def load_yolo(self):
model_path = os.path.expanduser(self.model_path)
assert model_path.endswith('.h5'), 'Keras model or weights must be a .h5 file.'
self.class_names = self.get_class()
self.anchors = self.get_anchors()
num_anchors = len(self.anchors)
num_classes = len(self.class_names)
# Generate colors for drawing bounding boxes.
hsv_tuples = [(x / len(self.class_names), 1., 1.)
for x in range(len(self.class_names))]
self.colors = list(map(lambda x: colorsys.hsv_to_rgb(*x), hsv_tuples))
self.colors = list(
map(lambda x: (int(x[0] * 255), int(x[1] * 255), int(x[2] * 255)),
self.colors))
self.sess = K.get_session()
# Load model, or construct model and load weights.
self.yolo4_model = yolo4_body(Input(shape=(416, 416, 3)), num_anchors//3, num_classes)
self.yolo4_model.load_weights(model_path)
print('{} model, anchors, and classes loaded.'.format(model_path))
if self.gpu_num>=2:
self.yolo4_model = multi_gpu_model(self.yolo4_model, gpus=self.gpu_num)
self.input_image_shape = K.placeholder(shape=(2, ))
self.boxes, self.scores, self.classes = yolo_eval(self.yolo4_model.output, self.anchors,
len(self.class_names), self.input_image_shape,
score_threshold=self.score)
def init():
# if (country == 'KRW'):
model_path = 'KRW_weight.h5'
anchors_path = 'model_data/yolo4_anchors.txt'
classes_path = 'model_data/KRW_classes.txt'
class_names = get_class(classes_path)
anchors = get_anchors(anchors_path)
num_anchors = len(anchors)
num_classes = len(class_names)
model_image_size = (416, 416)
# 分数阈值和nms_iou阈值
conf_thresh = 0.2
nms_thresh = 0.45
yolo4_model = yolo4_body(Input(shape=model_image_size + (3, )),
num_anchors // 3, num_classes)
model_path = os.path.expanduser(model_path)
yolo4_model.load_weights(model_path)
_decode = Decode(conf_thresh, nms_thresh, model_image_size, yolo4_model,
class_names) # 위 과정의 시간이 오래걸림
# else:
# model_path = 'JPY_weight.h5'
# anchors_path = 'model_data/yolo4_anchors.txt'
# classes_path = 'model_data/JPY_classes.txt'
return _decode
def load_yolo(self):
model_path = os.path.expanduser(self.model_path)
assert model_path.endswith('.h5'), 'Keras model or weights must be a .h5 file.'
self.class_names = CLASSES
self.anchors = np.array(anchors).reshape(-1, 2)
num_anchors = len(self.anchors)
num_classes = len(self.class_names)
self.sess = tf.compat.v1.Session()
# Load model, or construct model and load weights.
self.yolo4_model = yolo4_body(Input(shape=(self.input_size, self.input_size, 3)), num_anchors//3, num_classes)
# Read and convert darknet weight
self.load_weights(self.yolo4_model, self.weights_path)
self.yolo4_model.save(self.model_path)
self.input_image_shape = K.placeholder(shape=(2, ))
self.boxes, self.scores, self.classes = yolo_eval(
self.yolo4_model.output,
self.anchors,
len(self.class_names),
self.input_image_shape,
score_threshold=self.score
)
print('Dome.')
def jpy_count_coin(img): # img : str
model_path = 'JPY_weight.h5'
anchors_path = 'model_data/yolo4_anchors.txt'
classes_path = 'model_data/JPY_classes.txt'
jpy_classes = ['JPY_500', 'JPY_100', 'JPY_50', 'JPY_10', 'JPY_1', 'JPY_5']
count = {}
result = {}
total = 0
class_names = get_class(classes_path)
anchors = get_anchors(anchors_path)
num_anchors = len(anchors)
num_classes = len(class_names)
model_image_size = (416, 416)
# 分数阈值和nms_iou阈值
conf_thresh = 0.2
nms_thresh = 0.8
yolo4_model = yolo4_body(Input(shape=model_image_size + (3, )),
num_anchors // 3, num_classes)
model_path = os.path.expanduser(model_path)
yolo4_model.load_weights(model_path)
_decode = Decode(conf_thresh, nms_thresh, model_image_size, yolo4_model,
class_names)
try:
encoded_img = np.fromstring(base64.b64decode(img), dtype=np.uint8)
img = cv2.imdecode(encoded_img, cv2.IMREAD_COLOR)
except:
print('Open Error! Try again!')
else:
image, boxes, scores, classes = _decode.detect_image(img, True)
cv2.imwrite('predict.png', image)
with open('predict.png', 'rb') as img:
base64_string = base64.b64encode(img.read()).decode('utf-8')
count = collections.Counter(classes)
for key in tuple(count.keys()): # 딕셔너리 키 이름 변경
count[jpy_classes[key]] = count.pop(key)
for key, value in count.items():
total += int(key[str(key).find('_') + 1:]) * value
result['result'] = count
result['total'] = total
result['image'] = base64_string
# yolo4_model.close_session()
return result
def create_model(input_shape,
anchors,
num_classes,
load_pretrained=True,
freeze_body=2,
weights_path='model_data/yolo_weights.h5'):
'''create the training model'''
K.clear_session() # get a new session
image_input = Input(shape=(None, None, 3))
h, w = input_shape
num_anchors = len(anchors)
y_true = [Input(shape=(h//{0:32, 1:16, 2:8}[l], w//{0:32, 1:16, 2:8}[l], \
num_anchors//3, num_classes+5)) for l in range(3)]
model_body = yolo4_body(image_input, num_anchors // 3, num_classes)
print('Create YOLOv4 model with {} anchors and {} classes.'.format(
num_anchors, num_classes))
if load_pretrained:
model_body.load_weights(weights_path, by_name=True, skip_mismatch=True)
print('Load weights {}.'.format(weights_path))
if freeze_body in [1, 2]:
# Freeze darknet53 body or freeze all but 3 output layers.
num = (250, len(model_body.layers) - 3)[freeze_body - 1]
for i in range(num):
model_body.layers[i].trainable = False
print('Freeze the first {} layers of total {} layers.'.format(
num, len(model_body.layers)))
label_smoothing = 0
use_focal_obj_loss = False
use_focal_loss = False
use_diou_loss = True
use_softmax_loss = False
model_loss = Lambda(yolo4_loss,
output_shape=(1, ),
name='yolo_loss',
arguments={
'anchors': anchors,
'num_classes': num_classes,
'ignore_thresh': 0.5,
'label_smoothing': label_smoothing,
'use_focal_obj_loss': use_focal_obj_loss,
'use_focal_loss': use_focal_loss,
'use_diou_loss': use_diou_loss,
'use_softmax_loss': use_softmax_loss
})([*model_body.output, *y_true])
model = Model([model_body.input, *y_true], model_loss)
return model
def generate(self):
model_path = os.path.expanduser(self.model_path)
assert model_path.endswith(
'.h5'), 'Keras model or weights must be a .h5 file.'
# Load model, or construct model and load weights.
num_anchors = len(self.anchors)
num_classes = len(self.class_names)
is_tiny_version = num_anchors == 6 # default setting
try:
self.yolo_model = load_model(model_path, compile=False)
except:
self.yolo_model = yolo4_body(Input(shape=(None, None, 3)),
num_anchors // 3, num_classes)
self.yolo_model.load_weights(
self.model_path) # make sure model, anchors and classes match
else:
assert self.yolo_model.layers[-1].output_shape[-1] == \
num_anchors/len(self.yolo_model.output) * (num_classes + 5), \
'Mismatch between model and given anchor and class sizes'
print('{} model, anchors, and classes loaded.'.format(model_path))
# Generate colors for drawing bounding boxes.
hsv_tuples = [(x / len(self.class_names), 1., 1.)
for x in range(len(self.class_names))]
self.colors = list(map(lambda x: colorsys.hsv_to_rgb(*x), hsv_tuples))
self.colors = list(
map(lambda x: (int(x[0] * 255), int(x[1] * 255), int(x[2] * 255)),
self.colors))
np.random.seed(10101) # Fixed seed for consistent colors across runs.
np.random.shuffle(
self.colors) # Shuffle colors to decorrelate adjacent classes.
np.random.seed(None) # Reset seed to default.
# Generate output tensor targets for filtered bounding boxes.
self.input_image_shape = K.placeholder(shape=(2, ))
if self.gpu_num >= 2:
self.yolo_model = multi_gpu_model(self.yolo_model,
gpus=self.gpu_num)
boxes, scores, classes = yolo_eval(self.yolo_model.output,
self.anchors,
len(self.class_names),
self.input_image_shape,
score_threshold=self.score,
iou_threshold=self.iou)
return boxes, scores, classes
def __init__(self, conf_thresh: float = 0.8, nms_thresh: float = 0.8):
class_names = get_class(self.classes_path)
anchors = get_anchors(self.anchors_path)
model_image_size = (416, 416)
self._model: keras.Model = yolo4_body(
inputs=keras.Input(shape=model_image_size + (3, )),
num_anchors=len(anchors) // 3,
num_classes=len(class_names),
)
self._model.load_weights(os.path.expanduser(self.model_path))
self._decoder: Decode = Decode(
obj_threshold=conf_thresh,
nms_threshold=nms_thresh,
input_shape=model_image_size,
_yolo=self._model,
all_classes=class_names,
)
def create_model(input_shape, anchors_stride_base, num_classes, load_pretrained=True, freeze_body=2,
weights_path='model_data/yolo_weights.h5'):
'''create the training model'''
K.clear_session() # get a new session
image_input = Input(shape=(None, None, 3))
h, w = input_shape
num_anchors = len(anchors_stride_base)
max_bbox_per_scale = 150
iou_loss_thresh = 0.7
model_body = yolo4_body(image_input, num_anchors, num_classes)
print('Create YOLOv4 model with {} anchors and {} classes.'.format(num_anchors*3, num_classes))
if load_pretrained:
model_body.load_weights(weights_path, by_name=True, skip_mismatch=True)
print('Load weights {}.'.format(weights_path))
if freeze_body in [1, 2]:
# Freeze darknet53 body or freeze all but 3 output layers.
num = (250, len(model_body.layers)-3)[freeze_body-1]
for i in range(num): model_body.layers[i].trainable = False
print('Freeze the first {} layers of total {} layers.'.format(num, len(model_body.layers)))
y_true = [
layers.Input(name='input_2', shape=(None, None, 3, (num_classes + 5))), # label_sbbox
layers.Input(name='input_3', shape=(None, None, 3, (num_classes + 5))), # label_mbbox
layers.Input(name='input_4', shape=(None, None, 3, (num_classes + 5))), # label_lbbox
layers.Input(name='input_5', shape=(max_bbox_per_scale, 4)), # true_sbboxes
layers.Input(name='input_6', shape=(max_bbox_per_scale, 4)), # true_mbboxes
layers.Input(name='input_7', shape=(max_bbox_per_scale, 4)) # true_lbboxes
]
loss_list = layers.Lambda(yolo_loss, name='yolo_loss',
arguments={'num_classes': num_classes, 'iou_loss_thresh': iou_loss_thresh,
'anchors': anchors_stride_base})([*model_body.output, *y_true])
model = Model([model_body.input, *y_true], loss_list)
#model.summary()
return model, model_body
def load_yolo(self):
model_path = os.path.expanduser(self.model_path)
assert model_path.endswith(
'.h5'), 'Keras model or weights must be a .h5 file.'
self.class_names = self.get_class()
self.anchors = self.get_anchors()
num_anchors = len(self.anchors)
num_classes = len(self.class_names)
# Generate colors for drawing bounding boxes.
hsv_tuples = [(x / len(self.class_names), 1., 1.)
for x in range(len(self.class_names))]
self.colors = list(map(lambda x: colorsys.hsv_to_rgb(*x), hsv_tuples))
self.colors = list(
map(lambda x: (int(x[0] * 255), int(x[1] * 255), int(x[2] * 255)),
self.colors))
self.sess = K.get_session()
# Load model, or construct model and load weights.
self.yolo4_model = yolo4_body(Input(shape=(608, 608, 3)),
num_anchors // 3, num_classes)
# Read and convert darknet weight
print('Loading weights.')
weights_file = open(self.weights_path, 'rb')
major, minor, revision = np.ndarray(shape=(3, ),
dtype='int32',
buffer=weights_file.read(12))
if (major * 10 + minor) >= 2 and major < 1000 and minor < 1000:
seen = np.ndarray(shape=(1, ),
dtype='int64',
buffer=weights_file.read(8))
else:
seen = np.ndarray(shape=(1, ),
dtype='int32',
buffer=weights_file.read(4))
print('Weights Header: ', major, minor, revision, seen)
convs_to_load = []
bns_to_load = []
for i in range(len(self.yolo4_model.layers)):
layer_name = self.yolo4_model.layers[i].name
if layer_name.startswith('conv2d_'):
convs_to_load.append((int(layer_name[7:]), i))
if layer_name.startswith('batch_normalization_'):
bns_to_load.append((int(layer_name[20:]), i))
convs_sorted = sorted(convs_to_load, key=itemgetter(0))
bns_sorted = sorted(bns_to_load, key=itemgetter(0))
bn_index = 0
for i in range(len(convs_sorted)):
print('Converting ', i)
if i == 93 or i == 101 or i == 109:
#no bn, with bias
weights_shape = self.yolo4_model.layers[
convs_sorted[i][1]].get_weights()[0].shape
bias_shape = self.yolo4_model.layers[
convs_sorted[i][1]].get_weights()[0].shape[3]
filters = bias_shape
size = weights_shape[0]
darknet_w_shape = (filters, weights_shape[2], size, size)
weights_size = np.product(weights_shape)
conv_bias = np.ndarray(shape=(filters, ),
dtype='float32',
buffer=weights_file.read(filters * 4))
conv_weights = np.ndarray(shape=darknet_w_shape,
dtype='float32',
buffer=weights_file.read(
weights_size * 4))
conv_weights = np.transpose(conv_weights, [2, 3, 1, 0])
self.yolo4_model.layers[convs_sorted[i][1]].set_weights(
[conv_weights, conv_bias])
else:
#with bn, no bias
weights_shape = self.yolo4_model.layers[
convs_sorted[i][1]].get_weights()[0].shape
size = weights_shape[0]
bn_shape = self.yolo4_model.layers[bns_sorted[bn_index]
[1]].get_weights()[0].shape
filters = bn_shape[0]
darknet_w_shape = (filters, weights_shape[2], size, size)
weights_size = np.product(weights_shape)
conv_bias = np.ndarray(shape=(filters, ),
dtype='float32',
buffer=weights_file.read(filters * 4))
bn_weights = np.ndarray(shape=(3, filters),
dtype='float32',
buffer=weights_file.read(filters * 12))
bn_weight_list = [
bn_weights[0], # scale gamma
conv_bias, # shift beta
bn_weights[1], # running mean
bn_weights[2] # running var
]
self.yolo4_model.layers[bns_sorted[bn_index][1]].set_weights(
bn_weight_list)
conv_weights = np.ndarray(shape=darknet_w_shape,
dtype='float32',
buffer=weights_file.read(
weights_size * 4))
conv_weights = np.transpose(conv_weights, [2, 3, 1, 0])
self.yolo4_model.layers[convs_sorted[i][1]].set_weights(
[conv_weights])
bn_index += 1
weights_file.close()
self.yolo4_model.save(self.model_path)
if self.gpu_num >= 2:
self.yolo4_model = multi_gpu_model(self.yolo4_model,
gpus=self.gpu_num)
self.input_image_shape = K.placeholder(shape=(2, ))
self.boxes, self.scores, self.classes = yolo_eval(
self.yolo4_model.output,
self.anchors,
len(self.class_names),
self.input_image_shape,
score_threshold=self.score)
anchors_path = 'model_data/yolo4_anchors.txt'
# classes_path = 'model_data/voc_classes.txt'
classes_path = 'model_data/coco_classes.txt'
class_names = get_class(classes_path)
anchors = get_anchors(anchors_path)
num_anchors = len(anchors)
num_classes = len(class_names)
model_image_size = (608, 608)
conf_thresh = 0.2
nms_thresh = 0.45
yolo4_model = yolo4_body(Input(shape=model_image_size + (3, )),
num_anchors // 3, num_classes)
model_path = os.path.expanduser(model_path)
assert model_path.endswith(
'.h5'), 'Keras model or weights must be a .h5 file.'
yolo4_model.load_weights(model_path)
_decode = Decode(conf_thresh, nms_thresh, model_image_size, yolo4_model,
class_names)
while True:
img = input('Input image filename:')
try:
image = cv2.imread(img)
except:
