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.'
#---------------------------------------------------#
# 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 // 3, num_classes,
self.phi)
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):
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)
is_tiny_version = num_anchors == 6 # default setting
# 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 = tiny_yolo_body(Input(shape=(None,None,3)), num_anchors//2, num_classes) \
if is_tiny_version else yolo_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))
# 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, )) #声明一个定义输入图片尺寸的张量
"""
yolo_outputs #模型输出，格式如下[（?，13,13,255）（?，26,26,255）（?,52,52,255）] ?:bitch size; 13、26、52:多尺度预测； 255：预测值（3*（80+5））
anchors, #[(10,13)，(16,30), (33,23), (30,61), (62,45), (59,119), (116,90), (156,198),(373,326)]
num_classes, #类别个数，coco集80类
image_shape, #placeholder类型的TF参数，默认(416, 416)；
max_boxes=20, #每张图每类最多检测到20个框同类别框的IoU阈值，大于阈值的重叠框被删除，重叠物体较多，则调高阈值，重叠物体较少，则调低阈值
score_threshold=.6, #框置信度阈值，小于阈值的框被删除，需要的框较多，则调低阈值，需要的框较少，则调高阈值；
iou_threshold=.5): #同类别框的IoU阈值，大于阈值的重叠框被删除，重叠物体较多，则调高阈值，重叠物体较少，则调低阈值
"""
boxes, scores, classes = yolo_eval(self.yolo_model.output,
self.anchors,
num_classes,
self.input_image_shape,
score_threshold=self.score,
iou_threshold=self.iou)
return boxes, scores, classes
def generate(self):
# 打开文件
model_path = os.path.expanduser(self.model_path)
# 判断文件夹是否有.h5文件
assert model_path.endswith(
'.h5'), 'Keras model or weights must be a .h5 file.'
# 计算anchor数量
num_anchors = len(self.anchors) # 9
num_classes = len(self.class_names) # 80
# 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 // 3, 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, ))
# 讲计算结果进行转化，得出结果
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)
return boxes, scores, classes
def generate(self):
score = CONFIG.DETECT.SCORE
iou = CONFIG.DETECT.IOU
model_path = os.path.expanduser(self.model_path)
assert model_path.endswith('.h5'), 'Keras model or weights must be a .h5 file.'
num_anchors = len(self.anchors)
num_classes = len(self.class_names)
try:
self.yolo_model = load_model(model_path, compile=False)
except:
self.yolo_model = yolo_body(Input(shape=(None, None, 3)), num_anchors // 3, 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))
# draw 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))
# random color
np.random.seed(10101)
np.random.shuffle(self.colors)
np.random.seed(None)
self.input_image_shape = K.placeholder(shape=(2,))
boxes, scores, classes = yolo_eval(self.yolo_model.output, self.anchors,
num_classes, self.input_image_shape,
score_threshold=score, iou_threshold=iou)
return boxes, scores, classes
def generate(self):
'''
Parameters
----------
Returns
-------
boxes:
scores:
classes:
'''
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
try:
self.yolo_model = load_model(model_path, compile=False)
except:
self.yolo_model = yolo_body(Input(shape=(None, None, 3)),
num_anchors // 3, 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,每个类设置一种颜色
# colors:array, shape=(20, 3), eg:[(0,178,255), (255,153,0), ..., (255,0,0)]
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)
# 根据检测参数，过滤框
# <tf.Tensor 'Placeholder_366:0' shape=(2,) dtype=float32>
self.input_image_shape = K.placeholder(shape=(2, ))
# 图片预测
boxes, scores, classes = yolo_eval(self.yolo_model.output,
self.anchors,
num_classes,
self.input_image_shape,
score_threshold=self.score,
iou_threshold=self.iou)
return boxes, scores, classes
