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yolo.py
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yolo.py
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import os
import numpy as np
import copy
import colorsys
import tensorflow as tf
from timeit import default_timer as timer
from tensorflow.compat.v1.keras import backend as K
from tensorflow.keras.models import load_model
from tensorflow.keras.layers import Input
from tensorflow.keras.layers import Lambda
from tensorflow.keras.models import Model
from PIL import Image, ImageFont, ImageDraw
from nets.yolo4_tiny import yolo_body,yolo_eval
from utils.utils import letterbox_image
class YOLO(object):
_defaults = {
"model_path" : 'logs_2/last1.h5',
"anchors_path" : 'model_data/yolo_anchors.txt',
"classes_path" : 'model_data/new_class.txt',
"score" : 0.5,
"iou" : 0.3,
"eager" : False,
"max_boxes" : 100,
# 显存比较小可以使用416x416
# 显存比较大可以使用608x608
"model_image_size" : (320, 320)
}
@classmethod
def get_defaults(cls, n):
if n in cls._defaults:
return cls._defaults[n]
else:
return "Unrecognized attribute name '" + n + "'"
#---------------------------------------------------#
# 初始化yolo
#---------------------------------------------------#
def __init__(self, **kwargs):
self.__dict__.update(self._defaults)
self.class_names = self._get_class()
self.anchors = self._get_anchors()
if not self.eager:
tf.compat.v1.disable_eager_execution()
self.sess = K.get_session()
self.generate()
#---------------------------------------------------#
# 获得所有的分类
#---------------------------------------------------#
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
#---------------------------------------------------#
# 获得所有的先验框
#---------------------------------------------------#
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)
#---------------------------------------------------#
# 获得所有的分类
#---------------------------------------------------#
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)
# 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)
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)
#---------------------------------------------------#
# 检测图片
#---------------------------------------------------#
def detect_image(self, image):
start = timer()
# 调整图片使其符合输入要求
new_image_size = (self.model_image_size[1],self.model_image_size[0])
boxed_image = letterbox_image(image, new_image_size)
image_data = np.array(boxed_image, dtype='float32')
image_data /= 255.
image_data = np.expand_dims(image_data, 0) # Add batch dimension.
if self.eager:
# 预测结果
input_image_shape = np.expand_dims(np.array([image.size[1], image.size[0]], dtype='float32'), 0)
out_boxes, out_scores, out_classes = self.yolo_model.predict([image_data, input_image_shape])
else:
# 预测结果
out_boxes, out_scores, out_classes = self.sess.run(
[self.boxes, self.scores, self.classes],
feed_dict={
self.yolo_model.input: image_data,
self.input_image_shape: [image.size[1], image.size[0]],
K.learning_phase(): 0
})
print('Found {} boxes for {}'.format(len(out_boxes), 'img'))
# 设置字体
font = ImageFont.truetype(font='font/simhei.ttf',
size=np.floor(3e-2 * image.size[1] + 0.5).astype('int32'))
thickness = (image.size[0] + image.size[1]) // 300
for i, c in list(enumerate(out_classes)):
predicted_class = self.class_names[c]
box = out_boxes[i]
score = out_scores[i]
top, left, bottom, right = box
top = top - 5
left = left - 5
bottom = bottom + 5
right = right + 5
top = max(0, np.floor(top + 0.5).astype('int32'))
left = max(0, np.floor(left + 0.5).astype('int32'))
bottom = min(image.size[1], np.floor(bottom + 0.5).astype('int32'))
right = min(image.size[0], np.floor(right + 0.5).astype('int32'))
# 画框框
label = '{} {:.2f}'.format(predicted_class, score)
draw = ImageDraw.Draw(image)
label_size = draw.textsize(label, font)
label = label.encode('utf-8')
print(label)
if top - label_size[1] >= 0:
text_origin = np.array([left, top - label_size[1]])
else:
text_origin = np.array([left, top + 1])
for i in range(thickness):
draw.rectangle(
[left + i, top + i, right - i, bottom - i],
outline=self.colors[c])
draw.rectangle(
[tuple(text_origin), tuple(text_origin + label_size)],
fill=self.colors[c])
draw.text(text_origin, str(label,'UTF-8'), fill=(0, 0, 0), font=font)
del draw
end = timer()
print(end - start)
return image