def __init__(self, json_path, model_path):
self.epoch = 0
self.output_path = 0
self.model_name = 0
self.json_path = json_path
self.model_path = model_path
self.model = get_Model(training=False)
parser = argparse.ArgumentParser()
parser.add_argument("-w",
"--weight",
help="weight file directory",
type=str,
default="./checkpoint/2--LSTM+BN5--30--0.222.hdf5")
parser.add_argument("-t",
"--test_img",
help="Test image directory",
type=str,
default="./test_img/")
args = parser.parse_args()
# Get CRNN model
model = get_Model(training=False)
try:
model.load_weights(args.weight)
print("...Previous weight data...")
except:
raise Exception("No weight file!")
test_dir = args.test_img
test_imgs = os.listdir(args.test_img)
total = 0
acc = 0
letter_total = 0
letter_acc = 0
start = time.time()
for test_img in test_imgs:
from keras.callbacks import *
from Model import get_Model
from Samples_generation import *
K.set_learning_phase(0)
# 获取模型
model = get_Model(training=True)
# 载入模型权重
try:
model.load_weights(model_path)
print("...Previous weight data...")
except:
print("...New weight data...")
# 将损失最低的模型保存在名为1.hdf5的文件中
checkpoint = ModelCheckpoint(filepath=model_path,
monitor='loss',
verbose=1,
mode='min',
period=1)
# 损失函数实际上已经在model内部算好了并作为y_pred输出了，所以这里只需要写这样一个loss就可以了
model.compile(loss={'ctc': lambda y_true, y_pred: y_pred}, optimizer='adam')
model.fit_generator(generator=next_batch(),
steps_per_epoch=int(n / batch_size),
callbacks=[checkpoint],
epochs=3000)
def _main_(args):
# ----- Initialization -----------
input_path = args.input
output_path = args.output
dataset_name = args.dataset
trial_version = args.trial_version
net_h, net_w = 416, 416
#obj_thresh, nms_thresh = 0.7, 0.7
obj_thresh, nms_thresh = 0.7, 0.7
os.environ['CUDA_VISIBLE_DEVICES'] = "0,1"
if trial_version != "WITH_TRAIN" and trial_version != "WITH_TEST":
print("Wrong Trial version parameter")
return
#-START---- Model Loading------
if dataset_name == "parking":
try:
infer_model = load_model('W/' + trial_version +
'/parking_detection_' + trial_version +
'.h5')
anchors = [
9, 7, 10, 4, 16, 7, 17, 15, 24, 10, 30, 14, 57, 41, 71, 26, 99,
62
]
print("Parking detection weights loaded")
except:
print("Could not load parking detection weights")
return
elif dataset_name == "cctv":
try:
infer_model = load_model('W/' + trial_version +
'/cctv_detection_' + trial_version +
'.h5')
#anchors = [4,2, 8,7, 9,4, 13,5, 14,11, 19,16, 19,7, 24,10, 29,13]
anchors = [
8, 4, 9, 8, 13, 5, 15, 13, 17, 7, 20, 17, 22, 8, 26, 10, 29, 14
]
print("CCTV detection weights loaded")
except:
print("Could not load CCTV detection weights")
return
else:
print("Please specify dataset")
return
model = get_Model(training=False)
if dataset_name == "parking":
try:
model.load_weights('W/' + trial_version + '/parking_recognition_' +
trial_version + '.hdf5')
print("Parking recognition weights loaded")
except:
raise Exception("No parking recognition weight file!")
return
elif dataset_name == "cctv":
try:
model.load_weights('W/' + trial_version + '/cctv_recognition_' +
trial_version + '.hdf5')
print("CCTV recognition weights loaded")
except:
raise Exception("No cctv recognition weight file!")
return
#-END---- Model Loading------
image_paths = []
for fold in os.listdir(input_path):
for fname in os.listdir(os.path.join(input_path, fold)):
if fname.endswith('.jpg') or fname.endswith('.png'):
image_paths.append(os.path.join(input_path, fold, fname))
image_paths = sorted(image_paths)
print("Number of images to test: " + str(len(image_paths)))
# ----- Initialization -----------
# -START----- Main loop -------------
total_time = 0
for i, image_path in enumerate(image_paths):
#if i == 10:
# break
print('[{}] / [{}]'.format(i + 1, len(image_paths)))
image = cv2.imread(image_path)
tic = time.time()
boxes = get_yolo_boxes(infer_model, [image], net_h, net_w, anchors,
obj_thresh, nms_thresh)[0]
if len(boxes) != False:
box = boxes[0]
else:
print("Box not found! " + image_path)
continue
if box.ymin < 0 or box.ymax < 0 or box.xmin < 0 or box.xmax < 0:
print("Negative box found. Cannot proceed! " + image_path)
continue
label = get_label(image[box.ymin:box.ymax, box.xmin:box.xmax, 0],
model)
toc = time.time()
total_time += toc - tic
write_result(output_path, dataset_name, image_path, label, box)
# -END----- Main loop -------------
print('Avg. PT: {} ms.'.format(total_time / len(image_paths) * 1000))
