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yolov4-keras

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未验证 提交 3bb5d7b1 编写于 作者: B Bubbliiiing 提交者： GitHub
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FPS_test.py
浏览文件 @ 3bb5d7b1
......@@ -17,31 +17,20 @@ video.py里面测试的FPS会低于该FPS，因为摄像头的读取频率有限
'''
class FPS_YOLO(YOLO):
def get_FPS(self, image, test_interval):
#---------------------------------------------------------#
# 给图像增加灰条，实现不失真的resize
# 也可以直接resize进行识别
#---------------------------------------------------------#
if self.letterbox_image:
boxed_image = letterbox_image(image, (self.model_image_size[1],self.model_image_size[0]))
else:
boxed_image = image.convert('RGB')
boxed_image = boxed_image.resize((self.model_image_size[1],self.model_image_size[0]), Image.BICUBIC)
# 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.
#---------------------------------------------------------#
# 添加上batch_size维度
#---------------------------------------------------------#
image_data = np.expand_dims(image_data, 0)
image_data = np.expand_dims(image_data, 0)
#---------------------------------------------------------#
# 将图像输入网络当中进行预测！
#---------------------------------------------------------#
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})
K.learning_phase(): 0
})
t1 = time.time()
for _ in range(test_interval):
......@@ -50,7 +39,8 @@ class FPS_YOLO(YOLO):
feed_dict={
self.yolo_model.input: image_data,
self.input_image_shape: [image.size[1], image.size[0]],
K.learning_phase(): 0})
K.learning_phase(): 0
})
t2 = time.time()
tact_time = (t2 - t1) / test_interval
return tact_time
......
get_dr_txt.py
浏览文件 @ 3bb5d7b1
......@@ -13,7 +13,7 @@ from keras.layers import Input
from PIL import Image
from tqdm import tqdm
from nets.yolo4_tiny import yolo_body, yolo_eval
from nets.yolo4 import yolo_body, yolo_eval
from utils.utils import letterbox_image
from yolo import YOLO
......@@ -41,7 +41,7 @@ class mAP_YOLO(YOLO):
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 = 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] == \
......@@ -71,7 +71,7 @@ class mAP_YOLO(YOLO):
#---------------------------------------------------------#
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)
score_threshold = self.score, iou_threshold = self.iou)
return boxes, scores, classes
#---------------------------------------------------#
......@@ -81,13 +81,9 @@ class mAP_YOLO(YOLO):
f = open("./input/detection-results/"+image_id+".txt","w")
#---------------------------------------------------------#
# 给图像增加灰条，实现不失真的resize
# 也可以直接resize进行识别
#---------------------------------------------------------#
if self.letterbox_image:
boxed_image = letterbox_image(image, (self.model_image_size[1],self.model_image_size[0]))
else:
boxed_image = image.convert('RGB')
boxed_image = boxed_image.resize((self.model_image_size[1],self.model_image_size[0]), Image.BICUBIC)
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.
#---------------------------------------------------------#
......
kmeans_for_anchors.py
浏览文件 @ 3bb5d7b1
......@@ -86,7 +86,7 @@ if __name__ == '__main__':
# 运行该程序会计算'./VOCdevkit/VOC2007/Annotations'的xml
# 会生成yolo_anchors.txt
SIZE = 416
anchors_num = 6
anchors_num = 9
# 载入数据集，可以使用VOC的xml
path = r'./VOCdevkit/VOC2007/Annotations'
......
nets/ious.py
浏览文件 @ 3bb5d7b1
import math
import tensorflow as tf
from keras import backend as K
import tensorflow as tf
import math
def box_ciou(b1, b2):
"""
输入为：
......
nets/loss.py
浏览文件 @ 3bb5d7b1
......@@ -99,20 +99,21 @@ def box_iou(b1, b2):
# loss值计算
#---------------------------------------------------#
def yolo_loss(args, anchors, num_classes, ignore_thresh=.5, label_smoothing=0.1, print_loss=False, normalize=True):
# 一共有两层
# 一共有三层
num_layers = len(anchors)//3
#---------------------------------------------------------------------------------------------------#
# 将预测结果和实际ground truth分开，args是[*model_body.output, *y_true]
# y_true是一个列表，包含两个特征层，shape分别为(m,13,13,3,85),(m,26,26,3,85)
# yolo_outputs是一个列表，包含两个特征层，shape分别为(m,13,13,3,85),(m,26,26,3,85)
# y_true是一个列表，包含三个特征层，shape分别为(m,13,13,3,85),(m,26,26,3,85),(m,52,52,3,85)。
# yolo_outputs是一个列表，包含三个特征层，shape分别为(m,13,13,3,85),(m,26,26,3,85),(m,52,52,3,85)。
#---------------------------------------------------------------------------------------------------#
y_true = args[num_layers:]
yolo_outputs = args[:num_layers]
#-----------------------------------------------------------#
# 13x13的特征层对应的anchor是[81,82], [135,169], [344,319]
# 26x26的特征层对应的anchor是[23,27], [37,58], [81,82]
# 13x13的特征层对应的anchor是[142, 110], [192, 243], [459, 401]
# 26x26的特征层对应的anchor是[36, 75], [76, 55], [72, 146]
# 52x52的特征层对应的anchor是[12, 16], [19, 36], [40, 28]
#-----------------------------------------------------------#
anchor_mask = [[6,7,8], [3,4,5], [0,1,2]] if num_layers==3 else [[3,4,5], [1,2,3]]
......@@ -129,8 +130,8 @@ def yolo_loss(args, anchors, num_classes, ignore_thresh=.5, label_smoothing=0.1,
mf = K.cast(m, K.dtype(yolo_outputs[0]))
#---------------------------------------------------------------------------------------------------#
# y_true是一个列表，包含两个特征层，shape分别为(m,13,13,3,85),(m,26,26,3,85)
# yolo_outputs是一个列表，包含两个特征层，shape分别为(m,13,13,3,85),(m,26,26,3,85)
# y_true是一个列表，包含三个特征层，shape分别为(m,13,13,3,85),(m,26,26,3,85),(m,52,52,3,85)。
# yolo_outputs是一个列表，包含三个特征层，shape分别为(m,13,13,3,85),(m,26,26,3,85),(m,52,52,3,85)。
#---------------------------------------------------------------------------------------------------#
for l in range(num_layers):
#-----------------------------------------------------------#
......
predict.py
浏览文件 @ 3bb5d7b1
......@@ -8,6 +8,7 @@ predict.py有几个注意点
from keras.layers import Input
from PIL import Image
from nets.yolo4 import yolo_body
from yolo import YOLO
yolo = YOLO()
......
test.py
浏览文件 @ 3bb5d7b1
......@@ -5,7 +5,7 @@
#--------------------------------------------#
from keras.layers import Input
from nets.yolo4_tiny import yolo_body
from nets.yolo4 import yolo_body
if __name__ == "__main__":
inputs = Input([416, 416, 3])
......
train.py
浏览文件 @ 3bb5d7b1
......@@ -9,7 +9,7 @@ from keras.models import Model
from keras.optimizers import Adam
from nets.loss import yolo_loss
from nets.yolo4_tiny import yolo_body
from nets.yolo4 import yolo_body
from utils.utils import (WarmUpCosineDecayScheduler, get_random_data,
get_random_data_with_Mosaic, rand)
......@@ -67,19 +67,20 @@ def data_generator(annotation_lines, batch_size, input_shape, anchors, num_class
#---------------------------------------------------#
def preprocess_true_boxes(true_boxes, input_shape, anchors, num_classes):
assert (true_boxes[..., 4]<num_classes).all(), 'class id must be less than num_classes'
# 一共有两个特征层数
# 一共有三个特征层数
num_layers = len(anchors)//3
#-----------------------------------------------------------#
# 13x13的特征层对应的anchor是[81,82], [135,169], [344,319]
# 26x26的特征层对应的anchor是[23,27], [37,58], [81,82]
# 13x13的特征层对应的anchor是[142, 110], [192, 243], [459, 401]
# 26x26的特征层对应的anchor是[36, 75], [76, 55], [72, 146]
# 52x52的特征层对应的anchor是[12, 16], [19, 36], [40, 28]
#-----------------------------------------------------------#
anchor_mask = [[6,7,8], [3,4,5], [0,1,2]] if num_layers==3 else [[3,4,5], [1,2,3]]
anchor_mask = [[6,7,8], [3,4,5], [0,1,2]]
#-----------------------------------------------------------#
# 获得框的坐标和图片的大小
#-----------------------------------------------------------#
true_boxes = np.array(true_boxes, dtype='float32')
input_shape = np.array(input_shape, dtype='int32')
input_shape = np.array(input_shape, dtype='int32')
#-----------------------------------------------------------#
# 通过计算获得真实框的中心和宽高
# 中心点(m,n,2) 宽高(m,n,2)
......@@ -102,7 +103,7 @@ def preprocess_true_boxes(true_boxes, input_shape, anchors, num_classes):
dtype='float32') for l in range(num_layers)]
#-----------------------------------------------------------#
# [6,2] -> [1,6,2]
# [9,2] -> [1,9,2]
#-----------------------------------------------------------#
anchors = np.expand_dims(anchors, 0)
anchor_maxes = anchors / 2.
......@@ -126,10 +127,10 @@ def preprocess_true_boxes(true_boxes, input_shape, anchors, num_classes):
#-----------------------------------------------------------#
# 计算所有真实框和先验框的交并比
# intersect_area [n,6]
# intersect_area [n,9]
# box_area [n,1]
# anchor_area [1,6]
# iou [n,6]
# anchor_area [1,9]
# iou [n,9]
#-----------------------------------------------------------#
intersect_mins = np.maximum(box_mins, anchor_mins)
intersect_maxes = np.minimum(box_maxes, anchor_maxes)
......@@ -199,7 +200,7 @@ if __name__ == "__main__":
# 训练自己的数据集时提示维度不匹配正常
# 预测的东西都不一样了自然维度不匹配
#------------------------------------------------------#
weights_path = 'model_data/yolov4_tiny_weights_coco.h5'
weights_path = 'model_data/yolo4_weight.h5'
#------------------------------------------------------#
# 训练用图片大小
# 一般在416x416和608x608选择
......@@ -238,8 +239,8 @@ if __name__ == "__main__":
#------------------------------------------------------#
image_input = Input(shape=(None, None, 3))
h, w = input_shape
print('Create YOLOv4-Tiny model with {} anchors and {} classes.'.format(num_anchors, num_classes))
model_body = yolo_body(image_input, num_anchors//2, num_classes)
print('Create YOLOv4 model with {} anchors and {} classes.'.format(num_anchors, num_classes))
model_body = yolo_body(image_input, num_anchors//3, num_classes)
#------------------------------------------------------#
# 载入预训练权重
......@@ -251,10 +252,12 @@ if __name__ == "__main__":
# 在这个地方设置损失，将网络的输出结果传入loss函数
# 把整个模型的输出作为loss
#------------------------------------------------------#
y_true = [Input(shape=(h//{0:32, 1:16}[l], w//{0:32, 1:16}[l], num_anchors//2, num_classes+5)) for l in range(2)]
y_true = [Input(shape=(h//{0:32, 1:16, 2:8}[l], w//{0:32, 1:16, 2:8}[l], \
num_anchors//3, num_classes+5)) for l in range(3)]
loss_input = [*model_body.output, *y_true]
model_loss = Lambda(yolo_loss, output_shape=(1,), name='yolo_loss',
arguments={'anchors': anchors, 'num_classes': num_classes, 'ignore_thresh': 0.5, 'label_smoothing': label_smoothing, 'normalize':normalize})(loss_input)
arguments={'anchors': anchors, 'num_classes': num_classes, 'ignore_thresh': 0.5,
'label_smoothing': label_smoothing, 'normalize': normalize})(loss_input)
model = Model([model_body.input, *y_true], model_loss)
......@@ -284,6 +287,10 @@ if __name__ == "__main__":
num_val = int(len(lines)*val_split)
num_train = len(lines) - num_val
freeze_layers = 249
for i in range(freeze_layers): model_body.layers[i].trainable = False
print('Freeze the first {} layers of total {} layers.'.format(freeze_layers, len(model_body.layers)))
#------------------------------------------------------#
# 主干特征提取网络特征通用，冻结训练可以加快训练速度
# 也可以在训练初期防止权值被破坏。
......@@ -292,15 +299,10 @@ if __name__ == "__main__":
# Epoch总训练世代
# 提示OOM或者显存不足请调小Batch_size
#------------------------------------------------------#
freeze_layers = 60
for i in range(freeze_layers): model_body.layers[i].trainable = False
print('Freeze the first {} layers of total {} layers.'.format(freeze_layers, len(model_body.layers)))
# 调整非主干模型first
if True:
Init_epoch = 0
Freeze_epoch = 50
batch_size = 32
batch_size = 16
learning_rate_base = 1e-3
if Cosine_scheduler:
......@@ -324,9 +326,9 @@ if __name__ == "__main__":
model.compile(optimizer=Adam(learning_rate_base), loss={'yolo_loss': lambda y_true, y_pred: y_pred})
print('Train on {} samples, val on {} samples, with batch size {}.'.format(num_train, num_val, batch_size))
model.fit_generator(data_generator(lines[:num_train], batch_size, input_shape, anchors, num_classes, mosaic=mosaic),
model.fit_generator(data_generator(lines[:num_train], batch_size, input_shape, anchors, num_classes, mosaic=mosaic, random=True),
steps_per_epoch=max(1, num_train//batch_size),
validation_data=data_generator(lines[num_train:], batch_size, input_shape, anchors, num_classes, mosaic=False),
validation_data=data_generator(lines[num_train:], batch_size, input_shape, anchors, num_classes, mosaic=False, random=False),
validation_steps=max(1, num_val//batch_size),
epochs=Freeze_epoch,
initial_epoch=Init_epoch,
......@@ -335,11 +337,10 @@ if __name__ == "__main__":
for i in range(freeze_layers): model_body.layers[i].trainable = True
# 解冻后训练
if True:
Freeze_epoch = 50
Epoch = 100
batch_size = 16
batch_size = 2
learning_rate_base = 1e-4
if Cosine_scheduler:
......@@ -363,9 +364,9 @@ if __name__ == "__main__":
model.compile(optimizer=Adam(learning_rate_base), loss={'yolo_loss': lambda y_true, y_pred: y_pred})
print('Train on {} samples, val on {} samples, with batch size {}.'.format(num_train, num_val, batch_size))
model.fit_generator(data_generator(lines[:num_train], batch_size, input_shape, anchors, num_classes, mosaic=mosaic),
model.fit_generator(data_generator(lines[:num_train], batch_size, input_shape, anchors, num_classes, mosaic=mosaic, random=True),
steps_per_epoch=max(1, num_train//batch_size),
validation_data=data_generator(lines[num_train:], batch_size, input_shape, anchors, num_classes, mosaic=False),
validation_data=data_generator(lines[num_train:], batch_size, input_shape, anchors, num_classes, mosaic=False, random=False),
validation_steps=max(1, num_val//batch_size),
epochs=Epoch,
initial_epoch=Freeze_epoch,
......
yolo.py
浏览文件 @ 3bb5d7b1
import collections
import colorsys
import copy
import os
......@@ -10,7 +9,7 @@ from keras.layers import Input
from keras.models import load_model
from PIL import Image, ImageDraw, ImageFont
from nets.yolo4_tiny import yolo_body, yolo_eval
from nets.yolo4 import yolo_body, yolo_eval
from utils.utils import letterbox_image
......@@ -22,7 +21,7 @@ from utils.utils import letterbox_image
#--------------------------------------------#
class YOLO(object):
_defaults = {
"model_path" : 'model_data/yolov4_tiny_weights_coco.h5',
"model_path" : 'model_data/yolo4_weight.h5',
"anchors_path" : 'model_data/yolo_anchors.txt',
"classes_path" : 'model_data/coco_classes.txt',
"score" : 0.5,
......@@ -30,12 +29,7 @@ class YOLO(object):
"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),
#---------------------------------------------------------------------#
# 该变量用于控制是否使用letterbox_image对输入图像进行不失真的resize，
# 在多次测试后，发现关闭letterbox_image直接resize的效果更好
#---------------------------------------------------------------------#
"letterbox_image" : False,
"model_image_size" : (416, 416)
}
@classmethod
......@@ -95,7 +89,7 @@ class YOLO(object):
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 = 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] == \
......@@ -125,22 +119,19 @@ class YOLO(object):
#---------------------------------------------------------#
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)
score_threshold = self.score, iou_threshold = self.iou)
return boxes, scores, classes
#---------------------------------------------------#
# 检测图片
#---------------------------------------------------#
def detect_image(self, image):
start = timer()
#---------------------------------------------------------#
# 给图像增加灰条，实现不失真的resize
# 也可以直接resize进行识别
#---------------------------------------------------------#
if self.letterbox_image:
boxed_image = letterbox_image(image, (self.model_image_size[1],self.model_image_size[0]))
else:
boxed_image = image.convert('RGB')
boxed_image = boxed_image.resize((self.model_image_size[1],self.model_image_size[0]), Image.BICUBIC)
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.
#---------------------------------------------------------#
......@@ -206,6 +197,8 @@ class YOLO(object):
draw.text(text_origin, str(label,'UTF-8'), fill=(0, 0, 0), font=font)
del draw
end = timer()
print(end - start)
return image
def close_session(self):
......
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