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yolo.py
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import colorsys
import copy
import os
from timeit import default_timer as timer
import numpy as np
import tensorflow as tf
from PIL import Image, ImageDraw, ImageFont
from tensorflow.compat.v1.keras import backend as K
from tensorflow.keras.layers import Input, Lambda
from tensorflow.keras.models import Model, load_model
from nets.yolo4_tiny import yolo_body, yolo_eval
from utils.utils import letterbox_image
#--------------------------------------------#
# 使用自己训练好的模型预测需要修改2个参数
# model_path和classes_path都需要修改！
# 如果出现shape不匹配，一定要注意
# 训练时的model_path和classes_path参数的修改
#--------------------------------------------#
class YOLO(object):
_defaults = {
"model_path" : 'model_data/yolov4_tiny_weights_voc.h5',
"anchors_path" : 'model_data/yolo_anchors.txt',
"classes_path" : 'model_data/voc_classes.txt',
"score" : 0.5,
"iou" : 0.3,
"eager" : True,
"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" : (416, 416)
}
@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)
#---------------------------------------------------#
# 载入模型
#---------------------------------------------------#
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
#---------------------------------------------------------#
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})(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)
@tf.function
def get_pred(self, image_data, input_image_shape):
out_boxes, out_scores, out_classes = self.yolo_model([image_data, input_image_shape], training=False)
return out_boxes, out_scores, out_classes
#---------------------------------------------------#
# 检测图片
#---------------------------------------------------#
def detect_image(self, image):
# -------------------------------------#
# 物体实际距离：actual_distance 5
# 物体实际高度：actual_high (高度) car:1.5 people:1.7 bike:1.2
# 物体像素高度：pixel_high (高度) car:291 people:283 bike:212
# 焦距focus =（pixel_high * actual_distance）/ actual_high
# 焦距focus已知后，就可以根据像素高度测出物体实际距离，公式如下：actual_distance =（actual_high * focus）/pixel_high
# -------------------------------------#
# car：focus_car = (291 * 5) / 1.5 = 970
# people：focus_people = (283 * 5) / 1.7 = 714
# bike：focus_bike = (212 * 5) / 1.2 = 514
# -------------------------------------#
# 实际高度
actual_high_car = 1.5
actual_high_people = 1.7
actual_high_bike = 1.2
# 像素高度
pixel_high_car = 0
pixel_high_people = 0
pixel_high_bike = 0
# 焦距
focus_car = 970
focus_people = 714
focus_bike = 514
# 实际距离
actual_distance = " "
actual_distance_car = 0
actual_distance_people = 0
actual_distance_bike = 0
start = timer()
cx = 0
cy = 0
#---------------------------------------------------------#
# 给图像增加灰条，实现不失真的resize
#---------------------------------------------------------#
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.
#---------------------------------------------------------#
# 添加上batch_size维度
#---------------------------------------------------------#
image_data = np.expand_dims(image_data, 0) # Add batch dimension.
#---------------------------------------------------------#
# 将图像输入网络当中进行预测
#---------------------------------------------------------#
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.get_pred(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 = max((image.size[0] + image.size[1]) // 300, 1)
out_name = [0]*len(out_boxes)
for i, c in list(enumerate(out_classes)):
predicted_class = self.class_names[c]
out_name[i] = self.class_names[c]
box = out_boxes[i]
score = out_scores[i]
top, left, bottom, right = box
cy = (top+bottom)/2
cx = (left+right)/2
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')
# label为识别出的类型
print(label, top, left, bottom, right)
# 计算障碍物的实际距离
if "person" in str(label):
pixel_high_people = bottom - top
actual_distance_people = (actual_high_people * focus_people) / pixel_high_people
actual_distance = round(actual_distance_people,1)
print("distance_people:", actual_distance_people)
elif "car" in str(label):
pixel_high_car = bottom - top
actual_distance_car = (actual_high_car * focus_car) / pixel_high_car
actual_distance = round(actual_distance_car,1)
print("distance_car:", actual_distance_car)
elif "bicycle" in str(label) or "motorbike" in str(label):
pixel_high_bike = bottom - top
actual_distance_bike = (actual_high_bike * focus_bike) / pixel_high_bike
actual_distance = round(actual_distance_bike,1)
print("distance_bike:", actual_distance_bike)
else:
actual_distance = "null"
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') +" "+"distance：" +
str(actual_distance) + "m", fill="green", font=font)
del draw
end = timer()
print(round(end - start),1) # 打印时间
if len(out_boxes) != 0:
j = np.argmax(out_scores)
predicted_classes = out_name[j]
else:
predicted_classes = 'none'
return image, predicted_classes,cx,cy
def close_session(self):
self.sess.close()