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AECIF_Net.py
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import colorsys
import copy
import time
import os
import cv2
import numpy as np
import torch
import torch.nn.functional as F
from PIL import Image, ImageFilter
from torch import nn
from collections import Counter
from nets.AECIF_Net import AECIF_Net
from utils.utils import cvtColor, preprocess_input, resize_image, show_config
class HRnet_Segmentation(object):
_defaults = {
"model_path": 'logs/best_epoch_weights.pth', # weight path
"num_classes" : [7, 2], # [element, defect]
"backbone" : "hrnetv2_w48", # backbone name
"input_shape" : [520, 520], # [h, w]
"mix_type" : 0,
"cuda" : True,
}
def __init__(self, **kwargs):
self.__dict__.update(self._defaults)
for name, value in kwargs.items():
setattr(self, name, value)
#---------------------------------------------------#
if self.num_classes[0] <= 21:
self.colors = [ (0, 0, 0), (128, 0, 0), (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)]
self.colors_1 = [ (128, 128, 128), (128, 64, 0), (0, 0, 0), (128, 0, 0), (0, 128, 0), (128, 128, 0), (0, 0, 128), (128, 0, 128), (0, 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), (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))
self.generate()
show_config(**self._defaults)
def generate(self, onnx=False):
self.net = AECIF_Net(num_classes=self.num_classes, backbone=self.backbone, pretrained=False)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
state_dict = torch.load(self.model_path, map_location=device)
weights_dict = {}
for k, v in state_dict.items():
new_k = k.replace('module.', '') if 'module' in k else k
weights_dict[new_k] = v
self.net.load_state_dict(weights_dict)
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_e = self.net(images)[0][0]
pr_d = self.net(images)[1][0]
pr_e = F.softmax(pr_e.permute(1,2,0),dim = -1).cpu().numpy()
pr_d = F.softmax(pr_d.permute(1,2,0),dim = -1).cpu().numpy()
pr_e = pr_e[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_d = pr_d[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_e = cv2.resize(pr_e, (orininal_w, orininal_h), interpolation = cv2.INTER_LINEAR)
pr_d = cv2.resize(pr_d, (orininal_w, orininal_h), interpolation = cv2.INTER_LINEAR)
pr_e = pr_e.argmax(axis=-1)
pr_d = pr_d.argmax(axis=-1)
if count:
classes_nums_e = np.zeros([self.num_classes[0]])
classes_nums_d = np.zeros([self.num_classes[1]])
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[0]):
num = np.sum(pr_e == i)
ratio = num / total_points_num * 100
if num > 0:
print("|%25s | %15s | %14.2f%%|"%(str(name_classes[0][i]), str(num), ratio))
print('-' * 63)
classes_nums_e[i] = num
print("classes_nums_e:", classes_nums_e)
for i in range(self.num_classes[1]):
num = np.sum(pr_d == i)
ratio = num / total_points_num * 100
if num > 0:
print("|%25s | %15s | %14.2f%%|"%(str(name_classes[1][i]), str(num), ratio))
print('-' * 63)
classes_nums_d[i] = num
print("classes_nums_d:", classes_nums_d)
if self.mix_type == 0:
seg_img_e = np.reshape(np.array(self.colors, np.uint8)[np.reshape(pr_e, [-1])], [orininal_h, orininal_w, -1])
seg_img_d = np.reshape(np.array(self.colors_1, np.uint8)[np.reshape(pr_d, [-1])], [orininal_h, orininal_w, -1])
image_e = Image.fromarray(np.uint8(seg_img_e))
image_d = Image.fromarray(np.uint8(seg_img_d))
image_e = Image.blend(old_img, image_e, 0.7)
image_d = Image.blend(old_img, image_d, 0.7)
contours, hierarchy = cv2.findContours(np.uint8(pr_d), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
contour_map = np.array(image_e)
cv2.drawContours(contour_map, contours, -1, (255, 255, 255), thickness = 5)
contour_map = Image.fromarray(np.uint8(contour_map))
elif self.mix_type == 1:
seg_img_e = np.reshape(np.array(self.colors, np.uint8)[np.reshape(pr_e, [-1])], [orininal_h, orininal_w, -1])
seg_img_d = np.reshape(np.array(self.colors_1, np.uint8)[np.reshape(pr_d, [-1])], [orininal_h, orininal_w, -1])
image_e = Image.fromarray(np.uint8(seg_img_e))
image_d = Image.fromarray(np.uint8(seg_img_d))
elif self.mix_type == 2:
seg_img_e = np.reshape(np.array(self.colors, np.uint8)[np.reshape(pr_e, [-1])], [orininal_h, orininal_w, -1])
seg_img_d = np.reshape(np.array(self.colors_1, np.uint8)[np.reshape(pr_d, [-1])], [orininal_h, orininal_w, -1])
image_e = Image.fromarray(np.uint8(seg_img_e))
image_d = Image.fromarray(np.uint8(seg_img_d))
return image_e, image_d
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_e = self.net(images)[0][0]
pr_d = self.net(images)[1][0]
pr_e = F.softmax(pr_e.permute(1,2,0),dim = -1).cpu().numpy().argmax(axis=-1)
pr_d = F.softmax(pr_d.permute(1,2,0),dim = -1).cpu().numpy().argmax(axis=-1)
pr_e = pr_e[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_d = pr_d[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_e = self.net(images)[0][0]
pr_d = self.net(images)[1][0]
pr_e = F.softmax(pr_e.permute(1,2,0),dim = -1).cpu().numpy().argmax(axis=-1)
pr_d = F.softmax(pr_d.permute(1,2,0),dim = -1).cpu().numpy().argmax(axis=-1)
pr_e = pr_e[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_d = pr_d[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_e = self.net(images)[0][0]
pr_d = self.net(images)[1][0]
pr_e = F.softmax(pr_e.permute(1,2,0),dim = -1).cpu().numpy()
pr_d = F.softmax(pr_d.permute(1,2,0),dim = -1).cpu().numpy()
pr_e = pr_e[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_d = pr_d[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_e = cv2.resize(pr_e, (orininal_w, orininal_h), interpolation = cv2.INTER_LINEAR)
pr_d = cv2.resize(pr_d, (orininal_w, orininal_h), interpolation = cv2.INTER_LINEAR)
pr_e = pr_e.argmax(axis=-1)
pr_d = pr_d.argmax(axis=-1)
image_e = Image.fromarray(np.uint8(pr_e))
image_d = Image.fromarray(np.uint8(pr_d))
return image_e, image_d