基于python-yolo的批量截图

from yolo3.model import yolo_eval, yolo_body, tiny_yolo_body
from yolo3.utils import letterbox_image
import os
from keras.utils import multi_gpu_model

count=0
tt=3461
class YOLO(object):
_defaults = {
"model_path": 'model_data/trained_weights_final.h5', #训练模型的位置
"anchors_path": 'model_data/yolo_anchors.txt', #anchors列表
"classes_path": 'model_data/voc_classes.txt', #分类文件
"score" : 0.1,
"iou" : 0.3,
"model_image_size" : (416, 416),
"gpu_num" : 1,
}

@classmethod
def get_defaults(cls, n):
if n in cls._defaults:
return cls._defaults[n] else:
return "Unrecognized attribute name '" + n + "'"

def __init__(self, **kwargs):
self.__dict__.update(self._defaults) # set up default values
self.__dict__.update(kwargs) # and update with user overrides
self.class_names = self._get_class()
self.anchors = self._get_anchors()
self.sess = K.get_session()
self.boxes, self.scores, self.classes = self.generate()

def _get_class(self):
classes_path = os.path.expanduser(self.classes_path)
with open(classes_path) as f:
class_names = f.readlines()
class_names = [c.strip() for c in class_names] return class_names

def _get_anchors(self):
anchors_path = os.path.expanduser(self.anchors_path)
with open(anchors_path) as f:
anchors = f.readline()
anchors = [float(x) for x in anchors.split(',')] return np.array(anchors).reshape(-1, 2)

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 = 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))

# Generate colors for drawing bounding boxes.
hsv_tuples = [(x / len(self.class_names), 1., 1.)

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