第一次提交

.idea/.gitignore

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+# 默认忽略的文件
+/shelf/
+/workspace.xml

.idea/inspectionProfiles/profiles_settings.xml

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+<component name="InspectionProjectProfileManager">
+  <settings>
+    <option name="USE_PROJECT_PROFILE" value="false" />
+    <version value="1.0" />
+  </settings>
+</component>

.idea/misc.xml

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+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.8" project-jdk-type="Python SDK" />
+</project>

.idea/modules.xml

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+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectModuleManager">
+    <modules>
+      <module fileurl="file://$PROJECT_DIR$/.idea/pose-detect.iml" filepath="$PROJECT_DIR$/.idea/pose-detect.iml" />
+    </modules>
+  </component>
+</project>

.idea/pose-detect.iml

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+<?xml version="1.0" encoding="UTF-8"?>
+<module type="PYTHON_MODULE" version="4">
+  <component name="NewModuleRootManager">
+    <content url="file://$MODULE_DIR$" />
+    <orderEntry type="inheritedJdk" />
+    <orderEntry type="sourceFolder" forTests="false" />
+  </component>
+</module>

.idea/vcs.xml

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+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="VcsDirectoryMappings">
+    <mapping directory="" vcs="Git" />
+  </component>
+</project>

pose.py

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+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}')

ultralytics/__init__.py

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+# Ultralytics YOLO 🚀, AGPL-3.0 license
+
+__version__ = "8.2.63"
+
+import os
+
+# Set ENV Variables (place before imports)
+os.environ["OMP_NUM_THREADS"] = "1"  # reduce CPU utilization during training
+
+from ultralytics.data.explorer.explorer import Explorer
+from ultralytics.models import NAS, RTDETR, SAM, YOLO, FastSAM, YOLOWorld
+from ultralytics.utils import ASSETS, SETTINGS
+from ultralytics.utils.checks import check_yolo as checks
+from ultralytics.utils.downloads import download
+
+settings = SETTINGS
+__all__ = (
+    "__version__",
+    "ASSETS",
+    "YOLO",
+    "YOLOWorld",
+    "NAS",
+    "SAM",
+    "FastSAM",
+    "RTDETR",
+    "checks",
+    "download",
+    "settings",
+    "Explorer",
+)