5.20结组作业

跌倒检测main部分，但是只能传mp4

# import time
# from collections import deque
#
# import cv2
# import numpy as np
# import mediapipe as mp
#
# from stgcn.stgcn import STGCN
# from PIL import Image, ImageDraw, ImageFont
#
# mp_drawing = mp.solutions.drawing_utils
# mp_drawing_styles = mp.solutions.drawing_styles
# mp_pose = mp.solutions.pose
#
# KEY_JOINTS = [
#     mp_pose.PoseLandmark.NOSE,
#     mp_pose.PoseLandmark.LEFT_SHOULDER,
#     mp_pose.PoseLandmark.RIGHT_SHOULDER,
#     mp_pose.PoseLandmark.LEFT_ELBOW,
#     mp_pose.PoseLandmark.RIGHT_ELBOW,
#     mp_pose.PoseLandmark.LEFT_WRIST,
#     mp_pose.PoseLandmark.RIGHT_WRIST,
#     mp_pose.PoseLandmark.LEFT_HIP,
#     mp_pose.PoseLandmark.RIGHT_HIP,
#     mp_pose.PoseLandmark.LEFT_KNEE,
#     mp_pose.PoseLandmark.RIGHT_KNEE,
#     mp_pose.PoseLandmark.LEFT_ANKLE,
#     mp_pose.PoseLandmark.RIGHT_ANKLE
# ]
#
# POSE_CONNECTIONS = [(6, 4), (4, 2), (2, 13), (13, 1), (5, 3), (3, 1), (12, 10),
#                     (10, 8), (8, 2), (11, 9), (9, 7), (7, 1), (13, 0)]
#
# POINT_COLORS = [(0, 255, 255), (0, 191, 255), (0, 255, 102), (0, 77, 255), (0, 255, 0),  # Nose, LEye, REye, LEar, REar
#                 (77, 255, 255), (77, 255, 204), (77, 204, 255), (191, 255, 77), (77, 191, 255), (191, 255, 77),  # LShoulder, RShoulder, LElbow, RElbow, LWrist, RWrist
#                 (204, 77, 255), (77, 255, 204), (191, 77, 255), (77, 255, 191), (127, 77, 255), (77, 255, 127), (0, 255, 255)]  # LHip, RHip, LKnee, Rknee, LAnkle, RAnkle, Neck
#
# LINE_COLORS = [(0, 215, 255), (0, 255, 204), (0, 134, 255), (0, 255, 50), (77, 255, 222),
#                (77, 196, 255), (77, 135, 255), (191, 255, 77), (77, 255, 77), (77, 222, 255),
#                (255, 156, 127), (0, 127, 255), (255, 127, 77), (0, 77, 255), (255, 77, 36)]
#
# ACTION_MODEL_MAX_FRAMES = 30
#
# class FallDetection:
#     def __init__(self):
#         self.action_model = STGCN(weight_file='./weights/tsstg-model.pth', device='cpu')
#         self.joints_list = deque(maxlen=ACTION_MODEL_MAX_FRAMES)
#
#     def draw_skeleton(self, frame, pts):
#         l_pair = POSE_CONNECTIONS
#         p_color = POINT_COLORS
#
#         line_color = LINE_COLORS
#
#         part_line = {}
#         pts = np.concatenate((pts, np.expand_dims((pts[1, :] + pts[2, :]) / 2, 0)), axis=0)
#         for n in range(pts.shape[0]):
#             if pts[n, 2] <= 0.05:
#                 continue
#             cor_x, cor_y = int(pts[n, 0]), int(pts[n, 1])
#             part_line[n] = (cor_x, cor_y)
#             cv2.circle(frame, (cor_x, cor_y), 3, p_color[n], -1)
#             # cv2.putText(frame, str(n), (cor_x+10, cor_y+10), cv2.FONT_HERSHEY_PLAIN, 1, (0, 0, 255), 1)
#
#         for i, (start_p, end_p) in enumerate(l_pair):
#             if start_p in part_line and end_p in part_line:
#                 start_xy = part_line[start_p]
#                 end_xy = part_line[end_p]
#                 cv2.line(frame, start_xy, end_xy, line_color[i], int(1*(pts[start_p, 2] + pts[end_p, 2]) + 3))
#         return frame
#
#     def cv2_add_chinese_text(self, img, text, position, textColor=(0, 255, 0), textSize=30):
#         if (isinstance(img, np.ndarray)):  # 判断是否OpenCV图片类型
#             img = Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
#         # 创建一个可以在给定图像上绘图的对象
#         draw = ImageDraw.Draw(img)
#         # 字体的格式
#         fontStyle = ImageFont.truetype(
#             "./fonts/MSYH.ttc", textSize, encoding="utf-8")
#         # 绘制文本
#         draw.text(position, text, textColor, font=fontStyle)
#         # 转换回OpenCV格式
#         return cv2.cvtColor(np.asarray(img), cv2.COLOR_RGB2BGR)
#
#     def detect(self):
#         # Initialize the webcam capture.
#         cap = cv2.VideoCapture('./video.mp4')
#         image_h = cap.get(cv2.CAP_PROP_FRAME_HEIGHT)
#         image_w = cap.get(cv2.CAP_PROP_FRAME_WIDTH)
#         frame_num = 0
#         print(image_h, image_w)
#
#         with mp_pose.Pose(
#                 min_detection_confidence=0.7,
#                 min_tracking_confidence=0.5) as pose:
#             while cap.isOpened():
#                 fps_time = time.time()
#                 frame_num += 1
#                 success, image = cap.read()
#                 if not success:
#                     print("Ignoring empty camera frame.")
#                     # If loading a video, use 'break' instead of 'continue'.
#                     continue
#
#                 # 提高性能
#                 image.flags.writeable = False
#                 image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
#                 results = pose.process(image)
#
#                 if not results.pose_landmarks:
#                     continue
#
#                 # 识别骨骼点
#                 image.flags.writeable = True
#                 image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
#
#                 # mp_drawing.draw_landmarks(
#                 #     image,
#                 #     results.pose_landmarks,
#                 #     mp_pose.POSE_CONNECTIONS,
#                 #     landmark_drawing_spec=mp_drawing_styles.get_default_pose_landmarks_style())
#
#                 landmarks = results.pose_landmarks.landmark
#                 joints = np.array([[landmarks[joint].x * image_w,
#                                     landmarks[joint].y * image_h,
#                                     landmarks[joint].visibility]
#                                    for joint in KEY_JOINTS])
#                 # 人体框
#                 box_l, box_r = int(joints[:, 0].min())-50, int(joints[:, 0].max())+50
#                 box_t, box_b = int(joints[:, 1].min())-100, int(joints[:, 1].max())+100
#
#                 self.joints_list.append(joints)
#
#                 # 识别动作
#                 action = ''
#                 clr = (0, 255, 0)
#                 # 30帧数据预测动作类型
#                 if len(self.joints_list) == ACTION_MODEL_MAX_FRAMES:
#                     pts = np.array(self.joints_list, dtype=np.float32)
#                     out = self.action_model.predict(pts, (image_w, image_h))
#                     action_name = self.action_model.class_names[out[0].argmax()]
#                     action = '{}: {:.2f}%'.format(action_name, out[0].max() * 100)
#                     print(action)
#
#                     if action_name == 'Fall Down':
#                         clr = (255, 0, 0)
#                         action = '摔倒'
#                     elif action_name == 'Walking':
#                         clr = (255, 128, 0)
#                         action = '行走'
#                     else:
#                         action = ''
#
#                 # 绘制骨骼点和动作类别
#                 image = self.draw_skeleton(image, self.joints_list[-1])
#                 image = cv2.rectangle(image, (box_l, box_t), (box_r, box_b), (255, 0, 0), 1)
#                 image = self.cv2_add_chinese_text(image, f'当前状态：{action}', (box_l + 10, box_t + 10), clr, 40)
#                 image = cv2.putText(image, f'FPS: {int(1.0 / (time.time() - fps_time))}',
#                                     (50, 50), cv2.FONT_HERSHEY_PLAIN, 3, (0, 255, 0), 2)
#                 # Flip the image horizontally for a selfie-view display.
#                 cv2.imshow('MediaPipe Pose', image)
#                 if cv2.waitKey(1) & 0xFF == ord("q"):
#                     break
#
#         # Release the resources.
#         cap.release()
#         cv2.destroyAllWindows()
#
# if __name__ == '__main__':
#     FallDetection().detect()