-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathmyFind_handpoints.py
131 lines (108 loc) · 5.55 KB
/
myFind_handpoints.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
import argparse
import torch
from torchvision.models import shufflenet_v2_x1_5, shufflenet_v2_x1_0, shufflenet_v2_x2_0
from hand_data_iter.datasets import draw_bd_handpose
from models.mobilenetv2 import MobileNetV2
from models.resnet import resnet18, resnet34, resnet50, resnet101
from models.rexnetv1 import ReXNetV1
from models.shufflenet import ShuffleNet
from models.shufflenetv2 import ShuffleNetV2
from models.squeezenet import squeezenet1_1, squeezenet1_0
from utils.common_utils import *
def findhand(img):
# 定义x和y列表
lmlist = []
parser = argparse.ArgumentParser(description=' Project Hand Pose Inference')
parser.add_argument('--model_path', type=str,
default='./model_exp/shufflenet_v2_x2_0-size-256-model_epoch-97.pth',
help='model_path') # 模型路径
parser.add_argument('--model', type=str, default='shufflenet_v2_x2_0',
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,
''') # 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('--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
# --------------------------------------------------------------------------
ops = parser.parse_args() # 解析添加参数
# --------------------------------------------------------------------------
vars(ops)
# ---------------------------------------------------------------------------
os.environ['CUDA_VISIBLE_DEVICES'] = ops.GPUS
# ---------------------------------------------------------------- 构建模型
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(width_mult=1.0, depth_mult=1.0, 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
# 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)
# ---------------------------------------------------------------- 预测图片
with torch.no_grad():
img_width = img.shape[1]
img_height = img.shape[0]
# 输入图片预处理
try:
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)
lmlist.append([int(x), int(y)])
except:
print("pass")
return img, lmlist