-
Notifications
You must be signed in to change notification settings - Fork 116
/
inference.py
182 lines (151 loc) · 7.02 KB
/
inference.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
#-*-coding:utf-8-*-
# date:2021-04-5
# Author: Eric.Lee
# function: Inference
import os
import argparse
import torch
import torch.nn as nn
import numpy as np
import time
import datetime
import os
import math
from datetime import datetime
import cv2
import torch.nn.functional as F
from models.resnet import resnet18,resnet34,resnet50,resnet101
from models.squeezenet import squeezenet1_1,squeezenet1_0
from models.shufflenetv2 import ShuffleNetV2
from models.shufflenet import ShuffleNet
from models.mobilenetv2 import MobileNetV2
from torchvision.models import shufflenet_v2_x1_5 ,shufflenet_v2_x1_0 , shufflenet_v2_x2_0
from models.rexnetv1 import ReXNetV1
from utils.common_utils import *
import copy
from hand_data_iter.datasets import draw_bd_handpose
if __name__ == "__main__":
parser = argparse.ArgumentParser(description=' Project Hand Pose Inference')
parser.add_argument('--model_path', type=str, default = './weights/ReXNetV1-size-256-wingloss102-0.122.pth',
help = 'model_path') # 模型路径
parser.add_argument('--model', type=str, default = 'ReXNetV1',
help = '''model : resnet_34,resnet_50,resnet_101,squeezenet1_0,squeezenet1_1,shufflenetv2,shufflenet,mobilenetv2
shufflenet_v2_x1_5 ,shufflenet_v2_x1_0 , shufflenet_v2_x2_0,ReXNetV1''') # 模型类型
parser.add_argument('--num_classes', type=int , default = 42,
help = 'num_classes') # 手部21关键点， (x,y)*2 = 42
parser.add_argument('--GPUS', type=str, default = '0',
help = 'GPUS') # GPU选择
parser.add_argument('--test_path', type=str, default = './image/',
help = 'test_path') # 测试图片路径
parser.add_argument('--img_size', type=tuple , default = (256,256),
help = 'img_size') # 输入模型图片尺寸
parser.add_argument('--vis', type=bool , default = True,
help = 'vis') # 是否可视化图片
print('\n/******************* {} ******************/\n'.format(parser.description))
#--------------------------------------------------------------------------
ops = parser.parse_args()# 解析添加参数
#--------------------------------------------------------------------------
print('----------------------------------')
unparsed = vars(ops) # parse_args()方法的返回值为namespace，用vars()内建函数化为字典
for key in unparsed.keys():
print('{} : {}'.format(key,unparsed[key]))
#---------------------------------------------------------------------------
os.environ['CUDA_VISIBLE_DEVICES'] = ops.GPUS
test_path = ops.test_path # 测试图片文件夹路径
#---------------------------------------------------------------- 构建模型
print('use model : %s'%(ops.model))
if ops.model == 'resnet_50':
model_ = resnet50(num_classes = ops.num_classes,img_size=ops.img_size[0])
elif ops.model == 'resnet_18':
model_ = resnet18(num_classes = ops.num_classes,img_size=ops.img_size[0])
elif ops.model == 'resnet_34':
model_ = resnet34(num_classes = ops.num_classes,img_size=ops.img_size[0])
elif ops.model == 'resnet_101':
model_ = resnet101(num_classes = ops.num_classes,img_size=ops.img_size[0])
elif ops.model == "squeezenet1_0":
model_ = squeezenet1_0(num_classes=ops.num_classes)
elif ops.model == "squeezenet1_1":
model_ = squeezenet1_1(num_classes=ops.num_classes)
elif ops.model == "shufflenetv2":
model_ = ShuffleNetV2(ratio=1., num_classes=ops.num_classes)
elif ops.model == "shufflenet_v2_x1_5":
model_ = shufflenet_v2_x1_5(pretrained=False,num_classes=ops.num_classes)
elif ops.model == "shufflenet_v2_x1_0":
model_ = shufflenet_v2_x1_0(pretrained=False,num_classes=ops.num_classes)
elif ops.model == "shufflenet_v2_x2_0":
model_ = shufflenet_v2_x2_0(pretrained=False,num_classes=ops.num_classes)
elif ops.model == "shufflenet":
model_ = ShuffleNet(num_blocks = [2,4,2], num_classes=ops.num_classes, groups=3)
elif ops.model == "mobilenetv2":
model_ = MobileNetV2(num_classes=ops.num_classes)
elif ops.model == "ReXNetV1":
model_ = ReXNetV1(num_classes=ops.num_classes)
use_cuda = torch.cuda.is_available()
device = torch.device("cuda:0" if use_cuda else "cpu")
model_ = model_.to(device)
model_.eval() # 设置为前向推断模式
# print(model_)# 打印模型结构
# 加载测试模型
if os.access(ops.model_path,os.F_OK):# checkpoint
chkpt = torch.load(ops.model_path, map_location=device)
model_.load_state_dict(chkpt)
print('load test model : {}'.format(ops.model_path))
#---------------------------------------------------------------- 预测图片
'''建议 检测手bbox后，crop手图片的预处理方式：
# img 为原图
x_min,y_min,x_max,y_max,score = bbox
w_ = max(abs(x_max-x_min),abs(y_max-y_min))
w_ = w_*1.1
x_mid = (x_max+x_min)/2
y_mid = (y_max+y_min)/2
x1,y1,x2,y2 = int(x_mid-w_/2),int(y_mid-w_/2),int(x_mid+w_/2),int(y_mid+w_/2)
x1 = np.clip(x1,0,img.shape[1]-1)
x2 = np.clip(x2,0,img.shape[1]-1)
y1 = np.clip(y1,0,img.shape[0]-1)
y2 = np.clip(y2,0,img.shape[0]-1)
'''
with torch.no_grad():
idx = 0
for file in os.listdir(ops.test_path):
if '.jpg' not in file:
continue
idx += 1
print('{}) image : {}'.format(idx,file))
img = cv2.imread(ops.test_path + file)
img_width = img.shape[1]
img_height = img.shape[0]
# 输入图片预处理
img_ = cv2.resize(img, (ops.img_size[1],ops.img_size[0]), interpolation = cv2.INTER_CUBIC)
img_ = img_.astype(np.float32)
img_ = (img_-128.)/256.
img_ = img_.transpose(2, 0, 1)
img_ = torch.from_numpy(img_)
img_ = img_.unsqueeze_(0)
if use_cuda:
img_ = img_.cuda() # (bs, 3, h, w)
pre_ = model_(img_.float()) # 模型推理
output = pre_.cpu().detach().numpy()
output = np.squeeze(output)
pts_hand = {} #构建关键点连线可视化结构
for i in range(int(output.shape[0]/2)):
x = (output[i*2+0]*float(img_width))
y = (output[i*2+1]*float(img_height))
pts_hand[str(i)] = {}
pts_hand[str(i)] = {
"x":x,
"y":y,
}
draw_bd_handpose(img,pts_hand,0,0) # 绘制关键点连线
#------------- 绘制关键点
for i in range(int(output.shape[0]/2)):
x = (output[i*2+0]*float(img_width))
y = (output[i*2+1]*float(img_height))
cv2.circle(img, (int(x),int(y)), 3, (255,50,60),-1)
cv2.circle(img, (int(x),int(y)), 1, (255,150,180),-1)
if ops.vis:
cv2.namedWindow('image',0)
cv2.imshow('image',img)
if cv2.waitKey(600) == 27 :
break
cv2.destroyAllWindows()
print('well done ')