最终成功果展示

import time
from collections import deque
import requests
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.isOk = True
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(0) # 使用0表示默认摄像头
# cap.set(cv2.CAP_PROP_FOURCC, cv2.VideoWriter_fourcc('M', 'J', 'P', 'G'))
# cap.set(cv2.CAP_PROP_FRAME_WIDTH, 800) # 解决问题的关键！！！
# cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 600)
# cap.set(cv2.CAP_PROP_FPS, 30)

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.")
            # 如果是实时摄像头，不需要使用'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':
                if self.isOk == True:
                    requests.get('http://localhost:8080/book/isokfalse')
                    self.isOk = False

                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()

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()

posted @ 2024-06-17 21:11 WWW/com 阅读(12) 评论(0) 编辑收藏举报

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