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deeplab.py
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deeplab.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.deeplabv3_plus import DeepLab
from nets.deepv3 import DeepR50V3PlusD
from utils.utils import cvtColor, preprocess_input, resize_image, show_config
#-----------------------------------------------------------------------------------#
# "Loading the model here."
#-----------------------------------------------------------------------------------#
class DeeplabV3(object):
_defaults = {
#-------------------------------------------------------------------#
# "Loading the model here."
#-------------------------------------------------------------------#
"model_path" : 'model_data/ep090-loss1.074-val_loss1.188.pth',
#----------------------------------------#
# the number of classes +1
#----------------------------------------#
"num_classes" : 6,
#----------------------------------------#
# backbone
#----------------------------------------#
"backbone" : "resnet50",
#----------------------------------------#
# size of images
#----------------------------------------#
# "input_shape" : [512, 512],
"input_shape" : [896, 896],
#----------------------------------------#
# downsample
#----------------------------------------#
"downsample_factor" : 16,
#-------------------------------------------------#
#-------------------------------------------------#
"mix_type" : 0,
#-------------------------------#
#-------------------------------#
"cuda" : True,
}
#---------------------------------------------------#
# Deeplab Initializing
#---------------------------------------------------#
def __init__(self, **kwargs):
self.__dict__.update(self._defaults)
for name, value in kwargs.items():
setattr(self, name, value)
#---------------------------------------------------#
# Drawing boxes with different colors:
#---------------------------------------------------#
if self.num_classes <= 6:
self.colors = self.colors = [ (255, 0, 0), (255, 255, 255),(0, 0, 255),(255, 255, 0),(0, 255, 0),(0, 255, 255)]
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))
#---------------------------------------------------#
# get the model
#---------------------------------------------------#
self.generate()
show_config(**self._defaults)
#---------------------------------------------------#
# get all classes
#---------------------------------------------------#
def generate(self, onnx=False):
#-------------------------------#
# loading model
#-------------------------------#
# self.net = DeepLab(num_classes=self.num_classes, backbone=self.backbone, downsample_factor=self.downsample_factor, pretrained=False)
self.net = DeepR50V3PlusD(num_classes=self.num_classes)
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()
#---------------------------------------------------#
#---------------------------------------------------#
def detect_image(self, image, count=False, name_classes=None):
#---------------------------------------------------------#
#---------------------------------------------------------#
image = cvtColor(image)
#---------------------------------------------------#
#---------------------------------------------------#
old_img = copy.deepcopy(image)
orininal_h = np.array(image).shape[0]
orininal_w = np.array(image).shape[1]
#---------------------------------------------------------#
#---------------------------------------------------------#
image_data, nw, nh = resize_image(image, (self.input_shape[1],self.input_shape[0]))
#---------------------------------------------------------#
#---------------------------------------------------------#
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()
#---------------------------------------------------#
#---------------------------------------------------#
pr = self.net(images)[0]
#---------------------------------------------------#
#---------------------------------------------------#
pr = F.softmax(pr.permute(1,2,0),dim = -1).cpu().numpy()
#--------------------------------------#
#--------------------------------------#
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)]
#---------------------------------------------------#
#---------------------------------------------------#
pr = cv2.resize(pr, (orininal_w, orininal_h), interpolation = cv2.INTER_LINEAR)
#---------------------------------------------------#
#---------------------------------------------------#
pr = pr.argmax(axis=-1)
#---------------------------------------------------------#
#---------------------------------------------------------#
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])
#------------------------------------------------#
#------------------------------------------------#
image = Image.fromarray(np.uint8(seg_img))
#------------------------------------------------#
#------------------------------------------------#
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])
#------------------------------------------------#
#------------------------------------------------#
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')
#------------------------------------------------#
#------------------------------------------------#
image = Image.fromarray(np.uint8(seg_img))
return image
def get_FPS(self, image, test_interval):
#---------------------------------------------------------#
#---------------------------------------------------------#
image = cvtColor(image)
#---------------------------------------------------------#
#---------------------------------------------------------#
image_data, nw, nh = resize_image(image, (self.input_shape[1],self.input_shape[0]))
#---------------------------------------------------------#
#---------------------------------------------------------#
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()
#---------------------------------------------------#
#---------------------------------------------------#
pr = self.net(images)[0]
#---------------------------------------------------#
#---------------------------------------------------#
pr = F.softmax(pr.permute(1,2,0),dim = -1).cpu().numpy().argmax(axis=-1)
#--------------------------------------#
#--------------------------------------#
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():
#---------------------------------------------------#
#---------------------------------------------------#
pr = self.net(images)[0]
#---------------------------------------------------#
#---------------------------------------------------#
pr = F.softmax(pr.permute(1,2,0),dim = -1).cpu().numpy().argmax(axis=-1)
#--------------------------------------#
#--------------------------------------#
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):
#---------------------------------------------------------#
#---------------------------------------------------------#
image = cvtColor(image)
orininal_h = np.array(image).shape[0]
orininal_w = np.array(image).shape[1]
#---------------------------------------------------------#
#---------------------------------------------------------#
image_data, nw, nh = resize_image(image, (self.input_shape[1],self.input_shape[0]))
#---------------------------------------------------------#
#---------------------------------------------------------#
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()
#---------------------------------------------------#
#---------------------------------------------------#
pr = self.net(images)[0]
#---------------------------------------------------#
#---------------------------------------------------#
pr = F.softmax(pr.permute(1,2,0),dim = -1).cpu().numpy()
#--------------------------------------#
#--------------------------------------#
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)]
#---------------------------------------------------#
#---------------------------------------------------#
pr = cv2.resize(pr, (orininal_w, orininal_h), interpolation = cv2.INTER_LINEAR)
#---------------------------------------------------#
#---------------------------------------------------#
pr = pr.argmax(axis=-1)
image = Image.fromarray(np.uint8(pr))
return image