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hrnet.py
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hrnet.py
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import colorsys
import copy
import time
import cv2
import numpy as np
import torch
import torch.nn.functional as F
from PIL import Image
from torch import nn
from nets.FA_HRNet.hrnet import HRnet
# from nets.hrnet import HRnet
from utils.utils import cvtColor, preprocess_input, resize_image, show_config
#-----------------------------------------------------------------------------------#
# 使用自己训练好的模型预测需要修改3个参数
# model_path、backbone和num_classes都需要修改！
# 如果出现shape不匹配，一定要注意训练时的model_path、backbone和num_classes的修改
#-----------------------------------------------------------------------------------#
class HRnet_Segmentation(object):
_defaults = {
#-------------------------------------------------------------------#
# model_path指向logs文件夹下的权值文件
# 训练好后logs文件夹下存在多个权值文件，选择验证集损失较低的即可。
# 验证集损失较低不代表miou较高，仅代表该权值在验证集上泛化性能较好。
#-------------------------------------------------------------------#
"model_path" : '/data4/chezhihao/hrnet-pytorch-main/logs/Sentinel2_Build/best_epoch_weights.pth',
#----------------------------------------#
# 所需要区分的类的个数+1
#----------------------------------------#
"num_classes" : 2,
#----------------------------------------#
# 所使用的的主干网络：
# hrnetv2_w18
# hrnetv2_w32
# hrnetv2_w48
#----------------------------------------#
"backbone" : "hrnetv2_w48",
#----------------------------------------#
# 输入图片的大小
#----------------------------------------#
"input_shape" : [512, 512],
#-------------------------------------------------#
# mix_type参数用于控制检测结果的可视化方式
#
# mix_type = 0的时候代表原图与生成的图进行混合
# mix_type = 1的时候代表仅保留生成的图
# mix_type = 2的时候代表仅扣去背景，仅保留原图中的目标
#-------------------------------------------------#
"mix_type" : 1,
#-------------------------------#
# 是否使用Cuda
# 没有GPU可以设置成False
#-------------------------------#
"cuda" : True,
}
#---------------------------------------------------#
# 初始化HRnet
#---------------------------------------------------#
def __init__(self, **kwargs):
self.__dict__.update(self._defaults)
for name, value in kwargs.items():
setattr(self, name, value)
#---------------------------------------------------#
# 画框设置不同的颜色
#---------------------------------------------------#
if self.num_classes <= 21:
self.colors = [ (0, 0, 0), (255, 255, 255), (0, 128, 0), (128, 128, 0), (0, 0, 128), (128, 0, 128), (0, 128, 128),
(128, 128, 128), (64, 0, 0), (192, 0, 0), (64, 128, 0), (192, 128, 0), (64, 0, 128), (192, 0, 128),
(64, 128, 128), (192, 128, 128), (0, 64, 0), (128, 64, 0), (0, 192, 0), (128, 192, 0), (0, 64, 128),
(128, 64, 12)]
else:
hsv_tuples = [(x / self.num_classes, 1., 1.) for x in range(self.num_classes)]
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))
#---------------------------------------------------#
# 获得模型
#---------------------------------------------------#
self.generate()
show_config(**self._defaults)
#---------------------------------------------------#
# 获得所有的分类
#---------------------------------------------------#
def generate(self, onnx=False):
#-------------------------------#
# 载入模型与权值
#-------------------------------#
self.net = HRnet(num_classes=self.num_classes, backbone=self.backbone, pretrained=False)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
self.net.load_state_dict(torch.load(self.model_path, map_location=device))
self.net = self.net.eval()
print('{} model, and classes loaded.'.format(self.model_path))
if not onnx:
if self.cuda:
self.net = nn.DataParallel(self.net)
self.net = self.net.cuda()
#---------------------------------------------------#
# 检测图片
#---------------------------------------------------#
def detect_image(self, image, count=False, name_classes=None):
#---------------------------------------------------------#
# 在这里将图像转换成RGB图像，防止灰度图在预测时报错。
# 代码仅仅支持RGB图像的预测，所有其它类型的图像都会转化成RGB
#---------------------------------------------------------#
image = cvtColor(image)
#---------------------------------------------------#
# 对输入图像进行一个备份，后面用于绘图
#---------------------------------------------------#
old_img = copy.deepcopy(image)
orininal_h = np.array(image).shape[0]
orininal_w = np.array(image).shape[1]
#---------------------------------------------------------#
# 给图像增加灰条，实现不失真的resize
# 也可以直接resize进行识别
#---------------------------------------------------------#
image_data, nw, nh = resize_image(image, (self.input_shape[1],self.input_shape[0]))
#---------------------------------------------------------#
# 添加上batch_size维度
#---------------------------------------------------------#
image_data = np.expand_dims(np.transpose(preprocess_input(np.array(image_data, np.float32)), (2, 0, 1)), 0)
with torch.no_grad():
images = torch.from_numpy(image_data)
if self.cuda:
images = images.cuda()
#---------------------------------------------------#
# 图片传入网络进行预测
#---------------------------------------------------#
pr = self.net(images)[0]
#---------------------------------------------------#
# 取出每一个像素点的种类
#---------------------------------------------------#
pr = F.softmax(pr.permute(1,2,0),dim = -1).cpu().numpy()
#--------------------------------------#
# 将灰条部分截取掉
#--------------------------------------#
pr = pr[int((self.input_shape[0] - nh) // 2) : int((self.input_shape[0] - nh) // 2 + nh), \
int((self.input_shape[1] - nw) // 2) : int((self.input_shape[1] - nw) // 2 + nw)]
#---------------------------------------------------#
# 进行图片的resize
#---------------------------------------------------#
pr = cv2.resize(pr, (orininal_w, orininal_h), interpolation = cv2.INTER_LINEAR)
#---------------------------------------------------#
# 取出每一个像素点的种类
#---------------------------------------------------#
pr = pr.argmax(axis=-1)
#---------------------------------------------------------#
# 计数
#---------------------------------------------------------#
if count:
classes_nums = np.zeros([self.num_classes])
total_points_num = orininal_h * orininal_w
print('-' * 63)
print("|%25s | %15s | %15s|"%("Key", "Value", "Ratio"))
print('-' * 63)
for i in range(self.num_classes):
num = np.sum(pr == i)
ratio = num / total_points_num * 100
if num > 0:
print("|%25s | %15s | %14.2f%%|"%(str(name_classes[i]), str(num), ratio))
print('-' * 63)
classes_nums[i] = num
print("classes_nums:", classes_nums)
if self.mix_type == 0:
# seg_img = np.zeros((np.shape(pr)[0], np.shape(pr)[1], 3))
# for c in range(self.num_classes):
# seg_img[:, :, 0] += ((pr[:, :] == c ) * self.colors[c][0]).astype('uint8')
# seg_img[:, :, 1] += ((pr[:, :] == c ) * self.colors[c][1]).astype('uint8')
# seg_img[:, :, 2] += ((pr[:, :] == c ) * self.colors[c][2]).astype('uint8')
seg_img = np.reshape(np.array(self.colors, np.uint8)[np.reshape(pr, [-1])], [orininal_h, orininal_w, -1])
#------------------------------------------------#
# 将新图片转换成Image的形式
#------------------------------------------------#
image = Image.fromarray(np.uint8(seg_img))
#------------------------------------------------#
# 将新图与原图及进行混合
#------------------------------------------------#
image = Image.blend(old_img, image, 0.7)
elif self.mix_type == 1:
# seg_img = np.zeros((np.shape(pr)[0], np.shape(pr)[1], 3))
# for c in range(self.num_classes):
# seg_img[:, :, 0] += ((pr[:, :] == c ) * self.colors[c][0]).astype('uint8')
# seg_img[:, :, 1] += ((pr[:, :] == c ) * self.colors[c][1]).astype('uint8')
# seg_img[:, :, 2] += ((pr[:, :] == c ) * self.colors[c][2]).astype('uint8')
seg_img = np.reshape(np.array(self.colors, np.uint8)[np.reshape(pr, [-1])], [orininal_h, orininal_w, -1])
#------------------------------------------------#
# 将新图片转换成Image的形式
#------------------------------------------------#
image = Image.fromarray(np.uint8(seg_img))
elif self.mix_type == 2:
seg_img = (np.expand_dims(pr != 0, -1) * np.array(old_img, np.float32)).astype('uint8')
#------------------------------------------------#
# 将新图片转换成Image的形式
#------------------------------------------------#
image = Image.fromarray(np.uint8(seg_img))
return image
def get_FPS(self, image, test_interval):
#---------------------------------------------------------#
# 在这里将图像转换成RGB图像，防止灰度图在预测时报错。
# 代码仅仅支持RGB图像的预测，所有其它类型的图像都会转化成RGB
#---------------------------------------------------------#
image = cvtColor(image)
#---------------------------------------------------------#
# 给图像增加灰条，实现不失真的resize
# 也可以直接resize进行识别
#---------------------------------------------------------#
image_data, nw, nh = resize_image(image, (self.input_shape[1],self.input_shape[0]))
#---------------------------------------------------------#
# 添加上batch_size维度
#---------------------------------------------------------#
image_data = np.expand_dims(np.transpose(preprocess_input(np.array(image_data, np.float32)), (2, 0, 1)), 0)
with torch.no_grad():
images = torch.from_numpy(image_data)
if self.cuda:
images = images.cuda()
#---------------------------------------------------#
# 图片传入网络进行预测
#---------------------------------------------------#
pr = self.net(images)[0]
#---------------------------------------------------#
# 取出每一个像素点的种类
#---------------------------------------------------#
pr = F.softmax(pr.permute(1,2,0),dim = -1).cpu().numpy().argmax(axis=-1)
#--------------------------------------#
# 将灰条部分截取掉
#--------------------------------------#
pr = pr[int((self.input_shape[0] - nh) // 2) : int((self.input_shape[0] - nh) // 2 + nh), \
int((self.input_shape[1] - nw) // 2) : int((self.input_shape[1] - nw) // 2 + nw)]
t1 = time.time()
for _ in range(test_interval):
with torch.no_grad():
#---------------------------------------------------#
# 图片传入网络进行预测
#---------------------------------------------------#
pr = self.net(images)[0]
#---------------------------------------------------#
# 取出每一个像素点的种类
#---------------------------------------------------#
pr = F.softmax(pr.permute(1,2,0),dim = -1).cpu().numpy().argmax(axis=-1)
#--------------------------------------#
# 将灰条部分截取掉
#--------------------------------------#
pr = pr[int((self.input_shape[0] - nh) // 2) : int((self.input_shape[0] - nh) // 2 + nh), \
int((self.input_shape[1] - nw) // 2) : int((self.input_shape[1] - nw) // 2 + nw)]
t2 = time.time()
tact_time = (t2 - t1) / test_interval
return tact_time
def convert_to_onnx(self, simplify, model_path):
import onnx
self.generate(onnx=True)
im = torch.zeros(1, 3, *self.input_shape).to('cpu') # image size(1, 3, 512, 512) BCHW
input_layer_names = ["images"]
output_layer_names = ["output"]
# Export the model
print(f'Starting export with onnx {onnx.__version__}.')
torch.onnx.export(self.net,
im,
f = model_path,
verbose = False,
opset_version = 12,
training = torch.onnx.TrainingMode.EVAL,
do_constant_folding = True,
input_names = input_layer_names,
output_names = output_layer_names,
dynamic_axes = None)
# Checks
model_onnx = onnx.load(model_path) # load onnx model
onnx.checker.check_model(model_onnx) # check onnx model
# Simplify onnx
if simplify:
import onnxsim
print(f'Simplifying with onnx-simplifier {onnxsim.__version__}.')
model_onnx, check = onnxsim.simplify(
model_onnx,
dynamic_input_shape=False,
input_shapes=None)
assert check, 'assert check failed'
onnx.save(model_onnx, model_path)
print('Onnx model save as {}'.format(model_path))
def get_miou_png(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。
# 代码仅仅支持RGB图像的预测，所有其它类型的图像都会转化成RGB
#---------------------------------------------------------#
image = cvtColor(image)
orininal_h = np.array(image).shape[0]
orininal_w = np.array(image).shape[1]
#---------------------------------------------------------#
# 给图像增加灰条，实现不失真的resize
# 也可以直接resize进行识别
#---------------------------------------------------------#
image_data, nw, nh = resize_image(image, (self.input_shape[1],self.input_shape[0]))
#---------------------------------------------------------#
# 添加上batch_size维度
#---------------------------------------------------------#
image_data = np.expand_dims(np.transpose(preprocess_input(np.array(image_data, np.float32)), (2, 0, 1)), 0)
with torch.no_grad():
images = torch.from_numpy(image_data)
if self.cuda:
images = images.cuda()
#---------------------------------------------------#
# 图片传入网络进行预测
#---------------------------------------------------#
pr = self.net(images)[0]
#---------------------------------------------------#
# 取出每一个像素点的种类
#---------------------------------------------------#
pr = F.softmax(pr.permute(1,2,0),dim = -1).cpu().numpy()
#--------------------------------------#
# 将灰条部分截取掉
#--------------------------------------#
pr = pr[int((self.input_shape[0] - nh) // 2) : int((self.input_shape[0] - nh) // 2 + nh), \
int((self.input_shape[1] - nw) // 2) : int((self.input_shape[1] - nw) // 2 + nw)]
#---------------------------------------------------#
# 进行图片的resize
#---------------------------------------------------#
pr = cv2.resize(pr, (orininal_w, orininal_h), interpolation = cv2.INTER_LINEAR)
#---------------------------------------------------#
# 取出每一个像素点的种类
#---------------------------------------------------#
pr = pr.argmax(axis=-1)
seg_img = np.zeros((np.shape(pr)[0], np.shape(pr)[1], 3))
for c in range(self.num_classes):
seg_img[:, :, 0] += ((pr[:, :] == c ) * self.colors[c][0]).astype('uint8')
seg_img[:, :, 1] += ((pr[:, :] == c ) * self.colors[c][1]).astype('uint8')
seg_img[:, :, 2] += ((pr[:, :] == c ) * self.colors[c][2]).astype('uint8')
image = Image.fromarray(np.uint8(seg_img))
return image