xmax, ymax, class_id, confidence]
if top_left_x > 0 and top_left_y > 0 and bottom_right_x > 0 and bottom_right_y > 0:
preds_frame = [
int(top_left_x),
int(top_left_y),
int(bottom_right_x),
int(bottom_right_y), class_object, probability
]
lista_preds_frame.append(preds_frame)
cv2.rectangle(car_rgb_np, (top_left_x, top_left_y),
(bottom_right_x, bottom_right_y), bbox_color,
thickness) # filled
s = Shape(Llp[0].pts)
# cv2.imwrite('%s/%s_lp_car.png' % (output_dir,bname), car_rgb_np)
# cv2.imwrite('%s/%s_lp.png' % (output_dir,bname),Ilp*255.)
# writeShapes('%s/%s_lp.txt' % (diretorio_saida,bname_image_file),[s])
else:
print('imagem sem placa detectacada %s ' % img_path)
lista_preds_frame.append([0, 0, 0, 0, class_object, 0])
# metric_fn.add(np.array(lista_preds_frame), np.array([[0, 0, 0, 0, 0, 0, 0]]))
metric_fn.add(np.array(lista_preds_frame),
np.array(ground_truth_frame))
# if is_exibir_gt and len(LlpImgs):
# for bbox_gt in ground_truth_frame:
# cv2.rectangle(car_rgb_np, (bbox_gt[0], bbox_gt[1]), (bbox_gt[2], bbox_gt[3]), bbox_color_gt, thickness) # filled
# cv2.imwrite('%s/%s_lp_car.png' % (diretorio_saida, bname_image_file), car_rgb_np)
true_positive_frame, false_positive_frame, false_negative_frame, true_positive_prob_frame, false_positive_prob_frame = evaluate_precision_recall(
ground_truth_frame, lista_preds_frame, 0.5)
indicadores_validacao.true_positive += true_positive_frame
indicadores_validacao.false_positive += false_positive_frame
indicadores_validacao.false_negative += false_negative_frame
indicadores_validacao.true_positive_probability.extend(
true_positive_prob_frame)
indicadores_validacao.false_positive_probability.extend(
false_positive_prob_frame)
if class_object == 0:
indicadores_validacao.true_positive_car += true_positive_frame
indicadores_validacao.false_positive_car += false_positive_frame
indicadores_validacao.false_negative_car += false_negative_frame
else:
indicadores_validacao.true_positive_moto += true_positive_frame
indicadores_validacao.false_positive_moto += false_positive_frame
indicadores_validacao.false_negative_moto += false_negative_frame
print('calculando mAP')
mAP_pascal = metric_fn.value(iou_thresholds=0.5,
recall_thresholds=np.arange(0., 1.1,
0.1))['mAP']
print('VOC PASCAL mAP:' + str(mAP_pascal))
mAP_pascal_all_points = metric_fn.value(iou_thresholds=0.5)['mAP']
print('VOC PASCAL mAP in all points:' + str(mAP_pascal_all_points))
mAP_coco = metric_fn.value(iou_thresholds=np.arange(0.5, 1.0, 0.05),
recall_thresholds=np.arange(0., 1.01, 0.01),
mpolicy='soft')['mAP']
print('COCO mAP: ' + str(mAP_coco))
indicadores_validacao.imprmir_ftn_all()
indicadores_validacao.imprimir_precision_recall_all()
# indicadores_validacao.imprimir_probabilidades()
return mAP_pascal, mAP_pascal_all_points, mAP_coco, indicadores_validacao
bname = splitext(basename(img_path))[0]
Ivehicle = cv2.imread(img_path)
ratio = float(max(Ivehicle.shape[:2])) / min(Ivehicle.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, im2single(Ivehicle),
bound_dim, 2**4, (240, 80),
lp_threshold)
for i, lpt in enumerate(LlpImgs):
#cv2.imwrite('a%s/%s_lp.png' % (output_dir,bname),lpt)
if len(LlpImgs):
Ilp = lpt
Ilp = cv2.cvtColor(Ilp, cv2.COLOR_BGR2GRAY)
Ilp = cv2.cvtColor(Ilp, cv2.COLOR_GRAY2BGR)
s = Shape(Llp[i].pts)
cv2.imwrite('%s/%s%d_lp.png' % (output_dir, bname, i),
Ilp * 255.)
writeShapes('%s/%s%d_lp.txt' % ('points', bname, i), [s])
except:
traceback.print_exc()
sys.exit(1)
sys.exit(0)