pytorch目标检测ssd六__预测效果与预测过程代码详解

本篇博客是我学习(https://blog.csdn.net/weixin_44791964)博主写的pytorch的ssd的博客后写的，大家可以直接去看这位博主的博客(https://blog.csdn.net/weixin_44791964/article/details/104981486)。这位博主在b站还有配套视频，传送门：(https://www.bilibili.com/video/BV1A7411976Z)。这位博主的在GitHub的源代码(https://github.com/bubbliiiing/ssd-pytorch)。 侵删

这篇博客将要讲述ssd的预测效果与预测过程是怎么实现的

# Adapted from https://github.com/Hakuyume/chainer-ssd
def decode(loc, priors, variances):
boxes = torch.cat((
#首先计算先验框调整之后的中心的位置
priors[:, :2] + loc[:, :2] * variances[0] * priors[:, 2:],
#计算出调整后的先验框的宽和高
priors[:, 2:] * torch.exp(loc[:, 2:] * variances[1])), 1)
#计算先验框的左上角，
boxes[:, :2] -= boxes[:, 2:] / 2
#计算先验框的右下角
boxes[:, 2:] += boxes[:, :2]
return boxes

from ssd import SSD
from PIL import Image

#首先ssd.py的ssd类
ssd = SSD()

while True:
img = input('Input image filename:')
try:
image = Image.open(img)
except:
print('Open Error! Try again!')
continue
else:
r_image = ssd.detect_image(image)
r_image.show()

#import cv2
import numpy as np
import colorsys
import os
import torch
from nets import ssd
import torch.backends.cudnn as cudnn
from utils.config import Config
from utils.box_utils import letterbox_image,ssd_correct_boxes
from PIL import Image,ImageFont, ImageDraw
from torch.autograd import Variable

MEANS = (104, 117, 123)
class SSD(object):
_defaults = {
#训练好的模型的权重存放的位置
"model_path": 'model_data/ssd_weights.pth',
#我们分的类，对应的txt文件
"classes_path": 'model_data/voc_classes.txt',
#输入图片的大小
"model_image_size" : (300, 300, 3),
"confidence": 0.5,
}

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

#---------------------------------------------------#
#   初始化RFB
#---------------------------------------------------#
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
#---------------------------------------------------#
#   获得所有的分类
#---------------------------------------------------#
def generate(self):
# 计算总的种类
self.num_classes = len(self.class_names) + 1

# 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
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = ssd.get_ssd("test",self.num_classes)
self.net = model
model.load_state_dict(torch.load(self.model_path))

self.net = torch.nn.DataParallel(self.net)
cudnn.benchmark = True
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
#---------------------------------------------------#
#输入参数是我们输入进来的图片
def detect_image(self, image):
#首先获得我们输入进来的图片的shape
image_shape = np.array(np.shape(image)[0:2])
#给我们输入进来的图片加上letterbox_image，目的是防止我们的图片失真，给图片边缘加上灰条，使图片整体的大小变成300x300x3的大小
crop_img = np.array(letterbox_image(image, (self.model_image_size[0],self.model_image_size[1])))
#将图片转化为numpy的float64的大小
photo = np.array(crop_img,dtype = np.float64)

# 图片预处理，归一化，这里加了cuda
photo = Variable(torch.from_numpy(np.expand_dims(np.transpose(crop_img-MEANS,(2,0,1)),0)).cuda().type(torch.FloatTensor))
"""
这里将我们的图片传入网络进行预测
这里的预测结果其实是包含了：
我们这张图片里面，每一个类所对应的得分最高的两百个框的参数
"""
preds = self.net(photo)

top_conf = []
top_label = []
top_bboxes = []
#下面就是对每一个类进行遍历，从1开始（0是指背景），然后判断先验框是否大于了self.confidence，
#遍历完所有的类之后就得到我们这张图片所有的预测结果了
for i in range(preds.size(1)):
j = 0
while preds[0, i, j, 0] >= self.confidence:
score = preds[0, i, j, 0]
label_name = self.class_names[i-1]
pt = (preds[0, i, j, 1:]).detach().numpy()
#如果大于了self.confidence就把这个框及其得分标签等保留下来
coords = [pt[0], pt[1], pt[2], pt[3]]
top_conf.append(score)
top_label.append(label_name)
top_bboxes.append(coords)
j = j + 1
# 将预测结果进行解码，如果检测到预测框里面有物体，就开始检测物体，如果没有物体，就返回这张图片了
if len(top_conf)<=0:
return image
top_conf = np.array(top_conf)
top_label = np.array(top_label)
top_bboxes = np.array(top_bboxes)
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
boxes = ssd_correct_boxes(top_ymin,top_xmin,top_ymax,top_xmax,np.array([self.model_image_size[0],self.model_image_size[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 = (np.shape(image)[0] + np.shape(image)[1]) // self.model_image_size[0]

for i, c in enumerate(top_label):
predicted_class = 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)

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

总结一下，预测其实就是先将我们所有的先验框都来遍历一遍，遍历的时候将每个先验框框都用类别（就是我们分出的类）来对比一遍，然后选出每个类别对应得分最高的200个先验框，然后再次进行遍历，来判断是先验框否满足了我们设定的confidence的值，然后再判断是否含有物体，如果含有物体的话，我们就进行预测，反之就不进行预测了。