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