def generate(self):
self.score = 0.01
self.iou = 0.5
model_path = os.path.expanduser(self.model_path)
assert model_path.endswith(
'.h5'), 'Keras model or weights must be a .h5 file.'
# 计算anchor数量
num_anchors = len(self.anchors)
num_classes = len(self.class_names)
# 载入模型，如果原来的模型里已经包括了模型结构则直接载入。
# 否则先构建模型再载入
self.yolo_model = yolo_body(Input(shape=(None, None, 3)),
num_anchors // 2, num_classes)
self.yolo_model.load_weights(self.model_path, by_name=True)
print('{} model, anchors, and classes loaded.'.format(model_path))
# 画框设置不同的颜色
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))
# 打乱颜色
np.random.seed(10101)
np.random.shuffle(self.colors)
np.random.seed(None)
if self.eager:
self.input_image_shape = Input([
2,
], batch_size=1)
inputs = [*self.yolo_model.output, self.input_image_shape]
outputs = Lambda(yolo_eval,
output_shape=(1, ),
name='yolo_eval',
arguments={
'anchors': self.anchors,
'num_classes': len(self.class_names),
'image_shape': self.model_image_size,
'score_threshold': self.score,
'eager': True,
'max_boxes': self.max_boxes
})(inputs)
self.yolo_model = Model(
[self.yolo_model.input, self.input_image_shape], outputs)
else:
self.input_image_shape = K.placeholder(shape=(2, ))
self.boxes, self.scores, self.classes = yolo_eval(
self.yolo_model.output,
self.anchors,
num_classes,
self.input_image_shape,
max_boxes=self.max_boxes,
score_threshold=self.score,
iou_threshold=self.iou)
def generate(self):
self.score = 0.01
self.iou = 0.5
model_path = os.path.expanduser(self.model_path)
assert model_path.endswith(
'.h5'), 'Keras model or weights must be a .h5 file.'
#---------------------------------------------------#
# 计算先验框的数量和种类的数量
#---------------------------------------------------#
num_anchors = len(self.anchors)
num_classes = len(self.class_names)
#---------------------------------------------------------#
# 载入模型，如果原来的模型里已经包括了模型结构则直接载入。
# 否则先构建模型再载入
#---------------------------------------------------------#
try:
self.yolo_model = load_model(model_path, compile=False)
except:
self.yolo_model = yolo_body(Input(shape=(None, None, 3)),
num_anchors // 2, num_classes)
self.yolo_model.load_weights(self.model_path)
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))
# 画框设置不同的颜色
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))
# 打乱颜色
np.random.seed(10101)
np.random.shuffle(self.colors)
np.random.seed(None)
self.input_image_shape = K.placeholder(shape=(2, ))
#---------------------------------------------------------#
# 在yolo_eval函数中，我们会对预测结果进行后处理
# 后处理的内容包括，解码、非极大抑制、门限筛选等
#---------------------------------------------------------#
boxes, scores, classes = yolo_eval(
self.yolo_model.output,
self.anchors,
num_classes,
self.input_image_shape,
max_boxes=self.max_boxes,
score_threshold=self.score,
iou_threshold=self.iou,
letterbox_image=self.letterbox_image)
return boxes, scores, classes
def generate(self):
self.score = 0.01
self.iou = 0.5
model_path = os.path.expanduser(self.model_path)
assert model_path.endswith('.h5'), 'Keras model or weights must be a .h5 file.'
#---------------------------------------------------#
# 计算先验框的数量和种类的数量
#---------------------------------------------------#
num_anchors = len(self.anchors)
num_classes = len(self.class_names)
#---------------------------------------------------------#
# 载入模型
#---------------------------------------------------------#
self.yolo_model = yolo_body(Input(shape=(None,None,3)), num_anchors//2, num_classes)
self.yolo_model.load_weights(self.model_path)
print('{} model, anchors, and classes loaded.'.format(model_path))
# 画框设置不同的颜色
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))
# 打乱颜色
np.random.seed(10101)
np.random.shuffle(self.colors)
np.random.seed(None)
#---------------------------------------------------------#
# 在yolo_eval函数中，我们会对预测结果进行后处理
# 后处理的内容包括，解码、非极大抑制、门限筛选等
#---------------------------------------------------------#
if self.eager:
self.input_image_shape = Input([2,],batch_size=1)
inputs = [*self.yolo_model.output, self.input_image_shape]
outputs = Lambda(yolo_eval, output_shape=(1,), name='yolo_eval',
arguments={'anchors': self.anchors, 'num_classes': len(self.class_names), 'image_shape': self.model_image_size,
'score_threshold': self.score, 'eager': True, 'max_boxes': self.max_boxes, 'letterbox_image': self.letterbox_image})(inputs)
self.yolo_model = Model([self.yolo_model.input, self.input_image_shape], outputs)
else:
self.input_image_shape = K.placeholder(shape=(2, ))
self.boxes, self.scores, self.classes = yolo_eval(self.yolo_model.output, self.anchors,
num_classes, self.input_image_shape, max_boxes=self.max_boxes,
score_threshold=self.score, iou_threshold=self.iou, letterbox_image=self.letterbox_image)
label_smoothing = 0
regularization = True
# -------------------------------#
# Dataloder的使用
# -------------------------------#
Use_Data_Loader = True
# ------------------------------------------------------#
# 创建yolo模型
# ------------------------------------------------------#
image_input = Input(shape=(None, None, 3))
h, w = input_shape
print('Create YOLOv4 model with {} anchors and {} classes.'.format(
num_anchors, num_classes))
model_body = yolo_body(image_input, num_anchors // 2, num_classes)
# -------------------------------------------#
# 权值文件的下载请看README
# -------------------------------------------#
print('Load weights {}.'.format(weights_path))
model_body.load_weights(weights_path, by_name=True, skip_mismatch=True)
# ----------------------------------------------------------------------#
# 验证集的划分在train.py代码里面进行
# 2007_test.txt和2007_val.txt里面没有内容是正常的。训练不会使用到。
# 当前划分方式下，验证集和训练集的比例为1:9
# ----------------------------------------------------------------------#
val_split = 0.1
with open(annotation_path) as f:
lines = f.readlines()
#--------------------------------------------#
# 该部分代码只用于看网络结构，并非测试代码
# map测试请看get_dr_txt.py、get_gt_txt.py
# 和get_map.py
#--------------------------------------------#
from keras.layers import Input
from nets.yolo4_tiny import yolo_body
if __name__ == "__main__":
inputs = Input([416, 416, 3])
model = yolo_body(inputs, 3, 80)
model.summary()
# for i,layer in enumerate(model.layers):
# print(i,layer.name)
from nets.yolo4_tiny import yolo_body
from tensorflow.keras.layers import Input
# 输入的图像为
image_input = Input(shape=(416, 416, 3))
model = yolo_body(image_input, 3, 20)
model.summary()
for i, layer in enumerate(model.layers):
print(i, layer.name)