Pytorch: 目标检测网络-人体关键点检测
Copyright: Jingmin Wei, Pattern Recognition and Intelligent System, School of Artificial and Intelligence, Huazhong University of Science and Technology
文章目录
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+ Pytorch: 目标检测网络-人体关键点检测
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* Reference
* 人体关键点检测代码实现
本教程不商用，仅供学习和参考交流使用，如需转载，请联系本人。
Reference
RCNN(Regions with CNN Features)
人体关键点检测代码实现
通过检测人体的一些关键点，如关节、五官等等，描述人体的骨骼信息。
MS COCO 数据集是多人人体关键点检测数据集，具有关键点个数为 17 17 1 7 ，图像样本数多于 30 30 3 0 万张，也是目前的相关研究中最常用的数据集。
import numpy as np
import torchvision
import torch
import torchvision.transforms as transforms
from PIL import Image, ImageDraw, ImageFont
import matplotlib.pyplot as plt
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(device)
print(torch.cuda.device_count())
print(torch.cuda.get_device_name(0))
cuda
1
GeForce MX250
model = torchvision.models.detection.keypointrcnn_resnet50_fpn(pretrained = True).to(device)
model.eval()
KeypointRCNN(
(transform): GeneralizedRCNNTransform(
Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
Resize(min_size=(640, 672, 704, 736, 768, 800), max_size=1333, mode='bilinear')
)
(backbone): BackboneWithFPN(
(body): IntermediateLayerGetter(
(conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
(bn1): FrozenBatchNorm2d(64, eps=0.0)
(relu): ReLU(inplace=True)
(maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
(layer1): Sequential(
(0): Bottleneck(
(conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(64, eps=0.0)
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(64, eps=0.0)
(conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(256, eps=0.0)
(relu): ReLU(inplace=True)
(downsample): Sequential(
(0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): FrozenBatchNorm2d(256, eps=0.0)
)
)
(1): Bottleneck(
(conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(64, eps=0.0)
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(64, eps=0.0)
(conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(256, eps=0.0)
(relu): ReLU(inplace=True)
)
(2): Bottleneck(
(conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(64, eps=0.0)
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(64, eps=0.0)
(conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(256, eps=0.0)
(relu): ReLU(inplace=True)
)
)
(layer2): Sequential(
(0): Bottleneck(
(conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(128, eps=0.0)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(128, eps=0.0)
(conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(512, eps=0.0)
(relu): ReLU(inplace=True)
(downsample): Sequential(
(0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): FrozenBatchNorm2d(512, eps=0.0)
)
)
(1): Bottleneck(
(conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(128, eps=0.0)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(128, eps=0.0)
(conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(512, eps=0.0)
(relu): ReLU(inplace=True)
)
(2): Bottleneck(
(conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(128, eps=0.0)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(128, eps=0.0)
(conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(512, eps=0.0)
(relu): ReLU(inplace=True)
)
(3): Bottleneck(
(conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(128, eps=0.0)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(128, eps=0.0)
(conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(512, eps=0.0)
(relu): ReLU(inplace=True)
)
)
(layer3): Sequential(
(0): Bottleneck(
(conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(256, eps=0.0)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(256, eps=0.0)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(1024, eps=0.0)
(relu): ReLU(inplace=True)
(downsample): Sequential(
(0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): FrozenBatchNorm2d(1024, eps=0.0)
)
)
(1): Bottleneck(
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(256, eps=0.0)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(256, eps=0.0)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(1024, eps=0.0)
(relu): ReLU(inplace=True)
)
(2): Bottleneck(
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(256, eps=0.0)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(256, eps=0.0)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(1024, eps=0.0)
(relu): ReLU(inplace=True)
)
(3): Bottleneck(
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(256, eps=0.0)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(256, eps=0.0)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(1024, eps=0.0)
(relu): ReLU(inplace=True)
)
(4): Bottleneck(
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(256, eps=0.0)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(256, eps=0.0)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(1024, eps=0.0)
(relu): ReLU(inplace=True)
)
(5): Bottleneck(
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(256, eps=0.0)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(256, eps=0.0)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(1024, eps=0.0)
(relu): ReLU(inplace=True)
)
)
(layer4): Sequential(
(0): Bottleneck(
(conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(512, eps=0.0)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(512, eps=0.0)
(conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(2048, eps=0.0)
(relu): ReLU(inplace=True)
(downsample): Sequential(
(0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): FrozenBatchNorm2d(2048, eps=0.0)
)
)
(1): Bottleneck(
(conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(512, eps=0.0)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(512, eps=0.0)
(conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(2048, eps=0.0)
(relu): ReLU(inplace=True)
)
(2): Bottleneck(
(conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(512, eps=0.0)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(512, eps=0.0)
(conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(2048, eps=0.0)
(relu): ReLU(inplace=True)
)
)
)
(fpn): FeaturePyramidNetwork(
(inner_blocks): ModuleList(
(0): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))
(1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1))
(2): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1))
(3): Conv2d(2048, 256, kernel_size=(1, 1), stride=(1, 1))
)
(layer_blocks): ModuleList(
(0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(3): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
(extra_blocks): LastLevelMaxPool()
)
)
(rpn): RegionProposalNetwork(
(anchor_generator): AnchorGenerator()
(head): RPNHead(
(conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(cls_logits): Conv2d(256, 3, kernel_size=(1, 1), stride=(1, 1))
(bbox_pred): Conv2d(256, 12, kernel_size=(1, 1), stride=(1, 1))
)
)
(roi_heads): RoIHeads(
(box_roi_pool): MultiScaleRoIAlign(featmap_names=['0', '1', '2', '3'], output_size=(7, 7), sampling_ratio=2)
(box_head): TwoMLPHead(
(fc6): Linear(in_features=12544, out_features=1024, bias=True)
(fc7): Linear(in_features=1024, out_features=1024, bias=True)
)
(box_predictor): FastRCNNPredictor(
(cls_score): Linear(in_features=1024, out_features=2, bias=True)
(bbox_pred): Linear(in_features=1024, out_features=8, bias=True)
)
(keypoint_roi_pool): MultiScaleRoIAlign(featmap_names=['0', '1', '2', '3'], output_size=(14, 14), sampling_ratio=2)
(keypoint_head): KeypointRCNNHeads(
(0): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): ReLU(inplace=True)
(2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(3): ReLU(inplace=True)
(4): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(5): ReLU(inplace=True)
(6): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(7): ReLU(inplace=True)
(8): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(9): ReLU(inplace=True)
(10): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(11): ReLU(inplace=True)
(12): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(13): ReLU(inplace=True)
(14): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(15): ReLU(inplace=True)
)
(keypoint_predictor): KeypointRCNNPredictor(
(kps_score_lowres): ConvTranspose2d(512, 17, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
)
)
)
COCO_INSTANCE_CATEGORY_NAMES = [
'__background__', 'person', 'bicycle', 'car', 'motorcycle',
'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light',
'fire hydrant', 'N/A', 'stop sign', 'parking meter', 'bench',
'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant',
'bear', 'zebra', 'giraffe', 'N/A', 'backpack', 'umbrella', 'N/A',
'N/A', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard',
'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard',
'surfboard', 'tennis racket', 'bottle', 'N/A', 'wine glass',
'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple',
'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza',
'donut', 'cake', 'chair', 'couch', 'potted plant', 'bed', 'N/A',
'dining table', 'N/A', 'N/A', 'toilet', 'N/A', 'tv', 'laptop',
'mouse', 'remote', 'keyboard', 'cell phone', 'microwave', 'oven',
'toaster', 'sink', 'refrigerator', 'N/A', 'book', 'clock',
'vase', 'scissors', 'teddy bear', 'hair drier', 'toothbrush'
]
COCO_PERSON_KEYPOINT_NAMES = [
'nose', 'left_eye', 'right_eye', 'left_ear',
'right_ear', 'left_shoulder', 'right_shoulder', 'left_elbow',
'right_elbow', 'left_wrist', 'right_wrist', 'left_hip', 'right_hip',
'left_knee', 'right_knee', 'left_ankle', 'right_ankle'
]
17 17 1 7 个关键点分别是鼻子、左眼、右眼、左耳朵、右耳朵、左肩、右肩、左胳膊肘、右胳膊肘、左手腕、右手腕、左臀、右臀、左膝、右膝、左脚踝和右脚踝
image = Image.open('./data/objdetect/woman sport.jpg')
transform_d = transforms.Compose([transforms.ToTensor()])
image_t = transform_d(image).to(device)
print(image_t.shape)
torch.Size([3, 592, 590])
pred = model([image_t])
pred
[{'boxes': tensor([[ 77.9719, 120.6577, 454.7404, 585.7834],
[274.8855, 165.7962, 317.6319, 217.0340],
[227.4592, 116.2128, 446.5350, 484.9686]], device='cuda:0',
grad_fn=<stackbackward>),
'labels': tensor([1, 1, 1], device='cuda:0'),
'scores': tensor([1.0000, 0.1437, 0.0849], device='cuda:0', grad_fn=<indexbackward>),
'keypoints': tensor([[[373.2670, 176.3106, 1.0000],
[378.4282, 168.2023, 1.0000],
[368.8431, 163.0424, 1.0000],
[345.9863, 154.1969, 1.0000],
[346.7236, 154.9340, 1.0000],
[347.4609, 217.5896, 1.0000],
[311.3325, 208.7441, 1.0000],
[365.8938, 278.7710, 1.0000],
[245.7113, 260.3429, 1.0000],
[413.0821, 335.5296, 1.0000],
[234.6516, 334.7924, 1.0000],
[290.6876, 338.4781, 1.0000],
[301.0100, 330.3697, 1.0000],
[241.2874, 449.7839, 1.0000],
[379.9029, 436.5156, 1.0000],
[126.2661, 446.0983, 1.0000],
[399.0730, 560.3526, 1.0000]],
[[317.2634, 172.0179, 1.0000],
[317.2634, 176.4097, 1.0000],
[317.2634, 170.5540, 1.0000],
[303.9973, 169.8220, 1.0000],
[315.0523, 170.5540, 1.0000],
[298.1012, 177.8737, 1.0000],
[304.7343, 178.6057, 1.0000],
[284.8351, 210.0803, 1.0000],
[286.3091, 209.3483, 1.0000],
[284.0981, 208.6164, 1.0000],
[275.2540, 216.6680, 1.0000],
[286.3091, 213.7402, 1.0000],
[305.4713, 213.7402, 1.0000],
[275.2540, 216.6680, 1.0000],
[301.0492, 179.3376, 1.0000],
[284.8351, 209.3483, 1.0000],
[317.2634, 216.6680, 1.0000]],
[[371.9169, 175.5824, 1.0000],
[374.8575, 166.7323, 1.0000],
[368.9763, 162.3072, 1.0000],
[344.7162, 154.9321, 1.0000],
[344.7162, 154.9321, 1.0000],
[345.4514, 213.1955, 1.0000],
[310.1640, 209.5080, 1.0000],
[363.8302, 279.5716, 1.0000],
[246.9408, 258.1837, 1.0000],
[411.6152, 334.1475, 1.0000],
[236.6487, 334.1475, 1.0000],
[289.5797, 340.0475, 1.0000],
[305.7531, 334.8849, 1.0000],
[240.3244, 451.4118, 1.0000],
[380.0036, 438.8741, 1.0000],
[238.8541, 448.4617, 1.0000],
[239.5893, 447.7242, 1.0000]]], device='cuda:0',
grad_fn=<copyslices>),
'keypoints_scores': tensor([[18.6403, 14.2279, 17.8857, 6.0422, 16.7495, 8.1433, 13.2838, 12.0588,
14.6382, 11.9217, 12.6847, 6.2832, 7.8149, 8.2861, 6.8149, 4.7025,
8.9715],
[-2.1022, -2.7731, -0.9629, 0.2135, 2.5375, 2.8867, 3.1575, 1.6579,
-0.6801, -0.0918, -3.9865, -0.1694, 0.2302, -3.2699, -1.9082, -2.1317,
-3.7917],
[16.1880, 9.7519, 15.0690, 5.6050, 13.6298, 7.4073, 8.4592, 11.1084,
10.7398, 9.4384, 10.2107, 4.9596, 6.7067, 8.7411, 8.4057, -3.4510,
-3.6268]], device='cuda:0', grad_fn=<copyslices>)}]
</copyslices></copyslices></indexbackward></stackbackward>
上面的程序对图像进行预测后在pred的结果中包含以下内容：
- boxes: 检测出目标的位置。
- labels: 检测出目标的分类。
- scores: 检测出目标为对应分类的得分。
- keypoints: 检测出N个实例中每个实例的 K K K 个关键位置，其中每个点的数据格式为 [ x , y , v i s i b i l i t y ] [x, y, visibility][x ,y ,v i s i b i l i t y ] ，如果 v i s i b i l i t y = 0 visibility=0 v i s i b i l i t y =0 ，表示关键点不可见。
- keypoints_scores: 表示每个关键点的相应的分。
下面先可视化得分高于 0.5 0.5 0 .5 的目标：
pred_class = [COCO_INSTANCE_CATEGORY_NAMES[ii] for ii in list(pred[0]['labels'].cpu().numpy())]
pred_score = list(pred[0]['scores'].cpu().detach().numpy())
pred_boxes = [[ii[0], ii[1], ii[2], ii[3]] for ii in list(pred[0]['boxes'].cpu().detach().numpy())]
pred_index = [pred_score.index(x) for x in pred_score if x > 0.5]
fontsize = np.int16(image.size[1] / 30)
font1 = ImageFont.truetype('C:/windows/Fonts/STXIHEI.TTF', fontsize)
image2 = image.copy()
draw = ImageDraw.Draw(image2)
for index in pred_index:
box = pred_boxes[index]
draw.rectangle(box, outline = 'red')
texts = pred_class[index] + ':' + str(np.round(pred_score[index], 2))
draw.text((box[0], box[1]), texts, fill = 'red', font = font1)
image2
可视化出该人物和网络检测到的关键点位置：
pred_index = [pred_score.index(x) for x in pred_score if x > 0.5]
pred_keypoint = pred[0]['keypoints']
pred_keypoint = pred_keypoint[pred_index].cpu().detach().numpy()
pred_keypoint
array([[[373.26697, 176.31064, 1. ],
[378.42822, 168.20227, 1. ],
[368.84308, 163.04239, 1. ],
[345.9863 , 154.19691, 1. ],
[346.72357, 154.93402, 1. ],
[347.4609 , 217.58963, 1. ],
[311.33246, 208.74411, 1. ],
[365.8938 , 278.77097, 1. ],
[245.71129, 260.34286, 1. ],
[413.08206, 335.52957, 1. ],
[234.65157, 334.79245, 1. ],
[290.6876 , 338.4781 , 1. ],
[301.01 , 330.3697 , 1. ],
[241.28741, 449.7839 , 1. ],
[379.90286, 436.51562, 1. ],
[126.26609, 446.09827, 1. ],
[399.07303, 560.3526 , 1. ]]], dtype=float32)
fontsize = np.int16(image.size[1] / 50)
r = np.int16(image.size[1] / 150)
font1 = ImageFont.truetype('C:/windows/Fonts/STXIHEI.TTF', fontsize)
image3 = image.copy()
draw = ImageDraw.Draw(image3)
for index in range(pred_keypoint.shape[0]):
keypoints = pred_keypoint[index]
for ii in range(keypoints.shape[0]):
x = keypoints[ii, 0]
y = keypoints[ii, 1]
visi = keypoints[ii, 2]
if visi > 0:
draw.ellipse(xy = (x - r, y - r, x + r, y + r), fill = (255, 0, 0))
texts = str(ii + 1)
draw.text((x + r, y - r), texts, fill = 'red', font = font1)
image3
将上面的人物关键点检测定义为一个函数，以方便调用
model = torchvision.models.detection.keypointrcnn_resnet50_fpn(pretrained = True)
model.eval()
KeypointRCNN(
(transform): GeneralizedRCNNTransform(
Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
Resize(min_size=(640, 672, 704, 736, 768, 800), max_size=1333, mode='bilinear')
)
(backbone): BackboneWithFPN(
(body): IntermediateLayerGetter(
(conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
(bn1): FrozenBatchNorm2d(64, eps=0.0)
(relu): ReLU(inplace=True)
(maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
(layer1): Sequential(
(0): Bottleneck(
(conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(64, eps=0.0)
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(64, eps=0.0)
(conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(256, eps=0.0)
(relu): ReLU(inplace=True)
(downsample): Sequential(
(0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): FrozenBatchNorm2d(256, eps=0.0)
)
)
(1): Bottleneck(
(conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(64, eps=0.0)
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(64, eps=0.0)
(conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(256, eps=0.0)
(relu): ReLU(inplace=True)
)
(2): Bottleneck(
(conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(64, eps=0.0)
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(64, eps=0.0)
(conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(256, eps=0.0)
(relu): ReLU(inplace=True)
)
)
(layer2): Sequential(
(0): Bottleneck(
(conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(128, eps=0.0)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(128, eps=0.0)
(conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(512, eps=0.0)
(relu): ReLU(inplace=True)
(downsample): Sequential(
(0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): FrozenBatchNorm2d(512, eps=0.0)
)
)
(1): Bottleneck(
(conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(128, eps=0.0)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(128, eps=0.0)
(conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(512, eps=0.0)
(relu): ReLU(inplace=True)
)
(2): Bottleneck(
(conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(128, eps=0.0)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(128, eps=0.0)
(conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(512, eps=0.0)
(relu): ReLU(inplace=True)
)
(3): Bottleneck(
(conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(128, eps=0.0)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(128, eps=0.0)
(conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(512, eps=0.0)
(relu): ReLU(inplace=True)
)
)
(layer3): Sequential(
(0): Bottleneck(
(conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(256, eps=0.0)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(256, eps=0.0)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(1024, eps=0.0)
(relu): ReLU(inplace=True)
(downsample): Sequential(
(0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): FrozenBatchNorm2d(1024, eps=0.0)
)
)
(1): Bottleneck(
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(256, eps=0.0)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(256, eps=0.0)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(1024, eps=0.0)
(relu): ReLU(inplace=True)
)
(2): Bottleneck(
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(256, eps=0.0)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(256, eps=0.0)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(1024, eps=0.0)
(relu): ReLU(inplace=True)
)
(3): Bottleneck(
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(256, eps=0.0)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(256, eps=0.0)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(1024, eps=0.0)
(relu): ReLU(inplace=True)
)
(4): Bottleneck(
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(256, eps=0.0)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(256, eps=0.0)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(1024, eps=0.0)
(relu): ReLU(inplace=True)
)
(5): Bottleneck(
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(256, eps=0.0)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(256, eps=0.0)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(1024, eps=0.0)
(relu): ReLU(inplace=True)
)
)
(layer4): Sequential(
(0): Bottleneck(
(conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(512, eps=0.0)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(512, eps=0.0)
(conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(2048, eps=0.0)
(relu): ReLU(inplace=True)
(downsample): Sequential(
(0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): FrozenBatchNorm2d(2048, eps=0.0)
)
)
(1): Bottleneck(
(conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(512, eps=0.0)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(512, eps=0.0)
(conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(2048, eps=0.0)
(relu): ReLU(inplace=True)
)
(2): Bottleneck(
(conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): FrozenBatchNorm2d(512, eps=0.0)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): FrozenBatchNorm2d(512, eps=0.0)
(conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): FrozenBatchNorm2d(2048, eps=0.0)
(relu): ReLU(inplace=True)
)
)
)
(fpn): FeaturePyramidNetwork(
(inner_blocks): ModuleList(
(0): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))
(1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1))
(2): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1))
(3): Conv2d(2048, 256, kernel_size=(1, 1), stride=(1, 1))
)
(layer_blocks): ModuleList(
(0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(3): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
(extra_blocks): LastLevelMaxPool()
)
)
(rpn): RegionProposalNetwork(
(anchor_generator): AnchorGenerator()
(head): RPNHead(
(conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(cls_logits): Conv2d(256, 3, kernel_size=(1, 1), stride=(1, 1))
(bbox_pred): Conv2d(256, 12, kernel_size=(1, 1), stride=(1, 1))
)
)
(roi_heads): RoIHeads(
(box_roi_pool): MultiScaleRoIAlign(featmap_names=['0', '1', '2', '3'], output_size=(7, 7), sampling_ratio=2)
(box_head): TwoMLPHead(
(fc6): Linear(in_features=12544, out_features=1024, bias=True)
(fc7): Linear(in_features=1024, out_features=1024, bias=True)
)
(box_predictor): FastRCNNPredictor(
(cls_score): Linear(in_features=1024, out_features=2, bias=True)
(bbox_pred): Linear(in_features=1024, out_features=8, bias=True)
)
(keypoint_roi_pool): MultiScaleRoIAlign(featmap_names=['0', '1', '2', '3'], output_size=(14, 14), sampling_ratio=2)
(keypoint_head): KeypointRCNNHeads(
(0): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): ReLU(inplace=True)
(2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(3): ReLU(inplace=True)
(4): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(5): ReLU(inplace=True)
(6): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(7): ReLU(inplace=True)
(8): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(9): ReLU(inplace=True)
(10): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(11): ReLU(inplace=True)
(12): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(13): ReLU(inplace=True)
(14): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(15): ReLU(inplace=True)
)
(keypoint_predictor): KeypointRCNNPredictor(
(kps_score_lowres): ConvTranspose2d(512, 17, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
)
)
)
def keypoints_detect(model, image_path, COCO_INSTANCE_CATEGORY_NAMES, COCO_PERSON_KEYPOINT_NAMES, threshold = 0.5):
image = Image.open(image_path)
transform_d = transforms.Compose([transforms.ToTensor()])
image_t = transform_d(image)
pred = model([image_t])
pred_class = [COCO_INSTANCE_CATEGORY_NAMES[ii] for ii in list(pred[0]['labels'].numpy())]
pred_score = list(pred[0]['scores'].detach().numpy())
pred_boxes = [[ii[0], ii[1], ii[2], ii[3]] for ii in list(pred[0]['boxes'].detach().numpy())]
pred_index = [pred_score.index(x) for x in pred_score if x > 0.5]
fontsize = np.int16(image.size[1] / 30)
font1 = ImageFont.truetype('C:/windows/Fonts/STXIHEI.TTF', fontsize)
image2 = image.copy()
draw = ImageDraw.Draw(image2)
for index in pred_index:
box = pred_boxes[index]
draw.rectangle(box, outline = 'red')
texts = pred_class[index] + ':' + str(np.round(pred_score[index], 2))
draw.text((box[0], box[1]), texts, fill = 'red', font = font1)
pred_keypoint = pred[0]['keypoints']
pred_keypoint = pred_keypoint[pred_index].detach().numpy()
fontsize = np.int16(image.size[1] / 50)
r = np.int16(image.size[1] / 150)
draw = ImageDraw.Draw(image2)
for index in range(pred_keypoint.shape[0]):
keypoints = pred_keypoint[index]
for ii in range(keypoints.shape[0]):
x = keypoints[ii, 0]
y = keypoints[ii, 1]
visi = keypoints[ii, 2]
if visi > 0:
draw.ellipse(xy = (x - r, y - r, x + r, y + r), fill = (255, 0, 0))
texts = str(ii + 1)
draw.text((x + r, y - r), texts, fill = 'red', font = font1)
return image2
针对一张新的图像，运行函数，并查看程序的输出结果
image_path = './data/objdetect/kendo2person.jpg'
image = keypoints_detect(model, image_path, COCO_INSTANCE_CATEGORY_NAMES, COCO_PERSON_KEYPOINT_NAMES, threshold = 0.8)
image
针对动作复杂、服饰宽松、面部遮挡的任务，关键点的位置有时会出现错误。
Original: https://blog.csdn.net/weixin_44979150/article/details/122974942
Author: 宅家的小魏
Title: Pytorch：目标检测网络-人体关键点检测
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