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retinanet.py
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import colorsys
import os
import time
import numpy as np
import torch
import torch.nn as nn
from PIL import ImageDraw, ImageFont
from nets.retinanet import Retinanet
from utils.utils import (decodebox, letterbox_image,
non_max_suppression, retinanet_correct_boxes)
def preprocess_input(image):
image /= 255
mean=(0.406, 0.456, 0.485)
std=(0.225, 0.224, 0.229)
image -= mean
image /= std
return image
#--------------------------------------------#
# 使用自己训练好的模型预测需要修改3个参数
# model_path、classes_path和phi都需要修改！
# 一定要全部对应
# phi == 0 : resnet18
# phi == 1 : resnet34
# phi == 2 : resnet50
# phi == 3 : resnet101
# phi == 4 : resnet152
# 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 RetinaNet(object):
_defaults = {
"model_path" : 'model_data/retinanet_resnet50.pth',
"classes_path" : 'model_data/voc_classes.txt',
"input_shape" : [600,600,3],
"confidence" : 0.5,
"iou" : 0.3,
"phi" : 2,
"cuda" : True,
}
@classmethod
def get_defaults(cls, n):
if n in cls._defaults:
return cls._defaults[n]
else:
return "Unrecognized attribute name '" + n + "'"
#---------------------------------------------------#
# 初始化Retinanet
#---------------------------------------------------#
def __init__(self, **kwargs):
self.__dict__.update(self._defaults)
self.class_names = self._get_class()
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
#---------------------------------------------------#
# 生成模型
#---------------------------------------------------#
def generate(self):
#------------------#
# 载入模型
#------------------#
self.net = Retinanet(len(self.class_names),self.phi).eval()
#----------------------------------------#
# 载入权值
#----------------------------------------#
print('Loading weights into state dict...')
state_dict = torch.load(self.model_path)
self.net.load_state_dict(state_dict)
if self.cuda:
self.net = nn.DataParallel(self.net)
self.net = self.net.cuda()
print('{} model, anchors, and classes loaded.'.format(self.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))
#---------------------------------------------------#
# 检测图片
#---------------------------------------------------#
def detect_image(self, image):
#---------------------------------------------------------#
# 在这里将图像转换成RGB图像，防止灰度图在预测时报错。
#---------------------------------------------------------#
image = image.convert('RGB')
image_shape = np.array(np.shape(image)[0:2])
#---------------------------------------------------------#
# 给图像增加灰条，实现不失真的resize
#---------------------------------------------------------#
crop_img = np.array(letterbox_image(image, [self.input_shape[1], self.input_shape[0]]))
photo = np.array(crop_img,dtype = np.float32)
photo = np.transpose(preprocess_input(photo), (2, 0, 1))
with torch.no_grad():
images = torch.from_numpy(np.asarray([photo]))
if self.cuda:
images = images.cuda()
#---------------------------------------------------------#
# 传入网络当中进行预测
#---------------------------------------------------------#
_, regression, classification, anchors = self.net(images)
#-----------------------------------------------------------#
# 将预测结果进行解码
#-----------------------------------------------------------#
regression = decodebox(regression, anchors, images)
detection = torch.cat([regression,classification],axis=-1)
batch_detections = non_max_suppression(detection, len(self.class_names),
conf_thres=self.confidence,
nms_thres=self.iou)
#--------------------------------------#
# 如果没有检测到物体，则返回原图
#--------------------------------------#
try:
batch_detections = batch_detections[0].cpu().numpy()
except:
return image
#-----------------------------------------------------------#
# 筛选出其中得分高于confidence的框
#-----------------------------------------------------------#
top_index = batch_detections[:,4] > self.confidence
top_conf = batch_detections[top_index,4]
top_label = np.array(batch_detections[top_index,-1],np.int32)
top_bboxes = np.array(batch_detections[top_index,:4])
top_xmin, top_ymin, top_xmax, top_ymax = np.expand_dims(top_bboxes[:,0],-1),np.expand_dims(top_bboxes[:,1],-1),np.expand_dims(top_bboxes[:,2],-1),np.expand_dims(top_bboxes[:,3],-1)
#-----------------------------------------------------------#
# 去掉灰条部分
#-----------------------------------------------------------#
boxes = retinanet_correct_boxes(top_ymin,top_xmin,top_ymax,top_xmax,np.array([self.input_shape[0], self.input_shape[1]]),image_shape)
font = ImageFont.truetype(font='model_data/simhei.ttf',size=np.floor(3e-2 * np.shape(image)[1] + 0.5).astype('int32'))
thickness = max((np.shape(image)[0] + np.shape(image)[1]) // self.input_shape[0], 1)
for i, c in enumerate(top_label):
predicted_class = self.class_names[c]
score = top_conf[i]
top, left, bottom, right = boxes[i]
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(np.shape(image)[0], np.floor(bottom + 0.5).astype('int32'))
right = min(np.shape(image)[1], 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, top, left, bottom, right)
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[self.class_names.index(predicted_class)])
draw.rectangle(
[tuple(text_origin), tuple(text_origin + label_size)],
fill=self.colors[self.class_names.index(predicted_class)])
draw.text(text_origin, str(label,'UTF-8'), fill=(0, 0, 0), font=font)
del draw
return image
def get_FPS(self, image, test_interval):
image_shape = np.array(np.shape(image)[0:2])
#---------------------------------------------------------#
# 给图像增加灰条，实现不失真的resize
#---------------------------------------------------------#
crop_img = np.array(letterbox_image(image, [self.input_shape[1], self.input_shape[0]]))
photo = np.array(crop_img,dtype = np.float32)
photo = np.transpose(preprocess_input(photo), (2, 0, 1))
with torch.no_grad():
images = torch.from_numpy(np.asarray([photo]))
if self.cuda:
images = images.cuda()
_, regression, classification, anchors = self.net(images)
regression = decodebox(regression, anchors, images)
detection = torch.cat([regression,classification],axis=-1)
batch_detections = non_max_suppression(detection, len(self.class_names),
conf_thres=self.confidence,
nms_thres=self.iou)
try:
batch_detections = batch_detections[0].cpu().numpy()
top_index = batch_detections[:,4] > self.confidence
top_conf = batch_detections[top_index,4]
top_label = np.array(batch_detections[top_index,-1],np.int32)
top_bboxes = np.array(batch_detections[top_index,:4])
top_xmin, top_ymin, top_xmax, top_ymax = np.expand_dims(top_bboxes[:,0],-1),np.expand_dims(top_bboxes[:,1],-1),np.expand_dims(top_bboxes[:,2],-1),np.expand_dims(top_bboxes[:,3],-1)
boxes = retinanet_correct_boxes(top_ymin,top_xmin,top_ymax,top_xmax,np.array([self.input_shape[0],self.input_shape[1]]),image_shape)
except:
pass
t1 = time.time()
for _ in range(test_interval):
with torch.no_grad():
_, regression, classification, anchors = self.net(images)
regression = decodebox(regression, anchors, images)
detection = torch.cat([regression,classification],axis=-1)
batch_detections = non_max_suppression(detection, len(self.class_names),
conf_thres=self.confidence,
nms_thres=self.iou)
try:
batch_detections = batch_detections[0].cpu().numpy()
top_index = batch_detections[:,4] > self.confidence
top_conf = batch_detections[top_index,4]
top_label = np.array(batch_detections[top_index,-1],np.int32)
top_bboxes = np.array(batch_detections[top_index,:4])
top_xmin, top_ymin, top_xmax, top_ymax = np.expand_dims(top_bboxes[:,0],-1),np.expand_dims(top_bboxes[:,1],-1),np.expand_dims(top_bboxes[:,2],-1),np.expand_dims(top_bboxes[:,3],-1)
boxes = retinanet_correct_boxes(top_ymin,top_xmin,top_ymax,top_xmax,np.array([self.input_shape[0],self.input_shape[1]]),image_shape)
except:
pass
t2 = time.time()
tact_time = (t2 - t1) / test_interval
return tact_time