256 lines
9.5 KiB
Python
256 lines
9.5 KiB
Python
|
from ultralytics import YOLO
|
|||
|
|
import cv2
|
|||
|
|
import math
|
|||
|
|
import matplotlib.pyplot as plt
|
|||
|
|
|
|||
|
|
|
|||
|
|
def getAnglebyline(line1, line2):
|
|||
|
|
dx1 = line1[0][0] - line1[1][0]
|
|||
|
|
dy1 = line1[0][1] - line1[1][1]
|
|||
|
|
dx2 = line2[0][0] - line2[1][0]
|
|||
|
|
dy2 = line2[0][1] - line2[1][1]
|
|||
|
|
|
|||
|
|
# 求斜率
|
|||
|
|
m1 = dy1 / dx1
|
|||
|
|
m2 = dy2 / dx2
|
|||
|
|
insideAngle = math.atan(abs((m2 - m1) / (1 + (m1 * m2))))
|
|||
|
|
|
|||
|
|
angle = insideAngle / math.pi * 180
|
|||
|
|
if angle > -370 and angle < 370:
|
|||
|
|
angle = int(angle)
|
|||
|
|
|
|||
|
|
return angle
|
|||
|
|
|
|||
|
|
|
|||
|
|
def aspectRatio(boxes):
|
|||
|
|
boxes = boxes.cpu().numpy().astype('uint32')
|
|||
|
|
x = [boxes[0], boxes[2]]
|
|||
|
|
y = [boxes[1], boxes[3]]
|
|||
|
|
width = x[1] - x[0]
|
|||
|
|
height = y[1] - y[0]
|
|||
|
|
radio = width / height
|
|||
|
|
return radio
|
|||
|
|
|
|||
|
|
|
|||
|
|
def getAnglebypoint(point_a, point_b, point_c):
|
|||
|
|
a_x, b_x, c_x = point_a[0], point_b[0], point_c[0] # 点a、b、c的x坐标
|
|||
|
|
a_y, b_y, c_y = point_a[1], point_b[1], point_c[1] # 点a、b、c的y坐标
|
|||
|
|
|
|||
|
|
if len(point_a) == len(point_b) == len(point_c) == 3:
|
|||
|
|
# print("坐标点为3维坐标形式")
|
|||
|
|
a_z, b_z, c_z = point_a[2], point_b[2], point_c[2] # 点a、b、c的z坐标
|
|||
|
|
else:
|
|||
|
|
a_z, b_z, c_z = 0, 0, 0 # 坐标点为2维坐标形式，z 坐标默认值设为0
|
|||
|
|
# print("坐标点为2维坐标形式，z 坐标默认值设为0")
|
|||
|
|
|
|||
|
|
# 向量 m=(x1,y1,z1), n=(x2,y2,z2)
|
|||
|
|
x1, y1, z1 = (a_x - b_x), (a_y - b_y), (a_z - b_z)
|
|||
|
|
x2, y2, z2 = (c_x - b_x), (c_y - b_y), (c_z - b_z)
|
|||
|
|
|
|||
|
|
# 两个向量的夹角，即角点b的夹角余弦值
|
|||
|
|
cos_b = (x1 * x2 + y1 * y2 + z1 * z2) / (
|
|||
|
|
math.sqrt(x1 ** 2 + y1 ** 2 + z1 ** 2) * (math.sqrt(x2 ** 2 + y2 ** 2 + z2 ** 2)) + 0.01) # 角点b的夹角余弦值
|
|||
|
|
B = math.degrees(math.acos(cos_b)) # 角点b的夹角值
|
|||
|
|
return B
|
|||
|
|
|
|||
|
|
|
|||
|
|
# getAnglebypoint((3 ** 0.5, 1), (0, 0), (3 ** 0.5, 0)) # 结果为 30°
|
|||
|
|
# getAnglebypoint((1, 1), (0, 0), (1, 0)) # 结果为 45°
|
|||
|
|
# getAnglebypoint((-1, 1), (0, 0), (1, 0)) # 结果为 135°
|
|||
|
|
|
|||
|
|
|
|||
|
|
def is_fallen(keypoints, boxes):
|
|||
|
|
keypoints = keypoints.cpu().numpy().astype('uint32')
|
|||
|
|
Left_Shoulder = keypoints[5][:2]
|
|||
|
|
# if Left_Shoulder[0] + Left_Shoulder[1] == 0: self.ATHERPOSE += 1
|
|||
|
|
Right_Shoulder = keypoints[6][:2]
|
|||
|
|
# if Right_Shoulder[0] + Right_Shoulder[1] == 0: self.ATHERPOSE += 1
|
|||
|
|
Left_Hip = keypoints[11][:2]
|
|||
|
|
# if Left_Hip[0] + Left_Hip[1] == 0: self.ATHERPOSE += 1
|
|||
|
|
Right_Hip = keypoints[12][:2]
|
|||
|
|
# if Right_Hip[0] + Right_Hip[1] == 0: self.ATHERPOSE += 1
|
|||
|
|
Left_Knee = keypoints[13][:2]
|
|||
|
|
# if Left_Knee[0] + Left_Knee[1] == 0: self.ATHERPOSE += 1
|
|||
|
|
Right_Knee = keypoints[15][:2]
|
|||
|
|
# if Right_Knee[0] + Right_Knee[1] == 0: self.ATHERPOSE += 1
|
|||
|
|
Left_Ankle = keypoints[15][:2]
|
|||
|
|
# if Left_Ankle[0] + Left_Ankle[1] == 0: self.ATHERPOSE += 1
|
|||
|
|
Right_Ankle = keypoints[16][:2]
|
|||
|
|
# if Right_Ankle[0] + Right_Ankle[1] == 0: self.ATHERPOSE += 1
|
|||
|
|
|
|||
|
|
Shoulders_c = [(Left_Shoulder[0] + Right_Shoulder[0]) // 2,
|
|||
|
|
(Left_Shoulder[1] + Right_Shoulder[1]) // 2]
|
|||
|
|
|
|||
|
|
hips_c = [(Left_Hip[0] + Right_Hip[0]) // 2,
|
|||
|
|
(Left_Hip[1] + Right_Hip[1]) // 2]
|
|||
|
|
|
|||
|
|
Knee_c = [(Left_Knee[0] + Right_Knee[0]) // 2,
|
|||
|
|
(Left_Knee[1] + Right_Knee[1]) // 2]
|
|||
|
|
|
|||
|
|
Ankle_c = [(Left_Ankle[0] + Right_Ankle[0]) // 2,
|
|||
|
|
(Left_Ankle[1] + Right_Ankle[1]) // 2]
|
|||
|
|
|
|||
|
|
'''计算身体中心线与水平线夹角'''
|
|||
|
|
human_angle = getAnglebyline([Shoulders_c, hips_c], [[0, 0], [10, 0]])
|
|||
|
|
'''计算检测区域宽高比'''
|
|||
|
|
aspect_ratio = aspectRatio(boxes)
|
|||
|
|
'''计算肩部中心点与胯部中心点的垂直距离差'''
|
|||
|
|
human_shoulderhip = abs(Shoulders_c[1] - hips_c[1])
|
|||
|
|
|
|||
|
|
'''计算肩部胯部膝盖夹角'''
|
|||
|
|
Hip_Knee_Shoulders_angle = getAnglebypoint(Shoulders_c, hips_c, Knee_c)
|
|||
|
|
Hip_Knee_Right_angle = getAnglebypoint(Right_Shoulder.tolist(), Right_Hip.tolist(), Right_Knee.tolist())
|
|||
|
|
|
|||
|
|
'''计算胯部膝盖小腿夹角'''
|
|||
|
|
Ankle_Knee_Hip_angle = getAnglebypoint(hips_c, Knee_c, Ankle_c)
|
|||
|
|
Ankle_Knee_Right_angle = getAnglebypoint(Right_Hip.tolist(), Right_Knee.tolist(), Right_Ankle.tolist())
|
|||
|
|
|
|||
|
|
'''计算胯部膝盖是否处于相似的垂直位置'''
|
|||
|
|
vertical_threshold = Left_Knee[1] - Left_Shoulder[1]
|
|||
|
|
|
|||
|
|
'''计算胯部膝盖是否处于相似的水平位置'''
|
|||
|
|
horizontal_threshold = Left_Shoulder[0] - Left_Knee[0]
|
|||
|
|
|
|||
|
|
status_score = {'Stand': 0.0,
|
|||
|
|
'Fall': 0.0,
|
|||
|
|
'Sit': 0.0,
|
|||
|
|
'other': 0.0}
|
|||
|
|
_weight = ''
|
|||
|
|
|
|||
|
|
'''判断Shoulder、Hip、Knee是否被检测到'''
|
|||
|
|
if Knee_c[0] == 0 and Knee_c[1] == 0 and hips_c[0] == 0 and hips_c[1] == 0:
|
|||
|
|
status_score['Sit'] += 0.69
|
|||
|
|
status_score['Fall'] += -0.8 * 2
|
|||
|
|
status_score['Stand'] += -0.8 * 2
|
|||
|
|
_weight = f'[1]Sit:+0.2, Fall:-1.6 ,Stand: -1.6'
|
|||
|
|
|
|||
|
|
elif Shoulders_c[1] == 0 and Shoulders_c[0] == 0 and hips_c[0] == 0 and hips_c[1] == 0:
|
|||
|
|
status_score['Sit'] += -0.8 * 2
|
|||
|
|
status_score['Fall'] += -0.8 * 2
|
|||
|
|
status_score['Stand'] += 0.69
|
|||
|
|
|
|||
|
|
'''身体中心线与水平线夹角+-25'''
|
|||
|
|
if human_angle in range(-25, 25):
|
|||
|
|
status_score['Fall'] += 0.8
|
|||
|
|
status_score['Sit'] += 0.1
|
|||
|
|
_weight = f'{_weight}, [2]Fall:+0.8, Sit:+0.1'
|
|||
|
|
else:
|
|||
|
|
status_score['Fall'] += 0.2 * ((90 - human_angle) / 90)
|
|||
|
|
_weight = f'{_weight}, [3]Fall:+{0.8 * ((90 - human_angle) / 90)}'
|
|||
|
|
|
|||
|
|
'''宽高比小与0.6则为站立'''
|
|||
|
|
if (aspect_ratio < 0.6 and human_angle in range(65, 115)):
|
|||
|
|
status_score['Stand'] += 0.8
|
|||
|
|
_weight = f'{_weight}, [4]Stand:+0.8'
|
|||
|
|
|
|||
|
|
elif (aspect_ratio > 1 / 0.6): # 5/3
|
|||
|
|
status_score['Fall'] += 0.8
|
|||
|
|
_weight = f'{_weight}, [5]Fall:+0.8'
|
|||
|
|
if horizontal_threshold < 30:
|
|||
|
|
status_score['Fall'] += 0.6
|
|||
|
|
status_score['Sit'] += -0.15
|
|||
|
|
# if 25 < Hip_Knee_Shoulders_angle < 145 and 75 < human_angle < 125:
|
|||
|
|
# status_score['Sit'] += 0.8
|
|||
|
|
# status_score['Stand'] += -0.035
|
|||
|
|
# if vertical_threshold > 30:
|
|||
|
|
# status_score['Sit'] += +0.15
|
|||
|
|
# _weight = f'{_weight}, [6]Stand:-0.035, Sit:+0.15'
|
|||
|
|
# elif Hip_Knee_Shoulders_angle > 120 and 75 < human_angle < 125:
|
|||
|
|
# status_score['Stand'] += 0.2
|
|||
|
|
# elif Hip_Knee_Shoulders_angle > 120 and -25 < human_angle < 25:
|
|||
|
|
# status_score['Fall'] += 0.2
|
|||
|
|
# else:
|
|||
|
|
# status_score['Fall'] += 0.05
|
|||
|
|
# status_score['Stand'] += 0.05
|
|||
|
|
# _weight = f'{_weight}, [7]Stand:+0.05, Fall:+0.05'
|
|||
|
|
|
|||
|
|
score_max, status_max = max(zip(status_score.values(), status_score.keys()))
|
|||
|
|
|
|||
|
|
return status_max, score_max
|
|||
|
|
|
|||
|
|
|
|||
|
|
def draw_boxes(boxes, image, label):
|
|||
|
|
boxes = boxes.cpu().numpy().astype('uint32')
|
|||
|
|
x = [boxes[0], boxes[2]]
|
|||
|
|
y = [boxes[1], boxes[3]]
|
|||
|
|
X = x[0]
|
|||
|
|
Y = y[0]
|
|||
|
|
color = (0, 255, 0)
|
|||
|
|
cv2.rectangle(image, (int(x[0]), int(y[0])), (int(x[1]), int(y[1])), color, 2)
|
|||
|
|
cv2.putText(image, label, (int(X + 5), int(Y + 5)), cv2.FONT_HERSHEY_SIMPLEX, 1, color, 2)
|
|||
|
|
|
|||
|
|
|
|||
|
|
if __name__ == '__main__':
|
|||
|
|
|
|||
|
|
model = YOLO('yolov8x-pose.pt')
|
|||
|
|
source = 'images' # 换成自己的图片路径
|
|||
|
|
results = model(source)
|
|||
|
|
y_train = []
|
|||
|
|
for result in results:
|
|||
|
|
keypoints = result.keypoints.xy
|
|||
|
|
boxes = result.boxes.xyxy
|
|||
|
|
image_path = result.path
|
|||
|
|
image = cv2.imread(image_path)
|
|||
|
|
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
|
|||
|
|
# print(keypoints.shape)
|
|||
|
|
# print(image_path.split("images")[0])
|
|||
|
|
# with open(f"{image_path}", "r") as f:
|
|||
|
|
# for line in f.readlines():
|
|||
|
|
# line = line.strip('\n') # 去掉列表中每一个元素的换行符
|
|||
|
|
# y_train.append(line[0])
|
|||
|
|
# print(f"第{i}张图片：")
|
|||
|
|
# y2 = []
|
|||
|
|
for keypoin, box in zip(keypoints, boxes):
|
|||
|
|
# print(keypoin,box)
|
|||
|
|
# keypoint = keypoin.cpu().numpy().astype('uint32')
|
|||
|
|
status_max, score_max = is_fallen(keypoin, box)
|
|||
|
|
if status_max == 'Fall':
|
|||
|
|
draw_boxes(box, image, f'{status_max}')
|
|||
|
|
# y2.append(status_max)
|
|||
|
|
# y1.append(y2)
|
|||
|
|
plt.imshow(image)
|
|||
|
|
plt.show()
|
|||
|
|
|
|||
|
|
# print(y1)
|
|||
|
|
# class BinaryClassifier(nn.Module):
|
|||
|
|
# def __init__(self, input_features):
|
|||
|
|
# super(BinaryClassifier, self).__init__()
|
|||
|
|
# # 定义网络结构
|
|||
|
|
# self.fc1 = nn.Linear(input_features, 64) # 第一个全连接层
|
|||
|
|
# self.fc2 = nn.Linear(64, 32) # 第二个全连接层
|
|||
|
|
# self.fc3 = nn.Linear(32, 2) # 输出层，2个神经元代表两个类别
|
|||
|
|
#
|
|||
|
|
# def forward(self, x):
|
|||
|
|
# x = F.relu(self.fc1(x)) # 使用ReLU激活函数
|
|||
|
|
# x = F.relu(self.fc2(x)) # 使用ReLU激活函数
|
|||
|
|
# x = self.fc3(x) # 输出层，不需要激活函数，因为后面会使用softmax
|
|||
|
|
# return x
|
|||
|
|
#
|
|||
|
|
#
|
|||
|
|
# # 实例化网络模型
|
|||
|
|
# input_features = 17 # 输入特征数量
|
|||
|
|
# model = BinaryClassifier(input_features)
|
|||
|
|
#
|
|||
|
|
# # 定义损失函数和优化器
|
|||
|
|
# criterion = nn.CrossEntropyLoss() # 交叉熵损失函数，适用于多分类问题
|
|||
|
|
# optimizer = optim.Adam(model.parameters(), lr=0.001) # Adam优化器
|
|||
|
|
#
|
|||
|
|
# # 假设有一些训练数据
|
|||
|
|
# # X_train是训练特征，y_train是训练标签（0或1）
|
|||
|
|
# X_train = torch.randn(100, input_features) # 100个样本，每个样本17个特征
|
|||
|
|
# y_train = torch.randint(0, 2, (100,)) # 100个样本的标签，0或1
|
|||
|
|
#
|
|||
|
|
# # 训练网络
|
|||
|
|
# num_epochs = 10 # 训练轮数
|
|||
|
|
# for epoch in range(num_epochs):
|
|||
|
|
# # 前向传播
|
|||
|
|
# outputs = model(X_train)
|
|||
|
|
# loss = criterion(outputs, y_train)
|
|||
|
|
#
|
|||
|
|
# # 反向传播和优化
|
|||
|
|
# optimizer.zero_grad() # 清空过往梯度
|
|||
|
|
# loss.backward() # 反向传播，计算当前梯度
|
|||
|
|
# optimizer.step() # 根据梯度更新网络参数
|
|||
|
|
#
|
|||
|
|
# print(f'Epoch [{epoch + 1}/{num_epochs}], Loss: {loss.item():.4f}')
|